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VOLUME 3 - 2013 DOI: 10.5027/jnrd.v3i0.01 - DOI: 10.5027/jnrd.v3i0.13

INTEGRATED WATER RESOURCES MANAGEMENT LAND USE DYNAMICS AND BIODIVERSITY ENERGY EFFICIENCY AND RENEWABLE RESOURCES REGIONAL MANAGEMENT AND SUSTAINABLE LIVELIHOOD OF THE POOR


Volume III

Contents

Journal of Natural Resources and Development 2013; 03: 1-139

Site suitability analysis for Bay scallop aquaculture and implications for sustainable fisheries management in the Ha Long Bay archipelago, northern Vietnam.

1

Authors: Marconi Michele, Pham Thuoc, Nguyen Tien Canh, Pham Thi Khanh, Marincioni Fausto DOI: 10.5027/jnrd.v3i0.01 Hydromorphological assessment as a tool for river basin management: The German field survey method

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Authors: Georg Meir, Thomas Zumbroich, Jackson Roehrig. DOI: 10.5027/jnrd.v3i0.02 Purification and characterization of toxic waste in the aquatic environment using common carp, Cyprinus carpio

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Authors: Hanan Abd Al-Gawad, Enas M. Ramzy DOI: 10.5027/jnrd.v3i0.03 Community perception on climate change and climate-related disaster preparedness in Kathmandu Valley, Nepal

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Authors: Udo Nehren, Jishnu Subedi, Ina Yanakieva, Simone Sandholz, Jibraj Pokharel, Ajay Chandra Lal, Inu Pradhan-Salike, Muh Aris Marfai, Danang Sri Hadmoko, G端nther Straub DOI: 10.5027/jnrd.v3i0.04 A water productive and economically profitable paddy rice production method to adapt water scarcity in the Vu Gia-Thu Bon river basin, Vietnam

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Authors: Bhone Nay-Htoon, Nguyen Tung Phong, Sabine Schl端ter, Aldas Janaiah DOI: 10.5027/jnrd.v3i0.05 Natural resources endowment and economic growth: The west african experience

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Author: Mohamed Jalloh DOI: 10.5027/jnrd.v3i0.06 A proposal for tsunami mitigation by using coastal vegetations: some findings from southern coastal area of Central Java, Indonesia

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Author: Djati Mardiatno DOI: 10.5027/jnrd.v3i0.07 Public-private partnership as a responsive culture for green management in Bangladesh: A study of natural resources management at Lawachhara national park

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Authors: Mohammad Nashir Uddin, Mohammad Hamiduzzaman DOI: 10.5027/jnrd.v3i0.08 Irrigation management strategies for winter wheat using AquaCrop model

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Authors: M. H. Ali, I. Abustan, A. B. Puteh DOI: 10.5027/jnrd.v3i0.09 Indigenous perceptions of soil erosion, adaptations and livelihood implications: the case of maize farmers in the Zampe community of bole in the northern region of Ghana

114

Author: Bukari Francis Issahaku Malongza DOI: 10.5027/jnrd.v3i0.10 New focus of environmental education programs [Commentary]

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Author: Alexander Neaman DOI: 10.5027/jnrd.v3i0.11 Simulation of upward flux from shallow water-table using UPFLOW model

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Authors: M. H. Ali, I. Abustan, S. Islam DOI: 10.5027/jnrd.v3i0.12 Evaluation of methods for digital elevation model interpolation of tillage systems Authors: Setiawan M. A., Rutzinger M., Wichmann V., Stoetter J., Sartohadi J. DOI: 10.5027/jnrd.v3i0.13

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DOI number: 10.5027/jnrd.v3i0.01

Journal of Natural Resources and Development 2013; 03: 01-13

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JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT Site suitability analysis for Bay scallop aquaculture and implications for sustainable fisheries management in the Ha Long Bay archipelago, northern Vietnam. Marconi Michele a, Pham Thuoc b, Nguyen Tien Canh b, Pham Thi Khanh b, Marincioni Fausto a, *. a

Department of Life and Environmental Sciences, Marche Polytechnic University , Via Brecce Bianche, 60131 Ancona, Italy

b

Research Institute of Marine Fisheries, 224 Le Lai, Hai Phong, Vietnam

*

Corresponding author : f.marincioni@univpm.it, phone: +39 071 2204312; fax: +39 071 2204650.

Article history Received 11.08.2012 Accepted 17.10.2012 Published 07.01.2013

Keywords Argopecten irradians aquaculture Bay scallop Multi-criteria evaluation Sustainable exploitation Ha Long Bay

Abstract Mollusc culture if properly managed, may help decrease capture fisheries over-exploitation in Vietnam, and possibly become an alternative income for local fishermen. The definition and characterization of zones suitable for aquaculture is pivotal for its success and sustainable development, and this study aims at determining the suitability of Argopecten irradians (Bay scallop) culture in the Ha Long Bay Archipelago. Temperature, salinity, chlorophyll-a, total suspended solid and bathymetry, were compiled in an environmental suitability model. Distance of culture sites from landing points and fish markets were instead grouped in an infrastructural suitability model. In both models, developed with Geographic Information Systems, the suitability scores were ranked on a scale from 1 (unsuitable) to 6 (very-highly suitable). Results showed that 98 % of the studied area is environmentally suitable for such culture. However, overlaying the infrastructural factors the suitable zone decrease to 38 %. Advantages and disadvantages of two management options were then discussed: (a) strengthening fisheries infrastructures or (b) developing post harvesting processing plants.

Introduction Molluscs appear to be a key species for the development of aquaculture in the XXI century (Kongkeo 2001), and its production is increasing worldwide, enhancing both seafood yield and the economic-social welfare of coastal communities (Bourne 2000). This is particularly important for countries like Vietnam where traditional capture fisheries appear over-exploited (Pomeroy et al. 2009). As a matter of fact, the Vietnamese government had planned to put in

production 76 thousand hectares by 2010, expecting to harvest 380 thousand tons of mollusc, with an exported value of 760 million of USD (MOFI 2003). Indeed, molluscs’ production in Vietnam has increased from 40 to 170 thousand tons over the period 2000-2008 (FAO 2010); even so yields are still far from the optimistic forecast of the government. The Vietnam Association of Seafood Exporters and Producers asserts that Vietnam’s mollusc exports in 2010 reached 125


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thousand tons, earning 490 million of USD (VASEP 2011). In Vietnam, as in many other countries, the development of the mollusc aquaculture still face a number of hindrances including limited accessibility to suitable sites (Binh et al. 1997) and the apprehension regarding environmental impacts and multi-use conflicts (Ridler 1998). Inappropriate aquaculture activities may lead to an over-exploitation of the natural resources. To prevent these problems, several programs of stock enhancement have been implemented for various species of aquatic products worldwide (Bell et al. 2006), including an extraordinary success with scallops (Uki 2006). Such programs led to increased production through careful selection of both the aquaculture sites and the natural restocking operations, considered key factors for the success and sustainability of mollusc culture (GESAMP 2001). Argopecten irradians Lamarck (1819), commonly called Bay scallop (Figure 1), is a cultured molluscs species with high commercial value (Stotz and Gonzales 1997). Native of North American coastal water, Bay scallop was introduced in China in 1982 where it became the dominant cultured species of scallop (Zhang et al. 1986). The species

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was transplanted into Vietnam in 2004. The short life span of Bay scallop (18-22 months) is both strength and weakness. Indeed, farmers benefited from the short culture duration, allowing a rapid financial turnover (Milke et al. 2006), however, because that adult spawn only once in their life, if natural population of Bay scallops has declined, its natural recovery is limited (Goldberg et al. 2000). Therefore, accurate culture site selection is necessary to minimize environmental impact and over-exploitation risks, thus maximizing the overall economic return. To gather background information necessary to expand Bay scallop culture in Vietnam and to envision possible livelihoods alternatives for the local small scale fisheries, it was necessary to: (i) assess the biological and reproductive characteristics of this mollusc in northern Vietnam, (ii) identify its most suitable farming zones in the Ha Long Bay archipelago, using Analytic Hierarchy Process (AHP) and Geographic Information Systems (GIS), and (iii) evaluate the capacity of such culture to provide an alternative income to fisherman willing to give up capture fisheries.

Figure 1. Argopecten irradians (Lamarck, 1819), commonly called Bay scallop.

Materials and Methods Four subsequent research phases can be defined: (a) biological measurements on farmed Argopecten irradians, to determine its sexual maturity’s period in northern Vietnam; (b) environmental and infrastructural data collection of the studied area, (c) hierarchy and spatial analysis to develop the suitability models for Bay scallop aquaculture, and (d) scenarios analysis to increase Bay scallop culture and decrease capture fisheries over-exploitation.

Study area The sea area chosen for the study lies between 106°50’ to 107°10’ East and 20°40’ to 20°55’ North (Figure 2) and is deep between 6 and 25 meters. It includes the archipelago of Ha Long Bay, with the two inhabited islands of Cat Ba and Cat Hai and the surrounding waters. Ha Long Bay is a inlet near the mouth of the Bach Dang River with a


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mature karsts seascape of a multitude of sparsely tree-clad limestone pinnacles rising from the sea. Its waters, due to the thousands of pinnacle islands, are calm and turbid (Tran et al. 2004). The mean tidal excursion ranges between 3 and 4 meters, and the annual rainfall is about 1800-2000 mm (Tang 2001). The climate is characterized by a relatively dry Northeaster winter monsoon (November/December to May/June) and a wetter

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summer monsoon (June/July to October/November) (MOSTE 2000). Environmental condition are stable during the winter-monsoon season, with narrow range of water temperature and salinity, and low river discharge, whereas the summer monsoon season is environmentally unstable, accompanied by large river discharge events, causing large fluctuation of water temperature and salinity.

Figure 2. Study area

Sampling and measurement of biological parameters From November 2005 to October 2006, 105 sample of Argopecten irradians were purchased monthly from selected aquaculture operations located in the study area, growing juvenile Bay scallops supplied by the Northern National Broadstock Center (Cat Ba Island). Shell dimensions (length, height and width), body (total and soft tissue) and gonads weights were measured for each individual using a micrometer with accuracy of 0.01 mm and micro scale balance (± 0.01 gram). From these 105 scallops, 80 gonads were randomly chosen and analyzed with electron microscopy (Nikon Eclipse-50i). The gonad condition of each Bay scallop was classified into 5 stages (Chipperfield 1953; Sastry and Blake 1971). The spawning season was determined as the time when over 70 % of the individuals were at stage III (maturing) and IV (mature).

bathymetry have been documented to be the key environmental factors affecting scallops’ growth (Kingzet et al. 2002; Shriver et al. 2002; Ellis et al. 2002). Similarly, the culture sites’ distance from landing points and fish markets appear to be the pivotal infrastructural factor affecting aquaculture operations (Kingzet et al. 2002). Average temperature, salinity, chlorophyll-a, total suspended sediment and bathymetry for the months of Bay scallop’s sexual maturity, were collected in map format from the Ministry of Science Technology and Environment of Vietnam during the late 1990s (MOSTE 2000). The location of landing points and fish markets were instead digitized from the topographic map (scale 1:100 000) of Quang Ninh’s and Hai Phong’s provinces. These maps after digitization were overlaid to create the: environmental and infrastructural suitability models of Bay scallop culture in the study area. Spatial analysis to identify suitable culture sites

Environmental and infrastructural data collection The identification and classification of suitable culture sites was carried out combining the key environmental and infrastructural parameters affecting scallops growth and commerce. Sea temperature, salinity, food availability (i.e. chlorophyll-a), suspended sediment and

The Geographic Information System (GIS) used in this study was Quantum-GIS 1.6.0. The geographical data-sets were projected on a WGS 84 UTM zone 48-North coordinate system. The environmental factors were subjected to an Analytic Hierarchy Process (AHP) performed though a pair-wise comparison matrix


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(Saaty 1977). The AHP allowed incorporating the advices expressed by Vietnamese National experts and local farmers, of the relative weights exerted by these environmental factors on Bay scallop

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aquaculture (table 1). Distance of culture sites from landing points and fish markets were incorporated to create several buffer zones with different infrastructural suitability.

Table 1. Pair-wise comparison matrix incorporating the advices expressed by both, Vietnamese National experts and local aquaculturists, on the relative importance of the main environmental factors affecting Bay scallop aquaculture in Ha Long Bay and Cat Ba island (numbers show the rating of the row factors relative to the column factor); consistency ratio 0.77 %.

Salinity Salinity

Sea temperature

Chlorophyll-a

Suspended solid

Bathymetry

Weight

1

13/10

2

8/3

7

0.3579

10/13

1

4/3

2

5

0.2638

Chlorophyll

1/2

3/4

1

5/3

4

0.1963

Suspended solid

3/8

1/2

3/5

1

2

0.1278

Bathymetry

1/7

1/5

1/4

1/2

1

0.0542

Sea temperature

Subsequently, an aquaculture suitability scoring system, from 1 to 6 (slightly modified from Pérez et al. 2005), was applied to the geographical data-sets in order to normalize each considered environmental or infrastructural factor. The most suitable zones for Bay scallop culture received a score “6” while not suitable zones received a score “1” (table 2). The formula used to calculate the total suitability score for each location inside the study area, was Malczewski’s (2000): V(xi) = ∑j wj rij, which considered the relative weight and the suitability score of each environmental or infrastructural factor. In such formula,

“wj” represents the relative weight of environmental or infrastructural factor “j”, and “rij” is the score attributed to the factor “j” in the specific location “i”. The environmental and the infrastructural suitability maps thus obtained, were successively overlaid to compute the final aggregated suitability aquaculture map. The overlay was executed according to three scenarios (table 3), each giving a different importance to the environmental and infrastructural models.

Table 2. Suitability scores for Bay scallops aquaculture obtained from the normalization of environmental and infrastructural factors. Ranges and scores are modified from: Shriver 2002; FAO 1991, Korol 1985; Kirby-Smith 1970.

Score 1 Not suitable

Temperature Salinity (°C) range

Chlorophyll-a range (µg l-1)

Suspended solid (mg l-1)

Bathymetry (m)

Infrastructures distance (miles)

<5

1-40

1-2

> 55

<3

>8

5-14

10-40

2-5

40-55

3-6

6–8

3 Lowly suitable

15-18

13-37

4-5

25-40

6-10

5-6

4 Suitable

18-23

18-34

5-10

15-25

10-13

4-5

5 Highly suitable

23-26 and > 28

23-31

8-15

5-15

13-15

3-4

26-28

25-29

> 13

<5

> 15

<3

2 Very-lowly suitable

6 Very-highly suitable


DOI number: 10.5027/jnrd.v3i0.01

Scenarios analysis to increase Bay scallop culture and decrease capture fisheries Cat Ba and Cat Hai islands (Cat Hai district) are the only two populated islands of the studied archipelago, with roughly 23 thousands inhabitants of which 500 got their main income from capture fishing. An additional 3700 people live on floating villages off the coasts (Tuan 2009). Although there is no certainty about the number of people living on the floating villages that are engaged in capture fisheries, an approximate figure could be obtained considering the 2009 general census of Vietnam. Such census reports that the active (working) individuals in Vietnam amounts at 55 % of the total population (General Statistic Office of Vietnam 2010a). Applying this ratio to the floating village population, we can assume that about 2000 individuals are active, namely involved in capture fisheries. This would project at approximately 2500 the people nowadays engaged in capture fishing activities in Cat Hai district. Such figure is corroborated by the fishing boats registry of the Department of Agriculture and Rural Development of Hai Phong (2009), which counts in the district of Cat Hai about 500 vessels, developing roughly 20 thousands HP. It is also assumed that the Cat Hai fishing fleet has the same capture fishing productivity of the other Vietnamese fishing vessels operating in the larger context of the Tonkin Gulf, which in 2008 reached 0.24 ton HP-1 year-1 (RIMF 2009). Therefore, the capture fishing yield in Cat Hai district can be estimated at about 4800 tons per year, roughly equivalent to a total of 5.5 million of USD per year, or about 2300 USD year-1 per fisherman. Scientists, policy makers and planners agree on the fact that

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Vietnam’s fisheries resources are over-exploited (Pomeroy et al. 2009), yet there are divergences on defining the sustainable level of fishing activities. Several studies assessed the maximum sustainable yield for capture fishery in Vietnam as the one reached in the year 1990 (van Zwieten et al. 2002; Thang 2007), when the capacity of the Vietnamese fishing fleet was about half of nowadays. This estimation implies that to achieve sustainable capture fisheries in the district of Cat Hai, almost half of the local fishermen, circa 1250, should change the main source of their livelihoods. Hence, to assess the possibility of replacing capture fisheries with Bay scallop aquaculture, the potential production of such mollusc was calculated. The sea surface effectively used in a Bay scallop plant was computed as the product of the cage surface by the number of cages deployed in each hectare, multiplied by the number of hectares used for aquaculture operations. The cage for farming Bay scallops is a tube shaped net, woven with polyethylene threads, separated into eight chambers by plastic disks of 17.5 cm radius. Thus, the total cage surface amounts to 0.769 square meters (the cage’s number of chambers by its area). Considering that the culture cages are hooked one meter apart on a 100 meters long floating lines, and that the spacing between each floating line is eight meters, every hectare contains 13 floating lines. Thus, the total number of cages for each hectare is 1300. Therefore, multiplying 1300 (cages) by 0.769 (total cage surface) we obtain an effective farming surface of 1000 square meters per hectare of sea surface. The study area’s potential production was thus computed multiplying the effective farming surface by the Bay scallop culture productivity values (discussed in : Bay scallop aquaculture production assessment).

Results Study area environmental setting and circulating seawater Modeled surface temperature of seawater in the culture zones ranged between 19.7°C and 33.2°C. The lowest average temperature occurred in the winter (from January to March), reaching 21.0°C, gradually increasing to 33.2°C in June and July. Modeled seawater salinity varied from 24.7 to 33.0. It reached values higher than 30.0 from the end of November to May, slowly decreased to intermediate levels during the months of June and July, to markedly drop to values lower than 25.0 from August to October. Modeled seawater concentration of chlorophyll-a, averaged 11.5 µg l-1, ranging from a minimum value of 2 µg l-1, during the dry season (from November to May), to values higher than 15 µg l-1,(from August to October). Overall, chlorophyll-a concentration measured values higher than 13 µg l-1, in two third of the studied area. In terms of suspended solid in the seawater, the modeled concentrations averaged 20 mg l-1, with values ranging from 4 to 25 mg l-1 in the eastern section of the study area, and reaching peaks of 45 mg l-1 in the Red River Delta. Finally, the bathymetric data showed an average depth of the sea bottom at about 10 meters, ranging from about minus 2 to minus 15 meters. The sea bottom topography presents two main morphology types: i) an irregularly eroded limestone platform with emerged pinnacles

and submerged canyons reaching depth of 15 meters, in the eastern section of the study area, and b) the Red River delta, showing a more regular morphology averaging minus 5 meters, in the western part. Gonads development Gonads observation at the microscope indicated that Bay scallops were in stage I - II (not mature) from January to June. A small number of gonads entered in stage III as early as July, when their eggs and sperm could be distinguished (in some wet scanned specimens, the sperm was found to slowly move). The majority of sampled Bay scallops showed stage III gonads from mid-July to mid-August. Their gonads showed quick changes in volume and color, acquiring a pale orange color in the ovarian region and a creamy color in the testicular region. By the end of August through the end of October all collected scallops had mature gonads (stage III and IV), and by December most individuals had finished their reproductive cycle showing transparent and soft gonads (stage V). Data, summarized in Figure 3, show that Bay scallop’s spawning season in Ha Long bay and Cat Ba Island started in August and continued throughout November, with the spawning peak between September and October.


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Figure 3. Gonadsâ&#x20AC;&#x2122; development during the year

Site suitability analysis: environmental and infrastructural models Spatial analysis of the studied environmental and infrastructural factors were performed to: (i) display the areal extension of the six aquaculture suitability scores (shown in table 2) computed for each considered environmental and infrastructural factor (Figure 4), and (ii) to map the areas providing various conditions of growth and survival conditions for Bay scallops (Figure 5 and 6, and table 3). In particular the environmental suitability model showed that: (a) the zone located

in the proximity of the South-East coast of Cat Ba Island, circa 10 % of the total study area, is very-highly suitable (score 6) for growth of Bay scallops, (b) the zone stretching from the South and West coast of Cat Ba Island to the Nam Trieu estuary, roughly half of the study area, is highly suitable (score 5), (c) the Ha Long Bay sea zone, East and North of Cat Ba Island, about 39 % of the study area, is environmentally suitable (score 4), and (d) the inner part of Nam Trieu estuary and the deepest area far away from Cat Ba Island, only 2 % of the study area, is lowly suitable (score 3). This was the lowest environmental suitability score measured in the total study area.

Table 3. Percentage of total study area for each suitability score for the environmental and infrastructural models, as well as for the three formulated suitability scenarios for Bay scallop aquaculture in Ha Long Bay and Cat Ba Island.

Score

Environment

Environment

Scenario A

Scenario B

Scenario C

Infrastructures

(%)

(Environmental predominance) (%)

(environment tantamount infrastructures) (%)

(Infrastructural predominance) (%)

(%)

100

65

50

35

0

Infrastructure

0

35

50

65

100

1

Not suitable

0

0

0

0

34

2

Very-lowly suitable

0

1

9

34

24

3

Lowly suitable

2

37

46

28

12

4

Suitable

39

34

21

17

11

5

Highly suitable

50

20

15

10

8

6

Very-highly suitable

9

8

9

11

11


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Figure 4. Percentage of total study area for the six suitability scores computed for each one of the environmental and infrastructural factors. CHL-a is chlorophyll-a; TSS is total suspended solid; TEMP is temperature; SALT is salinity; BATHY is bathymetry; INFRA is infrastructure.


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Figure 5. Environmental suitability map for Bay scallop aquaculture in Ha Long Bay and Cat Ba Island. Suitability increases with score value (1 = Not suitable zones; 6 = Very-highly suitable zones).

The spatial analysis for the infrastructural suitability model, using the distance ranges shown in the last column of table 2, showed that in terms of available infrastructures for aquaculture, circa 11 % of the study area (Figure 6 and table 3) is very-highly suitable (score 6), some 8 % highly suitable (score 5), and another 11 % suitable (score 4). These waters surround the urbanized West coast of Cat Ba Island, which contains numerous landing points and fish markets. Conversely, 12 % and 24 % of the total studied area, appeared

respectively lowly (score 3) and very-lowly (score 2) suitable for Bay scallop culture. These are the Northern part of Ha Long Bay and the open water West of Cat Ba Island. Finally, about 34 % of the studied area is too far from both main fish markets or landing points, and rank unsuitable for aquaculture operations (score 1). These are the waters surrounding the rugged and undeveloped North and East coast of Cat Ba Island (Figure 6).

Figure 6. Infrastructural suitability map for Bay scallop aquaculture in Ha Long Bay and Cat Ba Island. Suitability increases with score value (1 = Not suitable zones; 6 = Very-highly suitable zones).


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The combination of the different environmental and infrastructural factors (table 3) produced three suitability scenarios. The first one, which give to the environmental factors the greatest relative importance (Figure 7A, table 3), showed that only 8 % of the study area is very-highly suitable (score 6), 20 % ranked highly suitable (score 5), and 34 % suitable (score 4). Conversely, some 37 % of the total surface area ranked lowly suitable (score 3) and 1 % very-lowly suitable (score 2). No zone was identified unsuitable (score 1) for Bay scallop culture. In scenario B, which attributes the same weight to the environmental and infrastructural models (Figure 7B, table 3), slightly expands to 9 % the very-highly suitable zone (score 6). However, the highly suitable and the suitable zones (scores 5 and 4) decrease sensibly from 54 to 36 %. Not surprisingly, the lowly and very-lowly suitable zones (scores 3 and 2) increase to 55 %. In this scenario too, no zone was identified unsuitable (score 1) for Bay scallop culture. Finally, in scenario C, which give to the infrastructural factors the greatest relative importance (Figure 7C, table 3), further expands to 11 % the very-highly suitable zone (score 6), yet the sum of the highly suitable and suitable zones (scores 5 and 4) decrease to 27 % of the total study area (circa half of that computed in the first scenario). Also the lowly suitable zone (score 3) decrease to 28 %, but the very-lowly suitable (score 2) sharply increase to 34 %. In the end the sum of the lowly and very-lowly suitable zone of scenario 3 is similar to that of scenario 2. Here too, no zone was identified as unsuitable (score 1) for Bay scallop culture. The most noticeable differences passing from scenario A to C (Figure 7) in the waters surrounding Cat Ba Island, are between Ha Long Bay (North and East), Cat Gia and Lan Ha Bays (South) and the western coast of Cat Ba and Cat Hai Islands in front of the Red River Delta (Figure 1). Increasing the importance of infrastructural factors the majority of Ha Long Bay, Cat Gia Bay and Lan Ha Bay waters decrease their aquaculture suitability score, with some zones passing from score 5 (highly suitable) to 2 (very-lowly suitable), whereas the water facing the Red River Delta increase the overall zone with score 6 (very-highly suitable). Bay scallop aquaculture production assessment Bay scallop aquaculture may yield between 45 and 52.5 tons hectare-1, if salinity, temperature and suspended solid are optimal, and the carbon content of waters is over 150 µgC l-1 (FAO 1991). In the study area these conditions are met in the highly and very-highly suitable zones for Bay scallop aquaculture, which are characterized by Chlorophyll-a concentration higher than 8 µgChl-a l-1 (table 2). As a matter of fact, considering that tropical coastal areas, are characterized by a carbon/ Chlorophyll-a ratio higher than 20 (Behrenfeld et al. 2005), the carbon concentration of the study area’s highly and very-highly suitable zones is higher than 160 µgC l-1. As mentioned earlier, a typical Bay scallop farm has an effective farming surface of 1000 square meters per hectare of sea surface, namely a ratio 1/10 between the effectively farmed zone and the total area occupied by the farming plant. Therefore, just the study area’s very-highly suitable zone, which extends for 7325 hectares, could easily yield a minimum 33 thousand tons year-1 of Bay scallop.

Figure 7. Maps of the three formulated suitability scenario for Bay scallop culture in Ha Long Bay and Cat Ba Island. In map (A) the environmental factors were more important than infrastructural ones (65 % and 35 % respectively); in map (B) the environmental and infrastructural factors have the same weight; finally in map (C) the infrastructural factors are more important than environmental one (65 % and 35 % respectively). Suitability increases with score value (1 = Not suitable zones; 6 = Very-highly suitable zones).


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Discussion The environmental suitability model developed for this study showed that 98 % of Ha Long Bay area is suitable to develop Bay scallop culture. Temperature and bathymetry showed no sensible differences, while salinity level, chlorophyll-a, and suspended solid concentration, displayed seasonal variations. These last three parameters are influenced by the tropical storms hovering over Northern Vietnam, which, between late summer and early autumn, cause rivers to discharge large amount of freshwater and sediments in the coastal waters. Several studies have shown that environmental suitability for Bay scallop culture decrease with low concentration of chlorophyll-a, high concentration of suspended solid and unstable salinity (Barber and Blake, 1991; Borcherding, 1995; Martınez et al., 2000a,b; Bohle, 1972, Palmer, 1980; Navarro and Gonzales, 1998). Indeed, molluscs living in estuaries and coastal ecosystem are exposed to long-term (rainy seasons) and short term (tidal regime) fluctuation of salinity and changes of suspended solid and nutrients concentrations. In the specific case of the studied species such changes appear to decrease the overall growth of adult scallops, which occur over a very narrow range of temperature and salinity, but affect much less the development of larvae capable to survive harsher conditions (Tettelbach and Rhodes 1981). Bay scallop appeared to adapt its life cycle to the local environmental conditions. The scallops grew both shell and body mass during the “dry stable-salinity season” (December-July); while during the “wet unstable-salinity season” (August-November), the scallops use the energy for gametogenic process and gonads development. The best environmental conditions for Bay scallop culture were found in the zones farther away from the direct impact of estuarine waters, namely the Southeast coast of Cat Ba Island. These waters offered an optimal compromise of salinity level and chlorophyll-a concentration. The Vietnamese government has recognized the environmental value of this zone, and included it in the National Protected Area of Cat Ba Island established in 1986 (Council of Ministers of Vietnam, Decision No. 79/CT). In terms of infrastructural suitability, the model detailed the West and South costs of Cat Ba Island as the most suited marine zones for Bay scallop culture. As a matter of fact, several fish markets, and landing points are present in the West and South coast of Cat Ba and Cat Hai Islands. Conversely, the uninhabited North and East coasts of Cat Ba Island and the Ha Long Bay archipelago, was the less suited zones for Bay scallop culture. The different combination of the environmental and infrastructural factors (table 3), highlighted the zones that are either ready to be exploited for scallop culture (information important to the fishing industry), or that are worth some investments to develop the necessary infrastructures (information important to the local government). Increasing weight of infrastructural factors over the environmental ones, lead to an overall decrease of suitability of Bay scallop culture, with the exception of the waters West and South of Cat Ba Island. Hence, infrastructural inadequacy appears to be a limiting factor for Bay scallop culture in the studied area. Decreasing distances and costs of transportation between the culture sites and landing points, markets or post harvesting processing facilities, would

very likely incentive fishermen to conduct aquaculture operations in the environmentally suitable, but currently undeveloped, zones of the Southeast coast of Cat Ba Island. Notwithstanding, much of these coasts are under the management of the Cat Ba National Park, and any infrastructural expansion have to consider the park’s restrictions to maintain as much as possible a pristine environment. Bay scallops are cultured without food supply and have a low environmental impact (Crawford et al., 2003), and if properly carried out is a sustainable activity to include in the coastal zone management scheme. In addition, properly executed poly-culture farming, where the wastes from one specie become resources for the other (Stead et al., 2002; Shumway et al., 2003), could further improve environmental sustainability of molluscs aquaculture. Poly-culture farming has been demonstrated to be a viable option for shellfish and seaweed (Yang et al. 2005), developing a “blue-green” revolution (Ahmed et al. 2012) in a marine environment. The use of this aquaculture model would make possible to expand Bay scallop culture into the protected areas of the Ha Long Bay archipelago, where seaweeds are already collected by local farmers as food or feed for husbandry. Indeed, these above described methods of sustainable aquaculture could provide an alternative livelihood source for local residents. As a matter of fact, the 7325 hectares (the very-highly suitable zones of the study area) could yield a minimum of 33 thousand tons year-1 of Bay scallop, that sold with an average price of about 0.8 USD per kg (General Statistic Office of Vietnam 2010b), would add up to 28 million of USD per year (3600 USD per hectare/year). For example, these aquaculture activities could provide an alternative livelihood source to the 1250 fishermen that should forgo capture fishing. Considering the above production figures, to maintain the average income of about 2200 USD year-1 per fishermen, it would be enough to assign 0.67 hectares to each fisherman. This amounts to 838 hectares, namely 11.4 % of the very-highly suitable zones for Bay scallop aquaculture. However, committing such a large portion of the waters surrounding Cat Ba and Cat Hai Islands to develop Bay scallop culture although feasible, it is not really sustainable. First of all because the area is used also for other lucrative activities such as tourism, transportation, etc. In addition, it is never wise to base the economy of a region on a mono-culture, both in terms of market vulnerability and ecological consequences. To achieve a sustainable level of fishing activities, two possible management scenarios for Bay scallop production and branding can be envisioned. The first scenario would require enhancing the fisheries infrastructures (landing points and fish markets) in the East part of Cat Ba Island, to increase the overall surface of the very-highly suitable zone. Therefore, the 775 hectares to be distributed to the fisherman would represent a lower percentage of the very-highly suitable zone. However, for this option to be implemented the Vietnamese government should allow the development of such infrastructures, also in the East part of Cat Ba Island, currently included in the conservation area of Cat Ba National Park. The second scenario, could represent a possible compromise between strict conservancy and massive aquaculture development, but would require the Government to consent and support the


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construction of post harvesting processing plants for Bay scallop. Such processing plants would strengthen the marketing capacity of the local fishermen, allowing them to brand their products and sell it in national and international markets. According to the Vietnam Association of Seafood Exporters and Producers in 2011 the price of exported molluscs averaged 3.6 USD per Kg. More precisely, US retailers acquired Bay scallop at 2-2.5 USD per Kg from South Chinaâ&#x20AC;&#x2122;s producers in 2006 (Deward 2006). These values represent a threefourfold increment from the local market average price of 0.8 USD per Kg (General Statistic Office of Vietnam 2010b). Obtaining a higher added-value on their products would allow even more fishermen to leave capture fishing (circa 250) without the need to allocate new marine areas to aquaculture operations, or constructing new fisheries infrastructures in the protected areas of Cat Ba National Park. Moreover, it should be considered that, although

11

this scenario would reduce the overall fishing fleet power to 10 000 HP (half of the currently deployed 20 000 HP), in fact it increases the pro capita fishing capacity of the remaining fishermen from 8 to 10 HP/fishermen. This would increase the capture and consequent income pro capita at circa 3150 USD per year. This estimate is obtained considering an average selling price of 3 USD for Kg of Bay scallop. Considering that to reach an annual income of 3,150 USD an aquaculturist would need to farm about 0.23 hectares of waters, the post-processing and branding scenario could be implemented by just using 5.2 % of the available highly suitable area. To achieve the same level of income without the post-processing and branding strategies an area twice as large would be required. Table 4 and figure 8 synthesize and compare advantages and disadvantages of these two mentioned management options for Bay scallop production.

Table 4. Two management scenarios for Bay scallop production and branding

Scenario

(a)

(b)

Action

Construction of fishing infrastructures (fish markets, landing points, etc.) in the East coast of Cat Ba island

Construction of post harvesting processing facilities in Cat Ba Island (West coast)

Goal

Extend the very-highly suitable zone for Bay scallop culture

Increase the added value of the final products (produce branding)

Advantages

Reduce the capture fishing pressure to the level of 1990 Maintain farmersâ&#x20AC;&#x2122; investments low No farmer coordination required

Reduce the capture fishing pressure to the level of 1990 No impacts to the conservation area of Cat Ba Island National Park New job opportunity in the mainland

Disadvantages

Impacts to the conservation area of Cat Ba Island National Park Resettlement of operations in the West part of Cat Ba Island

Continuous investments required to maintain production Coordination among farmers required develop and protect a name brand in the national and international markets

Conclusion Bay scallop have shown to be able to adapt its short life cycle to the strong bi-seasonal tropical climate. Moreover, the Ha Long Bay environmental condition (temperature, salinity, hydrodynamic and nutrients), that are optimal for Bay scallop, are quite common in several tropical embayment. Therefore, Bay scallop could be one of the sources for alternative income for tropical developing countries. The limiting factor appears to be the infrastructure development (landing points, roads and processing plants). Bay scallop may potentially be able to replace fisheries incomes. However, coastal managers have to consider that profits from aquaculture could not lead automatically to decrease fishing effort, but opposite be used for further investment in fishing (Sievanen et al. 2005). Therefore, strategies to downgrade capture fisheries should combine Bay scallop

farming with no-take zone and marine protected areas (MPAs), incentive the disposal of fishing vessel and implement efficient patrol to enforce such restrictions. The process to assign areas for farms is similar to the practice of creating MPAs (Eriksson et al, 2012) and may create space conflicts among different users. The successful design of MPAs in developing countries requires a participatory approach and good governance (Francis et al. 2002). In the present method, the involvement of local communities for the site selection is achieved through the AHP.


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Figure 8. Decreasing number of capture fishermen in the two proposed aquaculture scenario for Argopecten irradians. Currently in the Cat Ba Island archipelago there are 2500 capture fishermen deploying a fishing capacity of 20 thousands HP. With the post processing and branding scenario the pro capita year income will increase from 2300 USD (current fishermen income) to 3150 USD, with an increase of + 37 %.

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JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT Hydromorphological assessment as a tool for river basin management: The German field survey method Georg Meiera b *, Thomas Zumbroichb, Jackson Roehriga Cologne University of Applied Sciences - Institute for Technology and Resources Management in the Tropics and Subtropics, Betzdorfer Strasse 2, 50679 Köln, Germany a

b

Planning Bureau Zumbroich GmbH & Co KG, Breite Strasse 21, 53111 Bonn, Germany

*

Corresponding author : Georg Meier, Email: georg.meier@fh-koeln.de

Article history Received 18.09.2012 Accepted 15.10.2012 Published 04.02.2013

Keywords Hydromorphology River assessment Restoration Monitoring Rur River basin

Abstract Physical habitat characteristics are of great importance for the ecological integrity of rivers and creeks. The assessment of these hydromorphological qualities is a fundamental component of sustainable river basin management and ecologically oriented river development. This paper describes the German field survey method for hydromorphological assessement of streams and points at its potential as a tool for river basin management. We present examples for the application of the method at different management scales: analyzing the overall hydromorphological state at the river basin scale, describing specific hydromorphological characteristics at the river reach scale and monitoring the success of restoration projects at the river segment scale. We show that the German field survey method proved to be an easy-to-apply and efficient tool for river basin management since its introduction in the year 2000. Beside the method’s potentials also several drawbacks have to be considered regarding its application in other regions of the world.

Introduction The assessment of river ecosystems is gaining importance worldwide. Alone in the countries which implement the European Water Framework Directive (EC 2000) about 300 different biological stream assessment methods are in use (Birk et al. 2012). The evaluation of the ecological status of rivers based on biological indicators also plays an increasingly important role in other parts of the world (Thorne et al. 1997; Gerson Araujo et al. 2003; Bozzetti and Schulz 2004; Haase and Nolte 2008; Moya et al. 2011; Couceiro et al. 2012).

The question of whether or to what extent the state of a stream can be described as natural or unnatural, however, cannot be answered solely on biocenotic-taxonomic interpretations of biological indicators such as benthic invertebrates and fish. The use of these bioindicators has to take into account the hydromorphological characteristics of the watercourse under consideration in order to validate the interpretation of the biological sampling results. A comprehensive evaluation of ecological stream quality must therefore always include


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a hydromorphological assessment based on natural reference conditions. Only in this way the information obtained from biological monitoring can be interpreted correctly to recognize ecological deficits and target improvements (Verdonschot et al. 2012). Therefore, methods that characterize the hydromorpology of rivers and assess river habitat quality are becoming increasingly important as an element of decision-making in river basin management (Raven et al. 2002). Several methods for characterizing the physical structure of rivers and assessing habitat quality have been developed since the early 1990s and described in several reviews (Raven et al. 2002; Balestrini et al. 2004; Kondolf and Piégay 2005; Davy-Bowker and Furse 2006; Kamp et al. 2007; Šípek et al. 2010; Ilnicki et al. 2010; Scheifhacken et al. 2012). However, no detailed description of the German field

15

survey method in terms of validity, applicability, monitoring capacity and potential for the usage outside its designated geographical region is given so far. This paper describes the German field survey method for hydromorphological assessment of streams (Zumbroich 2008). We applied this method assessing 931 km of streams and creeks in the Rur River Basin in the Western German. The potential of the method as a tool for river basin management is presented by three examples: analyzing the overall hydromorphological state at the river basin scale, describing specific hydromorphological characteristics at the river reach scale and monitoring the success of restoration projects at the river segment scale. Furthermore, the applicability of the method is evaluated by interviewing 36 members of the Rur river basin mapping campaign.

Materials and Methods Research area The German field survey method was applied in the Rur River Basin which is located mainly in the German state of North RhineWestphalia sharing small parts with the Netherlands and Belgium (Figure 1). The river basin covers an area of 2340 km² and contains approximately 2500 km of rivers and creeks. The main Rur River

bridges a height difference along its 165 km course of 643 m, with its source located at 660 m.a.s.l. and its mouth at 17 m.a.s.l. The average annual rainfall is 855 mm. In the southern highland regions an annual rainfall of up to 1560 mm is possible (MUNLV 2009). The study area is dominated by rural land use types (grassland, forest and cropland) with the exception of several urban areas (approx. 10 % of the total area).

Figure 1. Research area – The Rur River basin


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This river basin was chosen for this study due to its great variability of river types. The Rur River basin takes part in two ecoregions according to Illies (Illies 1978; Hering et al. 2004) – the Western plains (Ecoregion 13) and the Western highlands (Ecoregion 8). The ecoregion approach serves as a basis to define 28 different German river types (Pottgiesser and Sommerhäuser 2008) of which ten can be found in the Rur River basin (Table 1).

16

The stream types of the Western plains are characterized by meandering planform, no pronounced valley forms and sandy river bed substrate with a high share of organic material. The stream types of the Western highlands are characterized by V- and U-shaped valleys, low sinuosity and high substrate diversity composed of sand, gravel, rocks and dead wood.

Table 1. Occurrence and share of stream types in the research area.

Water bodies

Length (km)

Share of overall length (%)

Small loess-loam-bottom streams in the lowlands

20

146

16

Small streams in floodplains

19

123

13

Mid-sized to large gravel-bottom streams in the lowlands

6

73

8

Small gravel-bottom streams in the lowlands

11

43

5

Small sand-bottom streams in the lowlands

4

25

3

Small streams with organic substrates

4

18

2

Small siliceous sandstone streams

4

14

1

Small siliceous cobble-bottom streams in lower-mountainous areas

49

334

36

Mid-sized siliceous cobble/boulder bottom streams in lower mountainous areas

14

134

14

Small cobble-bottom streams in calcareous lower mountainous areas

4

23

2

135

931

100

Ecoregion

Western plains

Western highlands

Stream type

 

The herein presented work focuses on the German part of the river basin covering 2085 km² (91 % of the total area). Only those streams were considered in this study, which comprise a catchment area of at least 10 km². Catchments with this minimum size represent the basic management units according to the European Water Framework Directive. Therefore 931 km of rivers and creeks of the Rur River Basin were assessed by using the German field survey method (approximately 37 % of the river basin’s streams).

German field survey method for hydromorphological assessment of streams The German field survey method assesses the structural quality of streams and serves as the basis for local to regional river maintenance and development (LAWA 2000). The streams are assessed over their full length, dividing them into segments. The segment length is determined by the channel width (e.g. a 100 m length is used

Overall sum

for a river up to 20 m wide; 500m-segments for a river wider than 20 m) (Scheifhacken et al. 2012). The determination a segment’s hydromorphological quality is based on 25 parameters, which are assessed by visual inspection (Table 2). For each parameter, the observed state is determined using a series of options: for example, low and very high are two of five options for describing flow diversity. The 25 single parameters are stepwise aggregated into six main parameters, which are further aggregated into river sections (river bed, river banks and floodplain) and a final overall score resp. class. This aggregation is based on simple mean value calculation. The plausibility of the field results is tested by a cross-check using index-based and functional unit approaches (Figure 1). Deviations between the computed index-based scores from the single parameters and the functional units derived from expert opinion are corrected and thereby the assessment quality is assured (Raven et al. 2002).


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Table 2. The 25 single parameters of the German field survey method and their aggregation into main parameters and river sections. River section

Main parameter

1 - Planform

River bed

2 - Longitudinal profile

3 - River bed structure

4 - Cross-section River banks

5 - River banks structure

Floodplain

6 - Floodplain

Code

Single parameter

1-1

Sinuosity

1-2

Erosion at bends

1-3

Bars

1-4

Special features (indicating natural channel dynamics, e.g. large wood, islands, widening)

2-1

Artificial barriers (limiting continuity of flow, sediment and migration for biota, e.g. weirs)

2-2

Artificial impoundments

2-3

Culverts

2-4

Riffles and steps

2-5

Flow-diversity

2-6

Depth-variability

3-1

Dominant substrate

3-2

Bed-fixation

3-3

Substrate-diversity

3-4

Bed features (e.g. scour and backwater pools, rapids, cascades)

4-1

Cross-section form

4-2

Cross-section depth

4-3

Bank erosion

4-4

Cross-section width variability

4-5

Bridges

5-1

Riparian vegetation

5-2

Revetment/Bank protection

5-3

Bank features

6-1

Land-use

6-2

Riparian buffer-strip

6-3

Impeding features

Figure 2. Workflow of the German field survey method (LAWA 2000); modified.


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The hydromorphological assessment is calibrated against a hypothetic natural or near-natural reference state of the above mentioned river types (Šípek et al. 2010). Therefore, a pre-requisite for the survey is to define the potential natural condition of a river as the basis for the hydromorphological quality assessment (Kamp et al. 2007). The final assessment comprises a seven-band classification ranging from ‘unchanged’ to ‘completely changed’ (Table 3). Analyzing the hydromorphological state at different spatial scales The overall hydromorphological state is analyzed for the entire the Rur River Basin. After scoring the overall assessment of each stream segment according to Table 3, the results were grouped into different regional subsets and compared with each other (Rur River vs. tributaries, Western Plains streams vs. Western Highlands streams). The potential difference regarding the hydromorphological quality of these subsets was determined by non-parametric Mann-WhitneyTest with a statistically significant level set at p < 0.05 in the Section Assessment results at different aggregation levels and spatial scales. The method’s capability for detecting specific hydromorphological potentials and deficits is given on the river reach scale. As an example the assessment results of the main parameter River Bed Structure and the single parameter Riparian Buffer-Strip for the Rur River are presented in Section Assessment results at different aggregation levels

18

and spatial scales. A detailed analysis of the 25 single parameters is demonstrated for three 500m-segments of the Rur River (one restored segment and one up- and downstream of the restoration, respectively) in Section Assessment results at different aggregation levels and spatial scales. The monitoring capacity of the method is tested by comparing the results with former assessments of the restored stream segment. The restoration effects were analyzed based on the differences in hydromorphological quality before and after the restoration. Evaluation of the method’s applicability We conducted interviews with 36 experienced staff-members of the mapping campaign using a standardized ordinal-polytomous questionnaire (Oppenheim 2000) with a five-step verbal rating scale (Table 6). All participants hold at least a Bachelor degree in Geography. The question of interest for this study was: ‘How do you rate the applicability of the single parameters of the German field survey?’. In this case the applicability signifies the assessability of the hydromorphological element or process, which is represented by each parameter and has to be observed and evaluated in the field (e. g. bank erosion). We analyzed the results by calculating the percentage of the campaign members answers for each single parameter.

Table 3. Scores and classes of the German field survey method for river habitat monitoring and assessment. The final scoring index is the result of mean value calculation of the single parameters.

Final scoring index

1.0-1.7

1.8-2.6

2.7-3.5

3.6-4.4

4.5-5.3

5.4-6.2

6.3-7.0

Final class

1

2

3

4

5

6

7

Description

Unchanged (natural)

Slightly changed

Moderately changed

Distinctly changed

Obviously changed

Strongly changed

Completely changed

Table 4. Standardized answers of the questionnaire regarding the assessment difficulties of the 25 single parameters of the German field survey method. Standardized answer

Description

Very easy

The assessment of the parameter is feasible without any difficulty in all cases.

Easy

The assessment is problematic only in exceptional cases.

Intermediate difficulty

The assessment is problematic in some cases.

Difficult

The assessment is often problematic.

Very difficult

The assessment is always problematic.

Results Assessment results at different aggregation levels and spatial scales River basin scale According to Figure 3 the overall hydromorphological quality

(final class according to Table 3) of the main Rur River shows to be significantly better than the hydromorphological quality of its tributaries (MEDIAN Rur River = 4.1, MED Tributaries = 4.7, p < 0.001). The overall hydromorphological quality inside the Ecoregion Western Highlands shows to be significantly better than the


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hydromorphological quality inside the Ecoregion Western Plains (MED Western Mountains = 4.0, MED Western Plains = 5.4, p < 0.001). This draws the attention regarding restoration efforts towards the tributaries of the Western Plains. By looking further into the different river sections (river bed, river

19

bank and floodplains) and their hydromorphological quality the restoration focus in consequence is on the river banks of tributaries in the Western Plains and the floodplains of tributaries in both the Western Plains and the Western Mountains (Table 5). Also the river bed of the Rur River and its tributaries inside the Western Plains should be taken into consideration for restoration.

Figure 3. Comparison of the overall hydromorphological quality inside regional subsets. The upper two histograms show the differences of hydromorphological quality by aggregating the water bodies qualities into main Rur River and its tributaries respectively. The two histograms below aggregate the same water bodies according to their ecoregion classification.

Table 5. Comparison of the hydromorphological quality inside regional subsets (Main river vs. tributaries; ecoregion Western plains vs. ecoregion Western mountains) and regading different river sections. (** indicates a significant difference at p <0.001; * indicates a significant difference at p <0.05).

Subset Rur River

River bed [MED]

River banks [MED]

Floodplains [MED]

4.0*

3.8**

4.0**

Tributaries

4.3*

4.8**

5.5**

p-value

0.04

<0.001

<0.001

Subset

River bed [MED]

River banks [MED]

Floodplains [MED]

5.3**

5.5**

5.5**

Western plains Western mountains p-value

3.3**

3.5**

5.0**

<0.001

<0.001

<0.001

River reach scale At the river reach scale specific river basin management can be handled, such as: Good habitat characteristics for benthic invertebrates and fish provided by river bed: selection of stream segments with unchanged,

slightly changed or moderately changed river bed structure (Assessment class of the main parameter River bed structure â&#x2030;¤ 3). Riparian buffer strips missing or not fulfilling certain requirements regarding width and vegetation composition: selection of segments with distinctly to completely changed buffer strips (Assessment class of single parameter Riparian buffer strip > 3)


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In the case of the Rur River improving river bed structures (e.g. installation of fixed large dead wood) and riparian buffer strip conditions (e.g. land use conversion and reforestation) should be focused in the lower and middle reach (Figure 4).

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a straight-forward evaluation of single stream segments and their hydromorphological qualities inside the river bed, river banks and floodplains. It also enables a fast comparison of adjacent segments. In Figure 4 for example, clear differences in all sections can be observed for the segment 458 and its adjacent segments up- and downstream.

River segment scale At the river segment level the hydromorphological differences represented by river beds, river banks and floodplains can be clearly observed in Figure 5. The 5-band representation of provides

The analysis of the single parameters show that the three segments mainly differ in terms of sinuosity, flow-diversity, depth-variability, cross-section variability and the characteristics of the riparian vegetation and nearby land use (Table 6).

Figure 4. Analysis of the assessment results regarding specific river basin management issues. The colors in this case do not represent assessment classes. They illustrate hydromorphological potentials (green) and deficits (red).

The method can also be used for a rapid monitoring of restoration success in terms of hydromorphological alteration. The segment 458 of the Rur River was assessed using the German field method before its restoration in 2001 and eleven years later in 2012 (Figure 5 and Table 7). The hydromorphological improvement can clearly be identified for all main parameters.

However, for a detailed comparison of a â&#x20AC;&#x2DC;before-after restorationâ&#x20AC;&#x2122; habitat quality taking into account species-specific ecological requirements, high-resolution assessment methods have to be applied (Harby et al. 2005; Mouton et al. 2007; Parasiewics and Walker 2007).


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Figure 5. Spatial comparison of a restored segment (ID = 458) with segments upstream (ID = 463) and downstream (ID = 453) using a 5-band representation of the hydromorphological quality (river bed, river banks left/right, floodplains left/right).

Parameter applicability The 36 members of the mapping campaign evaluated none of the 25 single parameters as very difficult to assess. The parameters sinuosity, special features, substrate diversity, bridges, land use and impeding features where evaluated as intermediate difficult or difficult by less than 10% of the campaign’s staff (light grey bars in Figure 5). With exception of the parameter substrate diversity these parameters are easy to detect in almost all situations. More than half of the 25 parameters where evaluated as intermediate difficult or difficult to assess by 10-50 % of the campaign’s staff (dark grey bars in Figure 5). Especially parameters related to the stream bed and stream bank cause some problems (erosion, bars, riffles and steps, bed fixation, bed features). These features are hard to detect in case of high turbidity, increased discharge and overgrown vegetation along the river banks. The problems caused by the identification of the riparian vegetation lies in the insufficient botanical skills of the staff – according to individual interviews with the members of the

mapping campaign. Six parameters (culverts, dominant substrate, cross-section form, cross-section depth, revetment/bank protection, riparian bufferstrip) were evaluated as intermediate difficult or difficult to assess by 50 % or more of the campaign’s staff (black bars in Figure 5). The description and assessment of anthropogenic structures like culverts is carried out by taking into account several technical parameters. Mapping staff with a geographic background sometimes lack of the necessary hydro-engineering knowledge. The assessment of the dominant substrate is often impeded by low visibility due to water turbidity. The comparison of anthropogenic altered cross-section form and -depth with the corresponding reference conditions has shown to be one of the most difficult assessment aspects. According to individual interviews this is due to the insufficient instruction in the user manual. The difficulties of detecting bank protection and delineating riparian buffer strips lay in the seasonal vegetation overgrowth.


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Table 6. Single parameter comparison of three 500m-segments (restored segment = ID 458, upstream segment = ID 463, downstream segment = 453).

Single Parameters

Segment 463

Segment 458

Segment 453

low

moderate

low

none

little

little

1-2

1-2

1-2

1-4 Special features

none

1 (island)

1 (island)

2-1 Artificial barriers

glide

none

none

>100 - 250 m

>100 - 250 m

none

2-3 Culverts

none

none

none

2-4 Riffles and steps

none

none

none

2-5 Flow-diversity

low

moderate

low

2-6 Depth-variability

low

moderate

low

dominant: gravel subordinated: sand, silt organic: none

dominant: gravel subordinated: sand, silt organic: dead wood

dominant: gravel subordinated: sand, silt organic: dead wood

none

none

none

low

moderate

moderate

1

2

1

4-1 Cross-section form

artificial

near-natural

artificial

4-2 Cross-section depth

deep

moderately deep

moderately deep

4-3 Bank erosion

none

little

little

4-4 Cross-section width variability

none

high

moderate

1

none

1

left and right: single trees

left and right: forest

left and right: single trees

left and right: reinforced with stones (500m)

left: none (250 - 500 m), reinforced with stones (50 - 100 m) right: none (250 - 500 m),reinforced with stones (100 - 250 m)

left: none (250 - 500 m), reinforced with stones (50 - 100 m) right: none (250 - 500 m),reinforced with stones (100 - 250 m)

left: 3 (dead wood) right: 3 (dead wood)

left: 1 (dead wood) right: 1 (dead wood)

left: forest (>50%); grassland, cropland, impeding features (10-50%) right: grassland (>50% ); forest, cropland, impeding features (10-50%)

left: grassland (>50%); forest, cropland, impeding features (10-50%) right: grassland (>50% ); forest, cropland, impeding features, urban areas (10-50%)

1-1 Sinuosity 1-2 Erosion at bends 1-3 Bars

2-2 Artificial impoundments

3-1 Dominant substrate 3-2 Bed-fixation 3-3 Substrate-diversity 3-4 Bed features

4-5 Bridges 5-1 Riparian vegetation

5-2 Revetment/Bank protection

5-3 Bank features

6-1 Land-use

6-2 Riparian buffer-strip

none left: grassland (>50%) right: cropland (>50%), grassland (10-50%)

left and right: narrow buffer strip (500m)

left: broad buffer strip (500m) right: forest (500m)

left: broad buffer strip (250 - 500m), none (100 - 250m), narrow buffer strip (100 250m) , forest (50 - 100m) right: broad buffer strip (250 - 500m), none (50 - 100m), narrow buffer strip (50 - 100m), forest (50 - 100m)

6-3 Impeding features

none

left: streets (medium distance) right: streets (large distance)

left and right: streets (medium distance)


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Table 7. Temporal comparison of the hydromorphological quality before and after restoration at the River Rur segment 458.

Survey 2001

Survey 2012

Improvement (classes)

1 - Planform

Main Parameters

6

4

2

2 - Longitudinal Profile

7

5

2

3 - River bed structure

5

4

1

4 - Cross-section

5

3

2

5 - River banks structure

5

3

2

6 - Floodplain

5

3

2

Final classification

6

4

2

Figure 6. Aerial photographs of the Rur River segment 458 before restoration in 2001 (left) and after restoration in 2012 (right). Source: Eifel-Rur Water Association.


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Figure 7. Combined percentage of the campaign members (n = 36) evaluating the assessment of the respective parameters as intermediate difficult or difficult (light grey bars: 0 – 10 % of campaign’s members; dark grey bars: >10 % - <50 %; black bars: ≥50 %).

Discussion In recent years the German field survey method for hydromorphological assessement has produced a most valuable primary data set on the morphological state of German streams. It has shown deficits (UBA 2010), provided strategic planning (LANUV 2011) and initiated many restoration projects (WVER 2009). Furthermore, it is accepted by the public and has found its way into the classrooms. The method is an easy-to-learn and easy-to-use tool for river basin management. The mapping campaign in the Rur River basin showed that professionals with a geographic background can apply the method after a oneweek crash course. The standardized assessment of 100m- or 500m-segments guarantees a consistent spatial and temporal comparison of river segments. The evaluation of 25 single parameters provides a sound basis for a wide range of specific scientific and management-related issues (e.g. long-term and restoration monitoring, hydromorphological deficit analysis, planning and prioritizing of restoration measures, comparative analysis of habitat quality and biological quality elements). Furthermore, the possibility to aggregate single parameter into main parameters and river sections (river bed, river banks and floodplains) allows a fast and straight-forward hydromorphological analysis of river segments, reaches and networks. Last but not least, the method is characterized by a high cost-benefit-balance: up to five kilometers (data preparation, mapping, post-processing) can be

assessed per day. However, for a convenient application in different geographical regions some limitations and specificities have to be addressed. For customized and optimized applications the following modifications are recommended: The strict 100m- resp. 500m-segment approach may mask high-value or low-value river reaches (see also Figure 4). A flexible definition of segment length may improve the realistic assessment of hydromorphological qualities along streams. In cases of long, hydromorphologically homogeneous stream sections (e.g. heavily modified or completely natural sections) a strict division into pre-defined segment lengths is not effective. In such cases a flexible division into homogeneous sections with varying lengths may be appropriate. Several of the 25 single parameters provide redundant information (e. g. depth-variability and flow-diversity, riffles/steps and bed features). A flexible set of the parameters for different purposes (e.g. overview assessment of entire rivers, detailed analysis of specific river segements) may improve the method’s efficiency. The access to rivers along their entire length – as required for the German survey method – is sometimes limited in other geographical regions. A combined approach of an overview survey using remote sensing techniques with detailed spot-checks in the field may overcome this issue. A major prerequisite for the application of this method is the definition of specific river types with a detailed description


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of reference states. Only with such a basis sound and consistent evaluation of hydromorphological deficits can be identified correctly and actions can be targeted towards an improvement of ecomorphological stream conditions. The authors of this paper currently work on the adaption of the method to different geographical regions.

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Gerson Araujo F., Fichberg I., Carvalho Teixeira Pinto B., Galvão Peixoto M. 2003. A Preliminary Index of Biotic Integrity for Monitoring the Condition of the Rio Paraiba do Sul, Southeast Brazil. Environmental Management 32(4), 516-526. Haase R. and Nolte U. 2008. The invertebrate species index (ISI) for streams in southeast Queensland, Australia. Ecological Indicators 8(5), 599-613. Harby A., Baptist M., Dunbar M. J., Schmutz S. (Eds.) 2005. State-of-the-art in data sampling, modelling analysis and applications of river habitat modelling. COST

Conclusion

Action 626 Report. Hering D., Meier C., Rawer-Jost C., Feld C. K., Biss R., Zenker A., Sundermann A., Lohse S., Böhmer J. 2004. Assessing streams in Germany with benthic invertebrates: Selection

In this study, we pointed out the potentials of the German field survey method for hydromorphological quality assessment. The method showed to produce valuable information about the hydromorphological conditions of rivers and creeks at different spatial and thematical scales. On the one hand overview maps of the hydromorphological state within entire river basins can be produced and on the other hand detailed questions about hydromorphological meso-habitat issues can be addressed. The herein presented method provides a cost-effective approach for sound ecological river development. Its results should therefore be considered in river restoration planning to improve the ecological integrity of streams in their entirety. However, specific issues such as the method`s applicability in different geographical regions address a need for further research.

of candidate metrics. Limnologica 34(4), 398-415. Illies J. 1978. Limnofauna Europaea, Gustav Fischer Verlag, Weinheim. Ilnicki P., Górecki K., Grzybowski M., Krzeminska A., Lewandowski P., Sojka M. 2010. Principles of hydromorphological surveys of Polish rivers. Journal of Water and Land Development 14(1), 3-13. Kamp U., Binder W., Hölzl K. 2007. River habitat monitoring and assessment in Germany. Environmental Monitoring and Assessment 127(1), 209-226. Kondolf G. M. and Piégay H. 2005. Tools in Fluvial Geomorphology, John Wiley & Sons. LANUV – North Rhine-Westphalia State Agency for Nature, Environment and Consumer Protection. 2011. Strahlwirkungs- und Trittsteinkonzept in der Planungspraxis – Arbeitsblatt 16 (The concept of radiaiting effects and stepping stones in the planning practice – Work Paper 16. LANUV, Recklinghausen. LAWA 2000. Gewässerstrukturgütekartierung in der Bundesrepublik Deutschland: Verfahren für kleine und mittelgrosse Fliessgewässer (engl.: Structural quality mapping of watercourses in the Federal Republic of Germany: procedures for small

Acknowledgments

and medium-sized rivers). German Working Group on Water Issues. KulturbuchVerlag, Berlin. Mouton A. M., Schneider M., Depestele J., Goethals P. L. M., De Pauw N. 2007. Fish habitat

This work was funded in by the European Regional Development Fund and the Ministry Innovation, Science and Research of the German State of North Rhine-Westphalia (Project z1009fh003). The authors are grateful to Dr. Antje Goedeking of the Eifel-Rur Water Association for providing the data used in this project.

modelling as a tool for river management. Ecological Engineering 29(3), 305-315. Moya N., Hughes R. M., Domínguez E., Gibon F-M., Goitia E., Oberdorff T. 2011. Macroinvertebrate-based multimetric predictive models for evaluating the human impact on biotic condition of Bolivian streams. Ecological Indicators 11(3), 840-847. MUNLV 2009. Bewirtschaftungsplan für die nordrhein-westfälischen Anteile von Rhein, Weser, Ems und Maas – 2010-2015) (engl.: Management plan for the North Rhine-

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Oppenheim A. N. 2000. Questionnaire Design, Interviewing and Attitude Measurement, Bloomsbury. Parasiewics P. and Walker J. D.

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microhabitat models (PHABSIM and HARPHA). River Research and Applications 23(8), 904-923. Pottgiesser T. and Sommerhäuser M. 2008. Beschreibung und Bewertung der deutschen Fließgewässertypen - Steckbriefe und Anhang (engl.: Description and Evaluation of German river types), German Federal Environment Agency, Berlin. Raven P. J., Holmes N. T. H., Charrier P., Dawson F. H., Naura M., Boon P. J. 2002. Towards a harmonized approach for hydromorphological assessment of rivers in Europe: A qualitative comparison of three survey methods. Aquatic Conservation: Marine and Freshwater Ecosystems 12(4), 405-424.

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in the Central Amazon region of Brazil. Ecological Indicators 18(1), 118-125. Davy-Bowker J. and Furse M. T. 2006. Hydromorphology - Major results and conclusions from the STAR project. Hydrobiologia 566(1), 263-265. EC (2000). Directive 2000/60/EC of the European Parliament and the Council of 23 October 2000 establishing a framework for Community action in the field of water. Official Journal fo the European Communities 327, 1-72.

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UBA – Federal Environmental Agency of Germany. 2010. Water Resource Management in Germany – Part 2: Water quality. UBA, Dessau-Roßlau. Verdonschot R. C. M., Didderen K., Verdonschot P. F. M. 2012. Importance of habitat structure as a determinant of the taxonomic and functional composition of lentic macroinvertebrate assemblages. Limnologica 42(1), 31-42. WVER – Water Association Eifel-Rur 2009. Auswirkungen naturnaher Rückbaumaßnahmen und naturnaher Laufabschnitte – Gezielte Nutzung von Strahlwirkungen und

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Trittsteineffekten zur Erreichung der Ziele der EG-WRRL im EZG Eifel-Rur (Effects of near-natural restoration measures and stream section – Targeted use of radiating effects and stepping stone effects to achieve the objectives of the WFD in the EifelRur river basin). WVER, Düren. Zumbroich T. 2008. Strukturkartierung - Multifunktionstalent ohne Grenzen? (engl.: Structure mapping - All-rounder without limits?). Wasser und Abfall 10(3), 32-36.


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JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT Purification and characterization of toxic waste in the aquatic environment using common carp, Cyprinus carpio Hanan Abd El-Gawad a, Enas M. Ramzy a,* a

Central Laboratory for Environmental Quality Monitoring, National Water Research Center, Qalubiya, Egypt

*

Corresponding author : dr_enas21@yahoo.com

Article history Received  22.08.2012 Accepted 07.10.2012 Published 04.03.2013

Keywords Fish Malathion Bioaccumulation Aquatic environment

Abstract The organophosphorus (OP) pesticide (malathion) is used heavily for many crops such as vegetable and cotton to control serious key insecticide in many areas of Egypt. This study has focused on the effect of malathion on aquatic environment and aquatic organisms. The experimental work was carried out using malathion at different dosage of water lasted 96h and was carried out undertaken laboratory conditions. It evaluated the sensitivity of organic toxic waste and their purification techniques for common carp, Cyprinus carpio by determining enzymes activity as biomarker indicators in various organs of the studied fish. The results showed that exposure to malathion caused a significant increase in enzyme activity and total protein contents in the investigated tissues and inhibition of brain and liver acetylcholinesterase (AChE). Moreover, among the tissues studied, it appeared that the brain, gills and liver were more sensitive to pollution and seemed to be the most appropriate tissues to monitor water pollution by OP pesticides. In this context for environmental biomonitoring, the evaluation of toxic waste purification can be done to assess sensitivity of aquatic organism in recycling water to meet national goals and environmental safety.

Introduction Natural fresh water are the ultimate recipient of most toxic substances, generated by industrial, agricultural and domestic activities which are released into the environment. Deleterious effects on aquatic ecosystems may result from toxicant exposure, through run-off, from agricultural field or directly through careless application that directly causes the death of an organism (acute effect) or produces

sub-lethal chronic effects on the organist that hinder its ability to develop, grow and reproduce in the ecosystem (Eason 2011). Organophosphorus pesticides (OPs) used in agricultural intensively has led to the contamination of aquatic environments and affected the non-target aquatic organisms, particularly fish (Meng and Kang 2008). Therefore, fish have been used as a bioindicator to assess the


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28

environmental impacts of pollution and evaluate the purification of water resources (Arias et al. 2007). Thus, many researchers have studied the monitoring OPs by biomarker measurements and their response to OPs elimination in the aquatic environment (Meng and Kang 2008).

coupling with activated carbon technique and its response to fish. High Liquid Performance Chromatography (HLPC) equipped with UV at 220 wave length was used to detect malathion residues in all environmental samples including water and fish organs (brain, gills, liver and muscles).

Malathion, is an organophosphorus pesticide (OPs) widely used in agriculture and domestic activities (Abhilash and Singh 2009). Correlation between depressed accumulation levels and manifestation of toxic effects has been reported for malathion and other OPs (Tilak et al. 2004) and (Henry 2006). Many studies have investigated the persistence and accumulation of OP insecticides in brain more than other fish tissues specially in carp tissues (Tilak et al. 2004). Also, the residues of OPs in water of various environments were detected and seem to decline over time (Henry 2006).

Chemicals

Rick and Edwards (2010) have recently shown environmental investigations that used environmental approaches for measuring biological effects and green chemical process to remove toxicity in aquatic environment and to provide valuable results for a decision maker. So-called screening systems that elimination of toxic pesticides from aqueous solutions and waste water using enzymes activity are urgently needed (Weiping et al. 2006). Because of their toxic and hazardous nature, removal of malathion from water and wastewater has been of concern to scientists; treatment options should be efficient, economic and safe (Kiang et al. 2006). Therefore, the biological treatment seems suitable for the degradation of hazardous aqueous pollutants for removing pesticide residue from contaminated water. On other hand, several reactions catalyzed by enzymes of specific microorganisms take place (Jiang et al. 2006) in Laboratory Scale Activated Sludge Unit (LSASU). The objective of this study is to assess the sensitivity of fish tissues to exposure to sublethal concentrations of malathion (2 mg malation/l) ½LC50 for 96h on common carp, Cyprinus carpio and then allowed to recover for 192h. The selected biomarkers are acetylchloinesterase (AChE), alkaline phosphate (ALP), alanine aminotransferases (ALT), asparatate aminotransferases (AST) in fish’s brain and liver and total protein contents (T.P.) in liver and muscles resulting from malathion residues in aquatic environment. These measurements have been used as an important bio-indicator to monitor exposure – effect relationships, damage by OP pesticides to increase the predictability of laboratory and field eco-toxicology data.

Organophosphorus insecticide, malathion (57% E.C.) KafrelZayat Company-Egypt: O-O dimethyl S(1,2-dicarbethoxy-ethyl) phosphorodithioate (IUPAC) (Fig.1).

Figure 1. Chemical Structure of Malathion and Malaoxon.

Purification of aqueous solution A survey was carried out to determine aqueous solution treatment process and disposal management procedure for hazard organic waste. Laboratory Scale Activated Sludge Unit (LSASU) is simply designed as biotechnology coupling with an activated carbon system to remove organic waste (Fig. 2). LSASU simply involves mixing of wastewater and a culture of microorganisms from the incoming sewage and sludge recycle. Sample of mixed liquor (homogenous) and sludge were enriched by bacterial culture of genus Pseudomonas under aerobic conditions.

Figure 2. Diagram of laboratory-Scale activated Sludge Unit and Activated Carbon Column.

Fish

Methodology A simple and versatile laboratory experiment was designed to measure the influences of sub-lethal toxicity levels of malathion, accumulation concentrations in studied tissues and residues degradation in aquatic system. The common carp, Cyprinus carpio was selected as a bioindicator to determine AST, ALT, ALP, AChE enzyme activity and total proteins (T.P.) of a screening system for toxic organic waste in aquatic environment with fish. Contaminated water samples were analyzed and treated to determine malathion residues, its degradation and purification approach of aqueous solution using biological treatment

The fresh water common carp, Cyprinus carpio fish was collected from Abo-saleh Island productions, Beni-suif governorate, Egypt and transported to laboratory in plastic ice-box containing dechlorinated and oxygenated water. Fish were acclimated for one month before beginning the experiment. The physico-chemical characteristics of the test water were measured (APAH 2005) and recorded. Means results were; pH: 7.5 ± 0.07, Temperature: 27 ± 0.05 ºC, dissolved oxygen 6.4 ± 0.024 mg/l, alkalinity 2.43 ± 0.18 meq as CaCO3, Hardness 97.8 ± 2.7 mg/l as CaCO3. The healthy fish samples had average total length (16 - 18.3) cm and average total weight (180200g) The fish were fed once daily with commercial dry pellets (25%


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protein) at a rate of 2% of the body weight (Sprague 1969) before beginning of the experiment and during exposure period and aquaria water was changed once daily for cleaning. Fish were transferred to experimental aerated aquaria (70x50x60 cm3, 100 liter capacity) at the beginning of the experiment. Determination of LC50 of malathion for common carp 9 concentrations of malathion (6, 5, 4, 3, 2, 1, 0.75, 0.50, and 0.25 mg/l) were prepared in 9 equal-sized aquaria, in addition to one aquarium for the control. 10 fish individuals were transferred for each individual malathion containing aquaria. The experiment was continued for 96h at each malathion concentration and water was changed daily to keep the concentration constant during the exposure period. At the end of the 96h, the mortality percentages were calculated according to probit analysis method (Finney 1971). This experiment was repeated twice and the average LC50 value of malathion was found to be 4mg/l. Fig.(3) showed that the mortality percentage of common carp, Cyprinus carpio fish exposed to special concentration of malathion in aquatic environment followed the linear equation (-8.2418x + 110.92) with R2 = 0.98. The recorded LC50 for malathion 4 mg/l (50% mortality).

29

Table 1. Environmental management Set

Dealing Conditions

Control group

Fish exposed to de-chlorinated and oxygenated tap water

Dealing group

Fish were exposed to ½LC50 malathion - 96hrs

Recovery group

Treated fish transferred to de-chlorinated and oxygenated water for 8 days till complete recovery.

Scarifying

Each fish was dissected for pieces of muscle, liver, gill and brain tissues collection and storage was carried out at - 4°C.

Bioaccumulation analysis Simple experimental set-up and measure procedure were carried out to assess malathion concentration and its degradation in water samples, easy generation of dose - and time- dependent responses and bioaccumulation in tissues. The methodology is demonstrated with several different dosages of organophosphorus malathion to provide the sensitivity of pesticide. Malathion in water and fish tissue samples was extracted, analyzed using HPLC equipped with UV at 220 wave length and following the procedure outlined according to the method described by (El- Sheamy et al. 1991). The method was applied as following: Extraction

Figure 3. Determination of LC50 of common carp, Cyprinus carpio fish exposed to malathion.

Acute experiment for 96h to ½LC50 of malathion 10 fish of common carp, Cyprinus carpio were transferred to individual aquaria containing 1/2 LC50 of malathion (2mg/l) for 96h, in addition to the control. Fish were dissected to collect tissues for each 24h interval during the exposure period and kept at -40C till complete analysis within few days after sampling. Experimental design 3 groups (10 fish each) were investigated under laboratory conditions i.e. control group, dealing group and recovery group (Table 1). First group was exposed to dechlorinated and oxygenated tap water, second group was exposed for 1/2 LC50 of malathion (2mg/l) for 96h (acute exposure) and the third group in which the treated common carp, Cyprinus carpio fish was transferred to malathion free water for 192h.

50 g fresh ground fish flesh was accurately weighed, transferred to a high speed blender and mixed for 8 min with 100 g anhydrous sodium sulfate in the presence of 150 ml petroleum ether. The extract was decanted through 500 ml Buchner funnel (containing 2 12 cm Whatman N-1 filter papers) in a suction flask. The residue in the blender cup was re-extracted with 2 100 ml portions of petroleum ether, blended for 8 min for each portion, decanted through the Buchner funnel, and pooled with the first extract. The obtained extract was poured through a 40 x 25 mm column of anhydrous sodium sulfate, and the eluate was collected in a 500 ml flask and placed in a rotary evaporator, to obtain the fat. Cleanup The extracted fat was transferred to a 125 ml separatory funnel with the aid of 15 ml petroleum ether; 30 ml acetonitrile saturated with petroleum ether was added and the mixture was shaken vigorously for 5 min. The layers were allowed to separate and the acetonitrile layer was drained into 1 l separatory containing 650 ml water, 40 ml saturated NaCl solution and 100 ml petroleum ether. Separation technique was repeated 3 times, beginning with the transfer of the extracted fat to the 125 ml separatory funnel. All the extracts were collected in the 1 l separatory funnel and mixed thoroughly for 30-45 seconds. The layers were allowed to separate and the aqueous layer was drained into another 1 l separatory funnel containing 100 ml petroleum ether was added and the mixture was shaken vigorously for 15 S. After the layers separation, the petroleum


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ether layer was combined with the previous one and washed with 2 100 ml portions of water. The petroleum ether layer was drawn off through a 50 x 25 mm column of anhydrous sodium sulphate. The eluted petroleum ether extract was evaporated to 10 ml in a rotary evaporator after which it was transferred to a florisil column prepared as described below. Cleanup by Florisil column A glass column, 22 mm id, was filled with Florisil (60- 100 mesh, P.R. grade; activated at 675°C for 3 h) to a height of 10 cm and topped with 1 cm anhydrous sodium sulfate. The column was prewetted with 40-50 ml petroleum ether; then the petroleum ether extract from the above (b) step was passed through the column at (5 ml/min). The column was eluted at the same rate using 200 ml eluting solvent (50% diethyl ether in petroleum ether). The eluate was concentrated to a dry film which was dissolved by 2 ml of n-hexane for determination. Lipid assay The lipid content of various tissues was determined in an attempt to reduce variability in malathion concentrations in specific tissues by providing an alternative method to sample weight for standardization. Prior to determination of lipid content, glass culturettes were dried in a desecrator until their weight no longer changed. The tissue samples were prepared for lipid extraction by homogenizing approximately 0.1 g of tissue in 2 ml of solvent, 2:1 dichloromethane: methanol (v: v) in a homogenizer. The homogenate was added to a culturette and a folch wash was performed with the addition of 1.5 ml of 0.88% sodium chloride in deionized water. The sample was centrifuged for 8 minutes at 1000 G. The hydrophilic phase was removed with a glass pipette and the lipophilic phase, solvent, was transferred into the pre-weighed, dehydrated culturette. The solvent was evaporated under nitrogen to dryness. The culturette was reweighed and lipid content was calculated on a µg/g basis.

30

4 µl of the reconstituted sample was injected in triplicate onto the analytical column. The injection of external standards confirmed the identity of the chromatographic peaks. Detection limits were calculated to be 0.04 µg for malathion and 0.10 µg for malaoxon using curves generated from standards. Standard curves were made from malathion and malaoxon standards, 100 µg/ml in methanol, in dilutions of 100 µg/ml, 75 µg/ml, 50 µg/ml, 25 µg/ml, and 10 µg/ml. All standards were run in triplicate. All tissue burdens were calculated on a ppm or mg/Kg wet weight basis. There were no detectable levels of analytes in blank samples. The calibration standard is measured every set of group samples for calibration and the resulted concentration of the standard did not exceed than 5% differs of the true value. Performance data were checked by control standard results which fall within designated control limits. The spike recoveries ranged between 80-120%. The sensitivity and recovery for the method were determined using samples spiked with 3 different concentrations (3, 10, 20 mg/kg) of the tested malathion. The results that given in Table 3 had been corrected for percent recovery of the malathion. Table 3. Recovery and limit of detection of applied method Pesticide

Spiked conc. µg/kg

Recovery %

3

91

1.9

Malathion

10

94

10.15

20

96

20.81

Limit of detection µg/kg

Biomarker measurements

Extraction efficiency was measured in all samples. Malathion, 0.1 µg, was injected into tissues (n=5) in the homogenizer. The samples were extracted and ran on the HPLC-UV for quantification. Extraction efficiency for sets of samples was between 85-110%.

Malathion residues were measured in aquatic environment and investigated organs of common carp. AST, ALT, ALP, AChE were chosen as representative’s bio-marker of organic waste effect in aquatic environment to be tested as screening system (Ellman et al. 1961) and (Mukhopadhyay et al. 1982) . Total proteins (T.P.) of each tissue of the control and treatedmalathion were extracted and evaluated (Mukhopadhyay et al. 1982) and (Snedecor and Cochran 1980) respectively. Concentration of each metabolite was expressed as µg/mg of tissue

Chromatographic quantification

Statistical analysis

Malathion burdens were determined using High Performance Liquid Chromatograph Agilent I I 00 Series with work station. The U.V detector set at 220 nm, and the analytical column Zorbax - C18,5 Um (4.6 x 150 mm) was used. The Mobile phase was Acetonitrile - Water at gradient as follows:

Differences between control and treated-malathion groups were evaluated by an analysis of variance (one-way ANOVA) at a significance level of 0.01 (Snedecor and Cochran 1980). Normality and homogeneity of variances were verified and a parametric one-way analysis (ANOVA) was performed on data. All results are expressed as a mean ± standard error.

Table 2. Analytical conditions Time / min

Acetonitrile

Water

Flow

0

50

50

0.7

2

30

70

70

5

50

50

50

Results and discussion Data summary Various irrigation canals and drains, within crops-growing areas in


DOI number: 10.5027/jnrd.v3i0.03

spraying seasons received varying levels of malathion exposure from agricultural practices that cause environmental impacts on water quality, water suitability fish as important part of non-target organism in aquatic system and major source for human beings.

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directly via vascular system. Therefore, the residue levels depending on uptake rate, degree of solubility, stability, octanol-water partition coefficient of OP and /or its rapid conversion to metabolites (Pandeya et al. 2011).

The risk of exposure to malathion is due to its effect on immune system of fish and other alterations of its physiological system. The data presented the evaluation of LC50 and the potential organic toxic on the common carp, Cyprinus carpio fish as well as its effect on enzyme activity that reflect organic waste effects of malathion on health fish using biomarkers measurements and total protein. Moreover, toxicity of organic waste using laboratory activated sludge unit that includes a culture of active micro-organisms (settled sewage) under aerobic conditions coupled with activated column was applied in a screening system for elimination of malathion contamination in aquatic environment to reduce economic degradation and protect farm’s fish under international guidelines to achieve national goals.

Figure 5. Bio-accumulation of Malathion in Common carp, Cyprinus carpio tissues.

Environmental profiling of toxic waste in aquatic environment 1/2 LC50 of malathion (2mg/l) was chosen as representative’s organic toxic level to be tested as acute effect for 24, 48, 72, 96 hours. Figure 4 demonstrated malathion levels in water samples that declined significantly after 96h from 1.189±0.001 mg/l to 1.086±0.001 mg/l while it was not and non-detected after 8 days of reference concentration.

Study of aquatic purification and disposal management The investigated concentrations of malathion for 24h (½ LC50) were selected for on-site treatment before declaring to be safe. Progress of organic matter decline The artificial wastewater at experimental 24hours gave a significant volume of wastewater loaded with toxic waste (1.189 mg/l) and other chemical pollutants. The environmental measurements in terms of chemical oxygen demand (COD), biological oxygen demand (BOD5) were determined for the measurement of total organic load and malathion was measured at end of treatment.

Figure 4. Degradation of Malathion in water samples.

The study showed that malathion persisted in tissues of fish reflecting the general levels of pesticide usage. There was a correlation between the level of malathion degradation in water and corresponding bioaccumulation in fish tissue samples. This means that OPs were degraded and the residue levels were dependent on uptake rate in common carp, Cyprinus carpio tissues (Fig.5). Generally, statistical analysis showed that the brain was the carp tissue in which most Malathion was accumulated. Therefore, the malathion was accumulated in the following order brain > gills> liver > muscles with highly significant elevation (P<0.05). This could be attributed to the rapid penetration and binding of insecticide residues in fish tissues since some types of OP pesticides were found within the fat phase in fish depending on the physico-chemical properties (lipophilic) (Pandeya et al. 2011). Further, gills are the main route of pollutants entry, transportation to different tissues took place

The toxic waste effluent sample for 24h was selected as a test for onsite treatment before disposal (1.189 mg/l) COD concentration (158 mg/l), and BOD5 (85 mg/l) exceeded the national standards limits (9/2009), showing that wastewater was heavily loaded with organic residues. The alkaline trend side of pH for wastewater fluctuated between “7.5-8.5”. There would be 2 sources of malathion removal in the system; one is pH itself and the second is the microorganisms that are major pathway of disappearance of malathion in soil, water, sediment and salt march environments as biologically mediate (Imran et al. 2006). Furthermore, the aerobic condition: O2-rich in surface wastewater was maintained to breakdown organic matter and performance of the system especially in removal of COD concentration (158 to 10 mg/l), and BOD5 (85 to 15mg/l) as function of retention time. Figure 6 illustrated the operating times for integrated aerobic system: 1h8h have been operated and settling of sludge for 2h that showed the measurements at different detention times in aeration zone reported the optimum operating conditions were 7-8h to complete decomposition.


DOI number: 10.5027/jnrd.v3i0.03

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Moreover, the study coupled the biological treatment with activated carbon column to check and ensure toxic waste purification (1.189 mg/l) to (<0.01 mg/l) that within the Egyptian Environmental Law (9/2009) as shown in Fig. 9. In addition, the treatment performance evaluated as malathion removal percentage and calculated as: Malathion degradation % = (C0 – C/ C0 X 10), where C0 and C are initial and final malathion concentrations, respectively.

Figure 6. Concentration of organic matter in effluent at different reaction times.

Trend of toxic organic waste degradation An aquatic experiment was designed to measure the removal of malathion and accumulated concentrations of its residues in aquatic environment under laboratory conditions and malathion degradation in aquatic system to be safe. Figure 7-8 for chromatography analysis showed a significant improvement of malathion removal rate with time and biodegradation percentage that is used to quantify pollution removal from toxic waste under the given experimental conditions and the degree of water purification from contaminants.

Figure 9. Performance degradation of Malathion– activated carbon.

These data agree with some studies that assessed the activated charcoal is the most commonly used adsorbent for removing pesticide residue from contaminated water to avoid toxic and hazardous nature that could be dangerous to the ecological balance (Christodoulatos et al. 1997) and (Jiang et al. 2006). The out activated column is collected and adjusted as physico-chemical characteristics of the used test water in experimental design. This treated water is used to check the sensitivity of malathion removal in water and fish tissue for 24h. The gills and muscles tissues were selected to represent toxic waste effect that are in direct contact with the malathion (<0.01 mg/l) and assess sensitivity of aquatic organism in recycling water to meet national goals and safe economic environment. The analysis showed that malathion concentration was within FAO/WHO limits for pesticides in fish (FAO/WHO 1997).

Figure 7. HPLC analysis of Malathion at different times.

Bio-indicator consequence The present results of work was conformed with environmental studies that bacterial culture of Pseudomonas played important role in pesticides degradation because these bacteria are oxidative, aerobic metabolically versatile, degrade aromatic hydrocarbon, oil, petroleum products and pesticides which used in situ bio-restoration (Balk et al. 2011).

Quantification of proteins in tissue Protein contents of 2 tissues of common carp, Cyprinus carpio control and malathion-treated (muscles and liver) are presented in Fig. 10. Malathion exposure increased significantly (p<0.01) total protein concentrations in all studied tissues that were (3.42±0.008, 3.91±0.03, 4.09±0.05, 4.75±0.04, 4.57±0.07)µg/g for muscles and (4.3±0.1, 4.07±0.01, 4.43±0.06, 6.64±0.3, 4.8±0.03) g/g for liver. The increase of total protein contents is due to activation of protein synthesis involved in defensive mechanisms such as production of metallothionein (Paris et al. 2002). Proteins content would therefore depend on the balance between induction of defenses and their inhibition by impact of pollutants toxicity (Dautremepuits et al. 2004). Biomarker measurements

Figure 8. Estimation of Malathion degradation by HPLC at different times.

The environmental risk assessment and eco-toxicology involved the use of biomarkers designed to highlight an early pollution (Ramon


DOI number: 10.5027/jnrd.v3i0.03

et al. 2007). To assess the impact of neurotoxic compounds on fish, we had evaluated AChE activity which was used as a biomarker of exposure to nerve agents such as organophosphours pesticides (Rendón-von et al. 2005).

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increased gradually due to exposure at exposed to malathion in liver than muscles that conformed with recent studied (Oruc and Usta 2007). The increase in the activity of enzymes (AST,ALT&ALP) may be due to liver damage (Ozmen et al. 2008) and (Vandana et al. 2008) and also may be due to high production of oxaloacetic acid and pyruvate and glutaric acid which in turn channeled into citric acid cycle to meet the increase energy demand during stress condition.

Figure 10. Total protein in various fish tissues.

Figure 11 clarified that AChE activity after malathion exposure in the pieces (brain: 9.6±1.47, 28.06±2.3, 51.88±1.3, 66.08±2.1, 174.6±1.3 ug/g and liver: 18.57±0.02, 28.8±0.01, 41.2±0.02, 47.2±0.005, 81.8±0.006ug/g), that reveals an inhibition during acute period of exposure with a dose dependant on the studied tissues. This decrease was more apparent in the brain > liver as shown in Fig.5. This is due to malaxon (a major metabolite of malathion) which and it was the main inhibitor of AChE (Rick and Edwards 2010) the enzyme that cleaves the neuro transmitter acetylcholine, thereby interfering with proper neurotransmission in cholinergic synapses and neuromuscular junctions. This could interfere with vital functions and also as indication of hepatic disorders (Chandrasekra and Pathiratne 2005). The study suggested that brain AChE activity was depressed by about 70% or greater in fish. This may lead to risk danger of death from the AChE-inhibiting poisoning (Safia et al. 2010).

Figure 11. Acetylcholinesterase (AChE) activity in various fish tissues.

Furthermore, ALT, AST and ALP enzymes are considered key enzymes in fish that can be used as response indicators to chemical pollution and to diagnose the impact of sub-lethal toxicity of environmental contamination in fish (Kory and 1993). Figure 12-A,B showed that ALT (Liver: 17.3±0.4, 37.9±0.8, 44.4±1.5, 50.3±0.6, 73.8±1.3 ug/g and muscles: 14.03±0.38, 33.9±1.07, 47.3±1.19, 53.3±1.15, 61.1±0.91), AST (Liver: 43.5 ±1.08, 63.3±1.7, 79.06±2.09, 96.6±1.3, 125.1±1.7 ug/g and muscles: 40.4±0.36, 46.34±1.25, 61.6±0.57, 84.9±1.08, 93.6±1.14 ug/g) and ALP (Liver: 9.506±0.5, 20.9±1.05, 30.2±1.1, 36.6±1.4, 45.1±1.4 ug/g and muscles: 8.21±0.23, 25.5±1.32, 32.6±0.8, 45.4±1.28, 54.08±0.75 ug/g)

Figure 12-A. Enzyme activity of liver in studied fish.

Figure 12-B. Enzyme activity of muscles in studied fish.

Conclusion The studied fish was selected to determine LC50 of malathion, acute ( ½ LC50) for 96h, characterization of toxic waste (malathion) effect on aquatic organism in environment using HLPC equipped with UV at 220 wave length and enzyme activity (AChE, AST, ALT, ALP activities) as biomarkers causing un-safety for fish populations. The results clarified that enzymes in tissues were affected by malathion at a low concentration, during an acute period and its toxicity increased dependently on dose. Furthermore, the results clarified that T.P depended on the balance between induction of defenses and inhibition with malathion toxicity impact. It can be deduced from the aforementioned results that LSASU in aquatic environment to reduce economic degradation and protect farm’s fish under international guidelines to achieve national goals.

Acknowledgments This research has been supported by National Water Research Center (Cairo). The authors are most grateful to the laboratory staff of various departments of Central Laboratory for Environmental Quality Monitoring. The authors would like to thank Professor Dr.Mohamed Nagy for his valuable help from comparative physiology, Zoology Department, Faculty of Science, Benha University.


DOI number: 10.5027/jnrd.v3i0.03

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Journal of Natural Resources and Development 2013; 04: 35-57

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JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT Community perception on climate change and climate-related disaster preparedness in Kathmandu Valley, Nepal Udo Nehren a, Jishnu Subedi b, Ina Yanakieva a, Simone Sandholz c, Jibraj Pokharel b, Ajay Chandra Lal b, Inu Pradhan-Salike b, Muh Aris Marfai d, Danang Sri Hadmoko d, Günther Straub a Cologne University of Applied Sciences, Institute for Technology and Resources Management in the Tropics and Subtropics (ITT), Betzdorfer Str. 2, 50679 Köln, Germany a

b

Pulchowk Campus, Institute of Engineering, Tribhuvan University, Pulchowk, Nepal

Workgroup Development Studies and Sustainability Science, Institute of Geography, University of Innsbruck, Innrain 52, 6020 Innsbruck, Austria c

d *

Faculty of Geography, Universitas Gadjah Mada, Bulaksumur 55281 Yogyakarta, Indonesia Corresponding author : udo.nehren@fh-koeln.de

Article history

Received  25.07.2012 Accepted  09.01.2013 Published 08.04.2013

Keywords Climate change Kathmandu Valley Disaster risk Community perception Preparedness

Abstract

Within the last decades, Kathmandu Valley in Nepal has been characterized by rapid population growth and related urbanization processes, leading to environmental degradation, pollution and supply bottlenecks in the metropolitan area. Effects of climate change are now putting additional stress on the urban system. In our research in Kathmandu, we carried out community and household surveys to analyze community perception on climate change and climate-related disaster preparedness. For this purpose, three categories of communities, 12 in all, were surveyed and interviewed: Squatter settlements, agricultural villages, and traditional villages. All settlements are located close to main rivers and therefore especially exposed to floods and droughts, and in slope position also to landslides. As a main result, we can conclude that people are generally aware of climate change and its potential consequences, such as climate change-related disasters. However, in their daily lives, climate change does not play a significant role and most communities have not taken any adaptation measures so far.


DOI number: 10.5027/jnrd.v3i0.04

Introduction

Nepal, situated in the Himalayas between China in the North and India in the South, is highly exposed to natural disasters such as floods, droughts, earthquakes, landslides, avalanches, and glacial lake outburst floods (GLOFs). Although only ranked 99th out of the 173 countries in the World Risk Index (Alliance Development Works 2011), the country is a “hotspot for geophysical and climatic hazards” (NSET 2008) as stated in the Global Risk Analysis report by the World Bank in 2005 (Dilley et al. 2005). In the Natural Disasters Risk Index (NDRI) published by the British risk advisory firm Maplecroft, Nepal is listed as number 18 of 229 countries, based on the frequency of natural disasters that occurred between 1980 and 2010 and related human losses in proportion to the total population (Maplecroft 2011). Nepal is also highly vulnerable to climate change impacts, ranked 13th in the Global Climate Risk Index 2010 (Germanwatch 2010). According to the IPCC (2012, p. 5), “a changing climate leads to changes in the frequency, intensity, spatial extent, duration, and timing of extreme weather and climate events, and can result in unprecedented extreme weather and climate events.” Already in 1999, Shrestha et al. predicted increased temperatures and higher variability of rainfall for the country based on climate data of the 1971-1994 period. Current regional climate models based on the Global Circulation Model (GCM) support these trends and predict a temperature increase between 0.5° C and 2.0° C by the 2030s and 3.0° C and 6.3° C by the 2090s. Moreover, the GCM predicts precipitation changes due to the monsoon shift associated with more erratic rainfall and increasing storm intensity (NCVST 2009). According to NCVST (2009), climate change impacts are particularly strong in the highlands and midlands, and increasing temperatures above average in higher elevations of the Himalayas may regionally lead to increased glacier melt and a higher risk of flashfloods, GLOFs, and landslides. However, there is much uncertainty about glacier melt in the Himalayas, and only little research has been carried out to prove a general trend of glacier retreat (Inman 2010). A recent publication by Bolch et al. (2012) comes to the result that the majority of Himalayan glaciers are melting, but corresponding to the global average, and much less rapidly than predicted by IPCC (2007), which has already been stated by other researchers before. Based on satellite data, they calculated a glacier area of about 40,800 km², 20% less than assumed, and point out a heterogeneous behavior of the glaciers in the northwestern Himalayas. With respect to the drainage of the major rivers Indus, Ganges, and Brahmaputra, they assume that the glacier shrinkage will not a have major impact in the coming decades. However, a greater variability in seasonal water drainage can be expected in the medium term, and single valleys can dry up seasonally. But what about the midlands, or “Hills”, as the Nepali say, the densely populated belt between 1,500 and 3,000 m a.s.l with the country´s political and cultural center of Kathmandu Valley? Kathmandu Valley (KV) is administratively divided into Kathmandu District, Lalitpur District and Bhaktapur District with a total area of 665 km². Rapid population growths from 1.6 million in 2001 to 2.5 million in 2011 (Government of Nepal, National Planning Commission Secretariat 2011) in the valley, related urbanization and suburbanization process and the lack of investments in infrastructure,

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planning, and environmental protection have led to severe problems which are ever-present and interdependent: Massive air and water pollution, lack of waste disposal and wastewater treatment, bad hygienic conditions , run-down buildings, poor road conditions, traffic gridlock during rush hours, and power shortages, among others. This untenable situation is an aftermath of the ten-year civil war from 1996 to 2006, and the still very fragile political situation today. During the civil war, KV received many refugees and migrants from other parts of the country, and also victims of natural disasters, which contributed to population growth in addition to the pull-effects of the metropolitan area. Many refugees are still living in squatter areas, often along the river banks, and are among the most vulnerable to disasters (Pradhan 2004, Thapa & Murayama 2010). Climate change will likely have additional negative impacts on living conditions in general and on climate-dependent disaster risk in KV in particular. With increasing temperatures and drier climate conditions in the non-monsoon seasons, as predicted by the regional climate model for the middle hills, the risk of droughts and forest fires will increase as well, and agriculture will face major challenges (NCVST 2009). Furthermore, erratic rainfall and increasing storm intensity in the monsoon season will exacerbate risks and uncertainties for farmers (NAPA 2010). Floods in the monsoon season are natural events in Nepal, but a shift of the monsoon pattern may also increase the floods risk (NCVST 2009). The land use / land cover change in urban Kathmandu between 1976 and 2009, as shown in figure 1, reflects the rapid urbanization in the valley, and the dramatic loss of cultivated land and forests associated with it. This urbanization and suburbanization process will likely continue for the next years (Thapa & Murayama 2009, Thapa & Murayama 2010). The already severe situation with continuous loss of agricultural land due to built-up areas (planned and often unplanned), increase of population and dramatic disaster risk preparedness (bad traffic infrastructure, food supply) may decline even more in a continuous downward spiral due to climate change impacts. Without suitable urban and regional planning strategies and instruments as well as investments in urban infrastructure, the vulnerability to climate change impacts and climate-related disasters will probably increase. A study carried out by the Pacific Disaster Center (2005) already noted that many houses in KV were constructed inappropriately to guard against environmental impacts, such as floods and landslides. Moreover, poor maintenance of traditional brick nogged timber frame buildings with open fire places will likely have high risks of fires in dry seasons. Because of the poor drainage and settlements near polluted rivers, epidemics like malaria and other diseases might occur or spread. Poor access to health facilities makes epidemics more dangerous, particularly in remote areas and for poor people (United Nations Development Program 2009). In addition, the loss of forest and agricultural land in KV will lead to a further deterioration of urban climate, water and soil resources, higher disaster exposure, and more difficult post-disaster recovery. Due to land scarcity, particularly the urban poor will be forced to settle in disaster exposed areas, such as floodplains and steep slopes. In our presented studies in KV, we analyzed the community


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perception about climate change and disaster preparedness in different settlement types along the main rivers Bagmati, Bishnumati, and Manohara. With our research we aim at gaining a better

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understanding of risk awareness and possible adaptation strategies to climate change and climate-related disaster in a highly vulnerable area.

Figure 1. Land use / land cover change in urban Kathmandu 1976 to 2009 (source: Rimal 2012)

Methodology Methodological approach and study sites Urban and peri-urban settlements along rivers are among the most vulnerable to climatic changes and climate-dependent disasters.

Therefore study sites in urban and peri-urban KV were selected along the main rivers: Bagmati, Bishnumati, and Manohara. Two principal investigations were carried out in settlements along these rivers:


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(1) Community survey in nine selected communities within the three river corridors of the main rivers Bagmati, Manohara and Bishnumati; (2) Household survey in three squatter settlements along the Bagmati river. This paper is bringing together the results from both studies to draw conclusions on the current situation and risk perception in different communities that have in common a highly vulnerable location, but that differ mainly in economic and social patterns. Community survey The community survey was carried out in September 2011 in the framework of a joint student project of three universities: Tribhuvan

University (Kathmandu, Nepal), Gadjah Mada University (Yogyakarta, Indonesia), and Cologne University of Applied Sciences (Cologne, Germany). The objective of the survey was to obtain knowledge about community perception related to climate change and disaster preparedness in three different types of communities with respect to settlement history, dependence on agriculture, and average living standard of the population: (a) Recent squatter settlements with low living standards, (b) established settlements that depend on agriculture with moderate living standards, and (c) old traditional settlements with moderate to elevated living standards. In each of the three river corridors, one type of settlement was selected (see table 1, figures 2-4 give an overview of the variety of settlements). Figures 5-7 show the location of the study sites in the three river corridors.

Table 1. Selected settlements in the three river corridors Bagmati River corridor

Manohara River corridor

Bishnumati River corridor

Squatter settlement

Balkhu

Paurakhi Gaun

Jagriti

Agricultural settlement

Sainbu village

Bode Gaun

Manamaiju settlement

Old traditional settlement

Basnyat village

Tikathali village

Tankeshwor settlement

Figure 2. Squatter house construction in Balkhu

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Figure 3. Established houses in Sainbu village

Figure 4. One a few remaining traditional houses in Tikhatali village


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Figure 5. Location of the three squatter settlements from the community survey. Source of the satellite images: Google Earth (2012).


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Figure 6. Location of the three agricultural settlements from the community survey. Source of the satellite images: Google Earth (2012).


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Figure 7. Location of the three traditional settlements from the community survey. Source of the satellite images: Google Earth (2012).

Household survey in three squatter settlements The goal of the household survey was the assessment of vulnerability and risks to climate change-related impacts of slum dwellers in selected squatter settlements in Kathmandu. With the help of a flood-risk map provided by Full Bright Consultancy and GEO Consult (2009), three squatter communities were selected, whereby their

locations in the flood prone areas along the Bagmati and Manohara Rivers and their ages were taken into consideration (see table 2). The age of the squatter settlements is closely related to their building and road infrastructure and community organization, which are in turn of particular importance for vulnerability and adaptation potential to climate change-related hazards. The household survey was carried out from September to December 2010.


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Table 2. Selected squatter settlements for the household survey Bagmati River corridor

Bagmati River corridor

Manohara River corridor

Squatter settlement

Thapathali

Sankhamul

Manohara

Established

2007

> 30 years

2002

Figure 8. Location of the three squatter settlements from the household survey. Source of the satellite images: Google Earth (2012).


DOI number: 10.5027/jnrd.v3i0.04

Research methods Community survey The community survey includes: (1) Inventory of infrastructure, living conditions, and key demographic figures in the settlement based on satellite images, field observations, secondary data, and questioning in the communities, (2) Identification of hazards in the community based on secondary data and questioning in the communities, (3) Assessment of community perception about climate change and disaster preparedness based on semi-structured interviews. The inventory includes relevant information on road and building infrastructure, water supply, sanitation, waste and energy, as well as living standards and employment in the settlements to allow a comparison of the living environment. For the identification of hazards, particularity older community members who have lived a long time in the settlement were asked. This way information about past and present hazards in the area, sensitivity of the people to these events (self-evaluation), and likelihood of disasters in the past and present (estimation) was gathered. The collected survey data were compared to secondary data, such as disaster reports and press releases. Also details about the severity of disaster events were taken from secondary sources. The assessment questions are listed in box 1 in the appendix. The interviews took place as group interviews with community representatives in a community room or in public space. In some cases, particularly in the squatter settlements, the community representatives were not willing to answer all questions and digressed to other topics, which seemed to be more important for their lives. Furthermore in the squatter settlements, women were underrepresented during the interviews or stayed in the background. In some cases women groups joined the meetings and expressed their opinions, but usually the interviews displayed the traditional organizational structures of the settlements, which are dominated by men, e.g., as wards. However, interest in participating in the survey was apparent and there was a particular interest in wanting to communicate with foreign students and researchers. A major barrier for the foreign researchers was the language, as the interviews had to be carried out in Nepali language. However, Nepali researchers translated into English, which always took time but ensured the active participation of the foreign researchers. On-site visits with representatives of the communities were very informative and useful. Household survey in three squatter settlements The household survey obtained key information on: 1. 2. 3. 4.

Living environment and education; Climate-related hazards (direct and indirect); Sanitation, hygiene, and diseases; Squatter dwellersâ&#x20AC;&#x2122; own perception of climate-related hazards / risks; 5. Adaptation to climate change induced hazards.

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The survey is based on semi-structured household interviews in the squatter settlements, interviews with key informants, as well as field observations. The questionnaire for the semi-structured interviews consisted of 30 questions related to the five main topics mentioned above (the complete set of questions is included in box 2 in the appendix). Additionally, secondary data on infrastructure, population, and living conditions (satellite images, maps, and census data) were included. The questionnaire form was translated from English into Nepali language and the survey was carried out with the help of Nepali students organized by the Nepal Centre for Disaster Management. The three squatter settlements differed much in the number of households and it was not possible to include all household in the survey. To ensure representativeness with a minimum sample of 10%, but at the same take into account limited human resources for the field campaigns (interviewers and translators) as well as time constraints, the questionnaires were distributed to 60 households in Thapathali (16.4% of total households), 63 households in Sankhamul (52.5% of total households), and 75 households in the Manohara Squatter Settlement (10.0% of total households). The surveyed households were randomly selected, whereas effort was made to interview families living in different types of housing structures. Additionally, representatives of youth community groups in Sankhamul and Manohara squatter settlements were interviewed, as well as experts from Lumanti, Child Watabaran Center Nepal and Full Bright Consultancy.

Results Community survey: Risk awareness, environmental changes, and community response In the following, we present the results of the community survey for the three different settlement types. The settlement inventory is presented in the form of short settlement profiles in tables 3 to 5, while the hazard identification and the community assessment on perception about climate change and disaster preparedness are presented in text form right behind each profile. Squatter settlements Table 3 gives a brief overview of the three squatter settlements regarding location, population, infrastructure, water and energy supply, sewage, water management, as well as living standards and employment. The interviewed community members in all squatter settlements were aware of disaster risk and climate change impacts. They stated that they have heard about climate change in the television, radio, newspaper, or from other people. The community members of Balkhu stated that the flood level has increased but the settlement has not yet been affected. With respect to climate change, they observed a lengthening of the summer, a shortening of the winter and in average less rainfall. They said that until now this development caused just small droughts but they fear that droughts could get more severe in the future.


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Table 3. Profiles of the squatter settlements. Balkhu Location, population, infrastructure

Located in the Bagmati River Corridor, Balkhu consists of ~300 households. Access is provided by an earthen road mostly in fair condition. Almost all buildings are single storey; poor ones are made of plastic sheet, better ones of brick wall in cement mortar. Other materials (tin/metal sheet, bamboo, ply/wood board) are also used.

Water supply, sanitation, waste, energy

No access to the city water supply system and underground system. Five tanks with a capacity between 12,000-15,000 liters are placed in the central area. Most buildings share public toilets; few have private toilets inside. All of the toilets are drained to the river. No solid waste collection. 70% of energy consumption is covered by electricity, 1% by firewood and the rest by fossil fuels (gas, kerosene).

Living standard and employment

The living standard is very low. Most people have low educations and find jobs only as day laborers. Due to poor economic levels, individuals are not able to invest in any kind of business or vocational training.

Parauki Gaun Location, population, infrastructure

Located in the floodplain of Manohara River, access is given by a sealed road in good condition. Inside the squatter, earthen roads are in bad condition, particularly in wet seasons. Mainly single storey buildings with temporary roof structures made of tin, metal or plastic sheets. 60% of the buildings are permanent, 30% semi-permanent, and 10% temporary. There are few shops, a community office building and a school.

Water supply, sanitation, waste, energy

Use of groundwater, a water tank and river water. Most households use hand pumps; public taps are hardly available. Consume of about 20 liters per person/day. No proper drainage system. Customized drainage system with a pipe; outflow to the river or to the main road. Public sanitation system directly connected to the river. Most households use shared toilets, only few buildings have individual toilets. No solid waste collection. No access to city electricity grid; generators used.

Living standard and employment

Very low living standard. In addition to unskilled labor on a daily basis, there are a few skilled laborers and small businesses. Few inhabitants work in a department store, some in the field of construction and street vendors.

Jagriti Location, population, infrastructure

Located in the floodplain of the Bishnumati River, the squatter is already 30 years old and consists of ~300 households. Access is provided by an all weather gravel road linked with an arterial road. Almost all buildings are single storey; poor ones are made of plastic sheet, better ones of brick wall in cement mortar. Other materials such as tin/metal sheets, bamboo, and ply/wood board are also used.

Water supply, sanitation, waste, energy

Access to the public water supply system; one concrete water tank (20,000 liters). Additionally three tube wells. In summer, water is rationed to 40 liters/family/day. Most buildings have private toilets draining to the river. There is a solid waste collection point; anybody caught throwing waste into the river gets two slaps in public. Around 40% of energy consumption is covered by electricity, 50% (cooking) by fossil fuels (gas, kerosene), and rest by firewood, petrol and diesel.

Living standard and employment

The living standard and education level are very low. Most people are semi- or unskilled and find jobs only as day laborers, house helpers or drivers. Only a few are running shops. Due to the poor economic level, people are not able to invest in business or vocational training. However, NGOs are running vocational trainings.

In Paurakhi Gaun, floods occurred three times within the last four years. Moreover, two medium-sized earthquakes without human victims but some material damage hit the area. Epidemics, in particular typhoid, diarrhea, and water borne diseases are common due to bad sanitation conditions with open defecation and unprocessed drinking water. Wind, which damages the roofs, has a medium impact. Although aware of possible climate change impacts, climate is not of main importance at the local community level. Economic and social problems, water and energy resources, and settlement status are major problems. Asked for a ranking of main risks for the settlement and its inhabitants, the community members named traffic accidents first, followed by water supply and energy, land tenure and legal

status of the settlement, and theft and drug addiction. Only at the fifth place were climate change and related disasters mentioned. In this context, the increasing appearance of snakes was pointed out as a major threat and seen as an indicator for a rain pattern change and rising temperatures. To reduce flood risk, the residents have taken the initiative to build an earthen dam along the riverside to prevent floods from reaching the houses. The community members of Jagriti provided very detailed information on their climate observations, which is probably due to the fact that this settlement has already existed for 30 years. However, with respect to rainfall, their statements were more related to wetter and drier years and seasonal variations than to long-term climatic changes.


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The information about temperatures also referred more to weather extremes than to climatic changes. However, people agreed that winters have been coming later within the last years. While changes in snowfall and hailstorm had not been observed, people had noticed less wind blow compared to the past years. Asked about changes in animal species diversity, people complained about substantial loss particularly of different species of birds along the river corridor that they associated with climate change. However, it is much more likely that the real reasons are loss of habitats and breeding places due to ongoing urbanization processes as well as massive pollution of the rivers and degradation of the riverine ecosystems. Concerning hazards and risks, the community members mentioned tuberculosis, malnutrition and water borne diseases as the main health risks. In contrast to the past, the flood risk seems to be reduced due to a decrease of the water level. People think that this is caused by sand mining in the riverbed and less rainfall. Related to that, the groundwater table has significantly dropped so that water scarcity has become a major problem. With respect to disaster risks, people are aware of living in a highly vulnerable settlement structure. However, they see the highest natural disaster risk in earthquakes

and not in climate-related disasters. Therefore they assume that people living in apartments and multistoried buildings are even at a higher risk. Apart from the predefined questions, people in the squatter settlements complained about insufficient national and international aid. In their daily life unemployment, miserable living conditions, insecure land tenure, insufficient water and energy supply, the horrible traffic situation, and epidemics are among the major problems. Therefore, from the perspective of the slum dwellers, climate change and climate change-related disasters are only additional problems. With respect to natural disasters, earthquakes are even seen as the greater threat. In Jagriti, people told us that in case of a flood they will be warned by upstream communities via mobile phones, but for earthquakes there is no early warning system. Finally it must be stressed that the answers from the community members about already apparent climatic changes rather refer to natural climate variations and weather phenomena than to a longterm climatic change. Effects, such as changes in the water table and species diversity cannot clearly be assigned to climate change; they are more likely results of land and ecosystem degradation, maybe overlapping with climate phenomena.

Figure 9. Main road in the squatter settlement of Balkhu.

Figure 10. Houses made of plastic and metal sheets (front) and bricks (back) in Balkhu.

Figure 11. Open drainage system in Balkhu.

Figure 12. Bagmati river bank near Balkhu.


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Agricultural settlements Table 4 gives a brief overview of the agricultural settlements. In Sainbu village, the last flooding was in 1981 after heavy rainfall towards the eastern side of Nakhu Khola River. After that no natural disasters have occurred in the community. The residents reported that they mainly suffer from bad environmental conditions, in particular a lack of clean drinking water. They are aware that their settlement is exposed to floods due to the location along the Bagmati River, but they are less concerned about the landslide risk, which is also given as the village is situated in the middle slope of the Bhainsepati hill. The vast majority of the interviewed community members were also aware of climate change, but people had no clear idea about the difference between climate change and changing weather conditions

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and therefore often. People perceive a change in rainfall intensity and reported that higher rainfall is positive for agriculture. They think they already feel a shift of the monsoon season and longer lasting summers compared to the past. One community member reported about earlier blossoming of some flowers and trees in higher mountain regions. Despite the knowledge about climate change and related disasters, the community has not taken any preventive measures. The main concern is in fulfilling basic needs related to nutrition, housing, and clothing. According to statements of educated people during the interview, community segregation is the main reason behind the unwillingness to act against climate change. They say that people of the community are grouped in various ways like social, economical

Table 4. Profiles of the agricultural settlements. Sainbu village

Location, population, infrastructure

The village is located on the eastern side of the Bagmati River. According to the Ward profile of Lalitpur Sub-Metropolitan City (LSMC) 2006-2007, Sainbu has a population of 10,971. The village has access to the motorway, but only 30-40% of the roads are black topped; the rest is earthen or graveled. Most of the houses are traditional with load bearing walls using brick in mud and brick in cement. Only a few recent houses (around 10-20%) are framed structures; they are mainly located near the market area.

Water supply, sanitation, waste, energy

The water supply system has reached the settlement. However, water is available only once every four days. So, people depend on deep bores and wells. Collection of solid waste is organized by the community organization, but unfortunately waste is deposited in the valley. Almost 100% of the households are connected to the national electricity grid, but 15-20% still use firewood. Some households also use gas or kerosene, about 1% solar energy.

Living standard and employment

The settlement is located in an elevated position; the floodplain is used for agriculture. Today only 15% of the population, especially women, works in agriculture. Most people are employed in the service sector or work on a daily wage basis. About 10% are engaged in small businesses such as grocery stores.

Bode Gaun

Location, population, infrastructure

Located in the Manohara river corridor, the settlement consists of 1,275 households with a population of around 7,000 people (Census 2001). It is linked to the old metal road from Bhaktapur to Thimi and Kathmandu. In the village brick and stone pavement is common; gravel and earthen roads are dominant in newly developed areas. Most buildings in the core area are traditional load bearing structures with sloped roofs, but modern flat roofs out of reinforced concrete are replacing the old traditional ones.

Water supply, sanitation, waste, energy

75% of the population is connected to the municipal water supply line; the rest use groundwater. Water quality is satisfactory, but the quantity is insufficient. The number of water-borne diseases is decreasing. The settlement has a sewer system in fair condition. Most of the sewer lines are connected to the treatment plant and solid waste is collected by the municipal authority. A biogas plant has been constructed but is not yet in operation. Electricity is the main source of light. 90% of the people use gas for cooking, the rest kerosene stoves and very few biogas.

Living standard and employment

More than 60% of the population is based on agriculture; the majority of the remaining population is engaged in business, especially agro-business. Around 5-7% are involved in pottery industries and few people work in the service sector and as day laborers. A shift in cash crop farming can be observed as mushroom farming, hatcheries and unseasonal vegetable farming have been increasing.

Manamaiju settlement Location, population, infrastructure

The village is surrounded by two rivers, Mahadev Khola and Sangle Khola, both affluents of the Bishnumati. 995 of 2,539 households are engaged in agriculture, livestock and poultry farming (Census 2001). The roads of the core settlement are brick paved, but in a poor condition. The access road is in good condition.

Water supply, sanitation, waste, energy

The village depends on groundwater (wells, tube wells) and the community members said that water supply is sufficient. There is no organized waste management and no access to the public energy grid.

Living standard and employment

Most of the dwellers of the settlement work in the agricultural sector; few also work in private businesses and service sectors.


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and political, which is, in their opinion, the main barrier for common action. However, on the other hand, people in the community collect money each year to further asphalt the main graveled road. Bode Gaun is less vulnerable to floods since the Manohara River, which ruined property in 1993 and in 1999, has been deepened. In the hilly north side of the core settlement, some landslides can be observed. In the past, people suffered severely from water-borne diseases, but nowadays this problem is decreasing. In contrast, more people are facing problems with civilization diseases such as diabetes, hypertension, and high blood cholesterol. The younger generation is also facing drug problems. Direct climate-related disaster risk is relatively low. Environmental problems such as depletion of groundwater, biodiversity loss, and loss of agricultural and green areas are predominately the result of suburbanization processes and land pressure, as the municipality is among the fastest growing areas in KV. Agricultural land is being converted into building plots day by day. Due to sand mining for construction works, the riverbed is cutting deeper, which makes surface irrigation almost impossible on the remaining agricultural plots. Therefore, farmers use mainly groundwater for irrigation. Probably due to the moderate immediate climate-related disaster risk, the community is not much aware of possible climate change impacts. However, the settlement is currently in a transformation process from an agricultural to a commercial and residential area. Decreasing open spaces and less agricultural production for selfsupply as well as degradation of water and soil resources make the settlement more vulnerable to disasters, such as droughts. In Manamaiju about 60% of the people are engaged in agriculture; the others work in the business and services sectors and fewer in the craft trade sector. According to the community members, unplanned urbanization and environmental degradation have become a threat to their livelihood. They think that there should be more support

from the central and local governments to improve this unsatisfying situation. With respect to climate change, the inhabitants provided very detailed information, but as in the other communities, they mixed to some extent climate change with weather events, and also explained environmental degradation as a result of urbanization with climate change impacts. In detail, people felt moderate changes in rainfall compared to 30 years ago, but in their explanations, they only compared the present year with previous ones and referred to seasonal variations in rainfall throughout the year. Related to temperatures, they felt that there are higher temperatures than before in Ashad (June-July) and complained that in the recent winter season there was no formation of ice crusts like before. Moreover, they observed less wind and a higher frequency of hailstorms compared to the past years, but no changes in snowfall. People observed a rapid decrease of animals in the community lands. They reported that only few of the formerly many bird species are still abundant, and that seasonal birds, such as the Koel (Eudynamys scolopaceus) are completely extinct. Moreover they stated that ants are increasing and no fishes are seen in ponds and rivers. When asked about health risks, people referred to a wide range of widespread illnesses such as diabetes, hypertension, sight and hearing problems, as well as neurological diseases. However, they also mentioned diseases which are typically related to unclean water and poor hygiene, such as typhoid, gastric problems, and dysentery. Floods have decreased recently due to sinking water levels, and community members are now more concerned about the drop of the groundwater table and resulting water scarcity as well as contamination of drinking water. Moreover, wind storms are still a threat for the community. However, the greatest natural risk for the inhabitants comes from earthquakes.

Figure 13. Traditional house in Sainbu village.

Figure 14. In the agricultural village of Sainbu.


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Figure 15. Small house gardening in Sainbu village.

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Figure 16. Urban agriculture is being pushed back by ongoing urbanization in Kathmandu Valley.

Table 5. Profiles of the traditional settlements. Basnyat village Location, population, infrastructure

Located on the eastern side of the Bagmati river, the 200-300 year old village consists of 75 households with a total population of about 350 (Ward profile of Lalitpur Sub-Metropolitan City 2006-2007). The village has good access to the motorway and roads are black topped. Buildings are permanent and in a good shape.

Water supply, sanitation, waste, energy

Basnyat was among the first villages in KV with access to tap water. Today, only 5% of the households use tap water, the rest depends on wells. Sewage is directly drained to the Bagmati River. Waste is collected. The village has a 100% electricity supply.

Living standard and employment

The community was traditionally based on agriculture. However, in 1975 most of the agricultural land was converted to a waste water treatment plant. The consequence was a loss of agricultural land resulting in a loss of income sources, changing the livelihoods of the community members from agriculture to services and private business. At present around 95% of the population has shifted to the service sector and only 5% still work in agriculture.

Tikathali village

Location, population, infrastructure

Tikathali village is located at the confluence of three rivers: Manohara, Hanumante, and Godawari. The settlement has existed for more than 300 years and currently consists of ~1,000 households. The main road is asphalted, but only 4 m wide; secondary roads are very narrow, unsealed, and often not suitable for four wheelers. Houses are nowadays constructed with modern construction materials (cement, RCC, metal, etc.). Most of the traditional houses have been demolished and replaced by modern ones.

Water supply, sanitation, waste, energy

The main water sources are wells and tube wells. Deep boring for water distribution in every household is under construction. UNICEF supports the village with 118 public toilets. There is no waste management. The government has provided electric facilities; 80% of the energy consumption is represented by LPG, 20% by kerosene. Few bio-gas plants are under construction.

Living standard and employment

The share of agriculture was around 75% in the past, but has dropped to presently 25%. About 20% of the households practice animal husbandry; in the past it was almost 90%.

Tankeshwor Location, population, infrastructure

The medieval settlement is located along the Bishnumati River. The majority of residents belong to the ethnic minority of the Newars who are involved in business rather than in agriculture. According to the respondents, 60% of the inhabitants are enrolled in business and about 20% in services. Due to an unmanaged dump site, there is a pungent smell in the village.

Water supply, sanitation, waste, energy

The dwellers of Tankeshwor depend on the municipal water supply. Sanitation is taken care of at the individual household level. There is a plant that could convert waste from slaughter houses to gas that could be used for cooking and lighting. There are still gas pipe connections in some of the nearby houses. However, it has never been in operation except for a demo project.

Living standard and employment

Most of the inhabitants are business and service oriented. Few are involved in agriculture.


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Old traditional settlements Table 5 provides an overview of the traditional settlements. The community members of Basnyat reported about the last flood event in 1981. After this, no more flood events have occurred. According to the residents, until the early 1980s the water in the Bagmati River was so clean that it could be drunk without any treatment. Nowadays people suffer from the bad smell and mosquitoes are breeding in the polluted river. In 1975, land for waste water treatment was acquired by the government of Nepal, but due to technical problems, the project failed and the former agricultural land turned into fallow land that looks like a pond. The conversion of a large area of agriculture land for this project resulted in a loss of income from agriculture in the community, and many people changed their livelihoods to service and business oriented sectors. Although there is no official awareness program on climate change, almost all inhabitants have heard about it on TV and radio. However, they usually perceive it as weather change and do not take any adaptive measures. They expressed that the natural disasters are dependent on the future. They further added that the Bagmati River is not connected to any glaciers, so that a severe flood may not occur. In general, people are busy earning their livelihoods and their main concern is to fulfill basic needs. Although the community receives support for physical and social infrastructure development, they felt that they do not get sufficient financial support from the national government.

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to minimize the flooding risk. Moreover, there is a future plan for a river corridor and a green belt on both sides of the river. A door to door waste collection scheme is being launched from the private sector and local people in order to overcome the solid waste disposal problems. People state that there is a change of the river course and that bank erosion has increased. Severe floods occurred in 1954, 1967, 1981, 1993, and 2002. In the latter, three people died and a few were injured. Moreover, rivers incise due to sand mining from the river beds and the annual change of the flow pattern is a severe problem for agriculture. With respect to climate change, people told about huge rainfall in some years and no rain or very little rain in other years, which is a major problem for agriculture. They relate the variability in precipitation to climate change. Moreover, they felt a temperature increase as well as a decrease in the occurrence of hailstorms and strong winds within the last years. In general, people are more aware of climate change in this village and there is a high level of community participation compared to other communities. According to the residents of Tankeshwar, rapid urbanization and growing traffic congestion with increasing number of accidents are major threats for the community. Earthquakes are another major risk, in particular due to the haphazard construction of most buildings. Floods are not seen as a hazard anymore due to the decrease of the water level, which was likely caused by sand mining. Asked about climate change, people think there were slight changes in rainfall and state that in the months of Ashad (June-July) there is no flood as there used to be in the past. They say that summers are uncomfortable due to rising temperatures and that nowadays mosquitoes are abundant even in the winter. Moreover they assume that there has been a moderate decrease in wind speed in the last years, but no changes in snowfall and hailstorms. Concerning animal and plant species diversity, people have noticed an extreme loss within the last years. The older people said that the fox and the wild cat were already becoming rarer some decades ago, but are still abundant. Today they have completely vanished. Birds such as pigeons are fewer and sparrows are not seen anymore. Moreover, the occurrence of Shisnu (sting nettle; Urtica dioica), which is used as a medical plant, has considerably decreased.

Figure 17. Protected river banks at Manohara River near Tikathali village.

According to the people in Tikathali, main hazards in their community are related to traffic, earthquakes, and floods. Major risks for the community result also from unplanned settlements and immigration from other districts, as the village is currently in the transformation process from an agricultural to an urban settlement. This leads to a massive loss of green areas and agricultural land. People reported that the intensive use of chemical fertilizers causes serious health risks and assume that an increase in respiratory diseases and visibility problems in the community is related to it. Pollution in the Hanumante and Manohara Rivers are caused by untreated sewage discharge and waste disposal. The community has started with a land pooling project to control haphazard settlements. The northern part of the Manohara River was prepared for land pooling and river training works are carried out

Figure 18. Afforestation with native tree species for flood protection in Basnyat village.


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Regarding health risks, people complain about typhoid, malaria, dengue, Japanese encephalitis and asthma. With respect to water availability, they report about the drop in the water table, which has caused water scarcity in drier months. They think that the construction of buildings near dry river beds has resulted in the fallen water table. In general, they think that the stakeholders, such as the central and local government, are responsible for supporting their livelihood. Household survey in three squatter settlements Infrastructure, population and living conditions Table 6 provides an overview about basic parameters of the three squatter settlements, as well as literacy rate, income generation and motivation to move to Kathmandu from the hills where the people from all three sites came from. Thapathali squatter settlement is one of the most recent squatter settlements in urban Kathmandu. Since 2007 it has been situated on the floodplain of Bagmati River, opposite a big solid waste dumping site. All buildings in the settlement are one storey and either temporary (built with plastics, flash sheets, Corrugated Galvanised Iron [CGI] sheets, or bamboo) or permanent (built with bricks or cement; roof from CGI sheets or concrete). The Thapathali squatter settlement has no access to the city water and electricity supply system. A community water tank has been built, which, at the time of the survey, was not water-supplied due to difficult access of the water tankers to the settlement. Drinking water is collected from public stone spouts or bought in plastic jars from informal water vendors. For household usage, groundwater is extracted with the help of hand pumps. The settlement has no developed toilet facilities. People practice open defecation on the bank of Bagmati River. Furthermore, there is no functioning waste collection and the wastes are mainly dumped into the river, sometimes also burned or buried. This creates a polluted environment. The Shankhamul squatter settlement is located in the Kathmandu District along the Bagmati River bank, opposite the Shankhamul

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Ghat. Almost all of the houses in the settlement have a single storey build with bricks and cement, and CGI sheet for roofing (permanent). There is electricity provided in the settlement and the main water sources are water tanks, tube wells and dug wells. Open defecation is less practiced in the community, since there are pit latrines for each household or shared between several families. Some latrines, however, drain directly into the nearby river. The solid waste in the community is burned, buried, and less disposed on the banks of the river. Traditional composting of organic matter is also popular among the households. The Manohara squatter settlement is situated on an open field at the bank of the Manohara close to the Tribhuwan International Airport. The settlement appeared in 2002 as a consequence of the Maoist insurgency in the country, when a large population wave migrated to the capital city in the search of security and economic stability. Due to the ongoing process of settling, there are different types of housing structures in the community, as well as different sanitation conditions. All buildings in the settlement are one storey, with the exception of one big three-storey building. The housing structures are temporary, semi-permanent, or permanent, which clearly shows that economic and social differences still exist in the community. There is no legal water supply to the settlement, however, electricity is available. Main drinking water sources are stone spouts owned by another neighborhood. To avoid conflicts of water ownership, women fetch water very early in the morning. Some households also buy water jars. For domestic purposes groundwater is used, extracted with hand pumps. Open defecation is widely practiced as there are not enough pit latrines to serve the whole population. The pit latrines built are private or shared between several families. Most of them are not deep enough and they have been used for years, thus they are almost filled up; others drain directly into the nearby river. The solid waste in the settlement is mainly dumped on the riverside. Traditional composting of organic materials is practiced in the community as well.

Table 6. Profiles of the three squatter settlements.

Approximate size of settlement Number of households

Thapathali squatter settlement

Shankhamul squatter settlement

Manohara squatter settlement

17,500 m2 of public land

16,700 m2 of public land

37,900 m2 of public land

366 households

120 households

750 households

Around 2,200 people

Around 660 people

Around 5,000 people

60 households including 272 people

63 households including 357 people

75 households including 418 people

Illiteracy rate among interviewed people

More than half of the women (75 out of 127) and more than a third of the men (65 out of 145)

20 out of 174 men and 42 out of 183 women in the interviewed households

Almost half of the women (90 out of 210) and a quarter of the men (60 out of 208)

Main reason for moving to Kathmandu

Poverty and disaster risk in the previous place of living, as well as better job opportunities in the city

Better job opportunities in the city and escaping from conflicts and disasters at the previous place of living

Better job opportunities in the city, and escaping from conflicts, poverty and disasters in the previous place of living

Daily wages

Services and small businesses

Daily wages, but main sources of income were more evenly distributed

Overall population Number of households and people interviewed

Main source of income generation


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Identification of climate-related hazards As determined in the semi-structured interviews, the squatter dwellers in Thapathali face regular water shortage during the dry season and are forced to use the water from the Bagmati River for washing clothes, vegetables that they have for sale, cleaning the house and other domestic purposes. Flooding in the settlement happens once or more times during every monsoon season, causing loss of cultivated land and household property. Five households reported that their houses had been washed away by bigger flooding events. Heavy rains produce house inundation and water stagnation in the settlement that often lasts for days or weeks after the rainfall. Typical health problems during the monsoon were reported to be diarrhea (39 out of 60 households), typhoid fever (31 households) and jaundice (8 households). Two families have experienced cholera, one family malaria, since they settled at the present place. The squatter dwellers in Sankhmul struggle with limited drinking water supply during the dry season and the households of the upper part of the squatter experience lack of groundwater as well. The community experienced the big flood event in 2002, during which many houses in the lower part of the settlement were completely flooded with water that reached over a meter. No help was received at that time from the local authorities or NGOs and many families had to move to neighbors and relatives until the water pooled away. According to the respondents, flooding happens in the area once in a few years and the last flooding event was in 2009. Consequences of such events were losses of properties, agricultural land and domestic

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animals, as well as house inundation and water stagnation for more than a week. The older squatter dwellers have observed increases in the population of mosquitoes and flies in recent years that could be due to both higher pollution of Bagmati River and its banks, and the changing temperature and precipitation pattern in the valley. Common diseases during the monsoon were reported to be diarrhea (25 out of 63 households) and intestinal worms (11 households). Three families had cases of typhoid fever, one of cholera, one of malaria, and one of jaundice. The squatter dwellers of Manohara experience a lack of drinking and sanitation water every dry season and they still use the polluted water of the Manohara River for washing clothes, domestic animals, utensils, for religious and other purposes. The settlement witnesses one or more flooding events every monsoon season. Flooding has already washed houses away and has caused loss of properties, domestic animals and cultivated land. Open drainage overflow, house inundation and water stagnation are the typical consequences of heavy rainfall in the area. The squatter dwellers have already observed an increase in the number of mosquitoes and flies in the area, reasons for which may be the polluted environment and changing climate conditions favorable for the insects’ breeding. Common health problems during the monsoon were reported to be diarrhea (58 out of 75 households), typhoid fever (27 households), intestinal worms (12 households), as well as two cases of malaria, two cases of cholera, two cases of dengue, one case of jaundice, one case of filariasis, and one case of viral encephalitis.

Table 7. Squatter dwellers’ own perception of climate related risks. Settlement

Thapathali

Sankhamul

Manohara

Perception of climate-related risks Water-borne diseases, floods and drought events are considered as very high risks by most of the respondents, whereas vectorborne diseases are seen as medium risk for the squatter population. According to the surveyed households, the main reasons for health problems in the family are rather environmental, such as polluted river and river bank, contaminated drinking water, and poor hygiene and open defecation. The majority of surveyed households consider draughts/drinking water deficiency as a very high risk, whereas vector- , waterborne diseases, and floods are seen as medium risks for the squatter community. Main reasons for the health problems in the family are believed to be mainly polluted river and river bank, contaminated drinking water, and increased number of mosquitoes and flies. Flood events and water-borne diseases are seen as very high risks in the community, followed by vector-borne diseases (medium) and droughts/lack of drinking water (some risk). Main reasons for health problems in the family are believed to be poor hygiene and open defecation, polluted river and river bank, and contaminated drinking water.

Applied adaptation strategies to climate related risks In the Thapathali squatter settlement there are no significant adaptation strategies to climate-related hazards. The settlement is located on the floodplain of the river and no protection measures have been taken against potential flooding during the monsoon season. Most of the households practice subsistence agriculture on the narrow band of land between the river and their houses. Limited or no protection space has been left between the cultivated land and

the river or between the cultivated land and the toilet spots (fugire 19). Considering water- and vector-borne diseases, the majority of respondents (38 out of 60 households) did not treat water in any way before drinking (compare figure 21). The rest used water disinfection techniques, such as filtering with filter candle, Solar Water Disinfection (SODIS), boiling or applying chlorine solution (PIYUSH). On the question if they wash hands after going to the toilet and before eating, the responses varied from “yes, with soap” to “only sometimes”. Most of the domestic work in the settlement is


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done outside the house due to lack of lighting and tap water. Families cook, wash and bathe behind or next to their houses, which highly increase their exposure to disease vectors. Children are considered at highest risk, as 22 out of 46 families with children (under 16 years of age) replied that their kids usually played on the bank of polluted Bagmati River. In the past, the Sankhamul squatter settlement was located closer to the river. However, due to serious flooding, the squatter dwellers moved upwards, away from the river for better safety. The housing structures have also changed during the more than 30 years of squatter existence. In the beginning, the shelters were simply built from locally available materials, such as bamboo, plastic sheets and hard boards (temporary housing structures). Later, with time and the growing sense of security that they would not be evicted, the squatter dwellers have invested in their houses. After 2004, a stronger squatter community has formed that decided to restrict further squatting in the area (Full Bright Consultancy, 2010). Several community water tanks have been constructed in the settlement. They are easily accessible by water tankers through the city-side road. Considering water- and vector-borne diseases, the majority of interviewed households (43 out of 63) applied drinking water treatment techniques, such as filtering, SODIS, boiling or PIYUSH. The population practices hand-

washing after defecation; nevertheless, hand-washing with soap, has been observed to be rare and hand-washing before eating has not been regularly practiced. Due to its location in an open field, the Manohara squatter settlement has been able to expand towards the land away from the riverside which reduces the risk for squatter dwellers of river flooding during the monsoon. Further protection against flooding has been accomplished through the elevation of the river bank with the help of sandbags (figure 20). Even considering the risk of water- and vector-borne disease, the big majority of interviewed households do not treat drinking water in any way; the rest of the respondents filter or boiled the water before drinking. Hand washing after defecation and before eating is practiced irregularly among the family members. Badly maintained open drainage systems in some parts of the settlement result in permanent water stagnation close to the households and along the squatter paths that not only look unpleasant, but may have negative effects on the health of the local population regarding vector breeding. Furthermore, children younger than five years of age are considered highly vulnerable to water- and vector-borne diseases as they were observed to play on the bank of the polluted river during the day of the survey.

Figure 19. Unprotected cultivated land next to toilet spot and river, Thapathali squatter settlement.

Figure 20. Sandbagging of river bank, Manohara squatter settlement.

Discussion Unmanaged and unplanned urban development is a well-known source of emerging environmental problems in developing countries (Marfai & King 2008). Uncontrolled and unplanned urban growth, partly due to an unstable political environment, has led to severe environmental problems in KV, such as a loss and degradation of urban ecosystems, degradation of water resources, sealing and contamination of soils, air pollution, and waste problems (ICIMOD/ MoEST/UNEP 2007). The change of the settlements patterns in the valley is mainly characterized by a loss of green open spaces and urban agriculture in favor of built-up areas. Beside environmental degradation, ongoing urbanization in KV is

also characterized by a change in socioeconomic conditions. The concentration of the industry and service sectors in Kathmandu as well as the unpredictable political situation in rural areas during past years has attracted and still attracts immigrants from other regions. This has led further population growth in the valley and growing land demands for housing, business, and transport infrastructure. As a result, more agricultural land is being converted to built-up land, with the consequence of job losses in the agricultural sector. At the same time land prices continue to rise, so that poor immigrants have been forced to live in squatter settlements in highly vulnerable and disasters prone areas, such as floodplains and landslide-prone slopes. The expansion of squatter settlements, in turn, has contributed partially to further environmental degradation particularly along the


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Figure 21. Drinking water treatment practices in households in the Thapathali, Sankhamul, and Manohara squatter settlements.

rivers (sewage discharge, waste disposal, etc.). Additionally, the valley had been devastated by earthquakes in the past and hence is one of the major concerns for the habitants. KV has also been severely affected by floods and fire in the past and the current unplanned urban growth has further aggravated the risk. Against this background, climate change can be just seen as another problem or stress factor in addition to the numerous ones that already exist in KV. Hence, the question arises if people are aware of this new threat and already take adequate measures to reduce its (future) negative impacts, in particular with respect to natural disasters. This question was the starting-point of this research carried out in 2010 and 2011 in 12 communities of three different types (six squatter settlements, three agricultural settlement, and three traditional settlements) in KV. As a main result, we determined that people in KV are in general aware of climate change and its possible impacts. They are informed by the media or relatives, friends and community members. However, when asked for observed climate changes and impacts they often cannot distinguish between climate and weather phenomena and describe seasonal changes or single weather events as manifestation of climate change. Nevertheless, it was frequently mentioned that the flowering period started earlier and winters arrived later within the last years. This observation is confirmed by climate models (compare section 1). Residents of all settlement types mentioned about observed changes

related to water resources. As for water quality of rivers, people generally complain about a significant deterioration of the status in the past years. Inhabitants of the squatter settlements are aware of the high flood risk due to the exposed location of the settlements and have adopted some affordable protective measures, such as protective walls made of sand sacks. However, people in the Jagriti squatter settlement reported that the flood risk seems to be reduced due to a sinking groundwater level, which might be caused by sand mining in the river bed and less rainfall. A sinking groundwater table has also been observed in the agricultural and traditional villages of Bode Gaun, Manamaiju, Tikathali, and Tankeshwar. Apart from sand mining in the river and reduced rainfall, people in Bode Gaun mentioned soil sealing as a further cause for groundwater depletion. Most communities see droughts as a major problem, in particular for domestic water supply and agriculture. This situation could further deteriorate with increasing urbanization and in addition, with increasing dry periods, as forecasted in the regional climate models (NCVST 2009, IPCC 2012). With respect to flora, fauna and biodiversity, people of all communities observe habitat and species losses. In general the observations in older settlements, which have a high proportion of agricultural land use and longer duration of residency, are more precise. People mention losses of habitats, animal and plant species, and often relate those with climate change impacts. However, it seems obvious that most species losses are rather related to ecosystem loss, fragmentation,


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degradation and pollution than to climate change. Particularly in agricultural and traditional villages, people are concerned about the loss and degradation of urban green areas. Although people are aware of climate change, it does not play an important role in their daily lives. Inhabitants of squatter settlements are facing more severe immediate threats, such as unemployment, social insecurity, and diseases, while people in agricultural and traditional settlements are moving forward with their daily business. Compared to studies in other third world countries, such as those by Marfai (2011) and Ward et al. (2011) in Indonesia, very similar conditions have been identified. Although some of the communities in Indonesia are in climate-hazard prone areas, the people there are not very worried about these risks and do not pay much attention to the alerts that are provided. This condition is a reflection of people in third world countries who usually have a higher tolerance to hazard conditions. They feel relatively safe from climate-related disasters, in particular floods, as their houses are mainly located in higher elevations, not in the floodplain. However, community members complain about environmental problems, such as a loss of green area and agricultural land, bad water conditions, and waste problems. Moreover they criticize the insufficient infrastructure, in particular bad road conditions and energy supply, and make the government at least partly responsible for this undesirable development. In squatter settlements, people complain about insufficient national and international aid to improve their living conditions. Over all hangs a cloud of fatalism. Only occasionally communities take the initiative to adapt to changing environmental conditions and related hazards. This is primarily the case in the event of an imminent threat, such as a flood, when the community takes joint protective measures in form of river bank protection. However, due to the insecure future and limited financial resources, most of the measures are low cost solutions with very limited effectiveness. In some of the agricultural and traditional villages environmental initiatives have been established, for example in Thikatali. However, community members of Sainbu village see political segregation within the community as the main obstacle for joint action. Adaptation measures to climate change impacts in the mid or long term are not taken by any of the investigated communities. Asked about disaster preparedness, residents of all settlements were particularly concerned about earthquakes and floods. However, in the squatter settlements people were also very worried about diseases, traffic hazards, and thefts than natural disasters. The household survey clearly states that the polluted environment and the bad sanitation and hygiene conditions already cause a number of water- and vector-borne diseases particularly in flood-affected areas, among others malaria, dengue, cholera, typhoid fever, viral encephalitis, and diarrhea. Despite of the fact that the water is the main source for many of these diseases, many households still use unsafe water without treatment or with treatments such as filtering which is ineffective against bacteria (figure 21). Although particularly the squatter settlements are exposed to climate-related disasters, only one settlement has implemented adaptation measures to reduce flood risk at a notable scale. This is the settlement of Shankhamul, which is the oldest among the surveyed squatters. Here the houses have been changed to more

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robust types of structures and the settlement has shifted up to avoid flood water. The community is also managing water supply through water tanks. Only 20% of the surveyed households, compared to 38% and 50% in Thapathali and Manohara settlements, respectively, are using un-treated water for drinking. This is partly due to sense of security of the land ownership and also partly due to increased community value because of age of the settlement. Considering the difficulties and obstacles, local communities have at least partly developed strategies to adapt to climate change impacts, but the measures are insufficient for an effective protection. Moreover, the present measures only focus on short term conditions, but initiating mid- and long-term adaptation strategies is very important as well. As stated by Marfai and King (2007) and Ward et al. (2011), local governments play vital roles in traditional hazard management, especially by usingstructural-technical measurements. Unfortunately, due to the high cost and effort, this is still a very difficult task in developing countries. Mapping the hazard and projected impact can be used to increase stakeholder awareness and involvement in ensuring the safety of the society from the hazards, as it´s given the most priority.

Conclusions and outlook Environmental degradation and pollution will continue and even accelerate in KV if no adequate political control mechanisms and planning instruments are introduced in the very near future. Population growth, lack of perspectives in rural areas and economic development will lead to further urbanization, which needs to be sustainably managed. However, currently politicians and urban planners seem unable to cope with these challenges. Against this background, climate change must be seen as only another stress factor in addition to the many that already exist. Therefore it is understandable that people in squatter settlements, although aware of climate change, do not care too much about it, as they are caught up in their daily struggle for survival. Furthermore their information mainly stems from informal sources that might also deliver partly wrong or incoherent information, thus a lack of adequate awareness raising is apparent especially in the most vulnerable communities. Members of the middle class in the agricultural and traditional villages are more concerned about the degradation of their living environment, but pass the buck to the politicians and show little selfinitiative. It is obvious that the 10 year civil war (1996-2006) has left its traces also in insufficient environmental policy and urban planning and an internal rift of the society, which prevents joint action. The Kathmandu Environmental Outlook (ICIMOD/MoEST/UNEP 2007) listed numerous problems and recommendations for policy and action, in particular to minimize emissions from vehicles, improve urban land use and ecosystem management, ensure safe and sufficient drinking water resources, improve waste management, and prepare people for natural disasters. In this context, they promoted institutional strengthening and resource allocation, but unfortunately success remains elusive for the most part. Therefore it seems also questionable, if the recommendations from the NAPA (2010) that are related to climate change adaptation will be sufficiently implemented


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in planning processes and action at regional and urban scale. However, awareness is already present, and the different ways of perceiving and dealing with climate change and other environmental impacts by different communities show that there is potential for concerted action if the preparedness measures taken are locally adapted to the respective communities. The high variability of perception that can be seen in this study shows that adaptation on a regional scale has to be downscaled to the community and household levels to be effective.

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Government of Nepal, National Planning Commission Secretariat, Central Bureau of Statistics (CBS), 2011. National Population Census 2011. Available online: http:// census.gov.np (last access: 15th July 2012). Inman M., 2010. Settling the science on Himalayan glaciers. Nature Reports Climate Change, published online 2 March 2010, doi:10.1038/climate.2010.19. IPCC - Intergovernmental Panel on Climate Change, 2007. Climate Change 2007: Synthesis Report - Contribution of Working Groups I, II and III to the Fourth Assessment Report of IPCC. IPCC – Intergovernmental Panel on Climate Change, 2012. Special Report on Managing the Risks of Extreme Events and Disasters to advance Climate Change Adaptation (SREX).

Acknowledgments

Maplecroft 2011. Natural Hazards Risk Atlas 2011. Marfai M.A., 2011. Community’s adaptive capacity due to coastal flooding in Semarang

The authors would like to acknowledge the student groups from Nepal, Indonesia and Germany who did the site surveys, namely:

Coastal City, Indonesia. International Journal of Seria Geografie. Annals of the

Albert Beele, Alicia Bustillos, Gretel Clausen, Louisa Kistemaker, Henning Korte, Nico Pährisch (Cologne University of Applied Sciences).

Marfai M.A., King L, 2008. Potential vulnerability implications of coastal inundation due

University of Oradea. E-ISSN 2065-1619. Year XXI, no. 2/2011 (December), pp. 209221. to sea level rise for the coastal zone of Semarang city, Indonesia. Environmental Geology, 54:1235-1245. http://dx.doi.org/10.1007/s00254-007-0906-4. Marfai M.A., King L., 2007. Monitoring land subsidence in Semarang, Indonesia.

Nursakti Adhi Pratomoatmojo, Kusuma Rahmawati, Fahrudin Hanafi, Anang Widhi Nirwansyah, Taufik HIdayatullah (Universitas Gadjah Mada).

Environmental Geology 53. Pp. 651-659. http://dx.doi.org/10.1007/s00254-0070680-3. NCVST - Nepal Climate Vulnerability Study Team, 2009. Climate Change Induced Uncertainties and Nepal’s Development Predicaments.

Shreema Rana, Nirjana Shrestha, Nisha Shrestha, Dipa Shakya, Shradha Tashniwal, Gita G.C., Sulav Nepal, Archana Bade Shrestha, Pratigya Shakya, Lijeena Shakya, Niyanta Shrestha, Asha Shree Rajbhandari, Smriti Rajbhandari, Shova Thapa, Leena Koirala, Pratigya Manandhar, Madhusudhan Baral, Yek Raj Adhikari, Suraj Shrestha (Tribhuvan University Kathmandu).

NSET - National Society for Earthquake Technology-Nepal, 2008. National strategy for disaster risk management in Nepal- final draft. Available online http://www.undp. org.np/pdf/NSDRMFinalDraft.pdf (last access: 15th July 2012). Pacific Disaster Center (2005): Kathmandu Valley, Nepal, Disaster Risk Management Profile. Available at http://emi.pdc.org/cities/CP-Kathmandu-08-05.pdf (last access: 15th July 2012). Pradhan, P. K. (2004). Population growth, migration and urbanisation. Environmental

Furthermore the field trips would not have been possible without the support of the Center for Natural Resources and Development – CNRD (http://www.cnrd.info), financed by German Federal Ministry for Economic Cooperation and Development (BMZ) and managed by German Academic Exchange Service (DAAD).

consequences in Kathmandu valley, Nepal. In J. D. Unruh, M. S. Krol, & N. Kliot (Eds.), Environmental change and its implications for population migration (pp. 177–199). Dordrecht: Kluwer Academic Publishers. Rimal B., 2012: Dynamics of land cover change in Kathmandu, Nepal. Geospatial World – Geospatial Communication Network. Published online 20/02/2012, http://www. geospatialworld.net/index.php?option=com_content&view=article&id=24089:d ynamics-of-land-cover-change-in-kathmandu-nepal&catid=158:urban-planning-

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Sensing, ISSN 2072-4292. Thapa R. B., Murayama Y. 2010. Drivers of urban growth in the Kathmandu valley, Nepal: Examining the efficacy of the analytic hierarchy process. Applied Geography 30, 70–83. Thapa R. B., Murayama Y. 2012. Scenario based urban growth allocation in Kathmandu Valley, Nepal. Landscape and Urban Planning 105, 140– 148. United Nations Development Program (UNDP), 2009. Global Assessment of Risk – Nepal Country Report. Available online: http://www.undp.org.np/uploads/ publication/2010102909383499.pdf (last access: 15th June 2012).

Government of Nepal, Ministry of Environment, 2010. National Adaptation Programme

Ward P.J., Marfai M.A., Poerbandono, A.E., 2011. Flood-risk in Jakarta. In: Aerts, J.C.J.H.,

of Action (NAPA) to Climate Change. Available online: http://www.napanepal.gov.np/

Botzen W., Bowman M., Dircke P., Ward P.J. (eds.). Climate adaptation and flood-risk

pdf_reports/NAPA_Report.pdf (last access: 15th July 2012).

in coastal cities. Earthscan, London, UK, forthcoming.


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Appendix Interview questions from community and household survey Box 1: Interview questions related to community perception about climate change and disaster preparedness 1. Are you aware of climate change? 2. What is your expression for climate change? 3. Have you observed changes within the last years in:

- Temperature - Precipitation - Wind - Floods - Other natural Disasters - Birds behavior - Live stock behavior - Occurrence of plants - Occurrence of animals - Agricultural productivity - Green area - Water availability - Water quality - Depth of groundwater table - Epidemics

4. What are the major risks for the community and its members? The following answer choices are given:

- Earthquake - Flood - Drought - Health risks / epidemics - Traffic - Loss of employment - Decreasing public security / crime - Others

5. How do you estimate future risks? 6. How do you respond do these risks? 7. Do you take any prevention measure to natural risks? 8. Are there public institutions supporting you in your daily life?

56


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Box 2: Questions of the household survey I.

Living environment and education

(1) (2) (3) (4) (5) (6) (7) (8)

II.

Climate-related hazards (direct and indirect)

(9) (10) (11) (12) (13) (14) (15)

III.

Sanitation, hygiene, and diseases

(16) (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) (27)

House construction (temporary, semi permanent, permanent) Number of floors Number of rooms Number of household members (gender, age) Number of years living in the squatter settlement Place of origin Reasons for moving to Kathmandu Number of literate / illiterate persons in the family

Experienced water scarcity in dry seasons (yes/no; frequency) Experienced flooding in wet seasons (yes/no; frequency) Material losses / dead or injured people during flood events Water stagnation after flooding/ heavy rainfall (yes/no; duration) Observed changes in mosquito population Observed changes in fly population Experienced natural disasters apart from floods and droughts (yes/no; frequency)

Household with/out toilet Sources of water for domestic use and drinking / use of river water Treatment of potable water Washing hands after defecation (yes/no; with/without soap) Washing hands before eating (yes/no; with/without soap) Discharge of solid waste? (place; treatment) Kids play on the river bank (yes/ no) Type of health problems encountered in the family Frequency of diseases in the family since living in the squatter settlement Types of diseases since living in the squatter settlement (diarrhea, intestinal worms, cholera, typhoid, viral encephalitis, malaria, dengue, filariasis) Reasons for diseases / health problems (own perception) Place of medical treatment (hospital, clinic, healer, self-treatment, etc.)

IV.

Own perception of risks

(28)

V.

Adaptation to climate change induced hazards

(29) (30)

Level of risk due to floods, droughts, vector-borne diseases, water-borne diseases, others

Sources of family income and expenditures for different purposes Expenditures for adaptation measures due to climate change induced hazards

57


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JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT A water productive and economically profitable paddy rice production method to adapt water scarcity in the Vu Gia-Thu Bon river basin, Vietnam Bhone Nay-Htoon a, Nguyen Tung Phong b, Sabine Schlüter a, Aldas Janaiah c Cologne University of Applied Sciences, Institute for Technology and Resources Management in the Tropics and Subtropics (ITT), TermaVietnam Program, Betzdorfer Str. 2, 50679 Köln, Germany a

b

Center for Training and International Cooperation, Vietnam Academy of Water Resources, 171 Tay Son – Dong DA, Hanoi, Vietnam

c

School of Agribusiness Management, Acharya N.G. Ranga Agricultural University. Rajendranagar; Hyderabad-500030, India

*

Corresponding author : nay-htoon.bhone@uni-bayreuth.de

Article history Received  01.09.2012 Accepted  22.01.2013 Published 04.05.2013

Keywords Water productivity Benefit cost ratio Water-wise rice Decision making Vu Gia-Thu Bon Vietnam

Abstract In Vu Gia-Thu Bon river basin, Vietnam, drought during the dry season affected negatively on rice production. High and uneven rainfall distribution cause flooding in the basin during wet season and cause severe agricultural drought during dry season. This study aimed to point out a higher water productive and economically efficient rice production method to adapt water scarcity in the region. Based on available secondary data, water productivity is calculated for different water saving rice production methods, according to Pereira, et al, (2012)’s irrigation water productivity and total productivity equations. The profit of technological change is calculated by partial budget analysis of rice production in that area and a sensitivity analysis supports to point out which input factor is sensitive to farmer’s benefit. Farmer’s psychological and social beliefs are used to create fuzzy logic based decision making model. Although water productivities (ranging 0.441 kg/m3/ha to 0.504 kg/m3/ha) are ranked as the second after System of Rice Intensification, we demonstrated that Alternate Wetting and Drying method is a recommendable method to the farmer after considering economic profitability and technical simplicity. The System of Rice Intensification method also could be a suitable method to adopt because this method is the highest water productive method (Water Productivities are ranging from 0.77 kg/m3/ha to 1.02 kg/ m3/ha) coupled with highest yield of rice, subject to certain ecosystem services and payment policies should be developed to subsidize the reduced benefit resulting from this method.


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Introduction

Vietnam is the second largest rice exporter in the world (Vu, 2012). Due to frequent changes in climatic conditions, increased drought frequencies and intensities were observed to reduce rice crop productivity as reported in 2011 (Duc, 2011). Vu Gia Thu Bon river basin (VGTB) shared most of NCC rice production areas (Quang Nam Statistical Office [QSO], 2010).According to the irrigation discharge record of Tu Cau pumping station (Duc, 2011), a larger amount of water is discharged when rice plants do not need so much water but less amount of water is discharged to the same area units when rice plants are in critical need. Water scarcity during flowering and grain filling time is more important for rice production than water scarcity at earlier time (Yoshida, 1981; Datta, 1981). Previous studies and time series data sets of climatic conditions clearly indicate changes in the drought frequency and hence irrigation water availability. Therefore crop production can have severe ecological implications. Under extreme climate change and sea level rise scenario, Chen, et al. (2012) forecasted that Vietnam will go from being a rice exporter to an importer in 2030. Therefore experimental climate impact research in the field of agronomy and ecology has been strongly focused to find a better adaptation strategy of rice plants to gain higher yield. Bouman, et al(2007) found that continuous flooding has less water productivity in their study in India and in the Philippines and they pointed out the “most promising option” for saving water and to get higher farm water productivity is “by reducing the depth of ponded water from 5 -10 cm to the level of soil saturation”. In the case of VGTB river basin, change in technology suggested by Bouman, et al (2007) might not work since the water scarcity and lower water productivity is strongly related to rain events and salinity intrusion to inland water body from the sea. Duc (2011) recommended an adaptation strategy for water scarcity in the VGTB basin. His recommendation is to construct two reservoirs to store river water before the salinity was intruded or to harvest rain water. His suggestion for irrigation water deficit period is pumping salinity intruded water to the reservoirs and diluting the salinity to an acceptable degree and supplying the diluted water. His recommendation and suggestion might work for the short term but it might not work for long term since the quality of the reservoir will be affected by the pumped saline water after long term use. Rice is the dominant crop in VGTB basin, and farmers’ economy is strongly depended on success and failure of rice production (Ha, 2011). Although changing rice based farming system to other types of farming system could be a possible solution, there is almost no other alternative farming system to replace rice due to the environmental and social limitations (Ha, 2011). Therefore it is very important to adapt or mitigate the irrigation water scarcity by a better long term approach. For long term and sustainable adaptation of irrigation water scarcity in VGTB basin, United Nations Convention to Convert Desertification [UNCCD] (2009)’s idea, “changing cropping pattern or practices is one of the options to adapt agricultural water scarcity” should not be neglected. Changing rice cropping pattern or field irrigation management practices or cropping practices could be the best solution to adapt irrigation water scarcity in the VGTB basin. Therefore, this study seeks to address the following two objectives:

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1. Finding a better solution to adapt water scarcity by means of cropping practices and crop management 2. Evaluate economically profitable and high water productive rice production methods. Water wise rice production Zawawi, et al., 2010 examined paddy rice water requirement in Seberang Perak rice cultivation area in Malaysia where the research area is characterized by humid monsoon climate with 2393 mm average annual rainfall and concluded that a growth cycle of 120 day rice crop needs 13,010 m3 of water input (rainfall and irrigation) for a season of rice grown on 1.82 Ha land. Yoshida (1981) noted that rice crop need 180-300 mm/ha/month of water for evapotranspiration plus percolation and 1,240 mm/ha/month of water for overall field operation process of a crop cycle. Based on this fact, water need for a 4 month duration rice crop is assumed as 19,600 to 24,400 m3/ha [(720 to 1200 mm for evapotranspiration plus percolation + 1240 mm for field operation) converted to a volumetric value of water for a ha]. After the first and second world water forum in 1997 and 2000, the need was recognized to find solutions to increase water use efficiency in agriculture since it is the highest water consuming production sector (Bindraban, 2001, pg. 5). Technologies to produce more rice with less water are under evaluation (ibid, 2001) and such technologies are generally named as water-wise rice production technologies or water saving rice production technologies by IRRI (Bouman, et al, 2002). Some popular Water-wise rice production technologies across the world are 1) Alternate Wetting and Drying (AWD) [for more information, see: IRRI 2009a], 2) System of Rice Intensification (SRI) [for more information, see: Bouman, et al., 2002], 3) Aerobic rice production [for more information, see: IRRI, 2009b.]. Aerobic Rice production method is not included in this study since yield and return profit of rice by this method is lower than conventional methods in most of aerobic rice research (Bouman, et al., 2002) which against one of the objectives of this study: to maximize water productivity without affecting the economic profitability . Water productivity A good expression for “water productivity” is what agronomists say “More crop per drop” (FAO, 2000) and it is defined as the ratio of end product and water consumed during the production process (van Halsema, et al., 2012). Water productivity is widely used as Economic water productivity [EWP] and production water productivity [WP] but the basic theory holds a universal truth for both productivity calculations. Both EWP and WP follow the main definition (end product/water consumed) but EWP is calculated from economics point of view by using monetary unit for both numerator and denominator while WP is calculated from production point of view by using unit of products produced and unit of water consumed as numerator and denominator.


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Water productivity is calculated for different scales in Agriculture from plant scale to irrigation system level without changing the main theory of WP. Water productivity is classified based on the scale where the productivity is calculated as Water Use Efficiency (WUE) for crop; farm WP for farm level to calculate WP from farmer’s point of view of irrigation water productivity and Total WP [WP] for irrigation water use plus natural precipitation (Pereira, et al., 2012). The term WP and WUE are mostly used in irrigation management field and some researchers use these two terms interchangeably. According to Barker et al. (2003) the definition of those two terms were confused till 1993 (some examples according to Barker et al.: Dinar, 1993 for economics literatures and Richards et al., 1993 for Plant science literatures). Barker, et al. mentioned that there is no single definition for both WUE and WP because these terms are used for different purposes such as field application efficiency, Conveyance efficiency which are different types of WUE and Total Factor productivity, Partial Factor productivity which are some types of WP. However, Barker, et al. differentiated WP and WUE in general. WP is a ratio of crop output to water input (the unit can be in monetary terms or weight of output per volume of water). WUE can be defined how much water is depleted beneficially for crop, in other words, how much water is consumed by crop. Seckler, et al. (2003) pointed that WUE, especially with the term beneficial water use, is quite similar to the term productivity in WP but not exactly the same. WUE purely focuses on the physical flow of water while WP focused on the value of water.

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land rice system rain water is always ponded on the field. When rice is grown in a flood prone region with flooded water, that system of rice farming is named as “flood-prone rice” and commonly occurred in some delta regions (Taniyama, 2002). Irrigated rice production is common in the VGTB river basin and rainfed rice is a rare cropping practice (Ibid, 2011). Rice is cultivated as two crops per year: Winter-spring rice [WS or dry season rice] and Summer- Autumn Rice [SA or wet season rice] (Ha, 2011; Duc, 2011, Tenbrock, 2011). Water resources for rice production are precipitation and some irrigation water from certain sources such as reservoirs, pumping stations, etc. For the rice production in the study area, irrigation water is supplied by Tu Cau pumping station where water from the Vinh Dinh river is pumped (Duc, 2011; Tenbrock, 2011). Since Vinh Dinh River is intruded by salinity in dry season, pumping station have to stop irrigation water supply when salt concentration content is more than 0.8 ‰ and at that time there was a water shortage for rice farms (Duc, 2011). Tu Cau pumping station mostly cannot supply irrigation water for WS crop in March and July and August for SA crop.

Figure 2. Rice crops and irrigation availability of a study site in VGTB river basin. Source: Adopted from Duc (2011)

Methodology

Study site

Figure 1. Water Productivity at different scale in Agricultural production. Source: Pereira, et al. 2012

Rice production system There are very few cropping system classifications based on water management practices but the rice cropping system is one. Rice cropping patterns across the globe are generally classified into two systems: upland rice (depended solely on rainfall) and low land rice (water is available for crop in terms of rainfall and irrigation). Where there is no irrigation water available and rainfall is abundant, rain-fed lowland rice system is also common (Datta, 1981). For rain-fed low

The Vu Gia Thu Bon [VGTB] river basin is one of the 9 biggest river basins in Vietnam. It is in Central Vietnam and covers major parts of Quang Nam Province, Da Nang province and a small part of Kon Tum Province. The two main rivers Vu Gia and Thu Bon which originated in the high mountains, Truong Son, are met by many tributes before flowing into the South China sea (Department of Natural Resources and Environment [DONRE], 2011 cited in Toan, 2011). In Vietnam, especially in Vu Gia - Thu Bon river basin [VGTB], water related problems affect negatively on agricultural production (Ha, 2011). One main problem is water scarcity and drought during summer (Ibid, 2011; Duc, 2011). According to time series data from 1976 to 2010, rainfall distribution in VGTB basin is a unimodal distribution pattern where the rainfall is concentrated in around 4 months duration period (September to December). However, as the basin is in a coastal area, along with the occurrences of Typhoon, abnormal heavy rainfalls are likely to be occurring. Annual average rainfall (1976-2010) at Da Nang station in VGTB area is 2162.53 mm.


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Figure 3. Location map of VGTB river basin.

Data analysis To conduct our study, secondary data of the World Bank Statistics, Asia Development Bank’s surveyed data, International Rice Research Institute’s Statistics, Food and Agricultural Organization’s Statistics and Vietnamese Statistical organizations such as General Statistical Office, Quang Nam province Statistical Office and statistical data from LUCCi project of the Institute of Technology and resources management in the Tropics and Subtropics [ITT] of Cologne University of Applied Science, cost of production of rice data set from Janaiah and colleagues (2004) are used. Water productivity According to Pereira, et al., 2012, irrigation WP [WPirri] and WP are calculated by the following equation to point out irrigation water productivity at farm level. WPirri = Ya /IWU

Where, WPirri is irrigation water productivity at farm level, Ya is actual yield and IWU is irrigation water used throughout a crop cycle. WPTotal = Ya /TWU Where, WPirri is irrigation water productivity at farm level, Ya is actual yield and TWU is total water used throughout a crop circle (i.e. Irrigation water used +precipitation). Economic profitability Whenever making a decision of technology change, cost and benefit of changing to a new technology is taken into account by a decision maker. Cost benefit ratio is the most accurate “reflection of profitability of various investment opportunities” (Beierlein, et al, 1995, p-226). Partial Budget analysis is based on the household survey data (428 rice farmers in 11 village clusters) of Janaiah et al., (2004). Fixed cost of production for unit area of rice is excluded during partial budget analysis since the authors assumed that change in fixed cost due to technology change is negligible.

Figure 4. Monthly rainfall distribution (mm) in VGTB river basin (Da Nang station: 19762010). Sources: Data from Thomas, et al, 2010; Duc, 2011; GSO, 2012


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Fuzzy logic and decision making model Rule based fuzzy set is applied as Bozma, et al. (2005) applied in their research on framers’ motivation to adopt integrated farming in Vietnam. This fuzzy set theory is applied since Zhang and Liu (2006) recommend it as a good method to model human decision making. Economic criteria (Total Variable Cost and Return on resources, i.e., Fertilizer, Labor, Irrigation water) were used to compare economic efficiency. Yield of rice produced per unit water (irrigation and precipitation) was used as a criterion for water productivity. For social and environmental criteria, since there is no primary data to refer, criteria are assumed based on Thompson, et al. (n.d) and Pannel, et al., (2006). Based on those major criteria, a decision matrix shown in Table (1) was developed to create a prototype fuzzy logic model (see Dunn, et al., 1995 for more information). To predict farmers’ adoption to new technology, a set of fuzzy logic rules are developed based on Pannel, et al. (2006)’s description on farmer’s adoption behavior and criteria in the table (1). The rules are: Rule 1: the method which has higher WP and higher benefit will be accepted by farmers. Rule 2: the method which has higher WP but which has medium return will be accepted by farmers. Rule 3: the method which has high WP and low benefit will not be accepted because farmers do care more on benefit than WP. Rule 4: the method which has medium WP and higher benefit will be accepted because this method will produce higher yield although water need is still less than conventional method. Rule 5: the method which has medium WP and medium benefit will be accepted since this method can produce higher yield than conventional method and also save more water than conventional method.

Rule 6: the method which has medium WP and low benefit will not be accepted because of its lower benefit. Rule 7: the method which has low WP will not be accepted even it produce higher yield since there is limited agricultural water resource. Table 1. Sample Decision matrix used to create the prototype fuzzy logic model. Source: Adapted from Dunn, et al., 1995. Criteria (highest=10, lowest=0) Technology   

Economics

Water

Social

RF

RL

RI

TVC

WP

PI

Environmental  rf

ir

Alt. 1

Con.

5

8

2

6

1

3

8

9

Alt. 2

AWD

4

5

8

8

7

6

8

9

Alt. 3

SRI

6

3

8

3

9

2

8

9

Notes: Con.= Conventional; AWD= Alternate Wetting and Drying; SRI= System of Rice Intensification; Alt. 1,2,3= Alternative 1,2,3; RF = Return on fertilizer use; RL = Return on Labour use; RI = Return on Irrigation applied; TVC= Total Variable Cost; WP = Water productivity; PI = Farmer’s interest to adopt; rf= possibility of environmental hazard due to the applied fertilizer; ir= Impacts on regional water cycle due to irrigation water waste in Con.; due to alternate wetting and drying process in AWD and SRI.

Based on the results of water productivity and economic profitability calculation and fuzzy logic sets and rules, a decision making model was developed as shown in figure (5).

Figure 5. Diagrammatic presentation of the Conceptual decision making model applied in this study.


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Result and discussion Water Productivity WPirri and WPTotal of Current rice production methods in the VGTB river basin is as low as 0.267 Kg/m3/ha and 0.246 kg/m3/ha in dry season rice (November to March). In the wet season (May to September), WPirri and WPTotal are 0.389 Kg/m3/ha and 0.277 Kg/m3/ ha. For dry Season Crop, WPirri of conventional method 0.267 kg/m3/ ha is a bit lower than the minimal WP value (0.4 kg/m3/ha) resulted in the research under similar condition in the Philippines (Tuong and Bouman, 2003 cited in Zwart and Bastiaanssen, 2004). Moreover, this result is still lower than WP (Irrigation is excluded) in Vietnam which resulted 0.30 Kg/m3 (Mainuddin and Kirby, 2009). For wet season Crop, WPirri of the conventional method is higher than that of dry season and the result is comparable to the result of Mainuddin and Kirby (2009). Moreover, it is just slightly lower than that of Tuong and Bouman (2003)â&#x20AC;&#x2122;s result. Both WPirri and WPtotal are calculated by using minimal water used to leachsoil salinity and percolation according to the recommendation of previous researche. However WPirri of dry season rice modeled in this study is lower than that of minimal WP value resulted in replicated researches under similar situations. This study excluded water leakage from the ponded field because of data unavailability. Therefore, in real situation, WPirri may lower than the current modeled result. In dry season, WPtotal of conventional method doesnâ&#x20AC;&#x2122;t differ so much from that of WPirri since dry season rainfall is very low in the study site (23.58 to 98.70 mm/month/annum) and irrigation water shared a larger amount of dry season total water use. However, wet season WPirri differs slightly between each other since 80% of annual precipitation is concentrated in wet season (ranging from 145.11 to 659.59 mm/ month/annum). Moreover, farmers apply additional irrigation water to the fields. Therefore total WP for wet season rice (0.277 kg/m3) is lower than that of wet season WPirri (0.388 kg/m3). This lower total WP in wet season seems to be because of mismanagement of rain

water. If rain water is harvested effectively and managed efficiently, additional irrigation water may not be needed for wet season rice production. Water-wise rice production methods accepted by the International Rice Research Institute (IRRI) are modeled by using the same data applied to conventional methods and theoretical assumptions based on literatures of Hoek, et al., 2001; Bouman, et al., 2002; Bouman, et al, 2007. Both Alternate Wetting and Drying method (AWD) and System of Rice Intensification (SRI) methods resulted in 0.441 kg/m3/ ha1 and 0.770 kg/m3/ha1 respectively for dry season total WP; 0.479 kg/m3/ha1 and 0.835 kg/m3/ha1 respectively for dry season irrigation WP. In Wet season, total WPs of AWD and SRI resulted as 0.504 kg/ m3/ha1 and 1.02 kg/m3/ha1 respectively and irrigation WPs of AWD and SRI resulted as 0.703 kg/m3/ha1 and 0.770 kg/m3/ha1 respectively. SRI resulted with the highest water productivity and AWD followed after SRI. WPs of SRI are almost two times higher than that of AWD in both dry and wet season. Although irrigation management of SRI and AWD are similar (Bouman, et al, 2002), the modeled WPs of SRI resulted significantly higher than that of AWD. Among three methods, SRI resulted with the highest water productivity and AWD and Conventional method followed respectively. Benefit and cost of technology change Sensitivity of input uses on benefit is analyzed decreasing and increasing the input uses to 30%, 20% and 10 %. The cost of irrigation and water management per hectare of rice field in the VGTB river basin is the third sensitive factor on benefit after cost of labor (the first sensitive) and the cost of fertilizer (the second). Based on the farm budget of the conventional rice production studied in Janaiah, et al. (2004), cost and benefit of rice production by AWD and SRI is modeled. Benefit and Cost of production practices change resulted as BC ratio of changing Conventional to AWD is higher than that of Conventional to SRI.

Figure 6. Tornado diagrams of change in benefit of rice production (, 000 VND) due to change in input use (+/- 10%, +/- 20% and +/- 30%). Source: Data from Janiah, et al, 2004 and Duc 2011.

Decision making model The calculated result of WPs and economic profitability of different cropping methods are compared as shown in figure (6). It was clear that AWD method is a new technology which resulted highest economic profitability with reasonable WPs. As described in the model (figure. 5), after the comparison of new

technologies in terms of WPs and economic efficiency, a prototype fuzzy logic model was developed based on criteria described in Table (2) and rules described in section (2.2.3) of this article. The result of the model was that farmers will likely adopt to the AWD method because of its higher WPs than conventional rice production method and because of its highest economic profitability.


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Table 2. Benefit and cost of changing conventional rice production method to water wise methods for a Hector of rice farm. Conventional

Î&#x201D; Revenue, cost and benefit

AWD

Î&#x201D; Revenue, cost and benefit

SRI

VND (,000)

EURO

VND (,000)

EURO

VND (,000)

EURO

VND (,000)

EURO

VND (,000)

EURO

Revenue

9,861.00

505.04

13,840.00

708.83

3,979.00

203.79

13,840.00

708.83

3,979.00

203.79

Cost

7,781.00

398.53

7,976.00

408.51

195.00

9.98

10,917.00

559.11

3,136.00

160.58

Benefit

2,080.00

106.51

5,864.00

300.32

3,784.00

193.81

2,923.00

149.72

843.00

43.21

Source: Data from Janiah, et al, 2004, Duc, 2011.

Figure 7. WPs and return benefit calculated and modeled for different cropping methods Source: Data from Janiah, et al, 2004, Duc, 2011.

Conclusion Our findings contribute to finding a better solution to mitigate water scarcity by means of cropping practices and crop management in the area and further reviews the production profitability based on rice production methods discussed in the study. This study pointed out one possibility of water demand management by changing cropping practices to obtain higher agricultural water productivity without affecting farmers benefit. The research focused only on the farm scale level by objectives to point out a suitable higher water productive rice production method to adapt regional water scarcity with economic efficiency for farmers. The study not only focuses on water productivity, crop harvestable yield but tries to include socioeconomic factors as well. Our study found that, AWD is the most suitable method to attain all objectives of this study but this result is only for the current water scarcity scenario. On the other hand, since SRI resulted in the highest water productivity by all means and highest return benefits, SRI method could be a suitable method to adopt if the local or central government will

manage certain ecosystem services and payment policy for farmers. By this means, the VGTB basin will have higher agricultural water productivity together with higher rice yield and higher benefit of rice farmers live by means of income from SRI method plus payments for ecosystem services for adopting highest water productive SRI method. SRI methods plus certain ecosystem services and payment policies could be a better solution to the water scarcity in the VGTB river basin under the worse water scarcity.

Acknowledgments The authors would like to thank to Prof. Dr. Harmat Gaese, Ex-director of ITT/CUAS for his suggestions and valuable comments. The authors also acknowledge to Mrs. Alexandra Nauditt of LUCCi project for sharing some data and reports of the project, Dr. Juan Carlos Torrico Albino of DANIRO project of ITT/CUAS for his valuable suggestions, cited authors who did their research in the VGTB river basin or in Quang Nam province, Vietnam and anonymous reviewers.


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4529-a9dd- d85b69fe6fea/25433/ieee_final.pdf. [Accessed 20 June 2012] Chen, C., et al. (2012). Climate change, sea level rise and rice: global market implications. Climatic Change (2012) 110:543–560. Datta., S. K. 1981. Principles and practices of rice production. First edition. John Wiley & Sons, Inc. A Wiley-Interscience Publication. New York. Duc, V. N. 2011. [unpublished work] Improvement of water supply for agricultural

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JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT Natural resources endowment and economic growth: The West African Experience Mohamed Jalloh a* a

Economic Policy Analysis Unit (EPAU) Macroeconomic Policy Department ECOWAS Commission, River Plaza Central Area , Abuja Nigeria

*

Corresponding author : medjal99@yahoo.com

Article history Received  15.10.2012 Accepted  23.01.2013 Published 03.06.2013

Keywords Natural resource endowments Economic growth Dynamic panel Corruption

Abstract This study aims at investigating the nexus between natural resource endowment and economic growth using a sample of West African countries. The study adopted a Barrow-type growth model to analyse the impact of natural resource wealth on economic growth. A dynamic panel estimation technique was employed using relevant data from West African Countries. The results from the panel regressions indicate that natural resource endowments have very minimal impact in terms of promoting economic growth in West Africa, more so in resource rich countries. In terms of relative effects, the results indicate that a 10% increase in natural resource export reduces growth in income per capita by approximately 0.4%. Part of the factors explaining this finding amongst others; include high corruption in the public sector as well as the frequency of civil conflicts in resource rich economies of West Africa. For the natural resources of the region to fully benefit its citizens, these countries require , urgently, to improve management of natural resource export revenues and to apply effective policy measures to eradicate/ mitigate incidences of rampant corruption in the public sector.

Introduction The continent of Africa has being noted for having considerable amount of natural resources endowment. According to the United Nations Economic Commission for Africa (UNECA), the continent of Africa is known to have more than 40 percent of the world’s reserves of Platinum Group Minerals (PGMs), phosphate, gold, cobalt, vanadium, vermiculite, chromite, manganese, and diamonds. The continent has also been ranked first in the production of platinum, gold, chromite, vanadium, cobalt, and diamonds. Africa’s importance as a source of oil, gas and other energy resources is increasing. In 2006, gas and oil reserves alone stood at about 7.9% and 8.6% of the world’s total production. It is also noted that Africa produces about 16 % of the

world’s uranium. Coal resources are concentrated in Southern Africa, with South Africa accounting for five percent of proven world coal resources and 98 percent of Africa’s output. Despite the abundance of natural resources in Africa, its distribution is not even across countries. While some countries are richly endowed with a wide range of natural resources, others barely have them. In West Africa, there is evidence of uneven distribution of natural resources member countries of the Economic Community of West African States (ECOWAS). Even prior to their attainment of independence, a good number of West African states are endowed


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with one or other forms of mineral resources ranging from diamond, gold, iron ore, crude oil, aluminum, uranium, bauxite , manganese, tine and columbite. While Sierra Leone, Liberia and Cote d’Ivoire are known for being rich in diamond, Ghana and Nigeria are well known for Gold and Crude oil respectively. Bauxite and iron ore are also contributing significantly in countries like Guinea, Guinea Bissau, Sierra Leone and Liberia. Other West African countries including Senegal, Mali, Guinea Bissau and Togo are rich in phosphate. Apart from Nigeria that has produce crude oil since the 1960s, its discovery in West Africa is of recent development, with Ghana, Cote d’Ivoire, Liberia and Sierra Leone recently emerging as oil rich economies. The question as to whether the discovery of natural resources has translated into improving the well-being of citizens of those countries or not has provoked a series of debates amongst economists over the last two decades. A good number of authors believed that the discovery of natural resources and the subsequent revenue generated thereof helped countries to address key socioeconomic concerns such as poverty, health, infrastructure, education and unemployment .Quite recently, however, proponents of the resource curse literature have linked the endowment of natural resources to a series of negative outcome like economic decline, corruption and autocratic rule (McNeish 2010). A number of studies, including Fearon and Laitin (2003), Humphreys (2005), and Ross (2006) have found a positive correlation between natural resources endowment and the onset of civil conflicts, particularly for oil and diamond rich economies. These findings has turned the optimistic notion on natural resource discovery into a more pessimistic one that saw the discovery of natural resources as more of a curse than a blessing for those economies in question. The so called resource curse argument which has been widely discussed in the economic literature hinges on the idea that natural resource abundance has the likelihood to impact negatively on economic growth rates (Gelb, 1988; Maloney, 2002; Ross 1999, 2001 , Auty, 1993; Sachs and Warner, 1995, 2001, Busby et al., 2004) . A common point of view is that the discovery of Natural resources have played a key role in the conflicts that have plagued a number of African countries over the last decade, both motivating and fuelling armed conflicts. It is also the case that revenues generated from the exploitation of natural resources are not only used for sustaining armies but also for personal enrichment and building political empires. Statement of the research problem Proponents of the resource curse literature have made the point that the possession of oil, natural gas, or other valuable mineral deposits or natural resources does not necessarily confer economic growth. A typical case in point is that, many African countries such as Angola, Sierra Leone, Nigeria, Sudan, and the Congo are rich in oil, diamonds, or other minerals, and yet their peoples continue to experience low per capita income and low quality of life. Meanwhile, the East Asian economies like Japan, Korea, Taiwan, Singapore and Hong Kong have achieved western-level standards of living despite being rocky islands (or peninsulas) with virtually no natural resources endowment. As noted by Jeffry (2010), the philosophy that natural resource riches are a curse rather than a blessing may seem paradoxical and has lead to

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an extensive literature. A good number of ECOWAS member states1 are largely endowed with one or more forms of natural resources (large forest reserves, marine resources, diamond, gold, manganese, phosphate, petroleum, iron ore, uranium, bauxite, manganese, tine and columbite). These resources have in one way or the other played a significant role in terms of boosting economic activities in their respective economies. Prior to the discovery of mineral resources in West Africa, most of these countries were largely dependent on agricultural resources for the livelihood of their citizens. Apart from Cape Verde, The Gambia and Niger, Agriculture, Forestry, and Fishing is the largest sector for every other country in this region and it accounts for between 20% – 50% of GDP. West African nations are major producers of cocoa, rubber, cotton, and timber. This sector is a major revenue earner for a good number of these countries. For instance, while Liberia is noted for its large rubber plantation, Ghana, Nigeria , Cote d’Ivoire and Sierra Leone have huge potentials in Cocoa and coffee plantations. Also, while Senegal, Niger and Burkina Faso have huge potentials for groundnut, cotton and Sorghum production, Mali is noted for cotton, livestock, millet and rice. Fishing is also a major activity in most countries along the coast of West Africa , including, Senegal, Guinea, Guinea Bissau, Sierra Leone , Liberia, Cote d’Ivoire, Ghana, Togo , Benin and Nigeria. The discovery of mineral and petroleum resources in West Africa has in one way or the other played a critical role in influencing the political economy of these countries. Apart from The Gambia that can hardly boast of any form of mineral resources, all other West African States have one or other forms of mineral resources. Diamond, Gold, Bauxite, iron ore, and recently crude oil are all part of Sierra Leone’s endowment of mineral resources. Guinea is among the top five bauxite producers in the World, whilst Ghana has abundant deposit of gold and recently discovered crude oil in commercial quantities. Nigeria has been leading in terms of crude oil production in West African for the past decades. An interesting question one would, however, like to ask is whether the presence or discovery of natural resources in West Africa has significantly impacted on the economies of these countries. Over the past two decades, a good number of researchers have observed a link between natural resource discovery and the outbreak of civil conflicts (Collier and Goderis, 2007; Le Billon, Philippe, 2003; and Swanson, Philip, 2002) . In the case of West Africa, the civil wars in Sierra Leone and Liberia that left over a half million people dead, provides perhaps a good example of military political entrepreneurship driven by natural resource exploitation. With respect to human casualties, the war that ripped apart the Democratic Republic of Congo (DRC) remained to be the worst in recent times, resulting in over four million deaths, and is perhaps the greatest example of a resource-fuelled war. Similar civil conflicts have taken place in countries like Nigeria, Cote d’Ivoire, Guinea Bissau, and Mali. In Cote d’Ivoire, natural resources have been a major factor in financing the conflict, and once again, both the government and the rebels have used these resources to their advantage. It is now widely recognised that the rebels are illegally exploiting mineral 1

Cape Verde, Cote d’Ivoire, Ghana, Guinea, Guinea Bissau, Liberia, Niger, Nigeria, Senegal,

Sierra Leone and Togo (constituting about 73% of ECOWAS Member States).


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resources and cocoa to finance the war. Roger Blench (2004) noted that, in some parts of Nigeria, disputes that began with natural resource conflict over access to resources have become framed in religious terms, presumably to further the interests of urban politicians. Increasing availability of modern weapons has increased the intensity and violence of such disputes. The financing of civil conflicts through illegal diamond trade in countries like Angola, Liberia and Sierra Leone led to international condemnation of the trade in “conflict diamonds” and the subsequent launch of the Kimberley Process. Other contributors to the literature on natural resources discovery have also linked natural resource discovery and public sector corruption (Leite and Weidmann, 2002, Sala-i-Martin and Subramanian, 2003, and Isham et al., 2005). Aslaksen (2007), for instance, found that oil increases corruption in the samples of both democratic and non-democratic countries, whereas minerals increase corruption only in the sample of non-democratic countries. The question that one needs to ask at this juncture is that, why are natural resource abundance economies prone to poor economic performance? Some of the key reasons that a good number of economists2 attempted to advance include issues ranging from the Dutch disease phenomenon, increased rent seeking behavior, corruption and undue bureaucracies, tendencies towards closed economic policies, and sometimes lack of labour-based learning and education. With a view to generate empirical evidence on the impact of natural resource abundance in the economies of West Africa, the following questions are worth raised in the context of ECOWAS member states: (i) are countries with abundant natural resource more corrupt than those with very little or without?, (ii) are natural resource abundance countries more prone to civil conflicts than their counterparts with very little or without?, (iii) are natural resource abundance countries growing faster than those with very little or without? The attempt to provide answers to these questions constituted the key problem of this research work. Objectives of the study This study aims at investigating the relationship between natural resources endowments and overall economic performance using data from ECOWAS member states. Specifically, the study seeks to determine: i. The relative effects of natural resources export on economic growth in West Africa ii. The impact of corruption , government effectiveness and human capital endowments on growth in West Africa An overview of natural resource endowments and growth performance in ECOWAS Member States. The exploration of natural resources, in particular precious minerals and petroleum products, started in West Africa since colonial period. In Nigeria, for instance, the search for oil started far back in 1903 when two companies, Nigeria Properties (Limited) and the Nigeria and West African Development Syndicate (Limited) commenced exploration for bitumen, coal and oil (Jones, Geoffrey ; 1981). According to Jones, 2

See for instance Codren and Neary (1982), Gelb (1988), Tornell and Lane (1999),

Baland and Francois (2000), and Auty (1993).

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geological investigations by Bernard A. Collins (1903-4, and 1904- 5) and A.H. Harrison (1904-5) officially confirmed the existence of vast bitumen deposits as well as the possibility of petroleum. Currently, Nigeria is the largest oil producer in Africa and among the top ten globally. Its effective pumping capacity is about 900 million barrels a year. Its recoverable reserves are estimated at 34 billion barrels. In recent years, the oil sector has accounted for over 40% of GDP, 95% of export receipts, and over 80 percent of government revenue. The sector is dominated by joint venture operations between the Nigerian government and six major international oil companies—Shell, Mobil, Chevron, Agip, Elf, and Texaco. Nigeria’s reserves of natural gas were estimated at around 159 trillion cubic feet of proven reserves, being among the ten largest in the world. However, gas production is currently less significant economically. Nigeria also has reasonable deposits of solid minerals like tin, columbite, iron ore and gold. Sierra Leone is another West African state that is richly endowed with a variety of solid minerals including diamonds, gold, rutile, bauxite, platinum and iron ore. Other identified minerals include chromite, lignite, clays, and base metals (copper, nickel, molybdenum, lead and zinc). The mining sector is the biggest foreign exchange earner for the country, accounting for about 90 % of total exports earnings, and about 30% of the country’s GDP. Until very recently when iron ore production was restarted, rutile, diamonds and bauxite were the key minerals exported. The country’s dependence on the mining sector is reflected by its high contribution to GDP and registered exports throughout most of the 1990s. Following a decade’s long exploration of oil deposits along its coast, the country has formally announced the discovery of crude oil in November 2010 by Anardako Oil Company with approximately 135 net feet of oil pay in two Cretaceous-age fan systems. Guinea is another West African country that has abundant natural resources. Guinea is known to have about 50% of the world’s bauxite reserves, along with diamonds, gold, and other metals. Until 1990 mining accounted for more than 20% of its GDP, supplied over 90% of exports and provided approximately 70% of fiscal revenues. Financial problems, however, hindered the bauxite/alumina sector since the late 1980s. In 2002, mining activities accounted for an estimated 17% of GDP, while mineral exports represented nearly 90% of total export earnings and 20% of domestic government income. The country’s solid minerals consisted mainly of aluminium, bauxite, diamond, gold, iron ore and salt. Other mineral resources included graphite, limestone, manganese, nickel, and uranium. According to Plunkert (2006), Guinea continued to rank among the world’s top five producers of bauxite. Mali’s mineral sector is dominated by gold mining, the country being the third largest gold producer in Africa. Other mineral resources included bauxite, iron ore, diamond, limestone, manganese, nickel, petroleum, phosphates, tin, and uranium. Mali’s Petroleum potential is promising and research and exploration are stepping up. Mali could also provide a strategic transport route for oil and gas exports and there is the possibility of connecting the Taoudeni basin to the European market through Algeria. The mining industry in Ghana is dominated by gold, diamond, bauxite, and manganese. These are important sources of export and government revenue. For example, gold represented 34% of the country’s exports (12% of GDP) in 2000-2003. In 2007 significant oil discoveries were made offshore of Ghana. Estimates of Ghana’s oil


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reserves vary between 1bn and 1.5bn barrels. Commercial extraction has commenced and revenues from oil are expected to reported in the next reporting cycle (covering 2010 and 2011). The Republic of Niger has large reserves of uranium and in 2003 was the fourth largest producer in the world. Although uranium exports accounted for 62% of exports by value and contributed 4.3% of government revenue in 2003, uranium-mining is a declining sector. Gold mining has been traditionally done at small scale, yet gold production started in Niger’s first commercial mine at Samira in late 2004. Gold contribution to the economy remains modest. Niger officially became an oil producer in 2011 with the opening of the country’s first refinery in Zinder. Niger expects to produce about 20,000 barrels of fuel a day, initially just for the local market. Oil reserves in Niger are estimated at 480 million barrels. Today, gold mining plays a significant role in Burkina Faso’s economy. According to a report by the Extractive Industry Transparency Initiative (EITI) of 2010, gold production more than doubled since 2008, and constituted one of Burkina Faso’s largest export products in addition to cotton. As at 2010, approximately sixty international companies - including Australian, Canadian and South African firms - were engaged in exploration and mining activities in Burkina Faso. Apart from gold, Burkina Faso is also known to have significant occurrences

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of phosphates and manganese (see EITI 2010 report). In spite of the current political crisis in Mali, the mining sector continues to flourish with very little deterrent on investors. According to a report by the Extractive Industry Transparency Initiative (EITI ) of 2009 , Mali’s mineral sector is dominated by gold mining, thus, resulting in making Mali the third largest gold producer in Africa. In 2009, gold exports from Mali alone accounted for more than 80% of the country’s export earnings and approximately 8% of its GDP. Other mineral resources included bauxite, iron ore, diamond, limestone, manganese, nickel, petroleum, phosphates, tin, and uranium. Mali’s petroleum potential is promising and research and exploration are stepping up. Figure 1 shows the relationship between natural resource export and economic growth for ECOWAS member countries in 2010. As can be observed in figure 1, Cote d’Ivoire, Nigeria, Sierra Leone and Togo performed relatively well in terms of export of natural resources as a share of their GDP. In 2010 alone, the share of export of natural resources to each of these countries’ GDP stood at around 37.0%, 37.7%, 27.8 % and 24.6% for Cote d’Ivoire, Nigeria, Sierra Leone and Togo respectively. These countries were followed by Liberia, Guinea, Ghana and Guinea Bissau with natural resources exports of around 17.8%, 16.9%, 14.7% and 13.2% of GDP respectively.

Figure 1. Natural Resources Export and Economic Growth in West Africa. Source: World Trade Statistics and World Bank publications

At the extreme end of the spectrum, The Gambia happened to be the least in terms of natural resource export to GDP ratio. This is not surprising because The Gambia’s economy is largely dependent on agricultural activities with virtually less precious mineral deposits such as clay, silica sand, titanium, tin and zircon. As can be further observed in figure 1 above, countries with higher natural resource exports appear to exhibit lower growth rates as demonstrated by Cote d’Ivoire, Guinea, Sierra Leone and Togo. Nigeria’s growth performance though not in tandem with its export of natural resources, it however, performed relatively better than other resource rich countries. Ghana, Niger and Cape Verde are few of those West African Countries where economic growth seems to be in tandem with natural resource export. At the other end of the spectrum, countries like The Gambia, Burkina Faso, Mali and Benin happened to attain moderate growth despite the fact that they have very low natural resource export as a percentage of their GDP. Though

it may be difficult to appraise the strength of correlation between economic growth and natural resource exports on the basis of Figure 1 as shown above, however, it provides a vivid comparison on the relationship between natural resource export and economic growth amongst West African Countries. Infact , the picture presented in figure 1 apparently provides a kind of evidence that is consistent with proponents of the resource course literature who argue that nations characterized with natural resource abundance are highly likely to exhibit lower growth rates than those without (Sachs and Warner, 1999: Papyrakis and Gerlagh, 2007; Ding and Field ,2005). As can be seen in Figures B1 – B15 of Appendix B, the relationship between natural resource export and economic growth appears vividly inverse especially for resource rich countries. This further provides support for the existence of the resource curse phenomenon amongst resource rich countries in West Africa.


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This study would also like to find out whether the poor growth performance in resource rich countries could be explained corruption in the public sector. This is because; theories of natural resource booms have highlighted issues of rent seeking behavior (Tornell and Lane, 1999; Baland and Francois, 2000; Torvik, 2002) and political corruption under fragile institutions (Robinson et al, 2006). As a matter of fact, the poor growth performance as exhibited by some of the resource rich West African states could have some relationship with corruption in the public sector. As can be observed in figure 2, though almost all west African states appear to be rated relatively high in terms of corruption in the public sector, countries with more natural resource endowments like Cote dâ&#x20AC;&#x2122;Ivoire, Nigeria, Sierra Leone, Guinea, Liberia and Togo seem to be rated higher in terms of

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public sector corruption through the rent seeking behavior of their public sectors. Thus, the poor performance in growth in some of the resource rich West African states may be linked with public sector corruption arising from rent seeking behavior leading to resource misallocation. Ironically, The Gambia, Burkina Faso and Mali are examples of low natural resource base countries with relatively higher ranking in terms of public sector corruption perception index published by Transparency International. However, though Burkina Faso is ranked relatively high in terms of public sector corruption their growth performance was encouraging as compared to other highly rated corrupt West African States.

Figure 2. Natural Resource Endowment and Corruption in ECOWAS Member Countries. Source: Transparency International and World Bank data 2010

Literature review Review of the theoretical literature It has been extensively argued in the economic literature that economies that are highly dependent on natural resources are characterized with booms and busts as the prices of raw materials fluctuate a great deal in world markets. It has also been noted that fluctuations in export earnings caused by instability in world market prices can trigger exchange rate volatility. The resultant volatility in exchange rates has the tendency of triggering some amount of uncertainty that can harmfully affect exports and foreign investment. A good number of authors (Codren and Neary,1982; Bruno and Sachs, 1982; Gelb, 1988) have advanced various arguments on natural resource abundance economies based on a phenomenon popularly known in the economic literature as the â&#x20AC;&#x153;Dutch diseaseâ&#x20AC;?, which assumes that the economy can be categorized into three key sectors: the tradable natural resource sector, the tradable non-resource (manufacturing) sector, and the non-traded sector. The argument purported by proponents of the Dutch disease is that, economies with larger natural resource sectors create a higher demand for non-tradable goods. As a consequence, there is disproportionate re-allocation of labor and capital from the tradable manufacturing sector to the natural resource sector. The resultant decline of activities in the manufacturing sector will thus increases economic dependence on natural resources, and thereby exposes economies

depending on natural resources to volatility of commodity prices. A key problem in this argument is that, it does not take into account the fact that in many economies, the resource and manufacturing sectors are not mutually exclusive. It should be noted that most manufactured products contain natural resources as inputs, and at the same time, the extraction of these natural resources requires the use of manufactured products such as machinery and refining plants as well. Some other harmful effects often linked with the Dutch disease phenomenon are the tendencies for the implementation of protectionist policies for affected lagging sector industries as well as high corruption in the public sector. In particular, Polterovich and Popov, (2006) has noted that the government may not be able to carry out effective and sound macroeconomic, social and industrial policies. Another popular argument in the economic literature on natural resource abundance has to do with the resource curse phenomenon. Proponents of the resource curse argument (Sachs and Warner, 1999: Papyrakis and Gerlagh, 2007; Ding and Field ,2005; and Bulte et al., 2005) argue that nations characterized with natural resource abundance are highly likely to exhibit lower growth rates than those without. Prebisch (1950) and Singer (1950) have argued that primary commodity exporters would suffer from a decline in the terms of trade, which would widen the gap between the rich industrial states and the poor resource exporting states. This is sometimes linked with a phenomenon known as immiserizing growth (Balassa, 1985;


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Bhagwati, 1958a & 1958b; Johnson , 1967; Deardorff, 1973; Smith ,1976; and Samuelson, 1975). The main argument of the theory of immiserizing growth is centered or the idea that an open economy experiencing an expansion in its productive capacity (resulting either from economic growth or technological progress) can become worse off if its terms of trade deteriorate sufficiently and offset the beneficial effects of economic growth. Other researchers, including Nurske (1958) and Levin (1960) also noted that prices of primary exports are highly subjected to sharp fluctuations and thus states that relied on primary exports would find these fluctuations transferred to their domestic economies, making export revenues , and hence foreign exchange supplies unreliable. Resource rich countries are also noted for being characterized by income inequality owing to the high level of corruption in the public sector of most resource rich countries. Gelb (1988) notes that countries that are rich in natural resources are more incline to exhibit unequal income distribution than those without. Palley (2003) further notes that such countries are characterized by features such as having a larger share of their population living in poverty, exhibiting greater tendencies for corruption , having more authoritarian regimes that spend more on the military. Since the last two decades, theories of natural resource booms have highlighted issues of rent seeking behavior (Tornell and Lane, 1999; Baland and Francois, 2000; Torvik, 2002) and political corruption under fragile institutions (Robinson et al, 2006). The production of natural resources is highly likely to generate some sort of economic rents. Gelb (1988) argues that governments earn most of their rents from the exploitation of natural resources. Isham et. al. (2003) provide an excellent review of the mechanisms whereby large revenues from natural resources enable governments to appease dissent and circumvent accountability, thereby insulating them from pressures for institutional reform; whereby governments successfully thwart pressures for modernization and institutional reform because their â&#x20AC;&#x153;budgetary revenues are derived from a small workforce that deploys sophisticated technical skills that can only be acquired abroadâ&#x20AC;?. Robinson et al (2006) formulates a theoretical model from which they discover that, by raising the value of being in power and by providing politicians with more resources which they can use to influence the outcome of elections, resource booms do have the tendency of increasing resource misallocation in the rest of the economy. To a very large extent, however, this relationship depends on the initial quality of institutions (political accountability). Several authors including Tornell and Lane (1999), Baland and Francois (2000), and Torvik (2002) have argued that countries without such institutions are highly likely to suffer from a political resource curse, and thus may increase their tendencies towards implementing inefficient activities through rent-seeking motivations. The relationship between conflict and the extraction of natural resources seem not to be so clear-cut, however, resource-rich countries do appear to be more susceptible to conflict than the resource-poor. What has been noted generally is the fact the risk of conflict seems to be greatest when resource extraction accounts for a substantial proportion (around 30%) of a countryâ&#x20AC;&#x2122;s GDP: in other words, in countries which are largely dependent on the export of primary commodities such as metal ores, oil and gas, the risk of conflict is very high. Several studies have attempted to document the

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relationship between natural resource abundance and civil conflicts (Ross 2004a; Humphreys 2005; Rohner, 2006). The economic literature have identified three key possible channels through which resource rich economies can be exposed to the risk of conflicts. Firstly, the export of natural resources provide opportunities for rebel predation during conflict and so can finance the escalation and sustainability of rebellion. Natural resources like diamond and oil provide high prospects for the financing of rebellion through such actions like kidnapping and ransoming of oil workers, tapping of pipelines and theft of oil, extortion rackets against oil companies or illicit mining and smuggling of diamonds. The most cited instances are those of the diamond-financed rebellions in Sierra Leone and Angola. In Nigeria too, particularly in the Southern oil rich State of Delta, armed criminals have been notorious in financing their operations through kidnapping oil foreign workers in exchange for offers of ransoms. A second channel through which resource rich countries can be exposed to the risk of conflicts arises from the motivation to loot. Rebellions are sometimes motivated by the desire to capture some kind of economic rents, either during or after conflict. Weinstein (2005) purports a convincing argument that rebel recruits in countries that are endowed with valuable natural resources are highly likely to be motivated by the desire to loot rather than the pursuance of any political cause. Indeed, this loot seeking behavior of rebel recruits is highly prevalent during the lawless conditions that prevail during conflict than during peacetime. As noted by Collier and Hoeffler (2006), an intermediate position between the objective of wartime looting and the capture of the state is the secession of the resourcerich region. Thus, Lujala, Gleditsch, and Gilmore (2005) find that conflicts are more likely to be located in the areas of a country in which natural resources are extracted. The third channel through which resource abundance may prompt civil conflict arises from the fact that governments of most resourcerich countries are less accountable to their citizens. It has been widely note in the literature that governments of most resource rich countries are highly inclined to hold on to power through vote-buying, voter intimidation and other forms of electoral fraud. Vicente (2007) and Collier and Vicente (2008) investigate votebuying in two resource rich democracies3 and show that it is both prevalent and effective. Besley (2006) find that there is a point at which elections fail to discipline those politicians whose interests are divergent from those of voters due to the prevalent of votebuying. Collier and Hoeffler (2005) show that in conditions of poor governance, incumbents are far more likely to win elections than in conditions of good governance. In most cases, governments that win elections through vote-buying are likely to be unaccountable to their citizens. This lack of accountability by such governments has the tendency of triggering rebellions/civil conflicts that may have adverse consequences to the economy. Firstly, a government that lacks accountability to its citizens will be perceived by its citizens as highly corrupt. In a non-democratic setting, the only way to remove this type of government from power is through armed conflicts. So in many cases, when the citizen feel that they cannot easily change 3

The two democratic countries of Sao Tome and Cape Verde were used in these

studies to investigate the relationship between governments of resource rich countries and vote-buying.


DOI number: 10.5027/jnrd.v3i0.06

their corrupt government through a democratic process , there is a high tendency that they will resort to taking arms against their governments. If, however, citizens are fully confident that they can easily change corrupt officials from governance through credible democratic processes, then the tendencies for civil conflicts arising from lack of accountability by government is likely to be mitigated. Review of the empirical literature As was mentioned before, a good number of researchers have arrived at similar conclusions that natural resource abundant economies have the tendency to grow more slowly than economies without substantial resources (Auty, 2001a; Rainis, 1991; Bulmer-Thomas, 1994; Sachs and Warner, 1995, 1997; Lal and Myint, 1996). In an attempt to investigate whether natural resource abundance leads to slower growth rates, Ding and Field (2005) endeavor to distinguish between natural resource dependence and natural resource endowment . They estimated two separate models using World Bank data and found that whilst natural resource dependence has a negative effect on growth rates, natural resource endowment has a positive and significant impact on growth. However, when a threeequation recursive model was estimated by introducing endogenous human capital and allowing for endogeneity in a resource dependence sector, the effects of natural resources on growth are found not to be statistically significant. Many studies have provided some empirical evidence in support of the Dutch disease phenomenon (Auty and Evia, 2001; Rodriquez and Sachs, 1999; and Fardmanesh, 1991). A major problem with all of these papers is that they tend to predict a monotonic effect of resources on development that is not always consistent with the cross-country evidence (Acemoglu, Johnson and Robinson, 2002). Although the Dutch disease literature has a lengthy theoretical degree, it appears to be the empirically least important mechanism. For example, Spatafora and Warner (2001) examined 18 oil exporting developing countries covering a period running from the mid 1960s until the 1980s. They found that Dutch disease effects are strikingly absent. Gelb (1988) provides an extensive empirical cross-country study of the Dutch disease phenomenon, where the effect of windfall on oil exporters was examined for a group of oil exporting countries, most of whom have spent large amounts of the windfall they gained in the wake of the 1973 oil boom. He finds that Ecuador, Iran, Nigeria and Trinidad and Tobago went through the Dutch disease, mainly due to a decline in Agriculture, over the first and second oil booms of 1972â&#x20AC;&#x201C;81, while Algeria, Indonesia and Venezuela went through a strengthening of their non-oil tradable. However, virtually all countries in the study showed no Dutch disease in manufacturing. A possible explanation for the missing Dutch disease was that these sectors were initially too small, and that price controls and subsidies by the government combined with active promotion of the sector kept them from being adversely affected. Services, however, did expand dramatically as a share of output in GDP. The study by Sala-i-Martin and Subramanian (2003) could also not find evidence of the Dutch disease in Nigeria due to oil price movement. This study highlighted an issue that is all too common in analyzing the impact of oil prices on macroeconomic variables in oil-

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exporters, which is the importance of knowing the type of spending, and not only the quantity. In another study, Spatafora and Warner (1995) finds a positive link between terms of trade shocks in oilexporting countries and their real exchange rate as well as public spending. They find that the reaction of public spending to shocks was stronger than that of private spending. However, they could not find evidence of the Dutch disease. Ross (2001) found that oil rents do inhibit democratic governance not only in the Middle East, as formally claimed in previous empirical studies, but also in other oil exporting countries like Indonesia, Malaysia, Mexico and Nigeria. Moreover, oil does greater damage to democracy in oil-poor states than in oil-rich ones. Thus oil inhibits democracy even when exports are relatively small, particularly in poor states. The majority of studies investigating the economic growth-resource curse nexus use a version of the neoclassical growth model (Solow, 1956), augmented to include measures of human capital (from Mankiw et al.,1992) and such transmission mechanisms such as institutions, democracy or Dutch disease. Studies are yet to incorporate all these different transmission mechanisms in a single model for empirical analysis to assess their various implications for oil exporting African countries. This study intends to bridge this gap. On the relationship between resource abundance and civil conflict, Collier and Hoeffler (2001) find that the probability of a civil conflict is 0.5 percent in a country with limited natural resources, but 23 percent in a country where natural resources account for 26 percent of GDP. They further noted that, for a good number of countries, including Iraq, Nigeria, Sierra Leone, Venezuela, former Zaire, Zambia, and many others, the abundance in natural resources like oil or mineral wealth has not effectively translated into economic and social wellbeing for the majority of the population. With respect to the relationship between natural resource abundance and economic growth, Nankani (1980) shows that between 1960 and 1976, the developing worldâ&#x20AC;&#x2122;s leading hard-rock mineral exporters had a per-capita GDP growth rate of 1.9 percent, half the rate of a control group of non-mineral states. Using regression analysis to measure the impact of minerals and other resources exports on economic growth in a sample of ninety seven countries, Sachs and Warner (2000) find that states with a high ratio of natural resource exports to GDP had abnormally slow economic growth rates between 1971 and 1989. The results from their regressions remained significant even after controlling for a wide range of growth related variables. In another study of thirty Sub-Saharan African countries, Wheeler (1994) found a negative correlation between economic performance and the share of hard-rock minerals in total exports. Auty (1993) finds that both major oil exporters and hard rock minerals exporters performed less well than their resource poor counterparts. On the contrary, however, Davis (1995) finds that between 1970 and 1991, twenty two developing states most dependent on minerals exports performed just as well as non-mineral states. Brunnschweiler and Bulte (2008) find that growth in GDP per capita from 1970 to 2000 is positively correlated with 1994 resource abundance. Similarly, Brunnschweiler (2008) finds that economic growth is negatively correlated with resource dependence but positively correlated with resource abundance. Michaels (2010) explores the effect of oil discoveries on economic growth and education and finds that the effects of oil discoveries in the southern United States were, â&#x20AC;&#x153;large


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and beneficial.” Specifically, this study finds that oil discoveries led to a sustained increase in income per capita. A good number of studies ( including Hall and Jones ,1999; Acemoglu et al. ,2002; Easterly and Levine ,1997; Dollar and Kraay, 2003; and Rodrik et al.,2002 ) have examined the relationship between political institutions and economic growth. Vijayaraghavan and Ward (2004) examined the relationship between institutional infrastructure and economic growth rates across 43 nations during the years 1975– 1990. Using a neoclassical growth framework, they integrate a broad set of institutional variables used as proxy for the overall institutional infrastructure of an economy. The results from this study indicate that security of property rights and size of government are the most significant institutions that explain variations in economic growth rates.

Methodology Model specification In this study, we adopt a Barrow-type (1991) growth model to analyse the impact of resource wealth on economic growth. Following the works of Mankiw et al (1992) , Sala-i-Martin (1992) ,Sachs and Warner (1999), Lederman and Maloney (2002) and Hoeffler (2002), and by controlling for non-natural resource factors that influence long run growth4, we specify a growth equation that accounts for the effects of natural resources as follows: yit = α + βNRit + λXit + μi + εit

(1)

Where yit is the growth rate of per capita GDP in country i at time t, NRit represent measures of natural resources abundance in country i at time t, Xit is a set of control variables, μi represents the unobserved country-specific effect and εit is the error term. In a more explicit form, equation 1 can be presented as follows: ln(y)it = α + βln(NR)it + λ1ln(COR)it+ λ2ln(HUMC)it+λ3ln(GE)it+μi+ ϵit (2) Where:

4

ln(y)it

= the natural log of GDP per capita in country i at time t

ln(NR)it

= the natural log of natural resource export as a share of GDP in county i at time t,

ln(COR)it

= the natural log of the corruption perception index of country i at time t,

ln(HUMC)it

= the natural log of the human capital of country i at time t

ln(GE)it

= the natural log of government effectiveness in country i at time t.

β

= measures the relative effects of natural resource endowment on per capita output

λi

= set of parameters capturing the relative effects of the control variables.

The traditionally based neoclassical theory of growth has been focusing only on

physical capita and labour inputs as the principal determinants of growth.

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Following James and Aadland (2010) , we test the resource curse phenomenon by contrasting the null hypothesis H0:β≥0 against the alternative hypothesis HA:β <0 . A rejection of the null hypothesis in favor of the alternative hypothesis will thus provide evidence that resource abundance countries exhibit slower economic growth than resource poor countries. Estimation techniques Equation 2 is the basis upon which we estimate an empirical relationship between natural resource abundance and growth in per capita income amongst ECOWAS member states. As noted in equation 2 above, if the unobserved country-specific effects, μi, are uncorrelated with the explanatory variables (i.e. if μi is orthogonal to all the explanatory variables) then we can apply the pooled OLS estimator to fit our model. However, when there is a strong correlation between the unobserved individual component, μi and the regressors of the model, the pooled OLS estimator is biased and inefficient. In this situation, the fixed effects model is a suitable candidate for carrying out estimations of the model’s parameters. If the standard random effects assumptions hold but the model does not actually contain an unobserved effect, the pooled OLS is efficient and all the associated pooled OLS statistics are asymptotically valid. To test for the absence of unobserved effect, we employ a simple AR(1) test for serial correlation. This test is appropriate because the idiosyncratic errors are serially uncorrelated under the null H0 : ση2 = 0, given that the explanatory variables are exogenous. The detection of serial correlation amongst the idiosyncratic errors thus validates the presence of unobserved effect. In many applications, however, the whole point of using panel data is to allow for the unobserved effects, ηi , to be arbitrarily correlated with the set of explanatory variables, thus necessitating the application of a fixed effects estimation procedure. In this study, the choice between the fixed effects and random effects model for the levels estimation will be based on the Hausman specification test. A large value of the Hausman test statistic leads to the rejection of the null hypothesis that the individual-specific effects are uncorrelated with the regressors and to the conclusion that fixed effects are present. Following the approach by Levine et al (2000) and Beck et al (2000), we endeavor to establish the relationship between growth in real per capital GDP in country i at time t to some exogenous factors as specified in equation 2 above. With a view to addressing potential endogeneity in the data, we follow a dynamic panel approach (Arrelano and Bond, 1991; Arrelano and Bover, 1995; and Blundell and Bond, 1998) in estimating the specified growth equation . Such a dynamic panel estimation technique is being developed by applying first difference transformation from the following equation: yi,t - yi,t-1 = (α - 1) yi,t-1 + β’Xi,t + ηi + εi,t

(3)

Where yi,t - yi,t-1 is the growth rate in real GDP per capita, Xi,t is the set of explanatory variables, including our measures of natural resource abundance, ηi is the unobserved country-specific effect, and εi,t is the error term. We proceed by rewriting equation (3) as: yi,t = α yi,t-1 + βXi,t + ηi + εi,t

(4)


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Now, by taking the first difference of both the endogenous and exogenous variables in equation (4), we have: yi,t - yi,t-1 = α’(yi,t-1 - yi,t-2) + β’(Xi,t - Xi,t-1) + (εi,t - εi,t-1)

(5)

As can be observed from equation (5) above, the lagged difference in per capita GDP is correlated with the error term, which by implication of the potential endogeneity of the explanatory variables X, necessitated the use of instrumental variables. To address this problem, the system difference estimator uses the lagged level of the explanatory variables as instruments under the conditions that the error term is serially uncorrelated and that that the lagged level of the explanatory variables are weakly exogenous. Following Blundell and Bond (1998), we employ two specification tests. The first is a Sargan test of over-identification restriction which tests the validity of the instruments. The second is a test of second order serial correlation of the error term, which tests whether the error term in the differenced equation model follows a first order moving average process. Data and sources The data set used in this study is obtainable from the World Bank Development Indicators, Country Tables of the African Development Bank, World Trade Statistics, Transparency International and ECOMAC Data base. Since 1995, Transparency International Publishes the Corruption Perception Index (CPI) annually ranking countries by their perceived level of corruption, as determined by expert assessments and opinion surveys. The CPI generally defines corruption as the misuse of public power for private benefit. It ranks countries on a scale from 10 (no corruption) to 0 (highly corrupt). For purposes of this study , we reverse the Transparency International scale in ranking countries by subtracting the rank assigned by Transparency International from 10 (no corruption) so that highly corrupt countries will have higher numerical ranking for ease of comparison in terms of growth performance. The government effectiveness index (GE) also publish by Transparency International is a measure that captures the perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation and the credibility of the government’s commitment to such policies. The growth rate data is obtained from the World Bank Publications. We also consider the quality of a country’s stock of human capital (HUMC) as part of its natural resources and therefore assesses its impact on economic growth by including it as one of the control variables in the specified model. This variable is obtainable from the World Bank Development Indicators.

Presentation and analysis of regression results Presentation of results The Study utilizes the Hausman specification test in choosing between the fixed and random effect models in estimating the specified equations in levels. The dynamic panel model (in which all the

74

variables are in first difference) is estimated following the approach by Arellano –Bond using Stata command. We use the overall sample which comprises of ECOWAS member states, excluding Liberia for which some data were missing. In order to capture differences in natural resource management policy between the Anglophone and Francophone countries, we categorise ECOWAS member states into two groups: those belonging to the West African Economic and Monetary Union (WAEMU) and those belonging to the West African Monetary Zone (WAMZ). Whilst members states of WAEMU are mostly Francophone countries comprising of Benin, Burkina Faso, Cape Verde, Cote d’Ivoire, Mali, Niger, Senegal and Togo, those of the WAMZ are mostly the Anglophone West African countries comprising of The Gambia, Ghana, Liberia , Nigeria , Sierra Leone and Guinea. Guinea is the only francophone country that decided to align itself with the WAMZ. On the basis of these sub-regional groupings, we then run separate regressions for each of the sub-economic region in order to assess whether differences in economic policies across francophone and Anglophone countries affect the relative effects of natural resource endowment on growth. The panel regression results for the overall sample of ECOWAS member states and those of the WAMZ and WAMEU sub-regions are presented in table 1. The result from the Hausman specification test as shown in the lower section of table 1 consistently indicate that the individual unobserved country-specific effects are uncorrelated with the explanatory variables, suggesting that the fixed effects model is preferable to the random effects model for the levels regression estimates . Hence for the levels estimates, we only consider the results from the fixed effects estimates in our discussion of findings. For the dynamic model, the results from the Sargan test, as shown in the lower section of table 1 indicate that the instruments are valid in all the dynamic panel regressions. Finally, the test for second order serial correlation shows no serious problem of serial correlation of the residuals from the dynamic panel regressions. Analysis of regression results From the regressions results presented in table 1, it could be observed that the coefficient of the variable representing level of corruption as derived from the corruption perception index (COR) is consistently negative in all the regressions. For the fixed effects model, the coefficients of the corruption perception variable are significantly negative for the overall sample of ECOWAS member states as well as the WAMZ and WAEMU sub-regions. Accordingly, the results indicate that corruption has a negative and significant effect on growth in the ECOWAS region. The results from the fixed effects model are also consistent with those from the dynamic model for all the regressions. In terms of relative effects, the results from the dynamic model indicate that a 10% increase in corruption will retard growth in real income per capita by around 5.7% for the overall sample of ECOWAS member states. With regards to the two regional sub-groupings, a 10% increase in corruption will retard growth in income per capita by 1.2% and 9.5% in WAEMU and WAMZ subregions respectively. The results thus indicate that corruption has a more significantly negative effect on growth in WAMZ member countries as compared to their WAEMU counterpart. This is because,


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75

Table 1. Panel estimation results: per capita income as the dependent variable Results for the Overall ECOWAS Sample Variables

Constant Ln(Y)_1

Fixed Effects

Random Effects

Results for WAEMU Member States

Dynamic Model

-2.0462

-0.1904

(-2.78)**

(-0.29)

_

_

0.7236 (28.30)***

_

Fixed Effects -0.9223 (-1.13) _

Results for WAMZ Member States

Random Effects

Dynamic Model

1.6488 (3.07)

_

_

Fixed Effects

Random Effects

Dynamic Model

-4.0047

-2.1297

(-2.60)**

(-1.74)

0.0.6186 (9.70)***

_

_

0.67084 (3.17)***

_

-0.5794

- 0.6188

-0.5741

-0.5211

-0.6015

-0.1232

-0.61352

- 0.5916

-0.9503

(-4.10)***

(-4.15)***

(-1.25 )

(-3.18)***

(-3.32)***

(-1.21 )

(-2.43)**

(-2.31)**

(-1.86 )*

-0.00274

0.000981

-0.0921

-0.0381

-0.3478

(0.01)

0.06607 (3.26)***

-0.0418

(-0.04)

(-0.45)

(-1.06)

(-0.32)

(-0.29)

-0.0059

-0.03215

-0.0396

-0.04619

-0.07571

-0.05795

-0.1278

-0.09713

(-0.09)

(-0.48 )

(-2.53)**

(-0.65 )

(-1.02)

(-1.37)

(-0.87)

(-0.85 )

(-0.56)

Ln(HUM)

2.3841 (11.29)**

1.7787 (9.64)***

0.3112 9 (6.34)***

2.1923 (.9.90)***

1.6488 (9.28)***

0.3570 (1.48)

2.8865 (6.08)***

2.33021 (5.48)***

0.7801 (2.46)**

No. of Obs.

150

150

150

90

90

90

60

60

60

R

0.5582

0.5502

_

0.7190

0.7446

_

0.5350

0.5173

_

F(5,50 ) = 15.3 (0.000)

Wald

Ln(COR) Ln(GE) Ln(NR)

2

F-Statistics Hausman Test

F(4,131 ) = 42.1 (0.000)

Wald Chi2(4) = 133.6(0.000)

Chi2(4) = 24.53 (0.0382)

Test for second order Serial correlation

_

_

Sargan Test

_

_

_

_ H0 :No autocorrelation Z = -4.56 (0.000) Chi 2(39) = 14.9 (0.999)

F(4,77 ) = 29.95(0.000)

0.0527 (1.11)

Wald Chi2(4) = 132.45 (0.000)

Chi2(4) = 24.53 (0.0382)

_

_

_

_

_

_ H0 :No autocorrelation Z = -4.13 (0.000) Chi 2(39) = 52.52 (0.0288)

Chi2(4)= 51.4 (0.000)

Chi2(4) = 8.76 (0.0675)

_

_

_

_

1.0658 (1.05) -0.3782

_ _ H0 :No autocorrelation Z = -4.76 (0.000) Chi 2(32) = 29.13 (0.6124)

Note: Where the variables are expressed in log form and t –values are reported in parenthesis , where (*) (**) and (***) implies significance of coefficient at the 10%, 5% and 1% level respectively. The variables entering the Dynamic model are in first difference and their coefficients are interpreted as growth elasticities. Both the fixed effects and random effects models are in levels. The dynamic model is based on the Arellano-Bond Estimation procedure.

when we critically examine the corruption perception index published by transparency international, we discover that that a good number of WAMZ member countries are highly ranked in terms of corruption than those of WAEMU member countries. As a result of this perceived high level of corruption in WAMZ member countries, the impact of corruption on growth is of a relatively higher magnitude than that of the WAEMU member countries. This empirical evidence is further corroborated by figure A1 of Appendix A which shows the relationship between corruption and growth in income per capita in ECOWAS member states. As can be vividly observed from figure A1of Appendix A, a good number of WAMZ member states like Nigeria, Sierra Leone , Liberia and Guinea were ranked higher in terms of corruption when compared to WAEMU member countries like Benin, Burkina Faso, Cape Verde and Cote d’Ivoire . In fact Benin happened to be the least ranked country in terms of corruption amongst ECOWAS member states by Transparency International. In terms of growth performance amongst WAMZ countries, Nigeria, though highly ranked in terms of corruption, its growth performance stood at around 8.7% in 2010. Nigeria’s growth performance was quite impressive when compared to Sierra Leone (which is also highly ranked in terms of corruption)

with a growth performance of around 4.9% in 2010. Despite Nigeria is highly ranked in terms of corruption, the hike in the price of crude oil, Nigeria’s main export, may have partly accounted for its impressive growth in 2010. Thus, both the empirical finding from the study as well as the relationship depicted in figure A1 consistently supported the works of Tornell and Lane (1999), Baland and Francois (2000), Torvik (2002), and Robinson et al (2006) who find that the discovery of natural resources is highly likely to generate some sort of economic rents that may result to higher tendencies for corruption in the public sector. The relationship between the variable representing government effectiveness (GE) and growth in income per capita is rather mixed. When we consider the fixed effect model, the coefficients of the government effectiveness variable are negative but rather insignificant in all the regression. However, on considering the dynamic panel estimation, the coefficients of the government effectiveness variable are positive but only significant for the overall ECOWAS sample. When we consider the WAMZ and WAEMU subgroupings separately, the coefficients of the government effectiveness variable are positive but rather insignificant. Since the government effectiveness (GE) variable is an index that captures the perceptions


DOI number: 10.5027/jnrd.v3i0.06

of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies, the results indicates that its impact on growth in income per capita is more effective at the regional level that at the sub-regional levels. In terms of relative effects, the results indicate that a 10% enhancement in government effectiveness in the ECOWAS region as a whole will induce growth in income per capita by approximately 1.0%. Though the results are positive for the two sub-regional groupings , they are rather insignificant . This therefore implies that, effective collaboration at the ECOWAS regional level in terms of enhancing government effectiveness has a more positive and significant effect on growth than at sub-regional levels. With regards to the relationships between natural resource export (NR) and economic growth, the results from both the fixed effects and dynamic panel estimations are rather mixed. For the fixed effects estimates, while the results are positive for the overall ECOWAS sample and WAEMU, they are rather negative for the WAMZ region. However, the results from the fixed effects regression are all insignificant in terms of enhancing growth in income per capita. When we consider the results from the dynamic panel estimation, it could be observed that only the coefficient from the overall ECOWAS sample is negative and significant at the 5% level. For the WAMZ and WAEMU samples, the results are also negative but rather insignificant. In terms of relative effects, the results from the ECOWAS sample indicate that a 10% increase in natural resource export reduces growth in income per capita by approximately 0.4%. Given that a good number of West African Countries are highly endowed in natural resources, these finding lends support to the resource curse phenomenon. A good number of researchers have also arrived at similar conclusions that natural resource abundant economies have the tendency to grow more slowly than economies without substantial resources (Auty, 2001a; Rainis, 1991; Bulmer-Thomas, 1994; Sachs and Warner, 1995 & 2000, 1997; Lal and Myint, 1996). Another plausible explanation for the negative relationship between natural resource abundance and growth in income per capita as found in this study could be linked to the high tendencies for civil conflicts in natural resource rich countries as noted by Collier and Hoeffler (2001). The study by Collier and Hoeffler (2001) find that the probability of a civil conflict is 0.5 % in a country with limited natural resources, but 23 % in a country where natural resources account for 26 % of GDP. They argued that, for a good number of countries, including Iraq, Nigeria, Sierra Leone, Venezuela, former Zaire, Zambia, and many others, the abundance of natural resources like oil or mineral wealth has not effectively translated into economic and social well-being for the majority of the population. As noted for the West African region, most of the civil conflicts, especially for countries like Sierra Leone, Liberia, Nigeria and Cote d’Ivoire, the abundance of natural resources may have accounted for part of the causes of their civil conflicts. In Sierra Leone, for instance, the ten year civil conflict was partly alleged to the scramble for the country’s diamonds (the so called Blood Diamond War). In essence, the negative relationship between natural resource abundance and growth could also be partly explained by the outbreaks of civil conflicts that tremendously plagued the economies of these countries over the past two decades.

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In terms of the relationship between the variable representing human capita (HUMC) and economic growth in West Africa, the results from both the fixed effect and dynamic panel regressions show a positive and significant impact on growth. On the basis of the estimates from the dynamic panel regressions, the coefficients of the human resource variable are positive in all the regressions but only significant for the overall ECOWAS sample and that of the WAMZ member states. In terms of relative effects, the results indicate that a 10% enhancement in the quality of human capital will induce a corresponding growth in income per capita by approximately 3.1% and 7.8% for the ECOWAS region as a whole and the WAMZ member states respectively. For the WAEMU sample, the results from the dynamic panel regression are rather insignificant. This therefore implies that the quality of human capital plays a significant role in promoting economic growth in the ECOWAS region and more so in WAMZ member countries.

Conclusion This study attempted to investigate the nexus between natural resource endowment and economic growth using a sample of West African countries. Though the West African region is noted for having abundance natural resources, their distribution amongst member countries of the Economic Community of West Africa (ECOWAS) is quite uneven. Whilst some countries are richly endowed with a plethora of natural resources like Sierra Leone, Cote d’Ivoire, Liberia, Nigeria, Ghana, Guinea, Guinea Bissau and Togo, others like Burkina Faso and The Gambia have very minimal endowments of natural resources. To address the question as to whether natural resource rich countries performed better in terms of economic growth than those with limited resources, the study followed the works of Mankiw et al (1992), Sala-i-Martin (1992) ,Sachs and Warner (1999), Lederman and Maloney (2002) and Hoeffler (2002) by adopting a Barrow-type (1991) growth model to analyse the impact of natural resource wealth on economic growth. A dynamic panel data estimation technique was employed following the procedures by Arrelano and Bond (1991), Arrelano and Bover (1995), and Blundell and Bond (1998). The results from the panel regressions indicate that natural resource endowments in West Africa have very minimal impact in terms of promoting economic growth, especially in resource rich countries. This finding is therefore in conformity with the resource curse literature which emphasis the point that natural resource abundant economies have the tendency to slowdown economic growth than economies without substantial resources. Part of the factors explaining this finding in the case of West Africa include amongst others, the gross mismanagement of natural resources exports revenues through high rates of corruption in the public sector as well as the frequency of civil conflicts in resource rich economies of West Africa. For the natural resource endowments of the West Africa region to fully benefit its citizens in terms of improving their living standards so as to reduce the menace of poverty and starvation, there is an urgent need for countries of the West African region to improve on the management of natural resource revenues through putting in place effective policy measures to eradicate or rather mitigate incidences of rampant corruption in the public sector. The introduction of severe punitive measures for corrupt public officials like confiscation of illicit


DOI number: 10.5027/jnrd.v3i0.06

wealth and property, imprisonment of public officers found guilty of corruption related crimes and rejection of such public officers from holding public offices, will significantly curb the adverse effects of corruption on economic growth. Such measures, if effectively implemented will reduce the tendencies for corruption, thereby increasing the potentials for resource rich countries in West Africa to effectively address the United Nations Millennium Development Goals (MDGs). Secondly, the Economic Community of West African States (ECOWAS) should continue its efforts and commitment on conflict prevention and resolution amongst member states so as to mitigate the adverse effects of civil conflicts on economic growth, especially in natural resource rich countries that are more prone to civil conflict as recently experienced by Sierra Leone, Liberia, Cote d’Ivoire, Mali and Guinea Bissau.

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Appendix A

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Figure A1. Corruption Ranking and Economic Growth in ECOWAS member states. Source: Transparency International, World Bank and authorsâ&#x20AC;&#x2122; calculation.

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Appendix B

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Figure B1. Natural Resource Export and Economic Growth in Benin (2002 - 2010). Source: Transparency International, World Bank and authors’ calculation.

Figure B2. Natural Resource Export and Economic Growth in Burkina Faso (2002 - 2010). Source: Transparency International, World Bank and authors’ calculation.

Figure B3. Natural Resource Export and Economic Growth in Cape Verde (2002 - 2010). Source: Transparency International, World Bank and authors’ calculation.

80


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Figure B4. Natural Resource Export and Economic Growth in Cote d’Ivoire (2002 - 2010). Source: Transparency International, World Bank and authors’ calculation.

Figure B5. Natural Resource Export and Economic Growth in The Gambia (2002 - 2010). Source: Transparency International, World Bank and authors’ calculation.

Figure B6. Natural Resource Export and Economic Growth in Ghana (2002 - 2010). Source: Transparency International, World Bank and authors’ calculation.

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Figure B7. Natural Resource Export and Economic Growth in Guinea (2002 - 2010). Source: Transparency International, World Bank and authors’ calculation.

Figure B8. Natural Resource Export and Economic Growth in Guinea Bissau (2002 -2010). Source: Transparency International, World Bank and authors’ calculation.

Figure B9. Natural Resource Export and Economic Growth in Liberia (2002 -2010). Source: Transparency International, World Bank and authors’ calculation.

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Figure B10. Natural Resources Export and Economic Growth in Mali (2002 -2010). Source: Transparency International, World Bank and authors’ calculation.

Figure B11. Natural Resources Export and Economic Growth in Niger (2002 -2010). Source: Transparency International, World Bank and authors’ calculation.

Figure B12. Natural Resources Export and Economic Growth in Nigeria (2002 -2010). Source: Transparency International, World Bank and authors’ calculation.

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Figure B13. Natural Resources Export and Economic Growth in Senegal (2002 -2010). Source: Transparency International, World Bank and authorsâ&#x20AC;&#x2122; calculation.

Figure B14. Natural Resources Export and Economic Growth in Sierra Leone (2002 -2010). Source: Transparency International, World Bank and authorsâ&#x20AC;&#x2122; calculation.

Figure B15. Natural Resources Export and Economic Growth in Togo (2002 -2010). Source: Transparency International and World Bank Development Indicators.

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Journal of Natural Resources and Development 2013; 03: 85-95

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85

JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT A proposal for tsunami mitigation by using coastal vegetations: Some findings from southern coastal area of Central Java, Indonesia. Djati Mardiatno a* a

Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia

*

Corresponding author : mardiatno@yahoo.com

Article history Receivedâ&#x20AC;&#x192; 05.11.2012 Acceptedâ&#x20AC;&#x192; 12.04.2013 Published 01.07.2013

Keywords Tsunami mitigation Coastal forest Allometry Breaking capacity Java Island

Abstract This research was conducted at the southern coastal area of central Java Island, Indonesia. It is aimed to identify several coastal vegetation characteristics for development of guideline for planning and design of tsunami mitigation. Survey method was applied to observe common coastal vegetation in the research area. Data collected from the survey consisted of vegetation parameters and coastal morphology. All selected vegetations were analyzed for their allometry relation of each species, maximum density, correlation between breaking moment and trunk diameter of each tree species, and correlation between trunk diameter and spacing between trees for each species. For coastal morphology, it was focused on topography and elevation from sea level. The results show that trees with the hard wood will be stronger to hold the pull moment on the main trunk. Younger trees with smaller diameter tend to be more flexible, thus they will unbreakable during the test. The other trees which have flexible trunk such as Terminalia catappa and Anacardium occidentale were often pulled out their roots than broken on their trunks. To obtain more extensive characteristic, it is necessary to carry out advanced measurements, especially on the older trees which have more than 10 cm diameter. Coastal areas consist of mud and sand materials tend to have a high tsunami risk, although mitigation treatments were different for both types. At the muddy area, the recommended vegetation are Avicennia marina and Rhizophora mucronata, meanwhile Casuarina equisetifolia and Anacardium occidentale, due to their high flexibility, will be more suitable on the sandy coast. Both types should be planted parallel to the shoreline. Casuarina is planted in the frontline followed by Anacardium behind it.


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Introduction

The southern coastal area of Java Island, Indonesia, is recognized as one of the high tsunami risk area. This region experienced to tsunamis in 1994 and 2006, resulted in huge fatalities. Both significant tsunamis in South Java were recognized as ‘tsunami earthquakes’. It is provided by the larger tsunamis than expected in consideration to the earthquake magnitudes (Geist, 1999; Seno, 2007; Lavigne et al. 2007). In Banyuwangi area, tsunami occurred on 3 June 1994. It was triggered by an earthquake with moment magnitude (Mw) 7.6 and killed 238 people (Latief et al. 2000). The next tsunami happened on 17 July 2006, destroyed a wider area from eastern part of West Java Province through Yogyakarta Special Region. It was triggered by an earthquake Mw 7.7, located in 34 km of depth in the Indian Ocean. Based on the reality that south Java region is very vulnerable to tsunamis, it is necessary to do tsunami mitigation. For tsunami mitigation purpose at that region, coastal forest is considered as an option to reduce the damage due to tsunami attack. Ohira et al. (2012) explained that “a 100 m of forest width could reduce 17.6% of the inundation flux”. The characteristic of vegetation combined with local tsunami potential is the essential factors to asses the effectiveness of the vegetation to reduce tsunami wave (Chaeroni and Widagdo, 2011). However, the

86

coastal forest condition is depending on the local environment (Kordi, 2012), thus it is necessary to examine some prospective vegetation for tsunami mitigation. This paper presents some finding of the research conducted in a part of southern coastal area of Central Java region, from Parangtritis (Yogyakarta) to Cilacap (West Java). The samples including some coastal areas as follows: 1) Parangkusumo; 2) Samas; 3) Glagah; 4) Ketawang; 5) Suwug; 6) Karangbolong; 7) Ayah; 8) Widara Payung; 9) Adipala; and 10) Kalipucang (Figure 1). Vegetation observed and examined were “Pandan” (Pandanus odoratissimus), “Akasia” (Acacia auriculiformis), “Bakau” (Rhizopora mucronata), “Cemara udang” (Casuarina equisetifolia), “Api – api” (Avicenia marina), “Ketapang” (Terminalia catappa), “Jambu mete” (Anacardium occidentale), and “Waru” (Hibiscus tiliaceus).

Objective The objective of this research is to find the breaking capacity and allometry of several coastal vegetations to support the development of guideline for planning and design of tsunami mitigative coastal forest.

Figure 1. Location of the research area.

Methodology Identification of Coastal Vegetation Types

Measurement of Coastal Trees Size and coastal topography

The vegetation had been surveyed namely Pandanus odoratissimus, Acacia auriculiformis, Rhizopora mucronata, Casuarina equisetifolia, Avicenia marina, Terminalia catappa, Anacardium occidentale, and Hibiscus tiliaceu. All mentioned vegetations are commonly found in the coastal area, though Acacia auriculiformis, Casuarina equisetifolia, Terminalia catappa, Anacardium occidentale and Hibiscus tiliaceus also could be found outside of the coastal area.

Trees size was measured in the field by using measurement tape. Topography was measured by using abney level, geological compass, tape measure, Garmin GPS - 76CSx series, Laser Ace telemetry and yallon. It was done by ploting position, azimuth profiling, back azimuth, and ranging, as the profiling method conducted with 3 yalon to obtain the slope data per segment (Figure 2).


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Figure 2. Profilling scheme.

Breaking capacity measurement of coastal trees Breaking capacity was directly measured in the field by pulling test using a force-gauge (Figure 2 - 3). Each experiment was conducted at least three times for each species. The breaking moment of a tree trunk or a 1¬st-order branch was calculated by the force and the arm length from the breaking node to the forcing point. The elasticity was also measured by using the following equation.

Where: F is the force acting on tree trunk or branch (N), E is a Young coefficient (Nm-2), I is the second moment of area (m4), larm is the arm length (m) from the breaking node of the branch to the forcing point, and Def is a deflection (m).

Measurement of breaking moment was started by identification of the dominant coastal vegetation with a high population level and various sizes to establish some vegetation which represent variation of trunk diameter (DBH) for the test. Some important parameter of vegetation were measured and recorded at the working sheet. After setting up the test equipment and measurement tool on the vegetation, breaking moment test were executed by pulling gradually from the deflection (def) at 10 cm, 20 cm, continuously, following by recording the measured force acting (F). To minimize branch influence to the results, it is necessary to give a notice related to the presence of vegetation’s branchs around the test location. All data taken were recorded refer to the test location.

Figure 3. Trees parameter for breaking moment determination (Sunarto et al, 2009). Where: HR – height, from root to soil; DR30 – trunk diameter at 30 cm above the root; DBH – trunk diameter at chest height level (1.3m above the soil)


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Vegetation analysis Vegetation data was analyzed by using Diversity Index of ShanonWiener (Dumbois and Ellenberg, 1974) as follows. Density of Type A = Sum of Individual Unit Area

Figure 4. Breaking moment test variables (Sunarto et al, 2009). ELF - Height of forcing point; ELB - Height of breaking node; larm - The arm length (ELFELB); def – The deflection of trunk (measured using tape measure above the ground/soil); F – The force acting on tree trunk or branch (measured using force gauge)

(1)

Relative Density of Type A (DR) = Density of Type A x 100% Sum of All Types Density

(2)

Domination of Type A = Sum of base area of type A Hectare

(3)

Relative Domination of Type A (DoR) = Domination of Type A x 100% Sum of All Types Domination

(4)

Frequency of Type A = Sum of Quadrate of Type A Sum of All Quadrate

(5)

Relative Frequency of Type A (FR) = Frequency of Type A x 100% Sum of All Types Frequency

(6)

INP (Important Value Index) type A = DR + DoR + FR

(7)

ID = - ∑ pi log pi (8) Where: ID: Diversity Index; Type A: certain vegetation type (e.g. type A); pi: Result from important value of vegetation type, divided by important value of all vegetation type.

Results and discussion Diversity Index and Vegetation Characteristic

Vegetation sampling Vegetation sampling was conducted using the nested sampling (plot), which are 20 x 20 m for tree, 10 x 10 m for the poles, 5 x 5 m for sapling, shrubs, and 2 x 2 for herbs (Figure 5). Random sampling technique was applied within transectline to the land (stratified random sampling).

Figure 5. Nested sampling (plot) for vegetation sampling.

The diversity Index in coastal area is relative low. The species found in the research area are the adaptive species to high level of water salinity and strong wind. The other factor influencing diversity index in this area is less water contents in the sandy area. This condition apparently forced certain species undergo some adaptation to the extreme condition. The adaptation on plant may happen structurally and physiologically. The diversity index in research area is shown at Figure 6 and Table 1. Meanwhile, vegetation distribution in research area is shown at Table 2.

Figure 6. Diversity index in research area.


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Table 1. Diversity index. No

Location

Trees

Poles

Sapling

Seedling

Herbs

1

Parangtritis

0.48

0.47

2

Glagah

0.24

0.52

3

Samas

0.26

4

Ketawang

5

Suwug

6 7 8

Widoro Payung

Shrubs

0.18

0.3

0.71

0

0.68

0.59

1.09

0.3

0.24

0.24

0

0.74

0

0.23

0

0

0.68

0.86

0.29

0.11

0

0

0

0.85

0.47

Ayah

0.29

0.53

0.69

0.44

1.13

0.21

Karangbolong

0.19

0

0

0.46

0.7

0

0.11

0

0.72

0.61

0.98

0.3

9

Adipala (Mangrove)

0

0.3

0.39

0.56

0.27

0

10

Kalipucang Karapyak

0.69

0.3

0.42

0.67

0.63

0.29

Table 2. Vegetation distribution in research area. No

Location

Vegetation

Herbs and Shrubs

1

Parangtritis

“Gamal” (Gliricidia sepium), Acacia mangium, cashew fruit (Anacardium occidentale) and “Siwalan” (Borassus flabellifer)

Vinca rosea, Melochia corchorifolia, Alternanthera sp, Cyperus pedunculatus, Brachiaria eruciformis, Calotropis gigantean, Ipomea marginata and Passiflora foetida

2

Glagah

“Pandan” (Pandanus tectorius and Pandanus odoratissimus), Morinda cytrifolia, Terminalia cattapa, Gliricidia sepium, Borassus flabelliver and “Kelor” (Moringa oleifera)

Passiflora foetida, Ipomea pescaprae, Ischaemum timorense, Synedrella nodiflora, Cynodon dactylon, Cyperus rotundus, Mimosa pudica, Brachiaria mutica, Calotropis gigantean, Tephrosia purpurea, Uraria lagopoides and Spinifex squarosus

3

Samas

Casuarina equisetifolia, Acacia mangium, coconut (Cocos nucifera) and Gliricidia sepium

Spinifex squarosus, Alternanthera sp, Calotropis gigantean, Brachiaria eruciformis, Uraria lagopoides, Cyperus pedunculatus, Passiflora foetida and Tephrosia purpurea

Ketawang

Cocos nucifera, Acacia mangium, Anacardium occidentale, Gliricidia sepium and Terminalia catappa

Pandanus odoratissimus, Euphatorium odoratum, Brachiaria mutica, Tridax procumbens, Cyperus pedunculatus, Ischaemum timorense, Digitaria ciliaris, Cyperus rotundus, Polytrias amaura, Ipomea pescaprae and Cynodon dactylon

Coconut (Cocos nucifera), Acacia mangium and Casuarina equisetifolia

Pandanus tectorius, Calotropis gigantea, Jatropha podagrica, Stachytarpheta indica, Polytrias amaura, Alternanthera sp, Uraria lagopoides, Phyllanthus simplex, Spinifex squarosus, Ischaemum timorense, Flueggea leucopyrus, Cyperus pedunculatus, Tephrosia purpurea and Cyperus rotundus

4

5

Suwug

6

Ayah

Casuarina equisetifolia, Hibiscus tiliaceus, Terminalia catappa, Canophylum inophyllum and Erytrina sp

Pandanus odoratissimus, Euphatorium odoratum, Tridax procumbens, Luffa cylindrical, Porophyllum ruderale, Euphorbia hirta, Cyperus rotundus, Brachiaria eruciformis, Ipomea pescaprae, Ipomea marginata, Uraria lagopoides, Solanum melongena, Polytrias amaura, Synedrella nodiflora Citrullus vulgaris, Alternanthera sp and Selaginella doederleinii

7

Karangbolong

Coconut (Cocos nucifera), Calophyllum inophyllum, Terminalia catappa, and Pandanus odoratissimus

Ipomea prescaprae, peanut (Arachis hypogea), Ischaemum timorense, Ciperus rotundus, Rotthboellia sp, and (Cynodon dactylon)

Coconut trees (Cocos nucifera) and Acacia mangium

Pandanus odoratissimus, Eupatorium odoratum, Spinifex squarosus, Brachiaria eruciformis, Cyperus pedunculatus, Ipomea marginata, Calotropis gigantean, Uraria lagopoides, Tephrosia purpurea, Tridax procumbens, Phyllanthus niruri, Stachytarpheta indica, Cynodon dactylon, Polytrias amaura, Dactyloctenium aegyptium and Ciperus rotundus

8

Widoro Payung

9

Adipala (Mangrove)

Rhizopora apiculata, Avicenia marina and Soneratia sp

Nypa fruticans, Argemone sp and Ischaemum muticum

Kalipucang Karapyak

“Keben” (Baringtonia asiatica), Cocos nucifera, Terminalia catappa, Lagerstroemia speciosa, Erythrina sp, Eugenia jambos, Calophyllum inophyllum and Thespesia populnea

Eleusin indica, Cyperus rotundus, Ischaemum muticum, Imperata cylindrical, Tridax procumbens, Rotthboellia sp, Cynodon dactylon, Pandanus odoratissimus and Ficus septica

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Anacardium occidentale, Gliricidia sepium and Terminalia catappa that found in Ketawang is still in immature stage. In Suwug, the dominant species that can be found is coconut (Cocos nucifera) and Acacia mangium, which is why Suwug is known as local farmer coconut farm. On the Suwug beach, Casuarina equisetifolia can also be found. In Adipala, west part of Cilacap, the individual level of sapling is prominent in mangrove ecosystem. The vegetation in the coastal area is typically characterized with adaptive trees to high salinity level of water and wind influence. The changes and the adaptation of trees structure have been made to adapt with extreme salinity condition. The adaptation has been remarked on trees with low branches. The branches free trunk is found nearly closed to the ground, this condition happen especially in sandy coast. Roots system has been known developed well, the root grows longer. It serves to find water sources and to provide support for wind shaky. The major trees species found in the sandy coast area i.e. Coconut (Cocos nucifera), Cashew fruit (Anacardium occidentale), Acacia (Acacia mangium), casuarina tree (Casuarina equisetifolia), Ketapang (Terminalia catappa), gamal (Gliricidia sepium), siwalan (Borassus flabellifer), waru (Hibiscus tiliaceus), nyamplung (Callophyllum inophyllum) and keben (Baringtonia asiatica). Mangrove vegetation is not found in the shore area, although some available individual is present in poles to seedling level. In protected forest, trees diversification is very high. Pole level on the beach is relatively rare. Mangrove area shows a little amount poles level. The diversity at poles level can be found in protected forest vegetation. Sapling grows below the trees branch and the poles. Human influence gives a big impact to the development of

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sampling. The rehabilitation of coastal reforestation program is the form of human influence to the sampling development. The program involves planting a certain species of plant i.e. casuarinas trees (Casuarina equisetifolia), ketapang (Terminalia catappa), nyamplung (Callophyllum inophyllum), keben (Baringtonia asiatica) and Coconut (Cocos nucifera). Sampling diversity increases in mangrove ecosystem. Level of sampling in mangrove ecosystem has a specific domination and unique from species Rhizopora apiculata. In protected forest, the sampling levels relatively balance. Seedling level is extremely influenced by trees ability to produce seeds. Seeds will be spread through the area by spreading mechanism with the assistance of wind, seeds eater animals and water. The spreading seeds will be growing fast during the rain. The highest seedling diversity could be seen in the protected forest and Ketawang area. Shrubs from Pandanus tectorius and Pandanus odoratissimus, Calotropis gigantean, and Euphatorium odoratum are abundantly found. Mangrove area is dominated by Nypa fruticans while the virgin forest shows more varied species of shrubs. Various shrubs have grown and developed with the adapatation ability to extreme condition. During the rainy season the herbs seeding and flowering fastly. The seed will be growing into mature seed and will be spreading with the assistance of wind and several species of birds. Correlation between breaking moment and tree trunk diameter According to trunk sturdy, the observed plant could be divided into woody plant, soft plant, and stem plant. Stem plant is not including in this research. Based on the field measurements, the value of breaking moment and elasticity is presented in Figure 7 â&#x20AC;&#x201C; Figure 10.

Figure 7. Breaking moment graphic (Nm). Source: data analysis.


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Figure 8. Elasticity Graphic (Nm2). Source: data analysis.

Figure 9. Broken vegetationâ&#x20AC;&#x2122;s graphic. Source: data analysis.

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Figure 10. Unbreak vegetationâ&#x20AC;&#x2122;s graphic (Source: data analysis).

According to the results, none of the cashew fruit trees was broken during the test. Cashew fruit (Anacardium occidentale) is grouped into woody plant in this research. It is different with soft trees like Avicenia marina and Rhizophora mucronata, because they were broken in each breaking test. It proofs that soft plant tends to be more fragile in comparison to woody plant. The result for cashew fruit is same with the research conducted by Tanaka et al. (2007) in Ban Thale Nok, Sri Lanka, after the Indian Ocean tsunami on 26 December 2004. The branches of cashew fruit were broken at the edge facing the coast, but most of the trees are still in good condition. A house with distance around 450 meters from the coast and behind the Anacardium occidentale trees was not damaged (Tanaka et al. 2007). Pandan (Pandanus odoratissimus) 6 cm in size was not broken during the test, meanwhile all plants with diameter above 6 cm were broken during the test (Figure 11). It shows that younger plants are more rigid than mature plants, though its trunk sturdy is grouped into soft plants. Further investigations on breaking moment and elasticity data are presented into characteristic curve and equation trendline. In present investigation characteristic curve of breaking moment and elasticity are approached using square equation n (power equation). Tanaka et al. (2007) and Thuy et al. (2011) stated that Pandanus odoratissimus have many aerial roots, and the moment of the drag force can be shared by the aerial roots. Therefore, they were able to withstand a tsunami of less than 5 m, even with debris attached to the aerial roots and additional force applied. However, if the drag moment exceeded the threshold for the breaking moment when the tsunami water was high, the trunks were broken just above the aerial roots. Considering the limitations of P. odoratissimus in reducing tsunami water depth and the other roles that coastal vegetation can play in mitigating tsunami-related damage, a forest with two layers in the vertical direction of P. odoratissimus and dense C. equisetifolia (mixed culture) was found to be effective for increasing drag and trapping floating debris in Kalutara, Sri Lanka (Tanaka et al. 2009). Terminalia catappa had never been broken during force test, except trees with 10 in diameter. It was broken after receiving moment of

2,3 kNm (deflection 50 cm). Different situation in Laem Son National Park, Sri Lanka, T. catappa is one of trees which categorized as most of the broken or uprooted tree, but it was not washed away but remained in place (Tanaka et al. 2007). In other hands, some cases show that T. catappa is the effective tree for escaping from tsunami by climbing it.

Figure 11. All plants with diameter above 6 cm were broken during the test. (Photos taken by Sunarto and Mardiatno)


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Acacia (Acacia auriculiformis) with trunk below 6 cm was broken during test; however its rigidity is rise sharply as increased in tree life periode. This finding has not supported with sufficient data. It needs several data about diameter range. Acacia tree with 10 cm diameter was not broken during the test however 4,5 kNm and deflected 20 cm long. Comparing to cashew fruit tree, it is already defleated 120 cm long when given 3.8 kNm. This can be concluding cashew frit tree relatively flexible compare to another tree in this study. Hibiscus tiliaceus trees show relatively low elasticity for plants with trunk less than 6cm but it rose sharply as increased in diameter cm in diameter. It needs further observation to compare force moment each hard woody tree, especially for mature tree with 10cm and 15cm in diameter. This observation will be developed more accurate and representative. There is plant limitation in nature to do this kind of observation. The data shows all woody plants less than 11 cm in diameters were broken when received breaking moment less than 1.5kNm. Hard woody plant such as Cemara udang (Casuarina equisetifolia) was not broken when breaking moment more than 3,5 kNm was given. It is apparently hard woody trees may restrain tsunami force moment or push moment. Tanaka et al. (2007) stated that two layers of vegetation in the vertical direction with C. equisetifolia and P. odoratissimus exhibited a strong potential to decrease the damage behind the vegetation cover in Kalutara, Sri Lanka. C. equisetifolia grows at high density when the trunk diameter is small (d < 0.07 m), but at this size it can be broken by a 5 m high tsunami. When the diameter of C. equisetifolia was larger than 0.1 m, the trunks were not broken by the tsunami and were effective at that height, but it is presumed that they had little effect in reducing the surface velocity when their diameter is large (d > 0.5 m) with large spaces between trunks (7â&#x20AC;&#x201C; 30 m) (Tanaka et al. 2007). Based on Tanaka et al. (2009), dense C. equisetifolia grown in beach sand were found to be especially effective in providing protection from tsunami damage due to their density and complex aerial root structure. This research has observed the trees which stay rigid after force moment test. The trees cannot return into their first condition since the trees have withdrawn from its substrate (soil). From the data we

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can presume that enormous moment or force is needed to break the trunk than to withdraw them from soil. For this reason trees withdrawn frequently found in after tsunami than trees with broken trunk. Tsunami Mitigation Model Using Vegetation Coastal areas consist of different materials and topography conditions tend to have a high tsunami risk. Nevertheless, the mitigation treatments were different for each type. When analysing the mitigation treatments, it is important to identify the correlation among different parameters, such as physical characteristics of the tsunami wave, local topography, features of the existing vegetation, and the nature of the built up environment (Sunarto et al. 2009). The sketch of topography in the research area is shown in Figure 12.

Figure 12. Sketch of topography in research areas (Source: field measurement)

Furthermore, existing coastal vegetation play a hugely important role in coastal community development and in maintaining the coastal environment. Wide, elongated, dense, and mature coastal vegetation growing along the shoreline can help to reduce the devastating impact of a tsunami and storm surge by decreasing their wave energies (Tanaka and Sasaki, 2008; JWRC, 2009; Sunarto et al. 2009). Tanaka et al. (2007) stated some vegetation function when the tsunami happened, i.e. the soft-landing effect, trapping effect, and escaping effect. Some illustration for the function of coastal vegetation during tsunami inundation can be seen in Figure 13.

Figure 13. The function of coastal vegetation during tsunami inundation.


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In southern coastal area of Central Java, the coastal material consists of two materials, i.e. muddy and sandy materials. Both materials tend to have a high tsunami risk (Mardiatno, 2008). Each material has different ways regarding the mitigation treatments for tsunami risk. At the muddy area, for example in Adipala, the recommended vegetation are “Api – api” (Avicennia) and “bakau” (Rhizophora) (Figure 14), meanwhile Casuarina and Anacardium, due to their high flexibility will be more suitable on the sandy coast, for example in Ayah (Figure 15). Both types are not recommendend to be planted in

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a mixing way (“tumpang sari”), but should be better planted in line or parallel to the shoreline. Casuarina should be planted in the frontline and then followed by Anacardium behind Casuarina line. This way is different comparing to Tanaka et al. (2007); Tanaka and Sasaki (2008); Tanaka et al. (2009); and Thuy et al. (2011). They proposed Pandanus odoratissimus and Casuarina equisetifolia to reduce the tsunami energy and for protection to tsunami debris from any objects on the coast.

Figure 14. Ilustration for mitigation treatments in muddy coast (ex: Adipala)

Figure 15. Ilustration for mitigation treatments in sandy coast (ex: Ayah)

Conclusions Trees with the hard wood will be stronger to hold the pull moment on the main trunk. Younger trees with smaller diameter tend to be more flexible, thus they will unbreakable during the test. The other trees which have flexible trunk such as Terminalia catappa and Anacardium were oftenly pulled out their roots than broken on their trunks. The more data taken, the more accurate the result, since they will get more comprehensive test. To obtain more extensive characteristic, it is necessary to carry out advanced measurements, especially on the older trees which have more than 10 cm diameter. Coastal areas consist of mud and sand materials tend to have a high tsunami risk. Nevertheless, the mitigation treatments were different for both types. At the muddy coast, the recommended vegetation are Avicennia and Rhizophora, meanwhile Casuarina and Anacardium, due to their high flexibility will be more suitable on the sandy coast.

Both types were not recommendend to be planted in a mixing way (“tumpang sari”), but should be better in line or parallel to the shoreline. Casuarina should be planted in the frontline followed by Anacardium.

Acknowledgements This research was funded by The International Centre for Water Hazard and Risk Management (ICHARM) of Public Works Research Institute (PWRI), Japan, in cooperation with Research Center for Disasters (PSBA) Universitas Gadjah Mada Yogyakarta, Indonesia. It was also supported by partial grants from Faculty of Geography fiscal year 2012. Many thanks are dedicated to Bachtiar Wahyu Mutaqin for this article typing and to the assistants who supported during field work and data compilation. High appreciation is also provided to the reviewers for their very useful advices.


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References Chaeroni and Widagdo, A.B (2011) Casuarina tree’s as a candidate of greenbelt to reduce tsunami energy, short lectures on “The role of coastal forest in reducing tsunami impact”, BPDP-BPPT, Yogyakarta. Dumbois, D.M. and Ellenberg, H. (1974) Aims and Methods of Vegetation Ecology, John Wiley and Sons, New York. Geist, E. L. (1999) Local Tsunamis and Earthquake Source Parameters, in Dmowska, R. and B. Saltzman (eds), Advances in Geophysics Volume 39: Tsunamigenic Earthquakes and Their Consequences, Academic Press, London, 117-209. JWRC (2009) Assessment of Tsunami Mitigation Functions of Coastal Forest/Trees and Proposal for Appropriate Forest Management Indonesia, Japan Wildlife Research Center, Japan. Kordi, M.G.H. (2012) Ekosistem Mangrove Potensi, Fungsi, dan Pengelolaan, Rineka Cipta, Jakarta

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Seno, T. (2007) Implications for tsunami earthquakes. Available online at: http://www.eri.u-tokyo.ac.jp/seno/implications.tsunami.eng.html (retrieved: 29-10-2007) Sunarto, Mardiatno, D. and Rahayu, L. (2009) Data Collection and Analysis of Coastal Vegetation Characteristics for Tsunami Disaster Mitigation at Southern Coast of Java Indonesia, Final Report, The International Centre for Water Hazard and Risk Management (ICHARM) of Public Works Research Institute (PWRI), Japan. Tanaka, N., Sasaki, Y., Mowjood, M.I.M., Jinadasa, K.B.S.N., (2007). Coastal vegetation structures and their functions in tsunami protection: Experience of the recent Indian Ocean tsunami, Landscape and Ecol. Eng. 3, 33-45. Tanaka, N. and Sasaki, Y. (2008) Limitations of Coastal Vegatation in the 2004 Indian Ocean Tsunami and 2006 Java Tsunami. Paper (unpublished), Graduate School of Science and Engineering, Saitama University, Japan.

Lavigne, F., Gomez, C., Gifo, M., Wassmer, P., Hoebreck, C., Mardiatno, D., Prioyono, D.

Tanaka, N., Nandasena, N.A.K., Jinadasa, K.B.S.N. Tanimoto, K., Sasaki, Y. and Mowjood,

and Paris, R. (2007) Field Observations of the 17 July 2006 Tsunami in Java, Natural

(2009) Developing Effective vegetation bioshield for tsunami protection, Civil

Hazard and Earth System Science, 7(1), 177-183. Mardiatno, D. (2008) Tsunami risk assessment using scenario based approach, geomorphological analysis, and geographic information system; A Case Study in South Coastal Areas of Java Island-Indonesia, Dissertation (unpublished), University of Innsbruck, Austria. Ohira, W., Honda, K., and Harada, K. (2012) Reduction of tsunami inundation by coastal forests in Yogyakarta, Indonesia: a numerical study, Nat. Hazards Earth Syst. Sci., 12, 85–95.

Engineering and Environmental Systems 26 (2), 163–180. Thuy, N.B., Tanaka, N., Tanimoto, K., (2011). Tsunami mitigation by coastal vegetation considering the effect of tree breaking, J. Coastal Conservation 16(1), 111-121.


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JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT Public-private partnership as a responsive culture for green management in Bangladesh: A study of natural resources management at Lawachhara national park. Mohammad Nashir Uddin a, Mohammad Hamiduzzaman a* Assistant Professor, Department of Public Administration, Shahjalal University of Science and Technology, Sylhet-3114, Bangladesh, PABX: 880-821-713491, 714479, Fax: 880-821-715257 a

*

Corresponding author : khoka08@yahoo.com

Article history Receivedâ&#x20AC;&#x192; 07.07.2012 Acceptedâ&#x20AC;&#x192; 09.04.2013 Published 05.08.2013

Keywords Public-private partnership Green management Responsive culture Natural resources maintenance

Abstract The study essentially aims to assess the public-private partnership (PPP) as a thriving strategy in natural resources maintenance that largely is dependent on stakeholdersâ&#x20AC;&#x2122; participation forest biodiversity and green management. In an age of climate change and global warming, as a threat due to unavoidable consequences of human activities, natural resource management is now one of the prime concern around the developed and developing countries in terms of creating responsible attitude towards green maintenance. Governments have, by and large, agreed on sustainable employ and conservation of forests in several international forums during the last three decades. In fact, public sector has already proved its inefficiency and ineffective mode to protect natural resources due to lack of skills, human and material resources, and rampant corruption which have encouraged the government to introduce the strategy of PPP. The study was conducted at Lawachhara national park through a sample survey by employing stratified sampling as well as some other tools of data collection incorporating both quantitative and qualitative approaches. It is evident in the study that most of the respondents commonly believe PPP may change the existing ineffective and inefficient mode of natural resources management. Another important finding included that challenges are not possible to overcome unless the active participation of the stakeholders are possible to ensure.

Introduction Maintenance and conservation of forest biodiversity and green resources is one of the foremost contemporary challenges in changing global environment. Almost three billion hectares, half of the original forest coverage worldwide, has already been disappeared; much of it has been ruined during the last three decades. Every year about 16 million hectares of green areas are being destroyed.

Existence of forests is highly important for conserving biodiversity because around 50 to 90 percent of all terrestrial species are living in the forests world-wide (Ahsan, 2009). Scores of these species are threatened, mainly because of habitat loss. Forest bio-diversity is also being threatened by unsustainable industrial logging, energy plant development, mining, building new infrastructure, conversion


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of forest areas to cultivable agricultural land, excessive vegetation removal, and firewood collection etc. Important underlying causes behind these threats lie in illegal logging and poverty of the surrounding population (Khan, 2008). In 1992, ‘the Forest Principles’ were adopted in Rio by the concerned leaderships of the world to protect and maintain bio-diversification for future generations in upcoming centuries. Since then governments from both developed and developing countries have agreed on sustainable use of forests resources and create an authoritative structure to build awareness among people throughout the world, among others the various conventions on Biological Diversity are being observed and the United Nations Forum on Forests (UNFF) is being working in different remote areas. Nonetheless, international negotiations for an international legally binding forest treaty have failed due to fundamental differences in visions regarding conservation of forests and forest resources. In other words, countries without large areas of forests want to conserve the world’s forests, and countries with large forests want to keep the right to decide by them to use their green areas in their own fashion. In recent past, it has been quite evident to notice some fundamental changes in the global institutional framework governing the use of natural resources. Although the development of public institutional arrangements continues, new political spaces for global forest governance have emerged. Characteristic of these arrangements is the engagement of private actors in authoritative decision-making, which was previously the prerogative of sovereign states. Thus, public-private partnership (PPP) has become widely accepted and popular in public sector green management likewise in different sectors and areas. The period of 1990s has experienced the establishment of the PPP as a key tool of public policy process across the world as an outcome of New Public Management (NPM) thought (Osborne, 2007). NPM has shifted the spotlight of management from public service to service delivery and since 1980s been emphasizing on privatization, market mechanism, contestability in the delivery of public goods and services, deregulation, and reinvention of the role of government. At the center of that NPM was a cut-back of public sector expenditure, a delegation of responsibilities to the private sector and fostering voluntary engagement of private sector aiming at providing public goods (Mitchel, 1991). The principles of NPM encouraged the establishment of Public Private Partnerships (PPPs) as a new management tool. Now it (PPP) has become a favorite tool for providing services, to the developing society and conservation of natural resources as well in both developed and developing countries. At the most general level, Public-Private Partnership (PPPs) is generally recognized as the long term cooperative institutional arrangements between public and private sectors to achieve various purposes. Hence, there is a wide range of PPPs with diverse features involved in different activities. Natural resource preservation is highly influenced by decisions of the management throughout the history of natural resource management around the globe. Since the birth of the country Bangladesh, different management decisions have played the key role both on the forests and the forest-dependent people; the Forest Policy of 1994 was a dire necessity to reformulate. Forestry development projects in different sector such as Forestry Sector Project (FSP) have been implemented with a major policy

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shift in favor of a participatory management of the country’s forests and protected areas. Local people and communities participated in developing, protecting and managing forests/plantations in lieu of their rights granted as per participatory benefit sharing agreements (PBSAs) signed between user groups (of participants) and land owning agencies such as the department of Forest (DoF) in case of forest land. The Nishorgo Support Project (NSP) of DoF aims at protecting and conserving the forests and biodiversity of the country’s protected areas (PAs) by building a gainful partnership between DoF and principal stakeholders based on mutual trust, and shared roles and responsibilities for biodiversity conservation and sustainable usage. International organizations, acting as governance agents of states, along with powerful transnational entrepreneurs, are also the chief political drivers of public-private cooperation. Both the theoretical and empirical analysis reveal that public-private institutions do not simply cope up to fill all or even the most urgent governance gaps; instead they tend to focus on areas of cooperation where the interests of powerful state and transnational actors intersect. NSP is, indeed, based on a certain normative framework of conserving the forests and ensuring better livelihood of the forest dwellers. The application of co-management approach for the forest conservation in Bangladesh has witnessed incorporation of the Ministry of Environment and Forest (MoEF) and its subservient Department of Forest of Bangladesh (DoF), certain private and transnational organizations, and the local communities in the project. In addition, the project NSP has been funded by the United States Assistance for International Development (USAID), which implements development programs in conformity with US foreign policy. It works in Kamalgang upazila of Maulvibazar district to conserve the Lawachara National Park (LNP) and support local people. Since PPP is the working arrangement based on mutual understanding between a public sector and any organization or individuals outside of the public sector, the co-management approach which is now in operation under NSP can be defined as PPP initiatives. It is expected, for the last few decades, from the Government of Bangladesh (GoB) is to protect all the natural resources and maintenance of greens of the country. Regarding the issue, the authority has already launched PPP in the field of biodiversity conservation since the last decade in the country where DoF and Nishorgo collaboratively are initiating the conserving activities involving the surrounding inhabitants especially of the national parks. NSP as a pilot project was started on 2003 and ended on 2008. NSP has started its second phase after it has got a new name of Nishorgo Network. It is commonly believed that this project does not successfully perform its role and cannot bring expected changes to the real scenario. The DoF and Nishorgo argued that the NSP is performing well in conservation activities with efficiency and effectiveness. Unfortunately, due to inadequate support from government and local people, it is now under threat. Though a lot of research works have been conducted in Bangladesh on natural resources. Those works failed to provide a good guidance to take dynamic policies to promote authoritative structure. It follows the drawbacks of the previous studies and will afford significant recommendations based on the platform. The outcome of this study may, hopefully, be useful for policy makers for their future policy issues in reshaping the existing thoughts in green management. In an


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age of climate change and global warming, bio-diversity conservation and green management are scorching issues too. Apart from this, no significant study focused on the core villagers. Therefore, it is indispensable to justify whether PPP as a management strategy is useful for maintaining bio-diversity by bringing about a tripartite (government, private sector, and stakeholders) in a developing country like Bangladesh. The main objective of the study is to review the existing PPP programs for natural resource management and chalk out the challenges for responsive administrative culture. Therefore, the study specifies: a) To justify the basis of on-going projects for green maintenance and distribution of functions and responsibilities between public (government) and private sector; b) To explain formal and informal actors and factors affecting initiation and performance of such projects and level of involvement of stakeholders of the specific region; c) To assess the success or failure of the project by comparing achievement with pre-project outcome; and d) To suggest future policy agenda to develop and nurture PPP in line with effective and responsive administrative culture in green management.

Background of the study Conservation of natural resources is highly motivated by the government decisions throughout the times past of resource management around the earth. In Bangladesh, since the birth of the country, different policies and regulations taken by the successive governments have been playing key role in protection and conservation of green resources and the forest-dependent people as well. But successive failures of the governments due to inefficiency and corruption, co-management approach has been taken place to regulate the functioning of the management system to forestry development in Bangladesh broadly reveal the principles of PublicPrivate Partnership (PPP). Participatory forestry projects, supported by donors and local NGOs, have taken to implement in Bangladesh on a large scale during 1980s. Forest Department (FD) of Bangladesh has initiated a community forestry project with the financial support from Asian Development Bank (ADB). In addition, forestry improvement projects, such as Forestry Sector Project (FSP), have been implemented after a major policy shift in favor of participatory administration of the country’s forests and protected areas. Local people and communities participated in developing, protecting and managing forests/plantations in lieu of their rights granted as per participatory benefit sharing agreements (PBSAs) signed between user groups (of participants) and land owning agencies (such as FD in case of forest land). Nishorgo project has some specific components for conserving the unique biodiversity of the protected areas. It broadly aims to reduce the dependence of the forest dwellers on the forest on the one hand, and arrange sustainable alternative means for livelihood for them, on the other. Promotions of eco-tourism and non-timber wood production are among the major constituents, which are being implemented in the protected areas (IRG, 2006: 16). The Study area

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located in West Vanugach Reserve Forest at Komolganj Upazila of Moulavibazar District which covers the area of one thousand and twelve hounded hectares. This national forest reservation project was formed according to the Wildlife (Preservation) (Amendment) Act of 1974. It lies between the Dholai River on the east, the Manu River on the north, and the road from Moulavibazar to Srimangal on the west. Though this park is surrounded by 18 villages of which two, Lawachara and Magurchara are located in the park. It also bordered by cultivated lands and six tea estates that provide a suitable ecosystem services to human and non-human inhabitants. A number of ethnic communities resides within the park and largely depend on it for their livelihood. According to the NSP site reports- about sixty five percent of the local people are poor or very poor – and earn their livelihoods as day laborers and fuel wood collectors. Of the reminder, five percent are rich and thirty percent are middle class. In contrast, among the tribal people, nearly ninety seven percent are poor or very poor, with the highest concentrations of poor found in Lawachara Punji (ninety eight percent), Magurchara punji (ninety six percent), Dolubari Punji (ninety five percent), and followed by Baghmara (fifty eight percent) (NSP, 2004). The main professions of the local people are betel leaf cultivation, lemon and pineapple cultivation, agriculture, fuel wood collection, and different types of day based wage labor. There are different NGOs named BRAC, Association for Social Advancement (ASA), Grmeen bank, Bangladesh Rural Development Board (BRDB) are engaged in providing micro-credit to empower local women. NSP facilitates a total number of 53 forest user groups in 16 villages surrounding LNP through shared responsibility with development partners and NGOs. There are twenty one male forest user groups and rests of them are convened by local female which are playing an imperative role to reduce direct dependency on forest resources. Participatory forestry projects, supported by donors, have been implemented in Bangladesh on a large scale since 1981 when a community forestry project was taken up by Forest Department (FD) with the financial support from Asian Development Bank (ADB). Sectoral forestry development projects such as Forestry Sector Project (FSP) have been implemented with a major policy shift in favor of a participatory management of the country’s forests and protected areas. Local people and communities participated in developing, protecting and managing forests/plantations in lieu of their rights granted as per participatory benefit sharing agreements (PBSAs) signed between user groups (of participants) and land owning agencies (such as FD in case of forest land). The Nishorgo Support Project (NSP) of FD aims to protect and conserve the forests and biodiversity of the country’s protected areas (PAs) by building gainful partnerships between FD and main stakeholders based on mutual trust and shared roles and responsibilities for biodiversity conservation and sustainable use.

Review of relevant literature PPP implies working arrangements based on a mutual pledge between civil sector organizations with any organization outside of the public sector on basis of a co-management approach which is now in procedure under Nishorgo project as it includes both state machineries and non-state organizations for the preservation of


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biodiversity and tropical forest. PPP is essential because it offers resources, information, and skills unavailable for making governance of the public institutions effective. There are different definitions for PPPs viewed from different angles which include ‘as a way of managing and governing organizations, as an institutional arrangement for financial relationship, as a development strategy, and also as a language game.’ The review of different definitions indicates that there is no precise agreed definition of PPP. However, there are common features across the different approaches as well as distinctive features. Several gaps have been identified related to issues of governance, management and policy design of PPPs (Khanom, 2009). Public-private partnerships have been implemented throughout the world since the 1970s with mixed results mainly due to the lack of long run commitments from governments and other parties involved, lack of scientific understanding regarding clear short term and longterm potential biophysical and socio-economic, policy and legal consequences, and lack of trust between the partners. It presents a Regional Irrigation Business Partnership (RIBP) model, which is capable of efficiently utilizing research output and government policies for sustainable public–private irrigation planning and investment as well as emphasizes on the principle that sharing risks, rewards, and responsibilities coupled with sufficient investment incentives motivating actors in management to generate better outcomes for the environment (Khan & Mustaq, 2009). The project under this study is the first of its kind in Bangladesh based on public private partnerships strategy and involving local stakeholders as beneficiaries for natural resources management. The key goal of social forestry is to involve the poor beneficiaries and then it is possible to measure the degree of attainment of this goal by collecting socioeconomic data before and after project implementation the degree of inclusion of the poor in social forestry using ex-post data. Longitudinal analysis is approximated through the use of ‘slow change’ socioeconomic variables and through logistic regression (Sunderlin, 1997). The conservation of biodiversity requires a significant commitment by governments, industry sectors and the wider community to encourage cultural change across community and industry sectors which ensures a long-term balance between sustainable land management and biodiversity conservation. At the regional level viable biodiversity conservation requires a range of management strategies that may include the establishment of statutory protected areas, a range of off-park conservation management measures and achievable guidelines for ecologically sustainable land management at the landscape scale. Monitoring the performance of protected areas in achieving biodiversity conservation requires a commitment by government to facilitate involvement and participation of the wider community. In this regard, four Australian case studies discussed how public private conservation partnerships are integrating sustainable land management and biodiversity conservation at the regional level (Thackway, 1999).

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Operationalization of key concepts PPP as a strategy Although the nature of partnership is often viewed differently, the key distinguishing features of public-private partnerships is the transfer of risk between partners especially in development projects. The appropriate allocation of risk is the big question to answer, but always necessary to the success of the partnership. PPP maintains the relationship among government agencies and private or nonprofit contractors that should be formed when dealing with services or products of highest complexity. In comparison to traditional contractor- customer relationships, they require radical changes in the roles played by all partners. Accordingly, in this study PPP is defined as a management strategy between DoF and NSP for efficient green maintenance at Lawachhara national park. Green management One of the major current global environmental challenges is the conservation of biodiversity and green resources. Deforestation and forest degradation is continuous despite international and national governmental agreements on forest conservation and afforestation. In recent years private regulation in the international forest governance system has increased. Partnerships between governments, business, NGOs and/or civil society have been developed. This focuses on natural resource management mechanism and initiatives that attempts to conserve the green bio-diversity of the forest and ensure the stakeholders’ interests as well.

Data and materials The nature of the study is of a scientific evaluation of project mainly by using the sample survey method along with an exploratorydescriptive design. Inductive method and theoretical approach were simultaneously used to get empirical findings which resulted from collected data. In addition, methodological triangulation of mixed method was deployed (survey with qualitative and quantitative analysis). The study was conducted on a project which contains the quality aspect of the research side by side, quantitative measurement of the data. It examined the challenges of PPP to boost up responsive culture at forest preservation and spontaneous attachment of people’s with the project. The study has applied a stratified sampling method to 4 core villages (locally termed as Punji) named Langurpur (18 respondents); Dolubari (10 respondents); Lawachara (13 respondents); and Magurchara (15 respondents). Then, a purposive sampling method was employed in the study to get information from ultimate sample units in the specific villages. A self-administered semi-structured questionnaire (including both open and close ended questions) was used to collect empirical data from 56 respondents: one from each family. Depending on the nature and objectives of the study and availability of resources, the study has also collected data through most common methods of data collection techniques: community mapping, transect walks, household interviews, focus group discussion, informal discussion, and personal observation. The data was processed through editing to improve their quality and coding to convert them to the numerical


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form representing attribute of variables. For the sake of quantitative analysis, upgraded Statistical Pragramme for Social Science (SPSS) software used to get appropriate combination of data.

INDEPENDENT VARIABLES Department of Forest (DoF) Transfer of technology Information dissemination Legal framework Financial regulation Appropriate usage of central resources

Nishorgo Support Program (NSP) Stake holder management Biodiversity conservation Socio-economic development Provide Alternative Income Generating (AIG) activities

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This study has formed a conceptual framework on the basis of the reviewing the relevant literature with defined objectives. The following variables have considered in analyzing data.

DEPENDENT VARIABLES

Green Management through PPP Using PPP Benefit sharing Structural decentralization

Figure 1. Conceptual framework of the study.

Findings and discussion Qualitative analysis Formation of protected area (PA) This means that every PA has a management plan that guides and controls the management of PA resources, the conservation of biodiversity, the uses of area and the development of Park facilities. This management plan provides development programs with framework activities and guidelines for sustainably managing the Lawachara National Park and its interface landscape. The study found that PPP can make sure adequate protection to the Park for the preservation of its ingredient biodiversity. Main activities carried out to achieve this objective include updating forest cover and interface landscape maps; demarcation of park boundaries and management zones; effective protection through control of illegal felling, forest fires and poaching; forest grazing; and stopping encroachment of the park lands. All the peripheral boundaries of the notified park area were identified, surveyed and marked on the ground to engage the people living there; support has been provided by DoF staff by gainfully associating local stakeholders and NGOs on a comanagement principle basis. Forest villagers are particularly helpful in forest protection efforts through joint patrol and intelligence sharing. Conflict resolution mechanism A conflict resolution mechanism has been established as part of co-management council/committee because park level conflicts arises due to forest extraction, forest land encroachment, forest land disputes, forest offences, forest grazing and local level politics. In case of organized smuggling an effective checking of tree felling and poaching require concerted efforts from DoF by using modern

equipments, arms and ammunition (guns, revolvers, etc.), and transport facilities to combat organized smugglers and poachers. This also requires setting up special protection force by augmenting the presence of DoF field staff, if necessary backed by Border Guard Bangladesh (BGB) staff. Communication network has been strengthened by installing a radio communication network and by mobilizing more walky-talkies, mobile telephones and vehicles. Ensuring ecological succession for wildlife conservation This project implements program introduced to maintain ecological succession in constituent forests by providing effective protection against biotic interference; to develop natural forests and plantations as good habitat favoring wildlife; to conserve the forest resources including the constituent biodiversity; and to establish appropriate co-management methods and practices through stakeholdersâ&#x20AC;&#x2122; consultation and active participation. The long-term management aim of maintaining the maximum possible area under forest cover along with its constituent biodiversity in the best possible condition achieved by zoning the park area and surrounding landscape such that: i) the areas of highest conservation value (forests and/or old plantations) are protected, regenerated and managed towards natural forest composition and structure, particularly in the core zone; and ii) the areas used to provide benefits to local people through sustainable use of forests are defined, and high impact activity areas, mainly as interface landscape zone. Zoning and sub-zoning for effective habitat management The core zone has the highest conservation value followed by interface landscape zones which of course are important for biotic


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life; these two broad zones are further subdivided into specific subzones. All of the total notified area of the park is designated as the core zone, which is sub-divided into 5 sub-zones: • Ecosystem sub-zone All the well-stocked areas are covered under this sub-zone, where main management objective is to protect and maintain remaining vegetation in good stocking and encourage natural regeneration to gradually bring back natural forests. More than half (57%) of the notified park has been designated as ecosystem management sub-zone covering existing forests/plantations areas with good biodiversity value. Subsidiary silvi-cultural operations are carried out whenever necessary to encourage natural regeneration. • Habitat management sub-zone This sub-zone is subject to management/manipulation of habitat for key wildlife species through selective management interventions. Habitat improvement works include rehabilitation of degraded areas, enrichment planting of fruit bearing species and palatable grasses, replacement of exotics by gradual canopy opening, maintenance of glades and water holes, soil/water conservation in identified microwatersheds and eradication of weeds. • Sustainable use sub-zone Nearly one-quarter of the notified park area is designated as sustainable use sub-zone comprising forests/plantations which can sustainably be used by local people by entering into participatory conservation and benefits sharing agreements. Short and long rotation plantations including those raised under FSP as buffer plantations is managed under benefit sharing agreements. However, these plantations are not clear-felled but instead are managed under selection felling (mainly of exotic species) so that the area can be naturally regenerated to ultimately include over the period in core zone as mixed forests. The traditional use of assigned forests for betel leaf cultivation by forest villagers of Magurchara and Lawachara included in this zone. • Village use sub-zone The habitations and cultivations with respect to Forest Villages (Magurchara and Lawachara) are included in village use sub-zone. All of the six Tea Estates (Fulbari, Khaichara, Jakchara, Gilachara, Noorjahan and Bharaura) surrounding the Park is typically very important part of the interface landscape zone of Lawachara Park. Some parts of these Tea Estates have so far not been brought under tea cultivation, and have over the period developed as unmanaged secondary vegetation. They provide additional wildlife and plant habitat as a transition. • Intensive use sub-zone Livelihood programs are taken for landscape development and establish proper linkages with appropriate livelihoods programs and other projects/initiatives that will reduce biotic pressure on forests. Up-scaling of skills are taken up for generating value additions through capacity building of local stakeholders. LDF is used to provide finance for the members of co-management groups and committees, and their federations are encouraged to set up microenterprises,

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particularly forests-based, to generate value additions locally. Though several programs have undertaken, maximum respondents (around 84 percent) were partially benefited from the programs due their limited involvement. The benefits from eco-tourism are also ploughed back locally for the development of local communities and the park. Networking with relevant NGOs acting in the landscape zones is established for rendering rural development services to local stakeholders. Benefit sharing Different fringe benefits have been initiated to support the park administration during the plan implementation period. Built facilities have been developed at Park HQ including the existing Lawachara Beat Office and BFRI facilities; rest stop/picnic area near Janakichara Nursery; Guard Bhagmara Camp near eastern Park boundary; and Chautali Beat Office. At each location, the design standards for both renovations and new construction are based on sound environmental considerations. Most of respondents (around 81 percent) agreed that the collective program was appropriate for facilitating themselves. Conservation research activities Another important program is providing tools/mechanisms for a better understanding of the park and its functions in sustainably managing forests and biodiversity. Keeping in view the funds scarcity for conservation research, appropriate collaboration and networking with relevant Bangladeshi research organizations are maintained. Conservation research includes diverse types of flora and fauna, status of endangered species, wildlife behavior, socioeconomic issues, silvi-cultural aspects, applied biological research, ecological issues, human-animal conflicts, impact of anthropogenic pressures on natural systems, etc. The results/findings of research studies adequately disseminated for their proper utilization by DoF field staff. Research dissemination and usage methods are standardized and circulated among DoF staff. Useful research outputs include in annual development plans of DoF for their field implementation. Conservation training programs Cross-country exchange visits and training are also arranged to learn from relevant experiences from similar projects being implemented in different Asian countries under PPP program. A working group is being supported under NSP for preparing and disseminating co-management best practices and lessons learned. Potential organizations for establishing and maintaining professional contacts include FAO (Bangkok office), RECOFTC (Bangkok), ICIMOD (Kathmandu), WII (Dehra Dun), CIFOR (Bogor), etc. There are great necessities of imparting conservation training to the DoF field staff responsible for managing Lawachara Park. Presently they do not have any specialized capacity for imparting PA management training, although adequate forestry training infrastructure has been developed under different donor funded projects. Of many forestry subjects only one paper relates to wildlife management being taught to cadre officers at Forest Academy, Chittagong. Other subordinate DoF staffs do not receive any significant training on PA management, although wildlife management is one of the many taught subjects. There is lack of faculty, particularly conservation at ecosystem and landscape levels by involving stakeholders. Some forest officers have


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undergone overseas training on wildlife and PA management but are presently working outside WNCC, thereby underutilizing their expertise. An exhaustive conservation training plan, covering both in-country and overseas training, has been developed under NSP and implemented over the project period. Quantitative analysis Around three-fourths (about 79%) of the participating respondents belong to male (Table 1); of them almost all people are between 25 to 45 years old (Table 2). This is because these ranges of people are able to participate in the work involved within the project area. Regarding the occupational engagement, around one-third of the respondents (32%) were engaged in betel leaf cultivation; and next to that who were engaged in lemon and fruits cultivation; all of the women respondents (21%) are homemakers, while others were engaged in fisheries (about 11%), livestock rearing, handloom, poultry, and banana cultivation (Table 3).

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Table 4. Educational status before participation in the project Year of schooling of the respondents 0≤ 5 10 15≥ Total

Number of respondents

Percentage

37 17 1 1 56

66.1 30.3 1.8 1.8 100.0

Cumulative percentage 66.1 96.4 98.2 100.0

Source: Field data Table 5. Educational status after participation in the project Year of schooling of the respondents 0≤ 5 10 15≥ Total

Number of respondents

Percentage

8 45 2 1 56

14.3 80.3 3.6 1.8 100.0

Cumulative percentage 14.3 94.6 98.2 100.0

Source: Field data Table 1. Gender of the respondents Types of respondents

Number of respondents

Percentage

Female Male Total Source: Field data

12 44 56

21.4 78.6 100.0

Cumulative percentage 21.4 100.0

Table 2. Age distribution of the respondents Age of respondents

Number of respondents

Percentage

25 <age ≤ 35 35 <age ≤ 45 age > 45 Total

6 12 38 56

10.7 21.4 67.9 100.0

Cumulative percentage

Table 6. Income before participation in the project

10.7 32.1 100.0

Income level of the respondents (BDT)

Source: Field data

Table 3. Types of the respondents according to occupational status Occupational status of the respondents

Number of respondents

Percentage

Betel leaf cultivation Lemon & Fruits cultivation Hand loom Fisheries Poultry Livestock rearing Banana cultivation Homemakers Total

18 15 1 6 1 2 1 12 56

32.1 26.8 1.8 10.7 1.8 3.6 1.8 21.4 100.0

Similarly, in case of income level before participation in the project, around 66% belonged to the income group ranging from BDT 35,000 and 55,000 and the highest income level was BDT 120,000 (around 2%). After participation, the minimum income level rose to BDT 45,000 and corresponding figure from BDT 45,000 to 55,000 decreased to 19% which means after project intervention around 81% people went up to BDT 55,000 above and the highest income level rose from BDT 120,000 to 140,000; whereas those 66% people could enter into income ranging from BDT 45,000 to BDT 65,000 (Table 6 & 7).

Cumulative percentage 32.1 58.9 60.7 71.4 73.2 76.8 78.6 100.0

Source: Field data

The educational background shows that most of the respondents (66%) were completely illiterate while almost all (97%) rarely completed primary level. After they have participated in the project, the corresponding illiteracy level noticeably declined to 14% (Table 4 & 5).

≤35000 to 45000 45000 to 55000 55000 to 65000 65000 to 75000 80000 to 12000 120000≥ Total

Number of respondents

Percentage

15 22 6 6 6 1 56

26.8 39.3 10.7 10.7 10.7 1.8 100.0

Cumulative percentage 26.8 66.1 76.8 87.5 98.2 100

Source: Field data

Table 7. Income after participation in the project Income level of the respondents (BDT) ≤45000 to 55000 55000 to 65000 65000 to 75000 75000 to 85000 85000 to 95000 95000 to 140000 140000≥ Total Source: Field data

Number of respondents

Percentage

24 13 13 1 2 2 1 56

42.8 23.2 23.2 1.8 3.6 3.6 1.8 100.0

Cumulative percentage 42.8 66.0 89.2 91.0 94.6 98.2 100


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In this regard, a significant portion of the respondents believe that their living standard had been changing due to the programs initiated in green preservation. Almost all respondents somehow depended on NSP AIG project of who around 16% were fully dependent. But most of the respondents believed that the success rate of the project activities is very high (about 81% agreed). Therefore, it can be said that if the rest of the people (around 84%) could be incorporated into the project coverage, the rate of fruitfulness of the project could have been enormous (Table 8 & 9). Table 8. Extent of dependency on the NSP AIG projects Pattern of dependency of respondents

Number of respondents

Percentage

9 47 56

16.1 83.9 100.0

Fully Partially Total

Cumulative percentage 16.1 100

Source: Field data Table 9. Success of DoF-NSP synergic efforts on LNP Response of respondents No Yes Total

Number of respondents

Percentage

11 45 56

19.6 80.4 100

Cumulative percentage 19.6 100

Response of respondents

Number of respondents

Percentage

11 45 56

19.6 80.4 100

No Yes Total

Cumulative percentage 19.6 100

Source: Field data

The project indirectly addressed another area namely hunting behavior of the residents which is a threat to conserve biodiversity in the forest area. Before the project intervention, almost all people were engaged in hunting; of who around 70% fully depended on hunting. However, after the project started its activities by involving local people, around 43% people completely abstained from hunting (Table 12 & 13). Table 12. Hunting before participation in the project Response of respondents

Number of respondents

Percentage

39 17 56

69.6 30.4 100.0

Fully Partially Total Source: Field data

Response of respondents

One of the important aspects of the project was that it involved itself in the areas the local people were engaged in to earn their living. Around 18% contribution was in each sector of livestock rearing, fisheries and nursery along with patrol group (about 21%); others involved horticulture (about 9%), handloom and CMC (7% each), and poultry (around 2%) (Table 10). Table 10. Types of participation in NSP AIG project

Livestock rearing Fisheries Nursery Horticulture Hand loom Poultry Patrol group Co-management Councils (CMC) Total

Table 11. Appropriateness of FD-NSP collective approaches

Cumulative percentage 69.6 100.0

Table 13. Hunting after participation in the project

Source: Field data

Pattern of Participation

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Number of respondents

Percentage

Cumulative percentage

10 10 10 5 4 1 12

17.9 17.9 17.9 8.9 7.1 1.8 21.4

17.9 35.7 53.6 62.5 69.6 71.4 92.9

4

7.1

100.0

56

100.0

Source: Field data

This type of program intervention has been welcome by the stakeholders. The project was also highly supported by the stakeholders. More than 80% people agreed on the appropriateness of the public private partnerships between DoF and NSP to perform joint activities to run the project (Table 11).

Number of respondents

Percentage

24 32 56

42.9 57.1 100.0

No hunting Partially Total Source: Field data

Cumulative percentage 42.9 100.0

Another important factor that prevented locals from remaining abstain from doing detrimental activities to forest biodiversity and green resources is their household saving which was possible because of income generating activities initiated by the project. Before launching the project around 52% people could not save a single coin after mitigating all sorts of household expenses. But after being involve in the project around 64% could afford to save money ranging from BDT 1,000 to 10,000. Different programs have been introduced for generating income of the forest villagers and it is observed that their savings have increased during this period. Table 14. Savings before participation in the project Level of savings (BDT) 0â&#x2030;¤ 1000 to 3000 3000 to 5000 5000 to 10000 10000â&#x2030;Ľ Total Source: Field data

Number of respondents

Percentage

29 2 20 3 2 56

51.8 3.6 35.7 5.3 3.6 100.0

Cumulative percentage 51.8 55.4 91.1 96.4 100.0


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Table 15. Savings after participation in the project Level of savings (BDT) 0≤ 1000 to 3000 3000 to 5000 5000 to 10000 10000≥ Total

Number of respondents

Percentage

19 1 11 21 4 56

33.9 1.8 19.6 37.5 7.1 100.0

Cumulative percentage 33.9 35.7 55.4 92.9 100.0

Source: Field data

Before the initiation of the project people from among stakeholders were self-employed like hunting, collection of resources from the forest as well as their own profession. During their involvement in the project around 29% got full employment and rest of the stakeholders got partial employment (only 1 to 3 months unemployed in all the year round) (Table 16). Table 16. Unemployment period around the year (in months) Year of unemployment 0 1 2 3 Total

Number of respondents

Percentage

16 7 10 23 56

28.6 12.5 17.9 41.1 100.0

Cumulative percentage 28.6 41.1 58.9 100.0

Source: Field data

The study also found that in spite of the project intervention, still some problems are remaining to successfully manage and maintain the task of conservation of biodiversity and green resources at the park area; this mainly because a significant portion of stakeholders (around 16 percent) could not be brought under the project activities. Those challenges mainly involve: illicit feeling of the stakeholders (about 31%); encroachment (around 27%); and disagreement with DoF officials (about 18%); another important problem is land related conflict between the authority and local people (9%) (Table 17). Table 17. Existing major problems at the forest Challenges faced by respondents Illicit feeling Encroachment Illegal hunting Land related conflict Disagreement with officials Total

DoF

Number of respondents

Percentage

Cumulative percentage

17 15 9 5

30.4 26.8 16.1 8.9

30.4 57.1 73.2 82.1

10

17.9

100.0

56

100.0

Source: Field data

Concluding remarks and policy recommendations The study of on PPP and the Natural Resource Management Procedure demonstrate a guide to improve our socio-economic status and bring positive changes without hampering the environment. The

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livelihood programs applied in the forest conservation project have provided the necessary essentials to analyze environmental project. It also suggests that the forest conservation project based on ‘comanagement’ approach or broadly PPP or in other forms might not be as harmless as it appears. And the officially declared values, norms, ideas which provide for the governance of such project are perhaps for something else in many cases. Thus one must not be misled in his/her judgment by only focusing on such normative framework of governance apparently based on certain universal values like transparency, participation, accountability etc. To understand the complex dynamics of such project, it is necessary to dig into the intrinsic relationship of the actors to assess who is influencing who and for what reason. Particularly attention is needed to focus on the political and economic interests that bind the actors together within an ideological framework and thus functioning and interacting with each other cutting across both state and non-state levels. NSP (provides a description of the park, an assessment of biodiversity, resources protection and management, human interactions, forest resources use patterns, interface landscape situation, past biodiversity management and practices, etc) with a documentation of main findings and issues. PPP should be based on a co-management approach comprising: i) Protection and conservation of all remaining ecosystems including natural forests and constituent biodiversity in the park; ii) Conversion of monocultures of exotic tree species into natural and manmade regeneration of indigenous species by gradually opening the canopy; iii) Identification of interface landscape and development of comanagement agreements (and linking PA conservation with benefit sharing arrangements) with key stakeholders to reduce ongoing habitat damage by helping them achieve sustainable livelihoods through participatory forest use and alternative income generation activities; and iv) Provision of support to better administration and management of the park including capacity development, infrastructure, training, and wider extension and communication. It is important to have sufficient flexibility needed for making required modifications and adjustments to management activities within the limits set by overall goals and objectives. Hence, although five year schedules of activities and inputs are presented, it is recommended that needed changes in timing, inputs and outputs will be reflected in annual work plans to be prepared by park managers every year. The strategic programs and priorities (comprises prescriptions for future development and management of the park with detailed guidelines) for a sustainable National Park. The plan, as a guide to development interventions, will be useful for the PA managers, planners, decisionmakers, researchers, donors and other stakeholders including local forests dependent communities. The scope, timing and relative emphasis on specific activities may be modified by the park managers on the basis of experience, success and progress as the plan is implemented. The overall levels of inputs indicated under each activity will be maintained to the extent possible in order to ensure reasonable success in management implementation.


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References Ahsan, M. M. I. (2009). Perception of tourism by indigenous communities living in and

Mitchell, R. K., Agle, B. R. & Wood, D. J. (1997). Toward a theory of stakeholder identification

adjoining Lawachara national park. Sylhet: Office of the assistant conservator of

and salience: Defining the principle of who and what really counts. cited in: Academy

forest, Wildlife management and nature conservation division. Bovaird, T. (2004). Public-private partnerships: From contested concepts to prevalent practice. International Review of Administrative Sciences, 70(2), 199-214.

of Management Review 22(4), 853 - 888. NSP. (2004). Site strategy for Lawachara National Park, Nishorgo Support Project: Dhaka, Bangladesh.

Khanom, N.A. (2009). Conceptual issues in defining public private partnerships (PPPs).

Sunderlin, W. D. (1997). An ex-post methodology for measuring poor people’s

Conceptual paper for Asian business research conference, Faculty of business and

participation in social forestry: An example from Java, Indonesia. Agro Forestry

government, Canberra: University of Canberra. Khan, S. & Mushtaq, S. (2009). Regional partnerships to assist public–private investments in irrigation systems. Journal of Agricultural Water Management, 3, 839–846. Khan, M.T. (2008). The nishorgo support project, the Lawachara national park, and the Chevron seismic survey: forest conservation or energy procurement in Bangladesh? Journal of Political Ecology, 17, 68-78. Klijn, E.H. & Teiseman, G. R. (2000). Governing public private partnerships: Analyzing and managing the process and institutional characteristics of public private partnerships. cited in Osborne S.P. (2007). Public Private Partnerships: Theories and Practices in International Perspectives, London: Routledge.

Systems, 37, 297–310. Thackway, R. & Olsson, K. (1999).

Public-private partnerships and protected areas:

Selected Australian case studies. Journal of Landscape and Urban Planning, 44, 8797. IRG (2007). Nishorgo Support Project 5th Year Work Plan: June 1, 2007 through May 30, 2008, Dhaka: Nishorgo Support Unit, available at http://www.nishorgo.org/files_pdf/Year_5_Work_Plan_ver10.pdf, accessed on 15 April 2012.


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JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT Irrigation management strategies for winter wheat using AquaCrop model M. H. Ali ab*, I. Abustan b a

Agricultural Engineering Division, Bangladesh Institute of Nuclear Agriculture (BINA), Mymensingh 2202, Bangladesh

b

School of Civil Engineering, University Sains Malaysia

* Corresponding author : hossain.ali.bina@gmail.com, mha_bina@yahoo.com

Article history Received  24.01.2013 Accepted  20.05.2013 Published 02.09.2013

Keywords Wheat AquaCrop Efficient water use Water productivity Irrigation management

Abstract Many regions of the world face the challenge to ensure high yield with limited water supply. This calls for utilization of available water in an efficient and sustainable manner. Quantitative models can assist in management decision and planning purposes. The FAO’s newly developed crop-water model, AquaCrop, which simulates yield in response to water, has been calibrated for winter wheat and subsequently used to simulate yield under different sowing dates, irrigation frequencies, and irrigation sequences using 10 years daily weather data. The simulation results suggest that “2 irrigation frequency” is the most water-efficient schedule for wheat under the prevailing climatic and soil conditions. The results also indicate decreasing yield trend under late sowing. The normal/ recommended sequence of irrigation performed better than the seven-days shifting from the normal. The results will help to formulate irrigation management plan based on the resource availability (water, and land availability from previous crop).

Introduction By 2030, the world economy is projected to double and the world population to increase by 1/3rd (Gurria 2009). To feed these people, crop production should be increased by 33%. The agricultural sector needs particular attention, as it accounts for about 70% of water use worldwide. In addition, competition for water resources by domestic, industrial, and agricultural uses – or between users and environmental needs are increasing. Another era on water resource is the pollution of water from agricultural fields, domestic use and industrial areas. Climate change is expected to worsen the situation of water availability (both in temporal and spatial scale). To fulfill the

demand of agricultural sector, overexploitation of groundwater has been occurred in many parts of the world. In Bangladesh, a substantial amount of rainfall occurs, but this is seasonal and concentrated during few months of the year (summer months, May to September), leaving the other months dry. The source of irrigation water for dry-season cropping is groundwater. Excessive use of groundwater is seriously threatening the sustainability of groundwater and, consequently the agricultural systems that rely on it. Considering the above facts, sustainable use of water resources and sustain the crop productivity under limited and variable water


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availability are clearly urgent. Strategic options for achieving sustainable agriculture in the country include improving water productivity in crop production, cultivation of low water-demanding crops, and adoption of water saving irrigation scheduling. Wheat, a low water demanding crop, shows a promising alternate option of rice (a high water demanding crop) cultivation during dry, winter period (Ali et al. 2007). For wheat cultivation in Bangladesh, recommended irrigation schedule (time and amount) is available for optimum sowing date. The recommended sowing date often has to shift due to land availability from previous crop (as multiple crops are grown in a year) and climatic calamities. Besides, optimum irrigation time/interval often cannot be materialized due to unavailability of irrigation water. Thus, the recommended irrigation schedule becomes questionable

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and uncertain in producing desired yield under diverse conditions. Simulation models capable of quantifying the effects of water on yield can be a worthy tool for evaluating different irrigation management options. The AquaCrop model, developed by FAO, is a water-driven simulation model to simulate the yield of field crops. AquaCrop has been validated and tested on barley (Arya et al. 2010), teff (Eragrostis tef) (Aaraya et al. 2010), potato (Patel et al. 2010), maize (Heng et al. 2009), cotton (Farahani et al. 2009; Garcia-Vila et al. 2009), quinoa ( Geerts et al. 2009), and wheat (Andarzian et al. 2011). In this study, we calibrated the AquaCrop model for winter wheat crop, and used the calibrated model to simulate wheat yield under different sowing dates, irrigation frequencies, and irrigation sequences; with a view to help develop better irrigation management plan under the above mentioned diverse situations.

Figure 1. Long-term average temperature, ET0, and rainfall for the wheat growing period.


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Materials and methods About the model AquaCrop AquaCrop version 3.1 was used in this study. It was developed by the Land and Water Division of FAO. AquaCrop predicts biomass production through simulation of crop green foliage. From biomass, grain yield is predicted using harvest index. Detail principles, methods, and capabilities of AquaCrop can be found in Raes et al. (2010) and Steduto et al. (2009). Field experimental Field experiment was conducted for three consecutive years (200203, 2003-04 and 2004-05) at the experimental farm of Bangladesh Institute of Nuclear Agriculture (BINA), Ishurdi, Bangladesh (coordinates are: latitude 240 06’ N, longitude 890 01’ E). The climate of the region falls under humid sub-tropic having summer dominant rainfall. Long-term average temperature, reference evapotranspiration, and rainfall pattern of the experimental site are depicted in Fig.1. The wheat growing period, November to March, is characterized by drywinter. Experimental details have been well documented elsewhere (Ali 2008; Ali et al. 2007). Here, only a brief description is given. The field soil texture was silty loam. The field capacity and wilting point of the field soil were 45% and 19% (by volume), respectively. The wheat cultivar was a semi-dwarf variety (average height is 88 cm). It is a 120-130 days cereal crop and suits the prevailing climate of winter season. Details of irrigation treatments are given in Table 1. The experimental design was a randomized complete block (RCB), with four replications. Table 1. Details of irrigation treatments Irrigation at growth phase †

Treatment CRI

Jointing to shooting

Booting to heading

Flowering to soft dough

0

0

0

0

T2

1

1

1

1

T3

0

1

1

1

T4

1

0

1

1

T5

1

1

0

1

T6

1

1

1

0

T7

1

0

1

0

T8

0

1

0

1

T9‡

1

1

1

1

T1

† ‘1’ indicates one irrigation at this stage, and ‘0’ indicates no irrigation (deficit). ‡ in addition to irrigation at each stage, irrigation was given when total available moisture within the root zone dropped below 50 %.

Soil moisture was measured in one replication by gravimetric method and/or neutron moisture meter. Access tubes were installed at the center of the plots. Measurements were taken at soil depths of 15, 30, 45, 60, 75, and 90 cm at sowing, at every growth stage, and

at physiological maturity. Soil moisture measured by gravimetric method (weight basis) was converted into volumetric proportion by multiplying with bulk density. Irrigation water was applied in the unit plots using hose pipe by calibrating the rate with large bucket of known volume. Evapotranspiration (ET) was calculated using the general water balance equation (as there was no runoff): ET =

I + P ± ΔW

(1)

Where, ET is crop evapotranspiration, I is irrigation water applied, P is effective rainfall, ΔW is change in soil moisture storage in the soil profile. The grain and straw yield were adjusted to 12 % moisture using the following equation: 100+mT Y adj = Yi x 100+mi

(2)

Where, mi the initial moisture content, Yi the initial yield (at mi moisture content), mT the targeted moisture content (here, 12 %), and Yadj the adjusted yield (at mT % moisture content). Calibration of AquaCrop The experimental data of the year 2002-03 was used to calibrate AquaCrop model. The calibration was performed against grain and biomass yield, and ET; for both well watered and water deficit conditions. Climatic data files were prepared with measured data except the CO2 concentration, which was taken as the default value of AquaCrop (Manua Loa Observatory records in Hawai, USA). Average value of three years calculated harvest index (HI) and crop coefficient (Kc) were used (and kept constant) in calibration process. The Model was run keeping the measured/observed data constant. Other crop, soil and growth parameters were initially gauged from literature value adjusting with crop cultivar and climatic condition. The parameter values were changed systematically realizing their practical range, literature value, suggested conservative parameters, and local conditions (crop characteristics, crop duration, soil and climatic condition). Special care was taken to the sensitive and moderately sensitive parameters of AquaCrop as noted by Greets et al. (2009). Validation/Evaluation of AquaCrop Independent data sets (for the year 2003-04 & 2004-05) were used to evaluate the performance of AquaCrop model. All the calibrated parameters (along with average HI and Kc values) were used in simulation process. The weather, irrigation, and initial condition of the particular years were used as input for simulation purpose. In evaluation of simulated output, graphical and statistical comparisons were made. The following statistics were used to indicate overall model performance: Mean bias or error (ME), mean absolute error (MAE), root mean square error (RMSE), and relative error (RE) (Loague and Green, 1991).


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Simulation study After calibration and validation/evaluation of the AquaCrop model, it was used to simulate yield for different irrigation treatments under different sowing dates and irrigation sequences. Weather input data Average of ten years daily weather data were used as input to calculate reference evapotranspiration and other input file for model run, which is fairly representative of the area. The data were collected from meteorological department, which is 1.5 km apart from the field side. For rainfall file in AquaCrop, long-term (30 years) monthly total rainfall was used, as the monthly rainfall is more representative than the daily values (which are erratic and uneven). Irrigation options Yields are simulated under different irrigation frequencies and sequences for different sowing dates (Table 2). Irrigation frequency options are: 2 and 3 irrigations; irrigation sequences are: normal/ recommended sequence, and 7 days shifting (late) from the normal date; sowing dates are: 7, 15, and 23 November.

109

air temperature, TB is a base temperature below which development is assumed to cease, m is date of sowing, n is target date up to which we want to calculate GDD, and Δt is the time step in days. The TB for the entire period (from sowing to maturity) is considered as 50C (Ali et al. 2004). After preparing the input files (according to the treatments mentioned in Table 2), the model AquaCrop was run to obtain the simulated output.

Results and Discussions Calibration of AquaCrop Figure 2 presents the visual goodness of fit of the model calibration at full irrigation (4 nos.) and deficit irrigation (2 nos). The coefficient of determination, index of agreement, relative error, and root mean squared error were 0.99, 0.994, 7.4%, and 0.45, respectively; which indicates that the model fitted the observed data set very well. Calibrated parameters of crop growth, morphology, and other soil & management aspects are tabulated in Table 3.

Table 2. Treatment combinations for simulation study Sowing date D1: Nov.7

D2: Nov.15

D3: Nov.23

Irrigation frequency N1 – 2 nos N2 – 3 nos N1 – 2 nos N2 – 3 nos N1 – 2 nos N2 – 3 nos

Irrigation sequence (days after sowing)

Combination

Treatment name

S1: 19, 52

D1-N1-S1

T1

S2: 26, 60

D1-N1-S2

T2

S3: 19, 39, 56

D1-N2-S3

T3

S4 : 26, 44, 64

D1-N1-S4

T4

S1: 22, 62

D2-N1-S1

T5

S2: 29, 69

D2-N1-S2

T6

S3: 22, 45, 65

D2-N2-S3

T7

S4: 29, 52, 72

D2-N1-S4

T8

S1: 22, 62

D3-N1-S1

T9

S2: 29, 70

D3-N1-S2

T10

S3: 22, 46, 66

D3-N2-S3

T11

S4: 29, 53, 74

D3-N1-S4

T12

Irrigation frequencies are based on the general recommendation for deficit irrigation in wheat. In case of irrigation sequences, the 1stsequence (S1) is based on the average degree-days (GDD) (which correspond to general recommendation of irrigation interval for different frequencies) for normal sowing date, which is 15 November. The degree-day is chosen for fixing sequence because, due to change in sowing date, the accumulation of thermal unit will vary accordingly (Ali et al. 2004). For each sequence, the GDD is translated again into Julian day (in terms of days after sowing) for implementation/ preparation of irrigation file for model run. The GDD is calculated following Nuttonson (1955): n

GDD = Σ (T A - T B) Δt i=m

(3)

where, TA is the average of daily maximum (Tmax) and minimum (Tmin)

Figure 2. Observed and simulated yield and biomass in calibration process.


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Table 3. Calibrated soil and crop parameters for wheat. Parameters

Growth and morphology

Production

Value

Way of determination *

Initial canopy cover, %

1.2

E

Maximum canopy cover, %

98

E

Canopy expansion, %/day

14.4

E

Canopy decline coefficient , %/day

10.7

E

Shape factor for stress coefficient for canopy expansion

1.7

E

P_upper threshold for canopy/leaf expansion

0.10

E

P_lower threshold for canopy/leaf expansion

0.45

E

P_upper threshold for stomatal closer

0.55

E

Shape factor for stomatal closure

0.2

E

P_upper for pollination

0.65

E

P_upper threshold for canopy senescense

0.5

Shape factor for stress coefficient for canopy senescense

0.4

Maximum effective rooting depth, m

0.60

F

Shape factor for root expansion (-)

1.7

E

E

Maximum evapotranspiration crop coefficient (Kc)

1.1

F

Decline in crop coefficient as a result of ageing (% per day)

0.01

E

Time to reach full canopy (d)

54

F

Time to reach maximum root depth (d)

67

F

Time to reach senescence ( d)

87

E

Base temperature ( 0C)

5

L

Cut-off temperature ( 0C)

35

L

Normalized crop water productivity â&#x20AC;&#x201C; before anthesis (WP), g.m-2

16

E

Normalized crop water productivity â&#x20AC;&#x201C; after anthesis (as of % WP)

30

E

Soil & Management parameter

Harvest index (%)

35

F

Soil water content at saturation (% vol)

49

F

Field capacity(FC) (% vol)

45

F

Permanent wilting point (PWP) (% vol)

19

F

Ksat (mm/d)

150

L

Height of soil bund (m)

0.15

F

Effect of mulches on reduction of soil evaporation (%)

0 (No mulch)

F

* F= Field observed/measured data; E= calibrated; L= comparing with the literature, adapted for the crop cultivar and/or local condition (soil/climatic).

Validation/evaluation of AquaCrop The simulated grain yield and biomass (for the year 2003-04 and 2004-05) are depicted in Fig.3(a) and 3(b). The data points except the simulated output for extreme water-deficit treatments (no irrigation, and only one irrigation) are close to the 1:1 line, which indicates reasonable prediction of grain and biomass yield. The statistical indicators of the simulation outputs are summarized in Table 4. The positive and negative values in mean bias and relative error indicate error in positive direction (increasing trend) and negative direction (decreasing trend), respectively. Mean error, mean absolute error, root mean square error, and relative error are reasonable, which indicate that the model can simulate yield with acceptable accuracy.

Table 4. Statistical indicators for model performance

Statistical/ performance indicators* Mean bias (t/ha) Mean absolute bias (t/ha) RMSE (t/ha) RE (%)

Grain yield

Biomass yield

Year 2003-04

Year 2004-05

Year 2003-04

Year 2004-05

-0.087

0.193

-0.193

0.123

0.175

0.391

0.525

0.371

0.240

0.420

0.747

0.413

6.27

12.98

7.39

4.02


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Figure 3. (a) Observed versus simulated grain yield during validation process.

111

Figure 3. (b) Observed versus simulated biomass during validation process.

Table 5. Simulated yield, water productivity, and water balance* under different simulation treatments/combinations. DOS

Irri. Sequence

Treatment

Grain yield,t/h

Biomass yield,t/h

Infiltrated water,mm

E, mm

T, mm

WP, Y/ET

WP, Bio/ET

S1

T1

3.37

9.89

205.3

87.5

152.9

1.51

4.44

S2

T2

3.33

9.72

205.3

84.6

151.5

1.53

4.44

S1

T5

3.27

9.59

207.6

79.6

165.1

1.39

4.07

S2

T6

3.24

9.43

207.6

76.1

164.9

1.40

4.07

S1

T9

3.12

9.31

207.7

76.1

172.6

1.32

3.90

S2

T10

3.11

9.11

207.7

72.6

171.9

1.33

3.88

S3

T3

3.78

10.88

255.3

87.3

170.0

1.57

4.53

S4

T4

3.74

10.71

255.3

84.5

168.5

1.59

4.55

S3

T7

3.70

10.66

257.6

79.3

185.7

1.45

4.17

S4

T8

3.65

10.46

257.6

76.0

184.7

1.45

4.16

S3

T11

3.56

10.28

257.7

75.9

193.3

1.37

3.96

S4

T12

3.52

10.10

257.7

72.4

192.9

1.37

3.95

2 irrigation frequency Nov.7 Nov.15 Nov.23

3 irrigation frequency Nov.7 Nov.15 Nov.23

Note: DOS = date of sowing; Infiltrated water = irrigation + rainfall; E = evaporation; T = transpiration; WP = water productivity (expressed in Kg/m3); Y=grain yield; ET= evapotranspiration; Bio = biomass * No drainage from the treatments.


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112

Simulation results under different perspectives Simulated grain & biomass yield, water-balance components, and the water productivity (WP) are summarized in Table 5. The results showed that the simulated yield and the WP are affected by the irrigation frequency (2 or 3 nos), sequence/timing of irrigation (at normal/recommended days or 7 days shifting from normal), and the sowing dates (7, 15 or 23 Nov.). Comparison within 2 irrigation frequencies Within the two irrigation frequency combinations, the irrigation sequence/timing S1 produced higher grain and biomass yield compared to sequence S2 in all sowing dates. The November 7 sowing (SD1) produced the highest followed by November 15 sowing (SD2), and the November 23 the lowest. Comparison within 3 irrigation frequencies Within the three irrigation frequency combinations, similar trends of those of the two frequencies are also observed. Within a particular irrigation frequency, the difference between the effects of sequences of irrigation is small. Sowing dates In different sowing dates, the simulated grain & biomass yield and the WP were affected differently. For both irrigation frequencies, the Nov.7 sowing performed the best and the Nov.23 sowing the worst. This may be due to the shortening of growing period and heat stress at the later stage (Figure 1). A shortening in growing cycle can reduce the potential time for biomass accumulation.

The differences among simulated yields under different scenarios/ treatments (combination of irrigation frequency, irrigation sequence, and sowing dates) are small, which may be due to combined effect (or interactions) of temperature or heat stress, solar radiation, rainfall (generated due to variation of sowing date), differential effect of water stress and consequent osmotic adjustment or adaptation (generated from irrigation sequence), and irrigation amount (generated from different frequency). Water balance In case of water balance components, transpiration (T) is more affected by irrigation frequency compared to evaporation (E). Within a particular irrigation frequency (2 or 3), the amount of transpiration increases with the late sowing dates (SD1 to SD3) coupled with decreases of evaporation. This may be due to the higher temperature at the later part of the wheat growing period (Fig.1.). Within the two sequences, the T & E varied a little. The water productivity of grain yield (Y/ET, kg/m3 of water) and biomass yield followed the similar trend of grain yield and biomass yield, respectively (because of direct functional relationship with WP). The amount of irrigation water under the simulated treatments and the corresponding irrigation water productivity (IWP) are presented in Table 6. The IWP shows similar trend as that of WP (based on ET). The IWP of 2 irrigation frequency combinations is higher than those of 3 irrigation frequencies (for all cases).

Table 6. Irrigation amount and irrigation water productivity (IWP) under different simulation treatments/combinations. Date of sowing

Irrigation sequence

Treatment

Grain yield(t/h)

Irrigation amount(mm)

IWP

S1

T1

3.37

100

33.70

S2

T2

3.34

100

33.37

S1

T5

3.27

100

32.72

S2

T6

3.24

100

32.38

S1

T9

3.16

100

31.58

S2

T10

3.11

100

31.14

S3

T3

3.78

150

25.17

S4

T4

3.74

150

24.94

S3

T7

3.70

150

24.67

S4

T8

3.65

150

24.33

S3

T11

3.56

150

23.71

S4

T12

3.52

150

23.44

2 irrigation frequency Nov.7 Nov.15 Nov.23

3 irrigation frequency Nov.7 Nov.15 Nov.23

Discussion During simulation, initial soil moisture at upper 3 layers (0.30 m each) was taken as 35%, 36%, and 38% (by volume), respectively (i.e. favored soil moisture, close to field capacity â&#x20AC;&#x201C; 45%). In practice, if the soil moisture is low (e.g. <28%), post-sowing irrigation may be

needed for proper germination and/or crop establishment. The simulation study demonstrates that â&#x20AC;&#x153;2 irrigation frequencyâ&#x20AC;? is the most water-efficient schedule for wheat under the prevailing climatic and soil conditions (Table 4, 5). Among the sowing dates, the Nov.23


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sowing produced the lowest yield for all irrigation frequencies and sequences. Among the sequences, the sequence S1 for 2 frequencies and sequence S2 for 3 frequencies performed better. So, these two sequences should be used, depending on 2 or 3 irrigation frequency, along with Nov.7 sowing is the first preference and Nov.15 sowing is the second preference. The time of sowing of wheat seed depends on the freeness/ availability of the land from previous crop, soil moisture status or irrigation water availability, and the availability of the farm resources (such as labour, farm machinery, seed, etc.). Based on the availability of the resource, the farmers have to decide regarding sowing date or irrigation frequency or sequence. Thus, the results of the present study will help to formulate management plan for higher yield and water efficiency.

Ali M. H., Hoque M.R., Hassan A. A., Khair M.A., 2007. Effects of deficit irrigation on wheat yield, water productivity and economic return. Agric. Water Manage. 92, 151- 161. Andarziana B., Bannayanb M., Steduto P., Mazraeha H., Baratid M.E., Baratie M.A., Rahnama A., 2011. Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran. Agric. Water Manage. 100, 1-8. Araya A., Habtub S., Hadguc K. M., Kebedea A., Dejened T., 2010. Test of AquaCrop model in simulating biomass and yield of water deficient and irrigated barley (Hordeumvulgare). Agric. Water Manage. 97, 1838–1846. Araya A., Keesstra S.D., Stroosnijder L., 2010. Simulating yield response to water of Teff (Eragrostistef) with FAO’s AquaCrop.model. Field Crops Res. 116, 196–204. Farahani H.J., Izzi G., Oweis T.Y., 2009. Parameterization and evaluation of the AquaCrop model for full and deficit irrigated cotton. Agron. J. 101(3), 469-476. García-Vila M., Fereres E., Mateos L. , Orgaz F.,

The FAO model “AquaCrop” was calibrated by matching observed yield and biomass data, and then validated with independent data sets. Subsequently, the calibrated model was used to simulate grain yield for different sowing dates, irrigation frequencies, and irrigation sequences with a view to develop appropriate irrigation management strategy for wheat. The simulation study shows a clear decreasing yield trend for winter wheat under late sowing. ‘Two irrigation frequency’ demonstrates water-efficient production, and the normal/recommended irrigation sequence performed better than the alternate sequence. The results will help to select appropriate irrigation management option for the prevailing conditions of weather and farm resources.

Steduto P., 2009. Deficit Irrigation

Optimization of Cotton with AquaCrop.Agron. J. 101(3), 478-487. Geerts S.,

Summary and Conclusion

113

Raes D., Garcia M., Miranda R., Cusicanqui J. A., Taboada C., Mendoza J.,

Huanca R., Mamani A., Condori O., Mamani J., Morales B., Osco V., Steduto P., 2009. Simulating Yield Response of Quinoa to Water Availability with AquaCrop. Agron. J. 101(3), 499-508. Gurria A., 2009. Sustainably managing water: challenges and responses. Water International, 34(4), 396-401. Heng L. K. , Hsiao T., Evett S., Howell T., Steduto P., 2010. Validating the FAO AquaCrop Model for Irrigated and Water Deficient Field Maize. Agron. J. 101(3), 488-498. Hsiao T.C., Heng L.K., Steduto P., Rojas-Lara B., Raes D., Fereres E., 2009. AquaCrop The FAO crop model to simulate yield response to water: III. Parameterization and testing for maize. Agron.J. 101(3), 101- 448. Loague K., Green R. E., 1991. Statistical and graphical methods for evaluating solute transport models: Overview and application. J. Contam. Hydrol. 7, 51-73. Nuttonson, M.Y., 1955.Wheat climate relationship and use of phenology in ascertaining the thermal and photothermal requirements of wheat.American Institute of crop ecology, Washington DC, pp. 388.

Acknowledgement The work was done under USM-TWAS Postdoctoral fellowship.

Patel N., Kumar P., Singh N., 2010. Performance evaluation of AquaCrop in simulating potato yield under varying water availability conditions.www.rid.go.th/thaicid/_6_ activity/Technical-Session/Subthem2/2.16-Neelam-p-P_kumar-Neetu_S.pdf, accessed on 23/12/2010.

References

Raes D., Steduto P., Hsiao T.C., Fereres E., 2010. AquaCrop, Version 3.1 – Reference Manual, FAO, Land and Water Division, Rome, Italy.

Ali M.H., 2008. Deficit irrigation for wheat cultivation under limited water supply

Steduto P., Hsiao T.C. , Raes D., Fereres E., 2009. AquaCrop - The FAO crop model to

condition. PhD Dissertation, Dept. of Irrigation & Water Management, Bangladesh

simulate yield response to water: I. Concepts and underlying principles. Agron. J.

Agricultural University, Mymensingh, Bangladesh.

101(3), 101- 426.


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JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT Indigenous perceptions of soil erosion, adaptations and livelihood implications: the case of maize farmers in the Zampe community of Bole in the Northern region of Ghana. Francis Issahaku Malongza Bukari a* a *

University for development studies. Faculty of integrated development studies. Department of environment and resource studies Corresponding author : francismalongza@yahoo.com

Article history Received  26.02.2013 Accepted  23.05.2013 Published 07.10.2013

Keywords Local farmers Food crop farming Soil erosion Adaptive strategies

Abstract Soil is an important natural resource which when effectively managed, could increase the livelihoods of households in sedentary agricultural communities. Soil erosion is however an emerging challenge as a cause of environmental degradation and this paper sought to ascertain the nature of soil erosion on maize farms, the effects of soil erosion on maize crop farmers and the effectiveness of local control measures on output levels and the livelihoods of the farmers. A cross-section of the community was taken and participants were selected non-probabilistically by snow-balling for questionnaire administration and focus group discussions. The study revealed that the local farmers perceived soil erosion as the wearing away of the top soil and nutrients, under the influence of running water during rainy periods and the slope of the land. The major effects of soil erosion were found to be the loss of fertile soils, reduction in the cultivable land area, the reduction in the crop yield and a fall in the living standards of farmers’ households. The findings also indicated that some of the adaptive strategies to reduce the effects of soil erosion include shifting cultivation, ridging across slopes, planting on raised mounds and avoidance of deep ploughing. It was further revealed that farmers who successfully applied the traditional methods improved upon their output levels per land area and the standards of living of their families It was recommended that modern agricultural extension services were needed, not to replace, but to complement the local knowledge systems in order to ensure sustainability.

Introduction The rising need for local Ghanaian farmers to advance themselves beyond subsistence economic system is creating the awareness of soil loss through erosion and the adoption of soil management practices as important aspects of traditional agriculture. This situation however, varies from one part of the country to another on the basis

of climate, vegetation and topographical characteristics. The Bole District in the Northern Region of Ghana which is ethnically dominated by the Gonjas, is located in the tropical continental climatic belt and savanna vegetation zone. Apart from the single maximum and inadequate rainfall with less denser vegetation, human activities


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such as deforestation, over cultivation, overgrazing and bush burning are occurring at increasing rates in the area due to rapid population growth in the midst of poverty (Gyabaah, 1994; GPRS, 2002-2004). The situation is worsened by other natural factors such as the impact of rainfall and the slope of the land, which increase the susceptibility of the soil to erosion, especially by water and subsequent fall in crop yields (Wischmier and Smith, 1965). In the face of these environmental problems and the associated negative effects on the livelihoods of the farmers, it became necessary to determine the indigenous adaptive strategies and their degree of relevance in solving the problem of soil erosion, with maize farming in the Zampe community of Bole as a case study. Objectives of the Study The main objective of the study was to ascertain how local farmers perceive soil erosion and adopt strategies to solve the problem. The Sub-Objectives of the study are: 1. To examine the nature of soil erosion on maize farms 2. To identify the effects of soil erosion on maize crop farmers 3. To assess the effectiveness of local control measures on output levels

Theoretical perspectives The study adopted a mixed theory approach, and the relevant ones are discussed below. Theory of Plant Tolerance “Tolerance is the ability of an organism to withstand harmful conditions within its cell tissues” (Acheampong, 2006: 120). The theory of plant tolerance is built mainly on the principle that, in any environment a given plant has the minimum, maximum and optimum requirements for growth (Acheampong, 2006). This study sought to examine how soil erosion by water affects the tolerance level of the maize crop in particular, and how local farmers respond to the situation. The Erosion and cultivated Crop Sustainability Theory The researcher emerged with this grounded theory after field observation of the local maize farmers. As illustrated in Figure 1, soil erosion leads to the depletion of soil fertility as the top soil is removed. Farmers observe the level of tolerance of the crops to the soil erosion and declining soil fertility by using crop yields per land area as the indicator (all other farm practices assumed to be constant). In response to negative indicators, farmers adopt control mechanisms, involving soil loss control and the improvement of the soil fertility. The theory concludes that the sustained cultivation of the crop by a farmer depends on the effectiveness of the adaptive control mechanisms. Thus, the paper examined how soil erosion causes soil loss, poor soil fertility and declining crop yields, as well as what farmers do to solve the problems for sustained cultivation and livelihood improvement.

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115

Soil erosion

Depletion of soil fertility

Cultivated crop sustainability level

Crop tolerance indication by yield

Adaptive control mechanisms Figure 1. The Erosion and Crop Sustainability Theory in Diagram (Self-designed)

Methodology This paper resulted from a mixed research design involving the crosssectional and the before-and-after study designs and qualitative and quantitative approaches. Maize farmers in the Zampe community of the Bole District constituted the sample frame, and a sample size of 100 was chosen using the non-statistical method. Respondents were reached by snow-balling, for primary data collection using questionnaire, focus group discussion (FGD) and observation by transect walk along cultivated areas. Retrospective and one-short types of questions were used in the instruments, and the face-to-face interview technique was used to administer the tools.

Results and discussions This section presents the interpretation, analysis and synthesis of the results of the study in relation to the objectives set. Indigenous Perceptions of Soil Erosion by water The survey revealed that all the maize farmers of Zampe experienced soil erosion by water on their farmlands, while almost half of them perceived it as a threat to productivity. Below is a presentation of how the indigenous farmers define soil erosion by water. Indigenous Definitions of Soil Erosion by Water 1. “During or after rain, running water carries top fertile soils in the direction in which it is moving” Implied in this definition is that running water is a major cause of the loss of top fertile soils from cultivated lands, and that the running water has a direction depending on the nature of the land. Thus the loss of soil fertility is the cause of worry to this particular farmer. 2. “Soil erosion by water is the carrying away of top soil by running water along slopes” This definition is a complement of the first one, as it specifies the exact nature of the land that facilitates the phenomenon of erosion, namely sloping land. With this awareness, such a farmer can predict the fate of crops cultivated along slopes, and hence what practices he should adopt to control the situation. This farmer shares the same views with another who said “Soil erosion occurs on sloping farm lands”.


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3. “Soil erosion is the washing away of the top soil which contains most of the plant nutrients by running water “ This farmer’s view rates slightly higher than the first ones because he does not just understand the concept of soil fertility, but that there are several elements in the top soil that collectively make soil fertile, and which are lost from their original places due to running water. 4. “Soil erosion by water is the mixing of original soils with different ones due to the transfer and deposition of soil on farm lands by running water’’ This definition simply points out that soil erosion causes conditional instabilities in cultivated soils since this process of mixing of soil can never allow a permanent texture to develop. In order words erosion by water does not permit the development of a soil with unique characteristics on the affected portion of a piece of farm land. 5. “Running water carrying top soil away and resulting into the creation of gullies’’ This is the view of a typical farmer who experiences water erosion as one in which running water concentrates on some specific spots, especially along steep slopes, and cutting deep grooves into the land. The main cause of worry to this farmer is how the process renders the portions of the land changed into gullies uncultivable. This farmland is obviously located along the alluvial fan zone of a high land area (Beaumont, 1993). 6. “Water erosion is the process by which running water carries top soil from a highland and deposits it at an end” This is what happens when a farmland is located between the alluvial plain (gentle sloping part of highland area) and the salt desert (flat land area) (Beaumont, 1993). Here the gentle sloping nature of the land may not give rise to gullies, but rill erosion which is between gulley and sheet erosion may occur. The energy of the running water soon terminates as it gets to the more level or flat land area and the soil is deposited. The implication is that a portion of this farm land would be fluvial (erosive) while another portion would be depositional. This would lead to a contrast between crop yields from the different areas; those on the fluvial zone would have less fertile soils, possibly displaced by running water and so have lower yields, while those at the depositional zone would have an accumulation of finer and more fertile soils with little fluvial distructabilities, and so have higher yields (Beaumont, 1993). 7. Finally, another intelligent middle-aged farmer defines soil erosion by water as a process whereby “running water caries soil from where it has a greater energy, to where its energy ends” This particular definition is the experience of a farmer who holds all other things constant, and concludes that the ability of running water to continue carrying soil along depends on the intensity and duration of rain and the length of slope factors, as implied in the universal soil loss equation (Wischmier and smith, 1965). I now compare these indigenous views of soil erosion to two classical definitions of the concept.

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Comparison of Indigenous Knowledge with Classical Definitions of Soil Erosion For the assessment of the relevance of the indigenous knowledge of soil erosion to the contemporary scientific world, it is worth restating some of the classical definitions of the concept for the purpose of comparison. According to Sumner (2000: 171), “Soil erosion is a series of processes leading to soil depletion in situ and the export of sediments towards downstream areas”. Bunnett and Okunrontiffa (1983: 50) also defined soil erosion as “the breaking up and wearing away of exposed rocks by moving water (rivers and waves), the wind and moving ice”. These classical notions of erosion are nothing different from the indigenous knowledge displayed above. The local participants explained the phenomenon based on how they experienced it on their farmlands (based on topography and specific cultural practices), which also influenced the type of erosion they were exposed to. Table 1 shows the types of soil erosion according to participants’ perceptions and experiences. Table 1. Common types of soil erosion in Zampe Type of soil erosion Frequency Percentage by water Sheet erosion Gully erosion Rill erosion Total

68 30 2 100

68% 30% 2% 100%

Though not the most popular type as indicated by the response rates in Table 1, the local farmers expressed the worry that after heavy rains, amounting to 1050 mm (40 inches) per annum in the Bole area (GhanaDistricts.com, 2013), gully erosion in particular, destroys significant portions of their farmlands. They added that on uniform and gentle sloping farmlands, gullies resulting from exposed tunnels caused by decaying roots of trees felled for land reclamation, also promote gully erosion. A later part of the article examines the actual effects of soil erosion as known to the farmers. Cultural Practices that Increase the Erodibility of Soil Figure 2, shows how the local farmers rated cultural practices according level of impact on soil erosion. The farmers explained that over-cultivation loosens soil particles, deforestation and overgrazing expose soils to the direct impact of rain drops to facilitate the breakup of soil particles. Additionally the removal of the natural vegetation for land reclamation reduces the roots that hold the particles together. These make the soil easily eroded by running water. Bush burning (for hunting and land clearing purposes) and overgrazing have rated low in Figure 2 because, according to the farmers, over-grazing in the community usually occurs only with the coming of Fulani herdsmen (See Bunnett and Okunrontiffa, 1983; Pickering and Owen, 1994). The Effects of Erosion on Maize Farming Figure 3 shows the estimated portions of farmlands affected by soil erosion, for respondents who said they experienced the phenomenon. The effects of this high level of soil erosion according to the farmers include the removal of the fertile top soils which support the growth


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and development of the maize crops; the development of badlands which reduce the cultivable land area; reduction in the capacity of the soil to store water for crop use; and the dislocation of the crops, causing livestock to graze on the fallen stalks and buds. The combined result of these effects is low output per land area, according to the farmers (See Barnett et al, 1972; Stocking, 1984; Russell and Russell, 2003; Hutchinson, 2008).

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the ways of minimizing the effects of slope on soil erosion. The maize crops are then planted on and in some cases, between the ridges. This checks the speed of running water and effectively prevents the transportation of soil particles and nutrients. Reduced ploughing is another common practice among the farmers of Zampe. Most farmers prefer to clear land with the hoe or cutlass, to reduce disturbance to the natural compactness of the soil particles. This practice however, goes along with mixed cropping to increase the resistance to erosion, such as cassava, pigeon peas, millet, maize and vegetable crops cultivated on the same farmland. This increases the soil cover while crops with stronger stems and deeper root systems protect weaker ones like maize, against water erosion and rain storms.

Figure 2. Cultural Practices that Influence Erosion by water

Figure 3 shows that a greater number of farmers (61%), have about ½ of their farmlands eroded by water, and some of them (48%) considered the phenomenon to be very serious, in view of the associated low output per land area.

Figure 4. Topographical distribution of maize farmlands

Figure 3. Portions of land area eroded by water

This reveals the application of the Erosion and Crop Sustainability Theory in a negative direction (stopping cultivation), as the maize crops become increasingly intolerant to the erosive effects (Acheampong, 2006). The next section discusses how the local farmers try to address the situation. Adaptive Strategies to Reduce Soil Erosion by Water In response to the negative effects, the indigenous farmers of Zampe device and adopt strategies to reduce soil erosion on their maize farms. Figure 4 shows how the Zampe farmers perceived the nature of their farm lands, which also influence water erosion and hence, the nature of adoptive control measures. It shows that 90% of the respondents had gentle sloping farm lands while 10% had steep sloping lands. Topographically therefore, the area is generally undulating. According to Wischmier and Smith (1965), the slope of land is contributory to soil erosion, and focus group discussion results with the farmers indicated that ridging across slopes is one of

On less sloping lands, farmers allow the maize crops to attain considerable heights after which they raise small mounds around individual plants to prevent root exposure and subsequent falling of plants due to erosive activity. This is accompanied by a weeding method involving cutting lumps of soil and capsizing them with special broad-bladed hoes, thus creating a rough land surface. This method of weeding is known as ‘KimutЄ’ in the Gonja language. This takes place at the last weeding before harvesting (a period rather associated with heavier rainfalls between August and early September). At this stage the crops are close to maturity or at the later stage of budding (“Kipige”), during which period maize crops easily fall due to their weight and combined effects of rain storms and running water. KimutЄ is therefore suitable for the protection of the crops against both wind and water erosion. Though quite classical, shifting cultivation and bush fallowing are also local practices among the farmers of Zampe. They use the development of rills, gullies and decline in crop yields as indicators to shift to virgin or previously fallowed lands. Farmers who do so are therefore subject to the application of the negative consequences of the Erosion and cultivated Crop Sustainability Theory and the Theory of Plant Tolerance, which they try to avoid.


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Effectiveness of the Adaptive Strategies About 93% of the farmers saw their practices as being capable of maintaining the tolerance level of the crops and hence sustaining their interests to continue cultivation, based on output levels (a positive application of the Erosion and Cultivated Crop Sustainability Theory- Figure 1). The remaining 7% who failed to see the relevance of their own control methods in reducing the impact of soil erosion are those threatened by declining yields per land area, and hence are allocating portions of the maize field to crops that are more tolerant to the prevailing conditions, such as cassava, potatoes, pigeon peas and agro- forestry related practices. In this regard, it is clear the willingness or ability of the farmer to sustain the cultivation of the maize crop declines.

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periods when they were highly conservative in their farming methods (before), and periods when they combined traditional with modern methods of soil management (after).

Possible Innovations Due to Access to Extension Services Figure 5 shows the accessibility of the farmers to agricultural extension services. About 45% of them have access to extension services while 55% do not. The extension services are mostly provided by the Agricultural Extension Department of the Ministry of Food and Agriculture (MoFA). The activities of the extension officers usually include demonstration of modern techniques of farming using modern equipment and fertilizers; introduction of improved breeds of seeds; erosion prevention and control methods and the prevention and treatment of livestock diseases.

Figure 6. Levels of adaptation to extension services

Estimated Outputs of Farmers before and after Adaptation of Traditional and Modern Erosion Control Strategies There was 100% response rate that the adaption of traditional and modern strategies in the control of soil erosion is effective. The comparison of Tables 3 and 4 confirms the contention. With an average farm size of 3.45 acres, the average output per acre before adaptation measures was 2.2 bags, while that after adaptation was 4.7 bags. This showed an increase of 100% over the output level before adaptation, which was quite a significant improvement. Table 2. Sizes of maize farmlands

Figure 5. Accessibility to extension services

These usually take place through field visits, home visits (by expressed need of the farmers) or organized meetings in the community. Level of Adaptation of Extension Services Figure 6 shows the level of responsiveness of the farmers to the methods of the extension officers. It indicates that most farmers are conservative to the traditional farming practices and so do not respond easily to the technical advice of extension officers. It is not at all bad to maintain traditional farming practices, provided the prevailing environmental conditions still respond to the effectiveness of such practices. In line with this, however, the World Bank (1994) advices that for best results, there is the need to combine conservatism with modernity in order to ensure the sustainability of an innovation. The next section reveals the outputs of the farmers in

Farm size (x) in acres

Absolute Number of Farmers (F)

Absolute number (f) multiplied by farm size (fx) (in acres)

0.5 2 5 7

10 40 45 5 ∑f= 100

5 80 225 35 ∑fx= 345

Average farm size = ∑fx/∑f = 345/100= 3.45 acres

Table 3. Output of maize farmers before erosion control measures Output level in bags (x)

Absolute number of farmers (f)

4 7 8 9 Total

20 10 40 30 ∑f=100

(fx) in bags 80 70 320 270 ∑fx= 740

Source: Field survey, 2009 Average output per farmer before erosion control= ∑fx/∑f= 740/100= 7.4 bags Average output per acre before erosion control= 7.4 / 3.35 = 2.2 bags


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Table 4. Outputs of maize farmers after adaption of erosion control measures Output level in bags (x)

Number of farmers (f)

4 9 15 20 Total

5 10 40 45 ∑f=100

(fx) 20 90 600 900 ∑fx=1610

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desertification and the associated effect of low crop yield (see Arku & Arku, 2010). The Effects of Soil Erosion on Farmers’ Livelihoods Table 5 shows respondents’ perceptions of the effects of soil erosion on their livelihoods. Table 5. Effects of soil erosion on farmers’ livelihoods

Source: Field survey, 2009 Average output per farmer after erosion control = ∑fx/∑f= 1610/100= 16.1 bags Average output per acre after erosion control= 16.1/ 3.35 = 4.7 bags

However, if the local maize farmers were more responsive to extension services by technical officials, the situation could have been better. This is because, according to the Ministry of Food and Agriculture (MoFA, 2009), maize farmers in Northern Ghana could harvest about 8 to 9 bags per acre with training and input support from extension officers, but without intervention the output level could be 7 to 8 bags per acre. This also reveals the negative effects of some of the traditional soil loss control measures like mixed cropping, on the output of maize by the Zampe farmers (who are also in Northern Ghana), hence the relatively lower yields per acre both before and after adoption of control measures. Local Farmers’ Perception of Climate Change and the Impact on Soil Erosion This section presents the outcome of a focus group discussion with a chief and five elderly maize farmers on indigenous perceptions of climate change, and how it affects soil erosion. The results showed that a major problem caused by climate change is the inability of the local farmer to predict the right time for planting; that during their childhood, farmers could use the position of the stars and the arrival of certain bird species to predict weather and the right time for specific farming activities. Erosion is a common phenomenon associated with farming, the farmers said, but the severity of erosion on the maize plant also depends on the stage of development they reach during the period of maximum rain and erosive activity. Accurate timing is however difficult today because of increasing variations in rainfall patterns from year to year. They also observed that in the past, the land was more wooded, and the thick vegetation constituted a check to the impact of running water, thereby reducing soil erosion, and linked rapid deforestation to modern population pressure: “There were no many houses as you see today, to call for the felling of trees to make homes, instead structures were constructed under trees to provide shade and fresh air’’. The chief recalled (Source: Focus Group Discussin with Maize farmers in Zampe-Bole, 09/09/09). The discussion also related deforestation to reducing rainfall, as well as low organic matter content of the soil, which also causes

Effect

Frequency

Percentage

Low output Food insecurity Low income Low savings Low investment capital formation Total

30 10 20 20

30% 10% 20% 20%

20

20%

100

100%

Source: Field survey, 2009

In the focus group discussion with the maize farmers, the participants identified low output, low income, low savings, low capital for investment and food insecurity as being the effects of soil erosion on their livelihoods. On low output, a participant elaborated: “The wind and running water do not just remove the soil. You see, if the soil were removed and our crops are still in place and doing well, we should have no reason to worry. But during certain times of the growing season, there are heavy rains and wind storms. These cause the removal of the soil by running water and our maize stalks are also uprooted and broken down all over the field. At the end, we have the problem of how to provide our families with food for the rest of the year”. (Source: Focus Group Discussin with Maize farmers in ZampeBole, 09/09/09). Another farmer added his voice to the above contribution by saying that: “Sometimes we manage to transplant some of the damaged crops if they are still at the early stages of development. But still, the good part of the soil is often carried away and we experience poor outputs. This affects our income and the ability to save for the following farming season, so that some farmers are not even able to farm again, or their farm sizes are reduced”. Low output

Low investment

Low capital

POVERTY

Low income

Low savings

Figure 6. The vicious cycle of poverty (Self-designed)

The contributions of the participants contained the factors that are responsible for the vicious cycle of poverty, that is low output; low


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income; low savings; low capital formation; and low investments in farming, which is the predominant occupation. These among others, are contributory to poverty; a condition of lack of the basic needs of life (Todaro and Smith, 2006).

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Arku, F. S, Arku, C . (2010). I cannot Drink Water on an Empty Stomach: A Gender perspective on Living with Drought. Gender and Development. Vol. 18, No. 1, 115124. Barnett, A.P, Creeker, J.R, Fernado, A., Jackson, W.A, Dodey A.E, and Holladay, J.H (1972). Soil Nutrients and Losses in Runoff with Selected Cropping Treatment in Tropical

Conclusion The study revealed that local farmers perceive soil erosion as the wearing away of the top soil and nutrients, under the influence of running water during rainy periods. The phenomenon is known to all farmers, but the severity of the impacts felt depends on the nature of the individual farmerâ&#x20AC;&#x2122;s land, as well as the cultural practices. Soil erosion also reduces soil fertility, affects the physical development of food crops and consequently reduces crop yields. Traditional farmers adapt strategies such as ridging across slopes, planting on raised mounds, shifting cultivation and mixed cropping to control the phenomenon. Farmers, who effectively combine traditional methods with the services of extension officers, are able to reduce the effects of soil erosion for better crop yields, improved household livelihood and reduce poverty.

Soil. Agron J. 64391-5. Beaumont, P. (1993). Dry lands: Environmental Management and Development. London: Routledge. Bekye, P.K (1998). Peasant Development: The Case of Northern Ghana. Acco: Leuven. Botchwey, A.B. (2006). Steps to Self- Reliance: for Groups and Communities. Cape Coast, Ghana: Catholic Mission Press. Bryan, R., Yair A. (1989). Badland Geomorphology and Piping. Norwich, U.K: Geo Books, Bunnett, R.B and Okunrontiffa, P.O. (1998). General Geography in Diagrams for West Africa. London: Longman. Dickson, K. B., Benneh, G. (2001). A New Geography of Ghana (4th ed.). Harlow, England: Pearson Education Ltd. Getis, A., Getis J. and Fellman, J.D (2006). Introduction to Geography. New York: Mc Graw Hill GhanaDistricts.com (2013). Northern Region, Ghana. Retrieved from http://en.wikipedia. org/wiki/Northern_Region,_Ghana (13/04/2013). Gyabaah, K.N (1994). Environmental Degradation and Desertification in Ghana. England:

Recommendations Though the traditional notions of soil erosion are quite comprehensive, they have technical limitations. For instance none of the traditional definitions could identify weathering or the fractional decomposition of soil as a precondition for soil erosion. Adequate education on the meaning and processes of soil erosion through extension services, could improve upon the farmersâ&#x20AC;&#x2122; understanding of the phenomenon and hence their ability to develop and practice better preventive methods. For example an understanding of fractional decomposition or the break up of soil particles by rain drops and other mechanical processes, would inform farmers on the need to ensure adequate soil cover during and after the farming season. Extension officers educating farmers on the prevention of soil erosion should not impose new technologies or ideas on their clients. They should rather involve them first in the identification of the weaknesses of the existing practices, and then in the development of improved or better methods of doing so, otherwise the acceptance and sustainability of the innovations could be doubtful.

Avebury Ashgate Publishing Ltd. Hudson, W.A (1973). Soil Conservation. London: Batsford . Hutchinson, C. F. (2008). Desertification.

USA: Redmond WA-Microsoft Encarta

Corporation. Millar, D. (2007). Learning Together: Towards Operational Methodologies for Endogenous Development Research, Education and Development. Tamale, Ghana: CAPTURED Program, University for Development Studies Ministry of Food and Agriculture (2009). Northern Ghana Food Security & Nutrition Monitoring System. FSNMS Bulletin for Fourth Quarter, 2009. Retrieved from http:// home.wfp.org/stellent/groups/public/documents/ena/wfp215677.pdf (14/04/2013). Pickering, K.T and Owen, L.A (1994). An Introduction to Global Environmental Issues. London: Routledge. Russel, E.J, and Russel, E.W (2003). Soil Conditions and Plant Growth. Delhi: Biotech Books Seini, W. (2002). Agricultural Growth and Competitiveness under Policy Reforms in Ghana. Legon, Ghana: Wilco Publicity Ltd. Stocking, H. (1984). Erosion and Soil Productivity: A review- Consultation Working Paper No. 1. Rome: FAO. Sumner, M. E (2000). Hand Book of Soil Science. NW Suite: CRC Press, Taylor and Francis Group. Todaro, P.M. and Smith, S. C. (2006). Development Economics. Harlow- England: Pearson

References Acheampong, P.K (2006). Social Studies II: Our Environment. Cape Coast, Ghana: Center for Continuing Education, University of Cape Coast.

Education Ltd. Twumasi, P.A (2001). Social Research in Rural Communities. University Press World Bank (1994). Quarterly Economic Development Report .

Accra, Ghana: Ghana


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JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT Commentary

New focus of environmental education programs Alexander Neamana* and Andrés Mariób a

Área del Medio Ambiente, Facultad de Agronomía, Pontificia Universidad Católica de Valparaíso, Quillota, Chile

b

Departamento de Formación Pedagógica, Universidad Metropolitana de Ciencias de la Educación, Santiago, Chile

* Correspondence: E-mail: alexander.neaman@ucv.cl, Fax: +56-32-2274570

Does environmental knowledge determines pro-environmental behavior? Humanity is currently facing various global environmental problems including climate change, ecosystem destruction, loss of biodiversity, soil erosion, decreased natural food resources, and a limited availability of energy and fresh water, among others (Diamond, 2011). In our opinion, the ultimate goal of environmental education is to achieve pro-environmental behavior of the population, i.e. environmentally friendly behavior. It is often believed that increased environmental knowledge leads to pro-environmental behavior, i.e. environmentally friendly behavior. For instance, Plotnikoff et al. (2008) suggest that the mass media should provide more information to encourage the population to make changes in their environmental behavior. Indeed, widespread publicity campaigns have been dedicated to environmental topics in various mass communication media, documentary films, and magazines with the objective of creating environmental awareness throughout society, in hopes that everyone would learn about these issues and take positive environmental actions. The opinion that increased environmental knowledge leads to pro-environmental behavior is disputed. For example, in their seminal paper in the field of environmental education, Hungerford and Volk (1990) demonstrated that knowledge does not lead to behavior change in the environmental dimension. Heimlich (2010) came to the same conclusion. In particular, Kellstedt et al. (2008) showed that the more informed one is about global warming, the less one feels personally responsible for the problem and becomes concerned about global warming. In other words, the research shows that the more actively the media publicizes the problem and the more informed people are, the less concerned about it individuals become. Similarly, Tal (2010) demonstrated that environmental education improved the knowledge of university students; however, there were few behavior changes with respect to the environment. On the other hand, several other studies have found weak relationships between environmental knowledge and behavior. The amount of behavioral variance that can be explained by environmental knowledge varies between 6% and 18% (Barazarte et al., in press, Frick et al., 2004, Geiger et al., in press). Integral environmental education Some researchers emphasized that altruistic, egoistic and biospheric values influence pro-environmental behavior (for instance, Clark et al., 2003). Specifically, it is assumed that altruistic, egoistic and biospheric values influence pro-environmental beliefs (or considerations), which in turn influence pro-environmental intentions and behavior (de Groot & Steg, 2009). There are also empirical finding that prosociality, i.e. sensitive dealing with others, is highly related to pro-environmental behavior (Kaiser & Byrka, 2011). Similarly, it is demonstrated that egoistic environmental concerns, i.e. concerns to the self in relation to the environment, are related to our egoistic relationship to each other (Isaac et al., 2012, Laitman & Ulianov, 2012). The latter authors consider that we cannot really try to correct our dealings with the environment while ignoring our dealings with each other. They suggest that we must educate people to become sensitive toward others, which in turn will make


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them being responsible in their approach to the environment. In our opinion, such an approach could be called “integral environmental education”. From “saving the environment” to “humans are an integral part of nature” It is important to analyze the current focus of our environmental education programs and to suggest a possible new focus. Environmental education programs often divide the world between “humans” and “environment”. This division makes us think that the environment is a supplement to humanity. Most likely, this disconnection with nature is the cause of inefficiency of informal environmental education. Therefore, it is important to understand principles of natural systems and focus our environmental programs according to these principles. Perhaps the most important principle of natural systems is interconnectedness and interdependence of all their parts. Many researchers emphasize the importance of this principle in natural systems (for instance, Harman & Sahtouris, 1998, Sahtouris & Lovelock, 2000). The human body is an example of such an interconnected and interdependent system. Within our bodies, the homeostasis among all cells and organs enable the body to maintain proper health. To remain healthy, each cell and organ operates according to the interests of the entire organism. Social scientists also recognize the principle of interconnectedness (for instance, Christakis & Fowler, 2010). In consequence, we should change the focus of our environmental education programs. The focus on “saving the environment” should be replaced with a focus on “humans are an integral part of nature”.

References Barazarte, R., Neaman, A., Vallejo, F. & García, P. (in press) El conocimiento ambiental y el comportamiento pro-ambiental de los estudiantes de la enseñanza media, en la Región de Valparaíso (Chile). Revista de Educación. Christakis, N. A. & Fowler, J. H. (2010) Connected: The surprising power of our social networks and how they shape our lives – how your friends’ friends’ friends affect everything you feel, think, and do (New York, NY, USA, Little, Brown and Company). Clark, C. F., Kotchen, M. J. & Moore, M. R. (2003) Internal and external influences on pro-environmental behavior: Participation in a green electricity program. Journal of Environmental Psychology, 23, 237-246. De Groot, J. I. M. & Steg, L. (2009) Mean or green: Which values can promote stable pro-environmental behavior? Conservation Letters, 2, 61-66. Diamond, J. (2011) Collapse: How Societies Choose to Fail or Succeed. Revised edition (London, UK, Penguin Group). Félonneau, M.-L. & Becker, M. (2008) Pro-environmental attitudes and behavior: Revealing perceived social desirability. Revue Internationale de Psychologie Sociale, 4, 25-53. Frick, J., Kaiser, F. G. & Wilson, M. (2004) Environmental knowledge and conservation behavior: Exploring prevalence and structure in a representative sample. Personality and Individual Differences, 37, 1597–1613. Geiger, S., Otto, S. & Diaz, J. S. (in press) A diagnostic environmental knowledge scale for Latin America [Escala diagnostica de conocimientos ambientales para Latinoamérica]. Psyecology. Harman, W. & Sahtouris, E. (1998) Biology revisioned (Berkeley, CA, USA, North Atlantic Books). Heimlich, J. E. (2010) Environmental education evaluation: Reinterpreting education as a strategy for meeting mission. Evaluation and Program Planning, 33, 180-185. Hungerford, H. & Volk, T. (1990) Changing learner behavior through environmental education. Journal of Environmental Education, 21, 8-21. Isaac, G., Levy, J. & Ognits, A. (2012) The benefits of the new economy. (Toronto, ON, Canada, ARI Publishers, www.ariresearch.org). Kaiser, F. G. & Byrka, K. (2011) Environmentalism as a trait: Gauging people’s prosocial personality in terms of environmental engagement. International Journal of Phychology, 46, 71-79. Kellstedt, P., Zahran, S. & Vedlitz, A. (2008) Personal efficacy, the information environment and attitudes toward global warming and climate change in the united states. Risk Analysis, 28, 1-14. Laitman, M. & Ulianov, A. (2012) A guide to the new world (Toronto, ON, Canada, ARI Publishers, www.ariresearch.org). Sahtouris, E. & Lovelock, J. E. (2000) Earthdance: Living systems in evolution (Lincoln, NE, USA, iUniversity Press). Tal, T. (2010) Pre-service teachers’ reflections on awareness and knowledge following active learning in environmental education. International Research in Geographical and Environmental Education, 19, 263-276.


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JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT Simulation of upward flux from shallow water-table using UPFLOW model. M. H. Ali a*, I. Abustan a and S. Islam b a

School of Civil Engineering, University Sains Malaysia

b

Department of Irrigation & Water Management, Bangladesh Agricultural University

* Corresponding author : hossain.ali.bina@gmail.com, mha_bina@yahoo.com. Permanent address: Agril. Engg. Division, Bangladesh Institute of Nuclear Agriculture

Article history Receivedâ&#x20AC;&#x192; 27.02.2013 Acceptedâ&#x20AC;&#x192; 10.06.2013 Published 04.11.2013

Keywords Capillary rise Shallow water-table Groundwater contribution Evapotranspiration Wheat Simulation model

Abstract The upward movement of water by capillary rise from shallow water-table to the root zone is an important incoming flux. For determining exact amount of irrigation requirement, estimation of capillary flux or upward flux is essential. Simulation model can provide a reliable estimate of upward flux under variable soil and climatic conditions. In this study, the performance of model UPFLOW to estimate upward flux was evaluated. Evaluation of model performance was performed with both graphical display and statistical criteria. In distribution of simulated capillary rise values against observed field data, maximum data points lie around the 1:1 line, which means that the model output is reliable and reasonable. The coefficient of determination between observed and simulated values was 0.806 (r = 0.93), which indicates a good inter-relation between observed and simulated values. The relative error, model efficiency, and index of agreement were found as 27.91%, 85.93% and 0.96, respectively. Considering the graphical display of observed and simulated upward flux and statistical indicators, it can be concluded that the overall performance of the UPFLOW model in simulating actual upward flux from a crop field under variable water-table condition is satisfactory. Thus, the model can be used to estimate capillary rise from shallow water-table for proper estimation of irrigation requirement, which would save valuable water from over-irrigation.

Introduction Rapid growth of irrigated agriculture throughout the world accompanying with decline in water-table and shortage in energy has become a vital concern in recent years. Crop needs irrigation when its demand is not fulfilled from rainfall, stored soil-water, and upward soil-water flux or capillary upward flux from saturated soil layer or water-table. The calculation of the soil water balance is an important tool to assess the water availability for crops throughout the growing season, the water flow required for irrigation, and the excess water flowing to drains. It has wide application in the planning

and management of both rainfed and irrigated agriculture as well as in the evaluation of water management strategies. In the presence of a shallow water-table, the upward movement by capillary rise from the groundwater to the root zone is an important incoming flux at the bottom boundary of the root zone. The upward transported water can cover part of or even the total requirement. The determination of the upward flux however is not simple and requires a good knowledge of all factors that affects the flow, such as the depth to groundwater, the capillary properties of the soil


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profile, the evaporative demand of the atmosphere, root water uptake characteristics, root depth, and the soil water content in the root zone. Sophisticated mechanistic models simulating water flow in unsaturated porous medium (Feddes et al. 1978, De Laat 1980, Belmans et al. 1983, Vanclooster et al. 1994, Simunek et al. 1998, Carpena et al. 2001) can be used to obtain reliable estimates for any type of environment but the data requirement are quite extensive and their use require great expertise. Raes (2002) developed a software tool, named UPFLOW to estimate capillary flux. The model estimates upward movement from a shallow water-table to the root zone during a specific period in a specific environment. But the evaluation of the model against field data is scarce. In this study, the model UPFLOW was used to estimate the capillary flux from shallow groundwater to the root zone of wheat crop, and evaluated its performance.

(Raes and Deproost 2003, Raes 2002). The input fields in main menu are given in Figure 1. The inputs in the UPFLOW model are: average evapotranspiration (ET) during the time period, initial mean soil water content, average depth of water-table (WT) below the soil surface, crop type, the soil type of various layers and their thickness, and the salt content of the groundwater (if groundwater contains salts). For the given environmental conditions, UPFLOW displays the expected steady upward flow [mm day-1] from the water-table to the topsoil, the simulated soil water content [vol%] in the topsoil, the amount of salt transported upward during the given period [t.ha-1. year-1, if the water-table contains salts,], the degree of water logging [%] in the root zone (if any), and a graphical display of the soil water profile above the water-table. In the model, the steady upward flow to the topsoil is estimated according to De Laat (1980, 1995):

Materials and methods

z=

General overview of the model UPFLOW

h

â&#x2C6;Ť 0

UPFLOW is a software tool developed to estimate the expected upward flow from a shallow water-table in a given soil profile and to evaluate the effects of environmental conditions on the upward flow

124

K(h)

dh

(1)

q + K(h)

Where z (m) is the vertical co-ordinate, q is the constant upward flux (m3.m-2.d-1) of water, h is the soil matric potential (m), and K(h) is the hydraulic conductivity (m.day-1)

Root zone (0.50 meter)

Saturation 42.0 vol% Field capacity 21.7 Wilting Point 10.0 Anaerobiosis Point 35.0 Field Capacity (equilibrium) 27.8 vol%

Figure 1. Input fields in Main menu of UPFLOW (after Raes, 2002).


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With the K-h and θ-h relations (where θ is ‘soil water content’) for the various soil layers of the profile above the water-table, UPFLOW is able to determine the maximum flux that can flow to the top soil by checking that the simulated soil water content (derived from the moisture profile) remains below the specified mean water content in the top soil. UPFLOW calculates the amount of water that the plant roots extract according to Feddes et al. (1978). Since the water flow inside the soil profile is assumed to be steady, the capillary rise from the water-table to the topsoil can never exceeds the ET demand of the atmosphere. Mean soil evaporation or crop evapotranspiration for a given period from climatic, soil and crop parameters are calculated in the model according to Allen et al. (1998). More details about UPFLOW can be found in Raes (2002) and Raes and Deproost (2003).

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Statistical indicators for model performance Evaluation of model performance should include both graphical display and statistical criteria. A model is a good representation of reality only if it can be used to predict an observable phenomenon with acceptable accuracy and precision (Loague and Green 1991). The model output was compared graphically with the observed field data. Addiscott and Whitmore (1987) concluded that any one method of measuring discrepancy between model output and observed data alone might be misleading, but several methods used together could summarize satisfactorily the closeness of a model’s estimates and measurements. For this reason, the following statistics were used to indicate overall model performance:

Input data file (i) Bias or Mean bias (Willmott 1982, Retta et al. 1996): For a model run, the required data are summarized in Table 1.

N

ME = 1 Σ (S i - M i) N i=l

Table 1. Data required for a model run

Crop

• Crop cover type (no crop cover/bare soil, cereals/ grasslands) • Root-water uptake rate at different sections of root zone • Crop coefficient, Kc (for the particular crop stage)

Soil

• Number of soil layers, and their thickness • Mean soil-water content of the profile • Anaerobiosis point of the soil • Saturated hydraulic conductivity of the soil profile

Weather

Water-table (WT)

• Maximum and minimum temperature, solar radiation (or latitude and day length for indirect estimation), wind speed, and relative humidity; for calculating reference crop evapo-transpiration, ET0 (for the particular crop period) • Depth to WT from the soil surface • Salt content of the groundwater (if any)

The input data for model run for various conditions (for different water-table depths at various growth stages) were taken from Mridha et al. (2001) [reported in Tables and graphs]. The study was conducted in lysimeter with wheat crop, maintaining different sub-surface water levels e.g. 45, 60, 75 & 90 cm from the surface. Measured quantity of water was added to the lysimeter tanks to maintain the required depth to water level. The soil in the lysimeter was silty clay loam with an average bulk density of 1.5 g.cm-3.

where S and M are the simulated and measured values for the ith observation and N is the number of observations. (ii) Mean Absolute Bias or Error (Fox 1981, Cob and Juste 2004): 1 i=N MAE = N Σ |S i - M i |

(iii) Root mean square error (RMSE): It quantifies the dispersion between simulated and measured data (Gabrielle and Kengni 1996, Quemada and Cabrera 1995): N

1 Σ (S √N

RMSE =

The model was run stepwise for different growth stages and different water-table conditions. The model simulated outputs were compared with the corresponding measured values reported by Mridha et al. (2001).

i=1

i

- M i)2

(4)

(iv) Relative error (RE) (Cob and Juste 2004, Loague and Green 1991): RMSE x 100 y where y is the mean of observed values.

(5)

RE =

Ideally, the value of ME, MAE, RMSE and RE should be zero. (v) Model efficiency: Model efficiency (EF) was calculated as (Borah and Kalita 1997, Law 1983):

Σ(measured-measured.mean) - Σ(simulated-measured)2 Σ(measured-measured.mean) 2

(6)

2

(vi) Index of Agreement (IA) (Willmott 1982, Lecina et al. 2003): N

ET = Kc × ET0

(3)

i=1

EF = The daily reference crop evapo-transpiration, ET0 (for the crop period) was calculated by ET0 software (Raes, 2000), using weather data for the period. The weather data were collected from nearby weather station (600 m apart from the field). Then, mean evapo-transpiration (ET) demand for the particular growth period was computed as:

(2)

d=1

Σ (O i - S i)2 i=1 N

Σ [O’i + S’i]2 i=1

, 0 ≤ d ≤1

(7)

where O’i = |Oi - S| , S’i = |Si - S| , Oi is the observed value, Si is the simulated value and S is the simulated mean. An ideal value of EF and d is unity.


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Results and Discussions Graphical display of simulated values Figure 2 presents comparison between simulated and observed capillary flux values for different stages of growing period of wheat. From the graph it is revealed that the model can estimate actual capillary rise with reasonable accuracy. Figure 3 presents the distribution of simulated capillary flux against observed field values around the 1:1 line. The data points lie around the 1:1 line, which means that the model output is reliable and reasonable.

condition, the values of relative error (RE) and the model efficiency (EF) will be 0% and 100%, respectively. So the RE value of about 27.91 % and EF value of about 85.93 % indicate that the performance of UPFLOW model in simulating actual upward flow or capillary rise is satisfactory. The limit of index of agreement (d) value is from 0 to 1. A higher value indicates a better agreement between the simulated and observed values. In this study the value of d (0.96) shows a good performance of the model. Some discrepancies are observed in graphical display, and the statistical parameters are also deviated from the ideal value. These may be due to inherent assumptions in the model principle, and also in the field data. For example, the model assumes the steady state condition, that is the flow does not change with time. But in reality, this may not be true (as the flux varies with the change in moisture level and atmospheric demand). Considering the above statistical parameters and graphical comparison, it can be said that the overall performance of the UPFLOW model in simulating actual upward flux from wheat field under variable water-table condition is satisfactory. Table 2. Statistical indicators of simulation performance

Figure 2. Comparison between simulated and observed actual capillary flux in wheat crop

Sl. no.

Statistical indicators

Value

1 2 3 4 5 6 7 8

R2 r Mean Bias (mm) Mean Absolute Bias, MAB (mm) Root Mean Square Error , RMSE (mm) Relative Error, RE (%) Model Efficiency, EF (%) Index of Agreement (IA)

0.86 0.93 0.82 7.71 8.77 27.91 85.93 0.96

Conclusion and implications

Figure 3. Distribution of simulated flux values around the 1:1 line.

Statistical indicators of simulation performance The statistical indicators of simulation performance are summarized in Table 2. The value of coefficient of determination (R2, 0.86) indicates that a good inter-relation exists between observed and simulated vales. The value of mean bias error (MBE) is equal to 0.818 mm. A positive value of MBE indicates overestimation and vice-versa. The mean absolute bias and root mean square error are 7.71 mm and 8.76 mm, respectively. The absolute bias is an indicator of overall bias in the model estimation. The magnitude of root mean square error (RMSE) is also a useful parameter of model performance. In an ideal

Comparison between simulated and observed capillary rise values for different stages of growing period of wheat indicates that the model can estimate actual capillary rise with reasonable accuracy. In distribution of simulated capillary rise values against observed field values, maximum data points lie around the 1:1 line, which means that the model output is reliable and reasonable. Considering the graphical display and statistical parameters, it can be concluded that the overall performance of the UPFLOW model in simulating actual upward flux from a crop field under variable water-table condition is satisfactory. At many locations, a saturated layer exists at shallow depth from the soil surface, from which the capillary fringe may reach within the effective root zone of the crop. In such locations, or locations having shallow groundwater of good quality (or, guarantee a natural salt balance if water is saline), the use of the shallow water-table to meet crop water demand is an important management option. Application of simulation model such as UPFLOW in quantifying the magnitude of capillary flux (in response to crop ET demand) under different field situations can help to suggest appropriate irrigation management to exploit shallow water-table efficiently, and thus reduce frequency of irrigation and save energy.


DOI number: 10.5027/jnrd.v3i0.12

References

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Law A.M., 1983. Statistical analysis of simulation output data. Operations Res. 31, 9831029. Lecina S., Cob M.A., Perez P.J., Villalobos F.J., Baselga, J.J.. 2003. Fixed versus variable bulk canopy resistance for reference ET estimation using the Penman-Monteith equation under semi-arid conditions. Agric. Water Manage. 60, 181-198. Loague K., Green R.E., 1991. Statistical and graphical methods for evaluating solute transport models: Overview and application. J. Contam. Hydrol. 7, 51-73. Mridha M.A.K., Rashid M.H., Islam M.S., Alam M.S., Hossain M.A., 2001. Lysimeter study on the effect of subsurface water levels on wheat production. J. Institution of Engrs. Bangladesh, Agril. Engg. Division, 28/AE (1), 91-98. Quemada M., Cabrera M.L., 1995. CERES-N model predictions of nitrogen mineralized from cover crop residues. Soil. Sci. Soc. Am. J. 59, 1059-1065. Raes, D., 2000. ET0: A software for calculation of reference evapotranspiration. Dept. of Land and Water Management, K. U. Leuven University, Leuven, Belgium. Raes D., Deproost, P., 2003. Model to assess water movement from a shallow water table to the root zone. Agric. Water Manage. 62, 79-91. Raes D., 2002. UPFLOW- Water movement in a soil profile from a shallow water table to the topsoil (capillary rise). Reference Manual, Version 2.1. Department of Land Management, K.U. Leuven University, Leuven, Belgium. Retta A., Vanderlip R.L., Higgin R.A., Moshier L.J., 1996. Application of SORKAM to simulate shattercane growth using forage sorghum. Agron. J. 88, 596-601.

De Laat P.J.M., 1995. Design and operation of a subsurface irrigation scheme with MUST.

Simunek J., Sejna M., van Genuchten M.Th., 1998. HYDRUS ID-Software Package for

In Pereira, L.S., B.J. van den Broek, P. Kabat and R.G. Allen (Editors). Cropwater-

Simulating the One-Dimentional Movement of Water, Heat and Multiple Solutes in

simulation models in practice. Wageningen Presss, The Netherlands, pp: 123-140.

Variable Saturated-Media. International Ground Water Modeling Center, Colorado

Feddes R.A., Kowalik P.J., Zaradny H., 1978. Simulation of field water use and crop yield. Simulation Monographs. PUDOC, Wageningen, The Netherlands. 189 p. Fox M.S., 1981. An organizational view of distributed systems. IEEE Transact. Systems, Man Cybernet. 11, 70-80. Gabrielle B., Kengni L., 1996. Analysis and field-evaluation of the CERES modelâ&#x20AC;&#x2122;s soil components: Nitrogen transfer and transformations. Soil. Sci. Soc. Am. J. 60, 142149.

School of Mines, Golden, Colorado, USA, p. 162. Vanclooster M., Viaene P., Diels J., Christiaens K., 1994. WAVE - A Mathematical Model for Simulating Water and Agrochemicals in the Soil and Vadose Environment, Reference and userâ&#x20AC;&#x2122;s manual, Institute for Land and Water Management. Leuven, Belgium. Willmott C.J., 1982. Some comments on the evaluation of model performance. Am. Meteorol. Soc. Bull. 63, 1309-1313.


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JOURNAL OF NATURAL RESOURCES AND DEVELOPMENT Evaluation of methods for digital elevation model interpolation of tillage systems. Muhammad Anggri Setiawana, Martin Rutzingerb, Volker Wichmannc, Johann Stoetterbc, Junun Sartohadia a

Department of Environmental Geography, Universitas Gadjah Mada, Indonesia

b

Institute of Geography, University of Innsbruck, Austria

c

AlpS GmbH – Center for Climate Change Adaptation and Technologies

* Corresponding author : panyidiksiti@gmail.com

Article history Received  18.03.2013 Accepted  30.07.2013 Published 27.12.2013

Keywords Detain topography Digital elevation model Interpolation Tillage systems

Abstract There are very little attempts of DEM evaluation in such a disturbed or discontinuous surface (e.g., in tillage area). Present study aims to evaluate common interpolation methods (triangulation, nearest neighbor, natural neighbor, minimum curvature, multiquadratic radial basis function (MRBF), ordinary kriging, and inverse distance weight) in representing the detail topography of two different tillage types, namely bench terrace and furrow. Evaluation procedure was conducted through a stepwise analysis by using combination between the accuracy level (coefficient of determination (R2), mean error (ME) and standard deviation error (S)) and the shape similarity analysis. This study also shows the application of break-line function during the interpolation process in order to optimize some interpolation methods and the usage of drainage sink area as another step in evaluating DEM quality. To achieve the aim of this study, two field-size of dry-land agriculture (tegalan) were observed by using a set of total station Nikon DTM 322 with 3” angle accuracy. These plots, namely Tieng (1652 m²) and Buntu (673 m²), are situated in the upper part of Wonosobo District, Central Java Province, Indonesia. Tieng plot represents the bench terrace system embedded with stones on its terrace risers and showing relatively smooth ground surface. On the other side, Buntu plot shows the ridges and furrows system that lays perpendicularly to the contour lines. In terms of R², ME and S, there were slight differences in results between each method, except the multiquadratic radial basis function which was failed to generate terrace form in Tieng. The final result shows that triangulation is the best fit method followed by natural neighbour at representing the bench terraces in Tieng plot. In the case of furrow in Buntu plot, natural neighbour is the most accurate method. Despite its superiority at representing the bench terrace, triangulation has larger sink drainage area compared to natural neighbour. This study has confirmed the robustness of a stepwise analysis between quantitative and qualitative assessment techniques for DEM accuracy. A fine value of quantitative parameter does not necessarily mean that it will fairly possess a good spatial accuracy.


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Introduction

Representing tillage systems by Digital Elevation Model (DEM) is an important task in environmental modelling, particularly for soil erosion and hydrological processes within agricultural areas. It is common to represent the characteristics of tillage system by an index instead of using the real dimension of it. For instance, Wischmeir and Smith (1978) introduced the tillage system index (P) for the Universal Soil Loss Equation model (USLE). Morgan (2005) used the soil surface roughness (RFR) to represent the effect of different tillage systems on soil erosion process. In a detailed analysis, it is however necessary to maintain the real dimension of the tillage systems in DEMs by using appropriate interpolation algorithms. Tillage activity changes the surface continuity, which can create specific morphologic structures such as ridges, terraces, and furrows. Some of those characteristics appear as regular patterns, others occur as abrupt changes i.e. discontinuities. Thus, it requires an adequate point sampling technique in order to maintain the surface structure of interest in the interpolated DEM (Aguilar et al., 2005). Surveying such an area could be efficiently done by means of topographic LiDAR technology (Fröhlich & Mettenleiter, 2004; Hack et al., 2004). Nevertheless, traditional survey techniques acquiring single points such as theodolite or differential GPS are still common in practical work, especially if the acquisition of topographic LiDAR data is not affordable. The limited number of sampling points from such a traditional survey requires a comprehensive investigation of the pros and cons of different interpolation methods in order to be able to produce a reliable and accurate DEM. Several interpolation methods have been developed and improved in order to provide high quality DEMs. Most of those methods were developed for certain purposes and therefore have their advantages and insufficiencies (Mitas & Mitasova, 1999; Moore et al., 1991; Webster & Oliver, 2007). Some studies have evaluated different interpolation methods by investigating (i) the influence of the sampling patterns of field measurements (Heritage et al., 2009; Merwade, 2009; Zimmerman et al., 1999), (ii) the density of sampling points (Aguilar, et al., 2005; Chaplot et al., 2006; Heritage, et al., 2009), and (iii) the morphological characteristics of the surface (Aguilar, et al., 2005; Chaplot, et al., 2006; Zimmerman, et al., 1999). Most evaluation studies on DEM interpolation are carried out on continuous surfaces. Thus, there is a demand on comprehensive investigations on point

129

interpolation methods applied to surfaces with regular patterns and step edges such as in tillage areas. Our study aims at finding the best fit interpolation method from the already published methods in order to represent two different tillage types, namely bench terrace and furrow. To improve the interpolation, some methods allow the integration of breaklines during the interpolation process. The resulted DEMs are investigated by a stepwise analysis using a combination between quantitative and shape similarity analysis. In terms of the quantitative analysis, parameters such as mean error (ME), error standard deviation (S), variables of linear regression (i.e. weighted coefficient of determination (wR²) and intercept value (a)), and sink drainage area are used in this study. After the calculation of quantitative parameters, the interpolated DEMs are visualized in 3D views and profile to analyze the shape similarity. Finally, rank classification is performed to decide on the best quality interpolation methods.

Material and Methods Study site Two field plot areas in dry-land agriculture (tegalan) were observed. These plots, namely Tieng (1652 m²) and Buntu (673 m²), are situated in the upper part of Serayu Watershed in Wonosobo District, Central Java Province, Indonesia (Fig. 1). These plot areas were initially selected for measuring and modelling the soil erosion rate under both different land use and soil conservation methods. In this area, the limited availability of arable land area has forced the local farmers to modify the hillslope surface into agricultural field. Regardless of the slope steepness, bench terraces and furrows have widely been adopted as the common tillage systems within this area. The Tieng plot has vertical terrace risers (ca. 2 m) and nearly plane terrace beds (Fig. 1a). An andesitic boulder from the early Holocene period is located on top of this plot. In addition, numbers of rocks are embedded along the terrace risers to reduce the soil erosion effect. The Buntu plot area (Fig. 1b) is identified by a set of ridges and furrows interrupted with a number of ditches, which are perpendicularly oriented to the main slope direction. Buntu plot has a relatively thin soil layer (30-40 cm) nearly approaching the bedrock.

Figure 1. The study area of two field-size agricultural areas, i.e. (a) Tieng and (b) Buntu


DOI number: 10.5027/jnrd.v3i0.13

Data acquisition and pre-processing There is always a trade-off between the number of sampling points to be taken and the desired resolution and target scale. In order to carry out an effective survey, this study followed the principal work of (Aguilar, et al., 2005) and (Heritage, et al., 2009) who described the crucial role of the sampling strategy according to the morphological characteristics of the surface. In our study, point sampling was done at every major change within the plot’s area. Demanding a high resolution DEM (10x10 cm for Tieng and 5x5 cm for Buntu) a total station (Nikon DTM 322 with 3” angle precision) was employed.

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the points observed along the terrace edges (excepting the validation points) were simply connected and converted into blanking file format by using the module of Export Surfer Blanking File in SAGAGIS (System for Automated Geoscientific Analyses) (Conrad, 2007).

Figure 3. Point data set of Buntu

Figure 2. Points data set of Tieng

At Tieng plot, the sampling points were taken along the edges of the terrace risers, on both the upper and lower part (Fig. 1a). At the spot with the andesitic boulder, the sampling points were surveyed right on its surface changes. As a consequence, each interpolation algorithm had to show its capability to interpolate both continuous (the boulder spot) and terraced surfaces (the bench terraces). The terrace bed was considered as plane surface with low surface roughness (2 to 5 cm differences in elevation). Hence, for practical reasons the terrace bed was not particularly observed since roughness did not exceed 10 cm. Figure 2 shows the point data set of Tieng. The field survey campaign yielded 277 sampling points. For validation purposes, we chose randomly 29 points from the total sampling points. To optimize the interpolation processes, the remaining data (248 points) were then linearly densified along the terrace’s edges (0.5 m interval). The densification process resulted in 2254 points which were used in the interpolation process. In addition, to derive the breakline features,

A thorough ground survey was also carried out at the Buntu plot. Visualizing every ridge and furrow as a straight line, the points were sampled both at the starting and the end point of each imagined line. This resulted in two sample points for each ridge and furrow. The point data set of Buntu is shown in Figure 3. In order to validate this sampling strategy some points were also measured in the middle part of the ridges and furrows located on the western part of the plot. In addition, sampling points were also taken on the positions of newly planted trees and along the middle site of the ditches. In total, the field survey yielded 2016 sample points. For validation purposes, we excluded 235 points from the original dataset. From those validation points, 171 points were derived from the middle site of furrows and ridges of the Buntu’s western part and 64 points were taken from the trees’ positions. Applying the same technique used at Tieng, linear densification with a 0.5 m interval was also carried out to the remaining original data set resulting in 6508 points for interpolation. Interpolation process Interpolation methods were selected in this study based on two considerations. First, is their availability in some readily GIS software


DOI number: 10.5027/jnrd.v3i0.13

packages and, secondly, is according to their complexities algorithm from the simplest principle to the most compound one. The selected interpolation methods for this study were nearest neighbour, triangulation, natural neighbour, inverse distance weighting (IDW), minimum curvature, multiquadratic radial basis function (MRBF) and ordinary kriging. To process those interpolation routines, GIS software of Surfer Version 8.0 (www.goldensoftware.com) was used in this study due to its complete functions in surface interpolation process. It is possible to incorporate the so-called break-line function, which can be used to integrate discontinuity features in the interpolation process. The SAGA-GIS (Conrad, 2007) was also deployed in proportion of validating and visualizing the DEMs. In the following a brief description to each interpolation method is provided. Some examples from studies using these surface interpolation methods in various fields are mentioned as well.

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assessment methods in order to get a more reliable measure of the spatial accuracy of DEMs. As mentioned in the previous section, it is not sufficient to merely use the statistical test for assessing the DEM accuracy. Thus, current study followed the work of (Desmet, 1997; Wood & Fisher, 1993; Yang & Hodler, 2000) that combined the quantitative and qualitative parameters in order to assess the DEM accuracy. The investigation phase of this study is depicted in Figure 4.

Assessment of the DEM accuracy DEM accuracies are often confirmed quantitatively with the root mean square error (RMSE). However, RMSE merely provides a standard deviation value of the elevation error calculated from sampled points and higher accuracy data. It assumes two conditions: First, the values of sampled points are in normal distribution and secondly, the mean error is equal to zero which might not always be the case (Fisher & Tate, 2006). The mean error can be vary because of inaccurate interpolation result. Furthermore, a DEM with a good RMSE does not always represent similarity in comparison to the real surface (Declercq, 1996; Yang & Hodler, 2000) due to a limited sampling data set. To overcome this shortcoming, (Fisher & Tate, 2006) recommended to apply the error standard deviation (S) in which the real value of mean error is considered. Another alternative technique in assessing the DEM quality can be represented through measuring the total area of sink drainages. Such sink drainages can occur due errors in the input data or imperfect interpolation (Wang & Liu, 2006). The size of those sinks can range from single cells up to groups of connected cells which do not have any down-slope path on its surrounding cells (Wang & Liu, 2006). As a consequence drainage paths end in such sinks instead of reaching the main outlet. A critical question, however, always arises on how to distinguish between real and spurious sinks, in particular, when working on overview scales (e.g. catchment area). It may be easier to recognize the spurious sinks as noise on a field-plot scale where sampling points are conducted at every major surface change. The basic consideration is that the more spurious sinks appear, the more spatial errors occur in the DEM. Other authors (Desmet, 1997; Fisher & Tate, 2006) have agreed that evaluating the DEM accuracy should consider both the quantitative and the shape similarity analysis. It is, in fact, a matter of practical reason whether to merely use the quantitative level or to apply the shape similarity analysis. On the one hand, the quantitative level is easier to compare but it lacks any measure on shape similarity (Yang & Hodler, 2000). On the other hand, shape analysis is more like a descriptive or qualitative assessment but has the ability to describe the similarity between interpolated and reference surface (Wood & Fisher, 1993). It remains a challenging task to combine these two

Figure 4. Stepwise analyses of the DEM accuracy

Instead of the RMSE, we used ME and S for the statistical test routine, as recommended by (Fisher, 1998; Li, 1994). The value of ME was kept without absolute value ((Fisher & Tate, 2006) to define whether the DEM is underestimate (negative) or overestimate (positive). Those equations are described as follows: ME = Σ (ZDEM - ZREF) n

(1)

S = Σ [(ZDEM - ZREF) - ME]2 (2) n - 1 Where; ME = Mean error S = Error standard deviation ZDEM = Height value from the DEM ZREF = Height value from the higher accuracy data (real measurement data) Incorporated with ME and S, further assessment was also carried out through the weighted coefficient of determination (wR²) and intercept value (a) based on the linear regression (Krause et al., 2005). The wR² is obtained from the calculation of R² and the gradient b through the following equation: |b| . R2 for b ≤ 1 wR2 = (3) |b|-1 . R2 for b > 1

{


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In terms of R² and its function, earlier authors (Caruso & Quarta, 1998; Desmet, 1997; Heritage, et al., 2009) have also encompassed them to identify DEM accuracy generated from different sampling strategies and interpolation methods. Finally, the last quantitative assessment parameter used was the total area of sinks drainage (Wang & Liu, 2006). It is considered that a larger area (m²) of sink drainage occurs if the DEM is less accurate. After all quantitative parameters were identified; visual analyses were then conducted to select the most similar DEM compared to the original shape of the terraces and furrow shape. Two simple visualization techniques were used in namely cross-section profiling and combination of shaded relief maps with 3D views. To produce such a representative profile for the test sites Tieng and Buntu, two perpendicular cross-section lines were drawn in each of the plot area (Figure 2 and Figure 3). For Tieng plot, line A-B represents the shape of the boulder and the terrace risers while line C-D depicts the terrace bed. The line E-F at Buntu plot represents the longitudinal shape of furrow and ditches, whilst the G-H illustrates the regular repetition of the ridges and the furrows. A DEM, which has relatively extreme quantitative value or dissimilar shape, was deliberately rejected from the final analysis. Rank classification was then performed among the potentially realiable DEMs based on every quantitative parameter. The best DEM on certain quantitative parameter will gain the smallest score. Rank classification has already been addressed by the work of (Chaplot, et al., 2006), which merely focused on RMSE value. To emphasize the reliability effect of each parameter to the DEM accuracy, weighting by factor of 2 was embedded to the parameter of S.

Result In order to derive optimum DEM result, crucial parameters for each interpolation method were properly adjusted beforehand, particularly for minimum curvature, IDW, MRBF and ordinary kriging. For minimum curvature, the maximum number of iterations was set between one to two times from the total grid nodes, i.e. 350,000 for Tieng and 400,000 for Buntu. The maximum residual value was set by default 0.022 for Tieng and 0.017 for Buntu yielded from 0.001 x (Zmax - Zmin). In the case of IDW, some combination values of distance power, search radius and maximum numbers were simulated to get the most appropriate value both of Tieng and Buntu. It was confirmed that combination value of 3, 5, and 2 gave the best performance for Tieng plot, while Buntu plot was 5, 1, and 4 for distance power, search radius and maximum numbers, respectively. For the MRBF, we used smoothing factor (R²) of 0.12 for both Tieng and Buntu. In the case of ordinary kriging, linier (Fig. 5a) and gaussian variogram (Fig. 5b) were likely more fit to Tieng’s data trend whilst power variogram (Fig. 6) was fit for Buntu area. In addition, to optimize the interpolation result in Tieng plot, breakline function was set into the minimum curvature, MRBF and ordinary kriging. Both of Tieng and Buntu’s data set were described in Table 1. The coefficients of variation (CV) for both areas were significantly low due to the regular repetition of the tillage forms. However, Tieng possessed higher CV (0.34%) than Buntu (0.08%) due to the occurrence of terrace riser that has distinct height to the terrace bed.

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(a)

(b) Figure 5. Variogram of Tieng plot with linier model (a) described by error variance nugget = 2.078E-010; linier slope = 1.34; anisotropy ratio = 1.4; angle = 45; and Gaussian model (b) identified by error variance of nugget effect = 0.9; Gaussian scale = 64; length= 22.5; anisotropy rasio =1, angle =0

Figure 6. Variogram of Buntu plot with Power variogram identified by nugget effect error = 0.015; power scale 3.5; length 21.55; power 1.88; anisotropy ratio =1; and angle = 0


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Table 1. Statistical description of morphology in Tieng and Buntu plot Plot area

Tillage form

Area (m²)

Av (m)

Max (m)

Min (m)

SD (m)

CV (%)

Tieng

Bench terraces

1652

1687.53

1699.72

1677.54

5.76

0.34

Buntu

Furrows

673

1676.36

1679.60

1673.70

1,26

0.08

Av = average elevation, Max = maximum elevation, Min = minimum elevation, SD = standard deviation, CV = coefficient of variation of the elevation

Table 2. Comparison of quantitative parameters for the DEM accuracy in Tieng plot Quantitative parameters

a

b

c

d

e

f

g

h

i

j

k

l

0.99

0.98

0.98

0.97

0.99

0.99

0.97

0.98

0.97

0.96

0.98

0.98

B

1.00

0.97

0.99

0.96

0.98

0.98

0.98

0.96

0.61

0.99

0.98

0.98

A

-7.06

57.87

21.55

64.67

33.24

38.77

33.96

60.88

1678.00

16.58

26.33

38.67

wR²

0.99

0.95

0.97

0.93

0.97

0.97

0.95

0.94

0.59

0.95

0.96

0.96

ME

0.06

0.06

0.21

-0.16

0.17

0.01

0.14

0.00

0.12

0.01

0.05

0.18

S

0.29

0.54

0.58

0.89

0.41

0.43

0.73

0.67

0.70

1.09

0.29

0.53

Sink area

5.16

120.41

22.51

100.50

10.66

25.51

21.47

21.01

19.58

23.26

4.08

50.04

a = triangulation, b = IDW, c = ordinary kriging gaussian variogram-breakline, d = ordinary kriging gaussian variogram, e = ordinary kriging linear variogrambreakline, f = ordinary kriging linear variogram, g = minimum curvature-breakline, h =minimum curvature, i = MRBF-breakline, j = MRBF, k = natural neighbour, l = nearest neighbour

Following the stepwise analysis in evaluating the DEM accuracy (Figure 4), the quantitative validation data from all of the interpolation results were initially calculated. Table 2 and 3 compile all of those data for both Tieng and Buntu plot. In order to observe the effectiveness of breakline function in Tieng plot, the DEMs resulted by minimum curvature, MRBF and ordinary kriging without breakline function were also included in this quantitative analysis. Overall, validation result by means of linear regression parameter in the Tieng plot exhibited fine results. The R² values of all DEMs were nearly perfect – close to 1. Likewise, the b values were also fine except the DEM resulted by MRBF-breakline method. It only produced 0.6, which then resulted in low value of wR². Among others, its intercept value was also extremely large and similar to the elevation average provided in Table 1. In contrast, the MRBF showed a better value of linear regression parameter when the breakline function is not included during the interpolation process. Meanwhile, the best value for the linear regression parameter was derived by the triangulation method with 0.99 of wR² and -7.06 of a value. The next quantitative analysis for Tieng plot was carried out by considering the S and ME values. The lower value of S and ME is gained, the more accurate DEM will be resulted. Focusing on the S value, both triangulation and natural neighbour showed the best value (0.29). However, the ME value of natural neighbour was slightly lower rather than that of triangulation. Although gaining lower accuracy than natural neighbour and triangulation, the S value of ordinary kriging linear variogram with and without breakline function were better compared to the remaining methods. Moreover, among the DEMs resulted by the group of ordinary kriging, implementation of breakline function could provide better S value. The S values between IDW, ordinary kriging gaussian variogram-breakline, and nearest

neighbour were slightly similar showing from 0.54, 0.58, and 0.53, respectively. Meanwhile, the ordinary kriging gaussian variogram tended to give an underestimate DEM result (-0.16 of ME) with high S value (0.89). Likewise, the MRBF also showed the most unsatisfactory result of S value (1.09). Analysing the sink area was the final step for the quantitative evaluation between the DEMs in Tieng plot. Among the DEMs, both small sink area was resulted by natural neighbour (4.08 m²) and triangulation (5.16 m²). Although both DEMs resulted by MRBF showed low value of S, they had smaller total sink area (19.58 m² for MRBF with breakline and 23.26 m² for MRBF without breakline function) compared to nearest neighbour (50.04 m²) and IDW (120.41 m²). Such small sink areas in MRBF could be affected by the smoothing factor during its interpolation process which is neither considered by nearest neighbour nor IDW. The exceptionally large area of sink drainage in IDW and nearest neighbour gave a reason to reject this method at representing terrace feature in Tieng plot. Likewise, the ordinary kriging gaussian variogram had also a potential reason to be rejected due to its large sink drainage (100.5 m²). After acquiring the result from quantitative analysis in Tieng plot, the qualitative analysis was then conducted by using the 3D view and cross profile. All of the 3D views of DEMs are depicted in Figure 7. In order to create a contrast view over the terrace features, the hillshading effect was also incorporated into each of DEMs. Those 3D views provided a general overview of each DEM in representing the step form of bench terrace. Meanwhile, Figure 8 illustrates the cross profile of Tieng plot in order to observe the detailed feature of the terrace riser and terrace bed in every DEM. First observation was given to the DEMs resulted from the variation of ordinary kriging method. As illustrated in Figure 5, the Tieng data


DOI number: 10.5027/jnrd.v3i0.13

set was fit to the linear and gaussian variogram. Based on the 3D view in Figure 7, both variogram obviously failed in representing the step feature of bench terrace. Although the DEM from linear variogram showed better view rather than that of gaussian variogram, it created such undulating surface instead of step form. In contrast, implementation of breakline function could result in better feature of bench terrace in the ordinary kriging with linear variogram but not for the gaussian variogram. For this reason, the ordinary kriging linear variogram without breakline and both DEMs from gaussian variogram were rejected for representing the Tieng plot.

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134

Other obvious failures in representing the bench terrace were shown in the MRBF and minimum curvature method. It was evident that the implementation of breakline function did not give any better result for both methods. All of them produced undulating surface instead of step form. In this sense, the DEMs resulted from both methods were potentially rejected. Beside the ordinary kriging linear variogram-breakline, there were others method that could depict the step form of the bench terrace in Tieng plot i.e. triangulation, IDW, nearest neighbour, and natural neighbour. Although they posed similar result, the gradation colour

Figure 7. DEMs of Tieng plot generated from 7 interpolation methods with additional breakline function for minimum curvature, MRBF and ordinary kriging


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DOI number: 10.5027/jnrd.v3i0.13

through the hill-shading technique showed different effect. The darker colour of hill-shading shows the steeper feature. Between the triangulation and natural neighbour methods had similar pattern showing dark colour on the terrace riser and smooth graduation of grey colour on the terrace bed. For the IDW, scattered black and white spot were found along the edge of terrace riser and on the terrace bed. In the 3D view of nearest neighbour, there were two distinct features embedded in its DEM. First, the stripping shadow on the terrace riser and secondly, the narrow black colour spot along the terrace bed. Some discrepancies showed in the 3D view were then thoroughly observed by cross profile illustrated in Figure 8. In order to simplify the analysis, the DEMs of MRBF, minimum curvature, ordinary kriging linear variogram and both ordinary kriging gaussian variogram were not included in the cross profile analysis due to their incapability based on the previous step analysis. For a visual comparison, we inserted some observed points into the cross-section profile.

135

Through the A-B profile, it was confirmed that both minimum curvature-breakline and MRBF-breakline failed to represent the plain shape of terrace bed despite implementing the break-line function. In the case of nearest neighbour, its profile exhibited such stepwise form through the boulder shape, even though it showed better form on the terrace form. Over the cross profile, IDW tended to produce small micro-topography right on the terraceâ&#x20AC;&#x2122;s edge. There were numbers of artefacts along the line indicating more sink occurrence compared to natural neighbour and triangulation. The C-D profile depicted a better view of inaccuracy of some DEMs. All of the profiles originated from minimum curvature-breakline, MRBF-breakline and ordinary kriging linear variogram-breakline were overestimating the observed points. Through this profile, the IDW also produced small topography along the plane terrace bed. In contrast, both triangulation and natural neighbour were pretty reliable in representing the terrace bed.

(a)

(b)

Figure 8. Cross section profile (Tieng) which is perpendicular to contour line (a) and along the terrace bed (b)


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In the case of Buntu plot, the wR² and intercept value were initially compared as the first step of the quantitative analysis. Among the tested interpolation methods have the same value of wR² (0.98) except for the nearest neighbour that only resulted in 0.93. Additionally, the nearest neighbour method exhibited the worst intercept value (-27.11). This was the first indication of inaccurate DEM resulted from nearest neighbour method.

(a)

(b)

(c)

(d)

(e)

(f)

(f)

Figure 9. DEMs of furrow visualized by means of the combination of 3D view and hill shading technique for triangulation (a), nearest neighbour (b), natural neighbour (c), minimum curvature (d), ordinary kriging (e), inverse distance weighting (f), and multiquadratic radial basis function (g)

136

After analysing the value of ME and S in Buntu plot, it was more evident that the nearest neighbour method was less accurate compared to other methods. It exhibited 0.29 of S and 0.05 of ME. That was another reason to reject the nearest neighbour in representing Buntu plot area. In contrast, other methods resulted in 0.1 of S. Between them the IDW was slightly better on the ME value (0.01). These results confirmed that based on the S value the IDW method was superior compared to other methods. The last quantitative analysis for Buntu plot was the sink drainage area. According to the values mentioned in Table 3, IDW showed the largest area of sinks (46.7 m²) while nearest neighbour resulted in smallest area (1.97 m²). Those values were contrary with earlier mentioned result of S, ME and wR². Thus, this required further analysis through the qualitative method. Meanwhile, between the ordinary kriging, minimum curvature and MRBF produced relatively similar sink area, which were actually larger than that of natural neighbour (13.06 m²) and triangulation method (10.24 m²). The first impression through the E-F and G-H profiles comparison was shown by the stepwise form yielded by nearest neighbour (Figure 10). Sharp tip were also identified at the ridge and furrow’s edges (figure 10a) which was in fact not found in Buntu plot. Figure 9a enhances those sharp edges depicted as distinct shadow areas right at the upper edge. Those sharp edges were exhibited by the triangulation method. Due to their contrived form, DEMs interpolated by means of nearest neighbour and triangulation were rejected for further analysis in Buntu plot. Another spurious rough surface was also found in the E-F profile of the IDW method. This rough surface could promote the occurrence of sinks (Table 3). Through the Figure 9f, group of depressions occurred along the furrows. Likewise, number of stripping shadow feature were also found on top of the ridges. Those features were actually the common effect of local extrema resulted by IDW method. Hence, IDW was also rejected to represent the furrow form in Buntu plot. Based on the E-F cross profile, three methods were likely to exhibit similar form, namely minimum curvature, MRBF and ordinary kriging. Unexpectedly, they exhibited undulating surface at the end of ridge’s edge and lower site of furrow before reaching the ditches area. In reality, this occurrence was not found along the furrow area in Buntu plot. Due to that dissimilarity, those three irregularly rough DEMs were also rejected for further analysis. Combination of quantitative and qualitative assessment of DEM accuracy have filtered some potential DEM and rejected spurious DEM at representing the terrace and furrow form. The terrace form in Tieng plot was well represented by means of triangulation and natural neighbour. The scoring procedure was then conducted between those DEMs and showing that the triangulation is better compared to natural neighbour (Table 4). In case of Buntu plot, there was only one method left, namely natural neighbour, which was regarded as the best fit DEM at representing the furrow. It fairly means that scoring and rank classification was not carried out in case of Buntu plot.


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Table 3. Comparison of quantitative parameters for the DEM accuracy in Buntu plot Quantitative parameters

Triangulation

IDW

Ordinary kriging

Minimun curvature

Nearest neighbor

0.99

0.99

0.99

0.99

0.99

0.94

0.99

b

1.01

1.01

1.01

1.01

1.01

1.02

1.02

a

-23.88

-18.10

-15.45

-15.25

-16.56

-27.11

-26.50

wR²

0.98

0.98

0.98

0.98

0.98

0.93

0.98

MRBF

Natural neighbor

ME

0.02

0.01

0.02

0.02

0.02

0.05

0.02

S

0.10

0.10

0.10

0.10

0.10

0.29

0.10

Sink Area (m²)

10.24

46.71

18.02

18.29

19.05

1.97

13.06

F

(a)

E

G

(b)

H

Figure 10. Cross section profile of Buntu plot which is perpendicular to contour line (a) and along the contour line (b)

137


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138

Table 4. Comparison of quantitative parameters for the DEM accuracy in Tieng plot Parameters

Weighting factor

Triangulation

Rank

Scores

Natural neighbor

Rank

Scores

wR²

1

0.99

1

1

0.96

2

2

Intercept

1

-7.06

1

1

26.33

2

2

Mean error

1

0.06

2

1

0.05

1

2

Error standard deviation

2

0.29

1

2

0.29

1

2

Sink drainage area (m²)

1

5.16

2

2

4.08

1

Total scores Rank

7 1st

1 9

2nd

Discussion Two issues should be addressed first before describing the result in detail. First, this study convinces the work of Heritage et al. (2009) that morphological changes can effectively be used as the basic sampling framework during the survey. The point sampling taken along the terrace’s edges (Tieng plot) and at the tip of ridge and furrow (Buntu plot) can be an efficient and effective technique for creating proper DEM data of bench terrace and furrow tillage. Secondly, the point density is of great importance to the DEM result. Without linear densification through the observed points, any tested interpolation method in this study can not produce optimum DEM. In this sense, our study is in accordance to Aguilar et al. (2005) who mentioned the principle key in generating the DEM data, i.e. sampling technique and number of point sampling. Each of the interpolation method tries to accurately represent the regular repetition of bench terraces and furrow forms. Any interpolation method, which is overdoing the smoothing process, will yield less accurate DEM either in Tieng or Buntu plot. Even for a more sophisticated interpolation method such as ordinary kriging can not provide optimum result to the discontinuous surface of bench terrace. That result corresponds to Mitas and Mitawova (1999) who underlined the incapability of kriging method in representing the local geometry – in this case, the step form of bench terrace. Natural neighbour and triangulation, in contrast, show better performance in representing the Tieng plot despite its simple algorithm. The break-line function is implemented to the ordinary kriging, minimum curvature, and MRBF to optimize the interpolation process in Tieng plot. This function constraints the grid calculation nearby the edges of terrace risers to have value as close as to the value along the break-line. Among them, ordinary kriging linear variogram exhibit relatively close to the bench terrace form, even though it is not as accurate as both triangulation and natural neighbour. Meanwhile, Minimum curvature and MRBF always attempts to give the smoothest surface to its DEM along the break-line area. As a result, there are irregularity shapes in the minimum curvature and MRBF. Through this study, we can confirm that assessing the DEM accuracy by mere statistical value (ME, S, and wR²) does not always provide reliable analysis. For instance, in the case of Buntu plot, all of the tested interpolation methods – excluding the nearest neighbour –

gain same value of S and wR². In fact, we found different final result after conducting a thorough qualitative technique by using the 3D view and cross profile analysis. Thus, this finding follows the earlier studies by Wood and Fisher (1993), Declercq (1996), Desmet (1997), Yang and Hodler (2000), and Fisher and Tate (2006) that stated the importance of combination between the quantitative and qualitative technique in assessing the DEM accuracy. Distinguished with other quantitative parameters which depend on the point’s comparison, the sink drainage area is promoted in this study as an indication of spatial error. Indeed, it has strong correlation to the shape similarity. The more artefacts occur at the DEM surface, the larger the sink area will be. It applies to all methods except the nearest neighbour at the furrow system. Despite its stepwise form on furrow, it yields the smallest sink area compared to others. It indicates that a more flat area occurred rather than a sink due to those stepwise forms. Those flat features are also considered as noise within the DEM because they can introduce a discontinuity to the surface flow (Garbrecht & Martz, 1997). The final result shows that triangulation is the best fit method followed by natural neighbour at representing the bench terraces in Tieng plot. In the case of furrow in Buntu plot, natural neighbour is the most accurate method. Despite its superiority at representing the bench terrace, triangulation has larger sink drainage area compared to natural neighbour. It means that triangulation will require more pre-processing to eliminate such sinks rather than natural neighbour. Eventually, there are found such a combination tillage form between bench terrace and furrow in the study area. In that features, natural neighbour might perform as the best fit method with minimum efforts.

Conclusions This study has confirmed the robustness of a stepwise analysis between quantitative and qualitative assessment techniques for DEM accuracy. A fine value of quantitative parameter does not necessarily mean that it will fairly possess a good spatial accuracy. Thus, this study suggests to not independently apply the quantitative parameters without crosschecking it by the shape similarity analysis. For further


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applications, it is always important to compare the accuracy of the DEM yielded from the readily possible methods instead of depending to one favourite method. In addition, the applicability of breakline function for optimizing such more sophisticated interpolation method such as ordinary kriging requires additional studies. However, with a proper point sampling technique, a simple interpolation can result in more reliable DEM. In this study, we conclude that natural neighbour performs as the best fit method at the furrow and triangulation generates the most accurate DEM at the bench terrace area.

139

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