Component 2A

eb

1656

2 0 Philippine Institutional Strengthening Capacity to 1 Adapt to Climate Change Outcome 3.1 Activity 3.3 1

Component 2A: Simplified Vulnerability Assessment Tools Combining Indigenous and Scientific Knowledge for the Agricultural Sector in Benguet and Ifugao

UPLB Foundation Inc.

Lanzones St., UPLB Campus, College, Laguna, 4031 PHILIPPINES Tel: (049) 536 3688 Fax: (049) 536 6265

2nd Mid-Term Progress Report for Component 1B of UPLBFI-SPICACC 3.1 Activity 3.3

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Prepared by Senior Researcher: Dr. Zita V.J. Albacea Team Members: Dr. Jose Nestor M. Garcia Dr. Amparo M. Wagan Prof. Nellwyn M. Levita Ms. Aizobelle P. Huelgas Mr. Rozrikk C. Abo-abo Engr. Meynardo I. Ricarte

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Executive Summary The third component of the study developed a simplified science-indigenous knowledge-based vulnerability assessment tool and adaptation strategies for the upland communities of Benguet and Ifugao. The simplified vulnerability assessment tool is an attempt to provide an easy to use guide for identifying vulnerable agricultural areas. Through this tool, the vulnerability assessment may be done in a quick but organized manner. This tool integrates variables often used in describing agricultural systems and designing appropriate interventions as well as actual experiences and observations in agriculture in Benguet and Ifugao. The UPLBFI project team believes that this may provide reliable conclusions as to what agricultural areas and communities in the province need in terms of appropriate adaptation measures. A Vulnerability Index from 0 to 10, with 10 as the highest vulnerability, was developed to give a comparative evaluation of various areas being assessed.

As a contribution to methodology and tool development, optimal planting dates of a crop were determined using two approaches. The first approach is based on rainfall probabilities and assumes that rainfall is the crop’s main source of water. It selects the dates that have the highest probability of meeting the water requirements of the crop at each stage of development. The second approach is based on yield probabilities. The yield probabilities were simulated using DSSAT CERES crop models parameterized for selected crops and validated under local conditions. One of the model inputs is the crop’s genetic coefficient. To date, crop coefficients are available for limited varieties and there are no known crop coefficients yet for rice varieties commonly planted in Ifugao and vegetables commonly planted in Benguet such as lettuce, cauliflower, carrots and beans. This study was thus limited to exploring yield simulation of varieties with existing crop coefficients such as IR72 rice variety and Magilis tomato variety.

The optimal planting dates for rice were constructed based on rainfall probabilities and yield probabilities of the IR72 variety. Although the two methods generally gave different planting dates, they both recommended the last week of August as the optimal planting date of rice in Banaue for a normal year. The optimal planting dates derived based on yield probabilities are more plausible, and therefore, recommended. This is because estimated crop yields using eco-physical crop simulation models account more for the agro-climatic environment and crop management strategies.

The optimal planting dates for tomato were obtained based on the yield probabilities of the Magilis variety. Results showed that generally the dates are different for the two adjacent locations namely, Baguio City and La Trinidad, except during wet years. These differences may be due to the fact that the number of historical years available for the two locations differs. Since the classification of years into three climatic conditions is highly dependent on the available historical data, some normal years in Baguio City were classified wet years in La Trinidad. Thus, these differences in the groupings of the historical years resulted in different recommended planting dates.

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To understand the weather scenarios for 2020 and 2050 in Baguio City, the historical analogues of these years were determined using four methods. The first two methods, which involved the cluster analysis of years, failed to find immediate historical analogue. Hence, two methods involving the use of the least average absolute deviation were explored. Results showed that among the 30 historical years considered, 1995 has the closest weather scenario to 2020 and 2050. This implies that during these years, Baguio City is expected to experience an average daily temperature similar to 1995, when highest values of temperature were observed among the 30 historical years. Thus, the recommended planting dates for tomato in 2020 and 2050 will be based on the optimal planting dates of year 1995. It is week 51, that is, during the third week of December.

However, given data limitations such as: a) incomplete historical weather data and b) unavailability of crop genetic efficients for varieties commonly planted in Ifugao and Benguet; the construction of optimal planting dates for selected varieties was carried out more as a methodological exercise to illustrate its usefulness as a tool for climate change adaptation planning—granted that there is access to complete historical data and available genetic coefficients for local varieties. The optimal/recommended planting dates in this report are not in any way endorsed by FAO and DA for inclusion or adoption into the current agricultural production systems of Ifugao and Benguet.

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Table of Contents Title

Page

Executive Summary

2

Introduction

5

Part 1: Simplified Vulnerability Assessment Tool

7

1.1

Conceptual Framework

7

1.2

Methodology

7

1.3

Results and Discussion

8

1.4

Conclusion & Recommendations

22

Part 2: Climate Change Adaptation Tools

23

2.1

Methodology

23

2.2

Results and Discussions

27

2.3

Summary and Conclusions

53

References

54

Annexes

56

Consistency of the reported crop sensitivity to climate-related hazards between Benguet and Ifugao.

56

Comparison of responses from different focus groups on climate change questions based on survey conducted in Benguet & Ifugao

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Adaptation Strategies of Benguet and Ifugao to drought

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Adaptation Strategies of Benguet and Ifugao to flooding.

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Adaptation Strategies of Benguet and Ifugao to landslide.

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Adaptation Strategies of Benguet and Ifugao to pests and diseases.

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Adaptation Strategies of Benguet and Ifugao to continuous rain.

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Adaptation Strategies of Benguet and Ifugao to typhoon.

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Interview Schedule used in the Pretesting of the Vulnerability Assessment Tool.

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Introduction Nowadays, climate change is a major environmental issue that cuts across the boundaries of different nations, sectors and professions around the world. As part of the global community, Philippines does its part in preparing and implementing adaptive measures that would minimize the risks and adverse impacts of climate change. The focus is mostly given to sectors and areas where climate change is currently observed or foreseen to have the greatest impact. These include disaster-prone settlements, high-risk population centers and food production areas.

Food production areas are considered to be highly vulnerable to climate change because crop and livestock productivity is highly dependent on climatic conditions. Changes in temperature and amount of rainfall coupled with the occurrence of extreme weather events such as drought and heavy rains may cause changes in growing seasons, heat stress in plants and animals, outbreaks of pests or diseases and increased soil erosion. These impacts of climate change can bring significant losses and damages to the agricultural sector. Since the agricultural sector is the largest contributor to the Philippine economy, it is important to assess its vulnerability to climate change.

Vulnerability of a particular group or unit to a variety of stresses is commonly characterized as a function of exposure, sensitivity, and adaptive capacity. Vulnerability assessment identifies a range of factors that may reduce response capacity and adaptation to the stressors. Thus, vulnerability assessment of the agricultural sector to climate change will facilitate the decision-making process of specific stakeholders of the agricultural sector about their options for adapting to climate change within the scope of their resources. When done at the local level, the climate change issues, problems and solutions should be contextualized and translated into a language that people can understand.

This study is focused on two provinces in Northern Luzon namely, Benguet and Ifugao. In 2007, the Presidential Task Force on Climate Change (2007) stated that the northern part of Luzon is one of the two regions in the Philippines that has warmed and dried the most. In addition, it is also the area that is most frequently hit by tropical cyclones since 1980. Climate change in Northern Luzon is felt especially in upland communities, where the main source of livelihood is agriculture. The vulnerability of the agricultural sector can be reduced if adaptation can be incorporated into production process. This also involves the assessment of vulnerability to climate hazards as well as the evaluation of risks associated with them. Thus, to help ensure sustainable food production and to reduce threats to food security, vulnerability assessment tools should be developed and appropriate climate change adaptation mechanisms should be undertaken by farmers. One such strategy is through improvements in the analysis and interpretation of weather and climate data. Adaptive measures include understanding climatic patterns through establishment of farm weather information and advisories, and adjusting cropping systems through determination of optimal planting dates.

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This study aimed to (1) design and develop simplified vulnerability assessment tools for the agricultural sectors of Benguet and Ifugao; (2) determine optimal planting dates for selected agricultural crops such as rice and vegetables using rainfall and yield probabilities for Benguet and Ifugao; (3) analyze the projected climatic variability in the two provinces through finding the historical analogues that resemble the anticipated climate conditions of 2020 and 2050 for Benguet and Ifugao; and, (4) recommend optimal planting dates for rice and vegetables for years 2020 and 2050.

To achieve these objectives, the study analyzed data and information gathered through from focus group discussion (FGD), key informant interviews (KII), and formal surveys in both pilot barangays and non-pilot municipalities. Indigenous knowledge and scientific information were integrated in a simple procedure for conducting vulnerability assessment at the farm level. The procedure involved a series of questions, which may be answered using scoring matrices and later tabulated and used as inputs in the calculation of vulnerability indices. On the other hand, determining the modified planting date under climate change conditions made use of advances in statistical science, crop science, database management, crop simulation modeling, and other techniques/fields.

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Part 1: SIMPLIFIED VULNERABILITY ASSESSMENT TOOL 1.1 Conceptual Framework for the Development of the Vulnerability Assessment Tool The conceptual framework (Figure 1) for the development of the vulnerability assessment tool was based on the climate change vulnerability concept presented by Allen Consulting (2005, cited by Smith 2010). Vulnerability is defined as “a function of the character, magnitude, and rate of climate variation to which a system is exposed, its sensitivity, and its adaptive capacity� (IPCC 2001, cited by Smith (2010). Allen (2005) demonstrated that exposure to a climate event combined with sensitivity to that event may be interpreted as potential harm and that potential harm may be offset by adaptive capacity, resulting in a particular vulnerability level for a system.

Figure 1. A framework for understanding vulnerability (Smith in press, adapted from Allen Consulting 2005 after IPCC 2001).

1.2 Methodology A series of steps were involved in the development of a simplified vulnerability assessment tool. These include the following: 1.2.1 Identification of climate and agriculture variables

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Based on the results of Component 1A of the project (Review and Screening of Available Vulnerability Assessment Tools for Their Application in the Agricultural Sectors in Benguet and Ifugao), the suggested climate and agricultural variables were examined as to their relevance and practical application to the conditions in Benguet and Ifugao at the community level.

1.2.2 Integration of scientific and indigenous agricultural knowledge Specific information (including indigenous knowledge and practices, community profile), about the province of Benguet and Ifugao collected during Focused-Group Discussions (FGD), Key Informant Interview (KII) and formal survey of Component 1B (Local Knowledge and Tools for Assessing Vulnerability of the Agricultural Systems to Changing Climate: The Case of Ifugao and Benguet Provinces (Formal Field Survey, FGD and KII Reports), were examined and integrated with the variables identified in component 1A, to describe sensitivity, potential impact and adaptive capacity of the agriculture sector (crops, livestock, farming community) of the province at the community level.

1.2.3 Development of qualitative measures for the variables identified A rating scale for each of the variables was produced with the corresponding qualitative descriptions to make the assessment simple and easy to understand and use.

1.2.4 Calculation of community-level vulnerability index A vulnerability index was formulated based on exposure to climate hazards, sensitivity to climate hazards and adaptive capacity of the agricultural sector of the province.

1.2.5 Pretesting of the vulnerability tool The vulnerability assessment tool was pretested on July 2011 by Component 2A in Paoay, Atok and Loo, Buguias, Benguet. The trainings were attended by 17 participants in Paoay and 20 participants in Loo, Buguias. The participants were composed of farmers and personnel from the Municipal Agricultural Office. The profiles of the participants from both provinces are presented in the Component 3B report.

1.3 Results and Discussion 1.3.1

Climate and Agriculture Variables for the Simplified Vulnerability Assessment Tool

Much of the discussion in this section of the report covers specific climate and agriculture variables included in the development of the simplified assessment tool for Benguet and Ifugao. Essential climate and agriculture data requirements include those identified and suggested in the Component 1A report, namely: Indigenous knowledge in the form of local observations and experiences of farmers and other stakeholders regarding Report for Component 2A of UPLBFI study for MDG-F 1656 Outcome 3.1

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climate-related hazards, exposure and sensitivity of crops, and local adaptation measures as reported in Component 1B. How this set of information was integrated to make the assessment location-specific and appropriate for the conditions in Benguet and Ifugao are elaborated here.

The climate and agriculture data requirements for assessing vulnerability and adaptive capacity are presented in the Component 1A report. For the simplified vulnerability assessment, these variables were grouped into the following a) Exposure to climate-related hazards, b) Sensitivity of the community to the climate-hazards, and c) Adaptive capacity of the community.

Variables referring to exposure to climate-related hazards include the following: type of hazard, seasonality of hazard, frequency of occurrence of hazard within a ten-year period, duration, impacts, prevalence and magnitude of damage per hazard. These variables are hazard-specific, hence, data to be collected differ with the hazards. To integrate climate conditions in the provinces of Benguet and Ifugao, responses from farmers, government officials, and the municipal planning and development officers in a survey (Component 1B report) were collated and examined (Annex B). Through this analysis, local perspectives on climate-related hazards, their impacts and local coping strategies were identified and included in the development of the assessment tool, making it even more specific for Benguet and Ifugao.

Variables for assessing sensitivity to hazards include sensitivity of the environment, farming systems and the people in the community. Sensitive environments are areas that are flood-prone, steep, drought and dry spell sensitive and denuded. Based on the discussions in Component 1B, the farming systems sensitive to climate-related hazards are rice terraces and vegetable areas. However, areas with large livestock populations and aquaculture were also included. Likewise, areas with high population densities, large number of subsistence farmers, an aeging population (e.g. inverted population pyramid), low incomes, and agriculturedependent livelihoods were also deemed to be sensitive to climate-related hazards.

Also integrated in this assessment tool are farmers’ experiences and observations on the sensitivity of their crops to climate and change (based on the survey conducted in Benguet and Ifugao and discussed in Component in 1B report). Trends in farmer rating of the sensitivity of their crops to climate change vary (Annex 3). Responses of crops to extreme heat, frequent thunders, long dry seasons and strong and frequent typhoons were consistent.

However, there were differences in the sensitivity ratings of crops to climate change hazards.

For example, sweet potato was rated by some to be highly sensitive to flooding while some rated the same crop as not sensitive to flooding at all. A closer study of the farmers’ sensitivity rating revealed that responses were based more on the location where the crops are grown rather than the agronomic and physiologic characteristics of the crops. These observations had important implications in the integration of indigenous and scientific knowledge into the assessment methodology.

Data and information referring to the community’s preparedness to climate-related hazards were also included in this simplified vulnerability assessment. These include the general knowledge of the local stakeholders to climate-related hazards, presence of early warning system, access to information and the presence of adaptation measures. Local

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adaptation measures in Benguet and Ifugao documented in the Component 1B report were considered in the design of this simplified assessment (Annex 4).

As regards to the information on adaptive capacity, these were classified into eight categories as suggested in the Component 1A report. These are: (1) physical capacity, (2) ability, (3) resource availability, (4) communication system, (5) degree of isolation, (6) availability of support systems, (7) economic capacity, and (8) technological ability. A DriverPressure-State-Impact-Response Model illustrating the effects of Climate Change on agriculture is shown below.

Driver: Society’s Agricultural Demand

• Climate Modeling • Bio-physical Char.

Pressure: Hazards of Climate Change Policy Response: Available Adaptation Options

• Crop Loss Assessment • Cost Effective Analysis

Barriers: Lack of technology, Funds, etc

State: Vulnerability of • M&E Monitoring

Impact: Less Agricultural Production

Agricultural Production to Climate Change • V&A Assessment • Economic Valuation

Figure 2. DPSIR framework of the effects of Climate Change on Agriculture.

The different hazards perceived to most likely affect the community members were identified during an orientation on the use of the vulnerability assessment tool. They cited these hazards based on past experiences and by describing some of the slow onsetting effects of climate change such as temperature rise and variations in rainfall patterns.

The next subsection illustrates and discusses how these climate and agriculture variables were grouped and supplied with qualitative interpretations or measures.

1.3.2

Qualitative Measures of the Climate and Agriculture Variables included in the Simplified Vulnerability Assessment Tool

To facilitate vulnerability assessment, the climate hazard and agriculture variables for a specific community or barangay was gathered using the corresponding percentage or qualitative description and measured in a scale of 0 to 10, with 10 having the highest score.

Table 1 is the exposure of the farming areas and their various farming systems to a particular hazard. These were obtained the Municipal or Barangay Agricultural Offices. The particular hazard was also evaluated in terms of frequency of occurrence and duration. Data was sourced from PAGASA and/or local weather stations.

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Table 1. Variables referring to the Exposure of the agriculture sector of Benguet and Ifugao to climate-related hazards.

Data Question Exposure Score Description Source

A1

What proportion of the community’s land area is related to agriculture?

0 - 10

A2

What proportion of the agricultural area could be directly affected by the hazard?

0 - 10

A3

What proportion of the community is dependent on agriculture?

0 - 10

A4

A5

How frequent does the community experiences this hazard in a 10-year period?

How long does this hazard affect the agricultural sector to cause damage?

0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10

Percentage of agricultural lands from total land area of Barangay Percentage of agric lands that could be affected by the hazard Percentage of Barangay population into dependent on agriculture 0 1 time 2 times 3 times 4 times 5times 6 times 7 times 8 times 9 times 10 times None Very short

MAO

MAO

MAO

Weather Data/ Past Experiences

Short moderate

Weather Data/ Past Experiences

Long Very Long

Table 2 summarizes the variables referring to the sensitivity or risk of the farming areas and communities in Benguet and Ifugao to climate change hazards. Oftentimes, the variables were described and rated in terms of experiences with a particular hazard. For example, the score can be described in terms of the percentage of crop lost due to a particular hazard such as typhoon and floods.

As suggested in the component 1A report, data requirements under each variable may differ (i.e. through reconnaissance, key informant or key informant panel interviews). The Report for Component 2A of UPLBFI study for MDG-F 1656 Outcome 3.1

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assessment team may even get information about these variables through a review and study of secondary data from Benguet and Ifugao.

Table 2. Variables referring to the Sensitivity of the agriculture sector of Benguet and Ifugao to climate-related hazards.

Description Data Score Question Risk/Impact Source

B1

What proportion of profit in agricultural production may be lost?

0 - 10

B2

What proportion of the agricultural assets of the community was damaged?

0 - 10

B3

What is the opportunity cost from the hazard? (not able to market, low growth rate of plants and animal)

0 - 10

B4

What is the proportion of subsistence farmers?

0 - 10

B5

What proportion of agriculture contributes to the community's income?

0 - 10

Percentage of agricultural production that maybe loss to the hazard Percentage of agricultural assets that could be destroyed by the hazard Percentage of agricultural opportunity cost that maybe loss to the hazard Percentage of subsistence farmers from the total number of farmers in the barangay Percentage of income derive from agricultural related activities of the barangay

Farmer

Barangay

Farmer

MAO

Barangay

Qualitative descriptions of the variables under adaptive capacity are presented in Table 3. Many of the variables were measured in terms of availability. These variables include resource availability and physical capacity. Other categories that refer to adaptive capacity were described and measured in terms of the percentage of the population or the farming community. For economic capacity, versatility of skills or the ability to earn income other than farming was included to differentiate it from the economic condition of the farming community (cited in Table 2), whose sole dependence on agriculture for income is used as a descriptor of sensitivity.

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Table 3. Variables referring to the Adaptive Capacity of the Agriculture sector of Benguet and Ifugao to climate-related hazards.

Item

C1

C2

C3

C4

C5

Adaptive Capacity Deficit

How much agricultural area is isolated during the hazard?

How much is the need for support systems during the hazard? (Ex. Bayanihan, Government and NGO support, Credit facility, etc.)

What proportion of the community cannot afford to spend for adaptation cost?

What proportion of people in the community has no other sources of income than agriculture?

What is the need of the community's technological adaptation (Knowledge of adaptation techniques both scientific and indigenous practices?)

Score

0 - 10

0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10

Description

Data Source

Percentage of agricultural land isolated over total barangay land isolated during hazard

Farmers/ MAO

None Very few Few

Farmers/ MAO

Moderate

Many Very Many None Very few Few

Barangay

Moderate

Many Very Many None Very few Few

Barangay

Moderate

Many Very Many None Very few Few

MAO

Moderate

Many Very Many

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1.3.3

Community-level Vulnerability Index

After having shown the qualitative descriptions and measures of the variables for assessing vulnerability of the agriculture sector of Benguet and Ifugao (Tables 1-3 of the previous section), a qualitative variable was quantified by the rating scale of 0 - 10 in terms of percentage or ranking of the indicator. The definition of ‘community-level’ was the Barangay’s spatial boundary.

The Community-level Vulnerability index was computed based on the framework shown in figure 1.

Exposure Index

Sensitivity Index

Potential Impact

Adaptive Capacity Deficit Index

Community-level Vulnerability Index Figure 3. Step by step procedure in computing the community-level vulnerability index.

Area Exposed Score + Population Exposed Score Exposure Index (EI) = ----------------------------------------------------------------- + Effect of 2 Frequency & Duration

Where, A1 Score * A2 Score Area Exposed Score = -------------------------------100 A3 Score Population Exposed Score = --------------10 A4 Score * A5 Score Effects of Frequency and Duration = (1 – (A3 Score /10)) * -----------------------------100

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The effect of a hazard’s frequency of occurrence and the duration it stays in the community takes into account the population whose livelihood is not related to agriculture but are consumers of agricultural products. When the frequency and duration of the hazard increases other sources of income such as mining and tourism will also be negatively affected. Thus, consumer buying capacity and/or access to agricultural products will also worsen the plight of the farmers with respect to the particular hazard.

The Sensitivity Index (SI) is computed as:

Sensitivity Index (SI) = Scores B1 + B2 +B3 + B4 + B5 5 Where, Potential Impact (PI) = EI * SI Adaptive Capacity Deficit Index (ACDI) = Scores C1 + C2 + C3 + C4 4 * 10 The Community-level Vulnerability Index (CLVI) is computed as:

Community-Level Vulnerability Index (CLVI) = PI * ACDI The result will range from 0 to 10, with 10 indicating the most vulnerable. The table below shows the qualitative interpretation of the CLVI:

Table 4. Qualitative interpretation of a vulnerability index Index Value Qualitative Interpretation 0 – 1.99 Lightly vulnerable 2 – 3.99 Moderately vulnerable 4 – 5.99 Vulnerable 6 – 7.99 Highly vulnerable 8 – 10.0 Extremely vulnerable

1.3.4

Results of VA Tool Pretesting

The Agricultural System Vulnerability and Adaptive Capacity Assessment Tool was pretested in Benguet. The pilot sites in Atok and Buguias were visited and both activities were attended by some 20 farmers, farmer leaders and local government officials, such as the Municipal Agricultural Officer.

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Based on the results of the pretest in Benguet (Paoay, Atok and Loo, Buguias) about the various hazards the local stakeholders have identified themselves, the resulting perceived community vulnerability was very close to the Index, as shown in Table 5.

During the pre-test, the tool was found easy to understand and use. However, there were specific comments and suggestions to make the tool more appropriate and practical for the agricultural conditions. For example, it was suggested that as much as possible, secondary agricultural and demographic data that are more reliable and based on municipal, barangay records should be obtained.

The tool was also found to provide an objective assessment of climate hazards, especially considering that the community has its own perceptions of vulnerability. For instance, in Paoay, Atok, their perceived typhoon vulnerability was 7, and the results showed 6.186, resulting in a qualitative interpretation of ‘Highly Vulnerable’. Their perceived vulnerability to Monsoon was 5, and the AgSys VACA results showed 3.648, having a qualitative interpretation of ‘Moderately Vulnerable’.

Perceptions about vulnerability and the corresponding ratings may vary among local stakeholders and the use of this tool may eliminate biases resulting from such differences. As such, it was suggested that the results of the assessment be disseminated to the local political leaders and policy makers in the municipality. During the pre-test, it was also suggested to include the Department of Social Welfare and Development (DSWD) and the Public Works and Highways (DPWH) as members of the assessment team so that other related aspects of the community may be considered in the assessment.

Table 5. Results of the pretesting of the vulnerability assessment tool in Paoay, Atok, Benguet. 26 farmers attended.

Item / Hazard

Typhoon

Monsoon

Exposure Index (EI) Sensitivity Index (SI) Potential Impact (EI x SI) Adaptive Capacity Deficit Index Vulnerability Index

0.950 7.400 7.030 0.880 6.186

0.950 6.000 5.700 0.640 3.648 Moderately Vulnerable

Qualitative Interpretation

Highly Vulnerable

Table 6. Results of the pretesting of the vulnerability assessment tool in Loo, Buguias, Benguet. 23 farmers attended Item / Hazard Exposure Index (EI) Sensitivity Index (SI) Potential Impact (EI x SI)

Typhoon 0.950 8.000 7.600

Landslide 0.538 3.600 1.937

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Flood 0.590 6.400 3.776

Hailstorm 0.201 2.400 0.482 16

Adaptive Capacity Deficit Index Vulnerability Index Qualitative Interpretation

1.3.5

0.720 5.472 Vulnerable

0.720 1.394 Lightly Vulnerable

0.540 2.039 Moderately Vulnerable

0.420 0.203 Lightly Vulnerable

Slow-onset Effects of Climate Change and the Vulnerability Index

The IPCC report (IPCC, 2007) states, that the earth will become warmer by an average 2 degree Celsius in this century. There is going to be greater energy available in the atmosphere that will stir-up the global weather systems and lead to more frequent reoccurrences of extreme weather conditions. These weather conditions may bring about more of the many hazards considered in the vulnerability assessment. To make this tool more effective, it should also be able to consider the effects/impacts of slow-onset climate change.

To factor in the slow setting effects of climate change, we need to analyze the projected differences in maximum-minimum temperatures, precipitation, typhoon occurrence and other weather conditions that bring about various hazards to upland communities. These factors will also affect the different stages of crop growth that determine yield. We could categorize these effects into two: the dry effect and the wet effects. The dry effect would be mainly due to increase in temperature that would cause hazards in progression: dry spells, droughts. The wet effects would be mainly due to increased precipitation and would cause more monsoon rains, typhoons, flooding and landslides. Some of these changes in temperature are already experienced by some respondents in the study of Component 1B and are being mitigated by simple procedures. For instance, amidst higher temperature in the field, the farmer either brings a broad hat or umbrella or goes to the field earlier or later in the day to avoid the hot noontime temperatures. In terms of dry spells, they are still capable of utilizing the irrigation systems of the community. Subtle variations in seasonal precipitation are covered by some indigenous indicators such as presence of insects and other animals— allowing them to adjust their planting time.

Dynamically downscaled climate change scenarios for Benguet and Ifugao for the years 2020 and 2050 were used (including rainfall, temperature and occurrence of extreme weather events). These projections were produced by PAGASA (2011) using PRECIS, also under the auspices of MDG-F 1656.

The projections show that there will be an increase in monthly average rainfall from June to October in Benguet in 2020 and 2050. This will have a corresponding increase in monsoon rain duration, typhoon occurrence, and flooding and landslides for the corresponding year. This can be introduced in the index as projected additional vulnerability by a coefficient/multiplier determined by experts in climatology, crop production and in the socioeconomic fields.

If we let Css be the coefficient of slow-setting effects of climate change for a particular hazard x, then the adjusted CLVIss of each community due to the slow setting effects of climate change will be: CLVIss = CLVI * Css

where, CLVI is the index result of the VA tool for the community

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Figure 4. Projected monthly average rainfall (mm) for 2020 and 2050 compared to observations (1971-2000) under medium-range emission scenario for Benguet, Philippines down-scaled using PRECIS model. (PAGASA, 2011)

1.3.6

Multi-dimensional Aspect of Vulnerability

Vulnerability Assessment (VA) is multidimensional and hazard-specific. Communities have different exposure, sensitivity and adaptive capacity to different hazards, leading to specific vulnerabilities for each hazard. A composite vulnerability index can facilitate multidimensional vulnerability assessment. This composite vulnerability index is the combination of all the vulnerability indices for all the hazards that the community

= 123 k experiences. That is, VulnerabilityV(H,H,H,...,H) different hazards that the community experiences.

where

H,H,H,...,H 123

k

are the

A spidergram can be used in assessing and presenting the overall vulnerability of a particular community. A spidergram is a graphical presentation that gives an overview of multidimensional scores. Compared to a bar graph, a spidergram provides more shape information for the human eye. Based on its shape, one can see an overall perspective of the profile.

For example, there are five hazards with their corresponding vulnerability index scores namely: typhoon-7.2, landslide- 9.4, flood- 6.2, hailstorm- 2.4 and drought- 3.5. The number of hazards will dictate the number of sides the spidergram will have. In this case, the spidergram is pentagon in shape. Having the same range of values of vulnerability and qualitative interpretation for all the hazards, shown in Table 7, once can easily construct the spidergram.

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Table 7. Qualitative interpretation for any hazard and over-all vulnerability index.

Vulnerability Index

Qualitative Interpretation

0.00-2.00

Very low vulnerability

2.01-4.00

Low vulnerability

4.01-6.00

Moderate vulnerability

6.01-8.00

High vulnerability

8.01-10.00

Very high vulnerability

Figure 5.Ex ample of spidergram of vulnerability of a community in 5 hazards.

In the above figure, it can be easily seen that vulnerability to landslide, typhoon and flood is very high. On the other hand, vulnerability of the community to drought and hailstorm is not that alarming compared to the three aforementioned hazards.

Another way of expressing the over-all vulnerability of a community is through composite vulnerability index. It combines the vulnerability index scores of all the hazards that the community experiences. The next section describes the procedure on how to combine the vulnerability index of hazards the community has.

1.3.7

Procedure in formulating a Composite Vulnerability Index (Gauran, 1999)

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1. Select a group of at least 10 experts on climate change as participants in a Delphi survey.

Delphi survey will be composed of three stages: Stage I: Identification of the hazards in the community for the possible inclusion in the composite indicator; Stage II: Validation and finalization of the hazards in the community that would compose the indicator; and Stage III: Determination of weights for each of the identified hazards.

2. Ask the experts to list down all the hazards that the community is experiencing through a questionnaire.

The results will be tabulated, showing their respective frequency: hazard with the highest frequency will be listed first. Experts must be updated with the results to prepare for the next step.

3. The experts will be asked again to identify the hazards of their choice with reference to the tabulated outcome of the first stage: this time they have to choose from the results of the first survey.

During each stage of the survey, it should be stressed that hazards will be used to measure the vulnerability of the community to climate change. This will ensure that only those hazards important or relevant to the community, identified by the majority of the experts, will be included in the formulation of the composite indicator.

4. A questionnaire in Stage III of the opinion survey will be given to the experts that will ask them to identify more important variables in a given pairwise comparison of variables.

The intensity of importance, ranging from 1 (equal importance) to 9 (absolute importance) of one variable over the other will be asked to be specified. A p x p of importance scores will be formed per expert: the elements of which are the provided pairwise importance scores. The Analytical Hierarchy Process (AHP) will be used to determine the weight of each hazard. The maximum eigenvalue of the matrix of importance scores along with its associated eigenvector will be obtained. The weight for the ith hazard will be taken as the ith element of the eigenvector after it was normalized, a process that is necessary to ensure that the sum of the weights is in unity. The mean weight that will be obtained for the ith hazard from all the experts will be considered the final weight for the ith hazard. nn

5. Compute for the composite index as: identified by the experts and wi

1.3.8

∑∑wHw,1 ii==11

iii

= , where

Hi

are the hazards

are the weights of the ith hazard.

Further Use and Application

The requirements and proper procedure for the use and application of this simplified vulnerability assessment tool is discussed in detail in section V of the component 1A report, Report for Component 2A of UPLBFI study for MDG-F 1656 Outcome 3.1

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which explains the conceptual framework, methodological procedure, data requirements and suggested data collection techniques. The entire assessment procedure consisted of three important phases: pre-assessment and planning phase, actual assessment or field work and post assessment phase.

What is discussed in the above sections are about the tool that will be used for the actual assessment or field work. However, there are a number of important considerations before and after embarking on the field work such as: a.

Purpose - The very basic purpose of the assessment is to determine specific agricultural production areas and farming communities that are vulnerable to certain climate change hazards. The output of vulnerability assessment will serve as a basis for planning and prioritizing adaptation measures.

b.

Level of assessment –This assessment tool is designed for the community level, i.e. barangay level. The assessment will show which barangay(s), including production areas and farming communities, are vulnerable.

c.

Vulnerability assessment team – For a more efficient assessment, a team of manageable size i.e. minimum five members; multi-disciplinary--each representing the municipal agricultural office, municipal development planner, municipal disaster coordinating council, and other disaster response organizations, must be organized.

d.

Data requirements – Before embarking on the actual field assessment, it is necessary that the team understands the area and circumstances through the use of secondary data. Some of the important information needed are climate statistics, agriculture data, and community maps. Note that some of the information required in the assessment tool can be supplied by the use of secondary data.

e.

Team workshops – The team members should level-off and understand each item in the assessment tool as well as the scoring system prior to the assessment activity or field work. Existing data that members are familiar with and have access to (e.g. DA standard for damage assessment) may be incorporated in the assessment tool, if necessary.

f.

Manual or guide in using the assessment tool – Although the tool is self explanatory and easy to understand, a manual or a guide in using the tool maybe needed to ensure that the assessment team has a common understanding about the tool, how to use it, knows the correct data and information that should be collected, and how to analyze and interpret the results.

g.

Site reconnaissance - An initial visit to the area ocular observations; informal interviews with resident guides in the area; resource and situational analysis of the community. It is also during this time that the team may formally inform the community leaders of the assessment activity that will be conducted in the area, its purpose, invite community participation in the said activity and make all other necessary arrangements.

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h.

Actual vulnerability assessment - The assessment tool contains hazard-specific variables, which first requires assessment by hazard. This can then be summed up based on the relative weights of the hazards. However, many of the variables in the assessment tool refer to the general assessment of the community and may not require separate analysis and evaluation by hazard.

i.

Conduct of key informant interviews – Key informants must be representatives of the major sectors in the community, e.g. rice producers, vegetable producers, women, private sector, others. It is best to have at least 2- 3 representatives from each sector as key informants. Interviews may be done as individual or as a group.

j.

Analysis of data – The assessment tool will enable the team to describe the climate-related hazards the community is facing, describe the vulnerabilities in terms of high risk areas and most vulnerable groups, and also identify and describe the capacities of the groups within the community.

k.

Validation of results – Through the use of visual aids and language and/or local dialect understandable to the community, results of the assessment should be discussed with the community to make sure that the team has done a realistic assessment of the area and community. It is also a way of enhancing the community’s awareness of their own situation.

1.4 Conclusion and Recommendations The simplified vulnerability assessment tool aims to serve as an easy to use guide for identifying vulnerable agricultural areas to climate change hazards. Through this tool, the vulnerability assessment may be done in a quick but organized manner. This tool, being a product of the integration of agriculture variables often used in describing agricultural systems and designing appropriate interventions, and actual experiences and observations about agriculture in Benguet and Ifugao Province, can provide reliable conclusions as to the adaptation measures needed in agricultural areas and communities.

There are other ways of rating and computing vulnerability. The more important aspect in assessing vulnerability of agriculture to climate change however is the combined technical and social information about the farming systems and the farming community under climate change. These serve as a reliable basis for identifying, planning and implementing appropriate adaptation measures that will enable the agriculture sector to overcome the adversities of climate change.

The vulnerability assessment tool should be able to identify the climate hazards that pose danger or harm to the community, to evaluate the adverse effects and impacts of the hazards on the agricultural system under consideration, and to determine the set of possible intervention and/or adaptation measures which can reduce the vulnerability of the community.

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Part 2: CLIMATE CHANGE ADAPTATION TOOLS 2.1 METHODOLOGY 2.1.1 Data Sets Used in the Study Sequences of daily historical weather data of Benguet and Ifugao were obtained from the respective local agro-meteorological stations of Baguio City (1971-2000), La Trinidad (1976-1990), and Banaue (1979-1993). The historical data of La Trinidad and Banaue include weather variables such as daily minimum temperature (째C), maximum temperature (째C), mean temperature (째C), amount of rainfall (mm) and solar radiation (MJ/m2). On the other hand, the Baguio City data set consists of the same set of temperature variables, amount of rainfall (mm) and relative humidity (%).

Daily forecasted weather scenarios for Benguet and Ifugao for 2020 and 2050 were requested from Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA). PAGASA used the PRECIS RCM (Providing Regional Climates for Impact Studies, Regional Climate Model) to simulate future changes in temperature and precipitation over the Philippines for two-time slices centered on 2020 (2006-2035) and 2050 (2039-2065) using the baseline years 1971 to 2000 and an A1B scenario. The A1B scenario is characterized by a future world of a rapid economic growth, global population that peaks in mid-century and declines thereafter, and the rapid introduction of new and more efficient technologies. However, only forecasted daily values of amount of rainfall, maximum temperature, mean temperature, minimum temperature, and relative humidity for Baguio City were available. Monthly and yearly summaries for each variable were produced for use the statistical analysis.

2.1.2 Determination of Optimal Planting Date Initially, the years were grouped according to three climatic classifications, namely: dry year (DY), wet year (WY) and normal year (NY). For each climatic classification, synthetic weather data was generated and the optimal planting date for rice and vegetables were selected based on rainfall and yield probabilities.

Classifying the Years According to the Three Climate Conditions For each location, the years considered in the study were classified as dry, wet or normal years based on the historical data of annual rainfall. These climate classifications were based on the Standardized Precipitation Index (SPI). SPI is the number of standard deviations that the observed cumulative precipitation for the year deviates from the climatological average (McKee, 1993). An SPI equal to zero indicates the median precipitation amount. The median precipitation value indicates that half of the historical precipitation amounts are below it and half are above it. Negative value of the index is obtained during drought and positive Report for Component 2A of UPLBFI study for MDG-F 1656 Outcome 3.1

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value during wet conditions. As the dry or wet conditions become more severe, the value of the index moves farther away from zero (NCDC, 2009). The SPI is computed as

RR− SPIi12n == i ,,.., SR where

Ri

is the annual rainfall for the ith year,

R is the mean or normal annual rainfall, and SR

is the standard deviation of the annual rainfall. In this study, the years are grouped into three climatic classifications based on SPI as shown in Table 8.

Table 8. Modified climatic classifications based on Standardized Precipitation Index (SPI) developed by Mckee in 1993.

SPI CLIMATIC CLASSIFICATION -1.00 and below Dry -0.99 to 0.99 Normal 1.00 and above Wet

Synthetic Weather Data It is of interest to perform crop yield simulation per climatic classification. Each simulation, however, requires daily weather data for at least 50 years. Since the number of years that were classified in each climatic classification is less than the required, the historical weather data were used to generate synthetic data using the Long Ashton Research Station Weather Generator (LARS-WG). LARS-WG is a weather generator that has the ability to imitate and preserve statistical properties like mean and variance and generate synthetic data with minimum variance (Semenov, 2002). To check if indeed the statistical properties are preserved, the distributions of the historical and synthetic data were compared graphically. If the two distributions resemble each other, then this implies that the synthetic data would not alter any information given by the historical data. In such cases, the generated synthetic data were combined with the historical data to form 50 years, that is, from 1960 to 2010.

Determining the Optimal Planting Date Based on Rainfall Probabilities This method selects the planting date that will most likely satisfy the water requirements through rainfall of a particular crop during the entire crop growing period. This method involves the following steps: (1) determining the information on the water requirements of the crop for each stage of crop growth development; (2) computing the rainfall probabilities given the sequences of weather data; and, (3) selecting the optimal planting date based on the estimated probabilities of rainfall.

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Water Requirements for Rice. According to Saseendran (1998), certain water requirements must be met for optimum production of rice. At least 200 mm of cumulative precipitation falling in the week preceding transplanting date is needed for successful transplanting and plant establishment. When rice reaches the physiological maturity, which is during the 110th day of the 125-day crop growth period, it will use at least 1200 mm of water.

Lastly, the rice grains must remain relatively dry during the last 15 days of the crop period following maturity, to avoid deterioration of rice quality. In this study, it was assumed that the quality of rice will be maintained if no more than 80 mm of rain falls during this 15-day period.

Estimating Rainfall Probabilities. Rainfall probabilities were computed based on the weekly summaries of the combined historical and synthetic rainfall data.

For rice, several factors including the conditions at harvest that affect rice quality, rain required during growth period, and the need for paddied soil at transplanting time were considered. The probability of getting puddied soil at transplanting time were assumed if at least 200 mm of rain accumulated during the week preceding the transplanting date. This probability is denoted as P>200mm. The probability of receiving at least 1200mm of rain during the growth period was denoted as Pw. The probability of a dry harvest, that is, receiving no more than 80 mm of rain for the first 15 days following maturity is denoted as Pd. The probability of all these events occurring in the same year (Q) was taken as product of the individual probabilities, that is, Q = (P >200 mm) Ă— Pw Ă— Pd. Selecting the Optimal Planting Date based on Rainfall Probabilities. The week with the highest P >200 mm, Pw, Pd and Q was selected as the optimal planting date for rice.

Optimal Planting Date Based on Yield Probabilities This approach selects the planting date that will most likely produce the highest crop yield. This method involves the following steps: (1) determining crop yields using a crop simulation model; (2) computing the probability of exceedance of specified crop yield levels; and, (3) selecting the optimal planting date.

Simulating Crop Yields. The effects and impacts of projected climate variability were simulated for the selected crops in the identified areas in the Cordillera Region using the process-based CERES crop models under the Decision Support System for Agro-technology Transfer (Tsuji et al., 1994). The DSSAT CERES crop models have been parameterized for selected crops, and have been validated under local conditions (Lansigan and Salvacion, 2007). Crop-specific genetic coefficients, crop management parameters (e.g. planting date, planting density, etc.), and projected weather under climate change were used as model inputs. These crop coefficients are already available for limited varieties only. Unfortunately, there are no known crop coefficients yet for rice varieties commonly planted in Ifugao and vegetables commonly planted in Benguet such as lettuce, cauliflower, carrots and beans.

Thus, this study is limited to exploring yield simulation of varieties with existing crop coefficients such as IR72 rice variety and Magilis tomato variety.

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Computing the Probability of Exceedance. To know which of the planting dates can be considered optimal, the probability crop yields exceeding a target yield, y*, was computed for a particular planting date. This probability was obtained by using the following formula provided by Gumbel (1958):

PYy [ >≈ *]

r n +1

where r is the rank of the yields arranged in descending order and n is the number of years.

Optimal Planting Date based on Yield Probabilities. The week with the highest probability of exceeding a high target yield was selected as the optimal planting date for the crop.

2.1.3 Finding a Historical Analogue To prepare the farmer of Benguet and Ifugao for the climate conditions in 2020 and 2050, it is necessary for them to understand such conditions in the simplest way possible.

One approach is to find the historical analogues of 2020 and 2050 in terms of climate conditions. This will relate the future conditions to what they have already experienced in the past. Four methods were implemented to find the historical analogues of the projected weather conditions in Baguio for 2020 and 2050. These are the following: (1) Cluster Analysis using five weather variables; (2) Cluster Analysis using the extracted principal components; (3) least absolute deviation analysis using five weather variables; and, (4) least absolute deviation analysis using the extracted principal components.

Cluster Analysis Using Five Weather Variables Yearly summaries were computed from the daily values of amount of rainfall, minimum, mean and maximum temperature and relative humidity. These yearly summaries were standardized so that variables will have comparable values. Cluster Analysis (CA) was employed to identify which years behave similarly based on the five variables under consideration. CA is a statistical technique that groups observations that are similar into clusters based on several characteristics (Aldenderfer, 1984). Initially, each year was treated as a separate cluster and they were merged into successively larger clusters using Hierarchical algorithm. To evaluate how one cluster is different from the other clusters, squared Euclidean distances were calculated. In choosing which clusters to merge, singlelinkage method was used. This method merges the two clusters with the least distance. A tree structure called the dendogram was presented to summarize the hierarchy of clusters created.

Finally, the historical analogue was determined by finding the observed years for which 2020 and 2050 were immediately clustered with.

This method, however, treats the five weather variables in equal weights and assumes that they are independent of each other. Thus, another method that would take into account the association of these weather variables and the proportion of the contribution of each variable to the overall variation was explored.

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Cluster Analysis Using the Extracted Principal Components Using the yearly summaries of five weather variables, Principal Component Analysis (PCA) was used to transform the same set of weather variables which are highly correlated into a smaller number of uncorrelated variables which we call principal components (Joliffe, 1986). The extraction of principal components was based from variance maximizing (varimax) rotation of the original variables. Using the Kaisier Criterion, the principal components with eigenvalues greater than 1 were retained. For each value in the data set, the corresponding scores of each principal component were computed using the regression method. The same clustering technique specified in the first method was implemented using the factor scores.

Absolute Deviation Analysis Using Five Weather Variables The years 2020 and 2050 were also compared based on the deviation of the daily values of the five variables to each of the available historical year. Comparing the years based on daily values instead of yearly summaries may give a better estimate of the variation between the years.

In this method, historical weather data for each year was compared with the weather sequence for 2020 based on the standardized daily values of the five weather variables. The average absolute deviations of the daily observations of a particular year from 2020 were computed. The historical analogue of 2020 was the year with the least average absolute deviation. The same procedure was done in finding the historical analogue of 2050.

Least Absolute Deviation Analysis Using the Extracted Principal Components Similar to the second method, this method accounts for the association of the five weather variables and the proportion of the contribution of each variable to the overall variation but uses daily values instead of yearly summaries. Using the daily values of historical weather data, the factor scores were extracted from the five weather variables using PCA. Each year was compared with the corresponding weather data of 2020 based on the factor scores. The average absolute deviations of the scores of a particular year from 2020 were computed. The historical analogue of 2020 was the year with the least average absolute deviation. The same procedure was done in finding the historical analogue for 2050.

2.2 RESULTS AND DISCUSSIONS 2.2.1 BAGUIO CITY The distribution of the total rainfall per year in Baguio City from 1971 to 2000 has no apparent long term trend and pattern as shown in Figure 6. Moreover, the total rainfall is highest during 1972, lowest in 1995 and has an average of 3877.85 mm. The projected total

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rainfall for 2020 and 2050, which are 4773.5 mm and 5239.4 mm, respectively, are higher than the average of the 30 historical years.

Figure 6. Total rainfall per year in Baguio City from 1971-2000, and projected for 2020 and 2050.

The distribution of the average daily minimum, mean and maximum temperature per year in Baguio City from 1971-2000 shows an increasing trend as seen in Figure 7. There is less variation in the yearly values of average daily minimum temperature compared to that of average daily maximum temperature. The forecasted average daily minimum, mean and maximum temperature for 2020 and 2050 are greater than any of the historical data.

Figure 7. Average daily minimum, mean, maximum temperature per year in Baguio City from 1971-2000, and projected for 2020 and 2050.

The average daily relative humidity per year in Baguio City generally increases during 1971 to 1985, decreases during 1986 to 1993 and drastically increases during 1994 to 1996. The forecasted average daily relative humidity for 2020 and 2050 greatly deviates from the general trend of the historical data.

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Figure 8. Mean daily relative humidity per year in Baguio City from 1971-2000, and projected for 2020 and 2050.

Years Grouped According to the Three Climate Conditions for Baguio City The 30 years of historical records of weather data were classified as dry, average or wet based on the precipitation level in Baguio City using Standardized Precipitation Index. Table 9 below shows that six out of the 30 years were classified as dry years, 20 were normal years and four were wet years.

Table 9. SPI and climatic classification of 30 historical years based on precipitation level in Baguio City for 1971-2000.

Climatic Climatic Year SPI Year SPI classification classification 1971 -0.15 normal 1986 0.74 normal 1972 2.89 wet 1987 -1.01 dry 1973 -1.13 dry 1988 -0.79 normal 1974 1.97 wet 1989 1.10 wet 1975 -1.39 dry 1990 0.70 normal 1976 0.37 normal 1991 0.16 normal 1977 -0.24 normal 1992 0.57 normal 1978 -0.10 normal 1993 -0.53 normal 1979 -0.80 normal 1994 -0.78 normal 1980 0.47 normal 1995 -1.49 dry 1981 -0.02 normal 1996 -0.03 normal 1982 -0.15 normal 1997 -1.11 dry 1983 -1.46 dry 1998 -0.06 normal 1984 -0.08 normal 1999 0.79 normal 1985 0.78 normal 2000 1.20 wet

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Comparison of Historical and Synthetic Weather Data for Baguio City Figures 9, 10 and 11 show the distributions of monthly means of historical and synthetic weather data for dry, normal, and wet years, respectively. Results show that the distribution of the synthetic weather data generated using LARS-WG resembles that of the historical. Since the properties of the historical and synthetic data are the same, then it is suitable to combine them for simulation.

(a) mean monthly rainfall

(c) mean monthly minimum temperature

(b) mean monthly solar radiation

(d) mean monthly maximum temperature

Figure 9. Comparison of historical and synthetic weather data for dry years in Baguio City.

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(a) mean monthly rainfall

(c) mean monthly minimum temperature

(b) mean monthly solar radiation

(d) mean monthly maximum temperature

Figure 10. Comparison of historical and synthetic weather data for normal years in Baguio City.

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(a) mean monthly rainfall

(c) mean monthly minimum temperature

(b) mean monthly solar radiation

(d) mean monthly maximum temperature

Figure 11. Comparison of historical and synthetic weather data for wet years in Baguio City.

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Optimal Planting Date for Tomato in Baguio City Based on Tomato Yield Probability Figure 12 shows that for dry years in Baguio City the optimal planting dates for tomato is week 51 (third week of December), for normal years it is week 8 (fourth week of February), and for wet years it is week 36 (first week of September).

(a) for dry years

(b) for normal years

(c) for wet years Figure 12. The probability of exceedance of tomato yields during dry, normal and wet years in Baguio City Report for Component 2A of UPLBFI study for MDG-F 1656 Outcome 3.1

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Finding the Historical Analogue for 2020 and 2050 in Baguio City Based on Cluster Analysis Using Five Weather Variables The years were clustered based on the standardized yearly summaries of rainfall, minimum, mean, and maximum temperature and relative humidity. The dendogram in Figure 13 reveals that pairs 1981 and 1991, 1982 and 1998, 1984 and 1992 are the most similar and hence they were joined to form the first few clusters. Consequently, the remaining historical years were sequentially added with these clusters. Year 1997 was the last historical year to join the group. As for the forecasted years, 2020 was joined with the cluster formed from all the historical years. Year 2050 was last to join the group. This implies that 2020 and 2050 are highly different from the historical years based on the five weather variables. Thus, there is no immediate analogue for years 2020 and 2050.

Figure 13. Dendogram of cluster analysis using five weather variables in Baguio City.

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Based on Cluster Analysis Using the Extracted Principal Components The years were clustered based on the two principal components extracted from the original set of five weather variables. The first principal component highly accounts for the minimum, mean, maximum temperature and relative humidity while the second principal component highly accounts for total rainfall. Using these two uncorrelated components, the dendogram in Figure 14 reveals that the historical years again behaved in a most similar fashion with each other. Similar to the results presented earlier, years 2020 and 2050 were the last to be part of the cluster. This implies that they have no immediate analogue years.

Figure 14. Dendogram of cluster analysis using the extracted Principal Components in Baguio City.

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Based on Least Absolute Deviation Analysis Using Five Weather Variables Using the daily absolute deviations of the standardized five weather variables, Tables 10 and 11 show that year 1995 has the least average deviation from 2020 as well as 2050.

This implies that 1995 is the year that behaved in the most similar manner to 2020 and 2050 using their daily observed rainfall, minimum, mean, maximum temperature and relative humidity.

Table 10. Average Absolute Deviations (AAD) of the historical years from 2020 using five weather variables in Baguio City.

YEAR AAD YEAR AAD 1995 0.5525 1998 0.6313 1994 0.5834 1992 0.6351 1973 0.5868 1988 0.6409 1983 0.5978 1979 0.6455 1993 0.6055 1975 0.6457 1990 0.6083 1991 0.6549 1984 0.6110 1982 0.6553 1987 0.6156

Table 11. Average Absolute Deviations (AAD) of the historical years from 2050 using five weather variables in Baguio City.

YEAR AAD YEAR AAD 1995 0.7531 1984 0.8827 1983 0.8244 1982 0.9240 1994 0.8248 1992 0.9243 1988 0.8428 2000 0.9281 1987 0.8462 1991 0.9394 1993 0.8516 1998 0.9423 1973 0.8737 1979 0.9445 1990 0.8815

Based on Least Absolute Deviation Analysis Using the Extracted Principal Components Applying PCA to the weather variables based on daily values resulted in two principal components. The first component is highly accounted by minimum, mean, maximum temperature and the second component is highly accounted by amount of rainfall and relative humidity. Computing for the daily absolute deviations of factor scores, year 1995 has the least average deviation from 2020 as well as 2050 as shown in Tables 12 and 13. This implies that 1995 behaved in the most similar manner to 2020 and 2050 using the factor scores.

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Table 12. Average Absolute Deviations (AAD) of the historical years from 2020 using two principal components in Baguio City.

YEAR AAD YEAR AAD 1995 0.5376 1993 0.6373 1984 0.5960 1992 0.6375 1994 0.5973 1998 0.6397 1987 0.5995 1975 0.6575 1990 0.6060 1979 0.6638 1983 0.6076 1982 0.6762 1973 0.6106 2000 0.6768 1988 0.6330 Table 13. Average Absolute Deviations (AAD) of the historical years from 2050 using two principal components in Baguio City.

YEAR AAD YEAR AAD 1995 0.7411 1973 0.9295 1988 0.8357 1982 0.9445 1987 0.8433 1992 0.9503 1983 0.8612 2000 0.9604 1994 0.8662 1991 0.9737 1984 0.8961 1979 0.9822 1990 0.9034 1998 0.9859 1993 0.9207

Table 14 shows that most of the forecasted values for 2020 and 2050 are higher than what was observed in 1995. Close values were observed among temperature variables. Since the immediate historical analogue of years 2020 and 2050 is 1995, this implies that during these years, Baguio City is expected to experience an average daily temperature similar to 1995, when highest values of temperatures were observed among the 30 historical years.

Table 14. Summary statistics for five weather variables in years 1995, 2020 and 2050 in Baguio City.

VARIABLE Rainfall Maximum temperature Minimum temperature Mean temperature Relative humidity

STATISTIC Total standard deviation average standard deviation average standard deviation average standard deviation average standard deviation

1995 2180.40 17.63 25.02 2.03 15.53 1.68 20.29 1.47 86.90 5.16

2020 4773.50 15.98 24.94 1.23 16.13 1.23 20.51 0.93 85.07 4.76

2050 5239.40 18.22 25.98 1.34 17.25 1.14 21.55 0.87 84.38 5.44

The recommended planting dates for corn and tomato in 2020 and 2050 will be based from the optimal planting dates of year 1995.

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2.2.2 LA TRINIDAD, BENGUET The distribution of the total rainfall per year in La Trinidad, Benguet from 1976 to 1990 has no apparent long term trend and periodicity as shown in Figure 15. Moreover, the total rainfall is highest during 1990, lowest in 1987and has an average of 3611.72 mm.

Figure 15. Total rainfall per year in La Trinidad, Benguet from 1976-1990.

The distribution of the average daily minimum, mean and maximum temperature per year in La Trinidad, Benguet for 15 years shows no long term trend with minimal fluctuations as seen in Figure 16. The lowest average daily minimum temperature was observed in 1989 with 13.82 degrees Celsius and the highest average daily minimum temperature was observed in 1990 with 14.99 degrees Celsius. Moreover, the lowest average daily maximum temperature was experienced during 1984 with 23.27 degrees Celsius and the highest average daily maximum temperature was experienced during 1987 with 24.29 degrees Celsius.

Figure 16 Average daily minimum, mean, maximum temperature per year in La Trinidad, Benguet from 1976-1990.

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The values of the mean daily solar radiation from 1976 to 1990 highly fluctuates from the general average. On the average, the mean daily solar radiation is 16.25 MJ/m2.

Figure 17. Mean daily solar radiation per year in La Trinidad, Benguet from 1976-1990

Years Grouped According to the Three Climate Conditions for La Trinidad, Benguet Table 15 shows that 3 out of the 15 years were classified as dry years, 9 were normal years and 3 were wet years. Year 1987 has the least value of SPI. This indicates that it has the driest climate among the historical years.

Table 15. SPI and climatic classification of 15 historical years based on precipitation level in La Trinidad, Benguet.

Climatic Year SPI Classification 1976 -1.23 Dry 1977 0.05 Normal 1978 -0.16 Normal 1979 -0.73 Normal 1980 1.03 Wet 1981 0.45 Normal 1982 0.42 Normal 1983 -1.46 Dry 1984 -0.48 Normal 1985 1.3 Wet 1986 0.56 Normal 1987 -1.63 Dry 1988 -0.11 Normal 1989 0.47 Normal 1990 2.09 Wet

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Comparison of Historical and Synthetic Weather Data for La Trinidad, Benguet Figures 18, 19 and 20 show the distributions of monthly means of historical and synthetic weather data for dry, normal, and wet years, respectively. Since the distribution of the synthetic weather data generated using LARS-WG resembles that of the historical then they can be combined for simulation.

(a) mean monthly rainfall

(c) mean monthly minimum temperature

(b) mean monthly solar radiation

(d) mean monthly maximum temperature

Figure 18. Comparison of historical and synthetic weather data for dry years in La Trinidad, Benguet

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( (a) mean monthly rainfall

(c) mean monthly minimum temperature

(b) mean monthly solar radiation

(d) mean monthly maximum temperature

Figure 19. Comparison of historical and synthetic weather data for normal years in La Trinidad, Benguet

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(a) mean monthly rainfall

(c) mean monthly minimum temperature

(b) mean monthly solar radiation

(d) mean monthly maximum temperature

Figure 20. Comparison of historical and synthetic weather data for wet years in La Trinidad, Benguet.

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Optimal Planting Date for Tomato in La Trinidad, Benguet Based on Tomato Yield Probability For target yields of at most 12500 kg/ha the optimal planting date of tomato for dry years in La Trinidad, Benguet is week 4 (fourth week of January), else it is week 36 (first week of September). Moreover, for normal and wet years the optimal planting dates are week 2 (second week of January) and week 36 (first week of September), respectively. These are based on the graphs shown in Figure 21.

(a) for dry years

(b) for normal years

(c) for wet years Figure 21. The probability of exceedance for tomato during dry, normal and wet years in La Trinidad, Benguet.

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2.2.3 BANAUE, IFUGAO The distribution of total rainfall per year in Banaue, Ifugao from 1979 to 1993 has no apparent trend as shown in Figure 22. The values of the total rainfall from 1979 to 1987 are more variable than those from 1988 to 1993.

Figure 22. Total rainfall per year in Banaue, Ifugao from 1979-1993 From 1979 to 1993, the average daily minimum, mean and maximum temperature per year in Banaue, Ifugao shows little amount of variation. On the average, the minimum, mean and maximum temperatures are 17.23, 21.23 and 25.23 degrees Celsius, respectively.

Figure 23. Average daily minimum, mean and maximum temperature per year in Banaue, Ifugao from 1979-1993

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The mean daily solar radiation per year in Banaue, Ifugao from 1979 to 1993 have no apparent long term trend and seasonality.

Figure 24. Mean Daily Solar Radiation Per Year in Banaue, Ifugao Years Grouped According to the Three Climate Conditions in Banaue, Ifugao Based on the values of SPI, 3 years were classified as dry years, 9 years as normal years and the remaining 3 years as wet years as shown in Table 16.

Table 16. SPI and climatic classification of 15 historical years based on precipitation level in Banaue, Ifugao.

CLIMATIC YEAR SPI CLASSIFICATION 1979 0.47 normal 1980 0.49 normal 1981 -0.28 normal 1982 -1.38 dry 1983 0.06 normal 1984 -2.06 dry 1985 -1.30 dry 1986 -0.05 normal 1987 1.73 wet 1988 1.13 wet 1989 0.10 normal 1990 0.33 normal 1991 1.07 wet 1992 -0.06 normal 1993 -0.25 normal

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Comparison of Historical and Synthetic Weather Data for Banaue, Ifugao Similar to the results of Baguio and La Trinidad, Benguet the synthetic weather data for Banaue, Ifugao are statistically the same as the historical weather data for dry, normal and wet years as shown in Figures 25, 26 and 27.

(a) mean monthly rainfall

(c) mean monthly minimum temperature

(b) mean monthly solar radiation

(d) mean monthly maximum temperature

Figure 25. Comparison of historical and synthetic weather data for dry years in Banaue, Ifugao

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(a) mean monthly rainfall

(c) mean monthly minimum temperature

(b) mean monthly solar radiation

(d) mean monthly maximum temperature

Figure 26. Comparison of historical and synthetic weather data for normal years in Banaue, Ifugao

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(a) mean monthly rainfall

(b) mean monthly solar radiation

(c) mean monthly minimum temperature

(d) mean monthly maximum temperature

Figure 27. Comparison of historical and synthetic weather data for wet years in Banaue, Ifugao.

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Optimal Planting Date for Rice in Banaue, Ifugao Based on Rainfall Probability of a Dry Year The probability of having puddied soil at transplanting time (P>200mm) is high between week 27 and week 43. The chances of successful transplanting operations are highest during these weeks. The probability of receiving (Pw) 1200mm of rain during the growth period is high if the transplanting date is between week 26 and week 32. Among the transplanting dates with high P>200mm and high Pw, the probability of having dry harvest period (Pd) is high if the transplanting date is between week 29 and week 32.

Figure 28. The probabilities of satisfying the water requirements during transplanting (P>200mm), vegetative growth period (Pw) and harvest period (Pd) during Dry Years in Banaue, Ifugao.

The probability (Q) of meeting the water requirements in all stages during dry years in Banaue, Ifugao is shown in Figure 29. This probability is high when transplanting takes place between week 29 and 32, with a peak at week 30. Thus, week 29 (third week of July) to week 32 (first week of August) are the optimal planting dates for rice in Banaue, Ifugao.

Figure 29. The probability of meeting the water requirements of rice crop in all stages (Q) during a dry year in Banaue, Ifugao.

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Based on Rainfall Probability of a Normal Year For a normal year in Banaue, Ifugao it is more likely to achieve the water requirement at transplanting period when the transplanting date is between weeks 18 and 46. Moreover, during the crop growth period, the chance of meeting the water requirement is high when the transplanting was done between week 16 and 37. Among the transplanting dates with high P>200mm as well as high Pw, the probability of having dry harvest is high if the transplanting date is between week 33 and 37.

Figure 30. The probabilities of meeting water requirements during transplanting (P>200mm),vegetative growth (Pw) and harvest period (Pd) during a normal year in Banaue, Ifugao.

Based on the graph of Q values in Figure 27, the optimal transplanting date is between week 33 (third week of August) and 37 (second week of September), with a peak at week 34 (fourth week of August).

Figure 31. The probability of meeting the water requirements of rice crop in all stages (Q) during a normal year in Banaue, Ifugao.

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Based on Rainfall Probability of a Wet Year The likelihood of achieving the water requirement at transplanting period is high when the transplanting date is between weeks 20 and 44. During the growth period, the probability of meeting the water requirement is high when the transplanting was done between week 14 and 39. Among the transplanting dates with high P>200mm and high Pw, the probability of having dry harvest is high if the transplanting date is between week 36 and 39.

Figure 32. The probabilities of meeting water requirements of rice crop during transplanting (P>200mm), vegetative growth (Pw) and harvest period (Pd) during wet years in Banaue, Ifugao.

The probability of satisfying the water requirements of rice crop in all stages is high when the transplanting took place between week 36 (first week of September) and week 39 (fourth week of September), and highest at week 37 (second week of September).

Figure 33. The probability of meeting the water requirements of rice crop in all stages (Q) during wet years in Banaue, Ifugao.

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Based on Yield Probability Figure 34 shows that the optimal planting date of rice for dry years in Banaue, Ifugao are week 34 (fourth week of August) and week 35 (fifth week of August), for normal years it is week 35 (fifth week of August) and for wet years any week between week 29 (third week of July) and week 33 (third week of August).

(a) for dry years

(b) for normal years

(c) for wet years Figure 34 The probability of exceedance for rice during dry, normal and wet years in Banaue, Ifugao Report for Component 2A of UPLBFI study for MDG-F 1656 Outcome 3.1

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2.3 SUMMARY AND CONCLUSIONS Two approaches were used to determine the optimal planting dates of a crop. The first approach is based on rainfall probabilities. This approach assumes that rainfall is the crop’s main source of water. It selects the dates that have the highest probability of meeting the water requirement of the crop at each stage of development. The second approach is based on yield probabilities. The yield probabilities were simulated using DSSAT CERES crop models that have been parameterized for selected crops, and have been validated under local conditions. One of the model inputs is the crop’s genetic coefficient. These crop coefficients are already available for limited varieties only. Unfortunately, there are no known crop coefficients yet for rice varieties commonly planted in Ifugao and vegetables commonly planted in Benguet such as lettuce, cauliflower, carrots and beans. Thus, this study is limited to exploring yield simulation of varieties with existing crop coefficients such as IR72 rice variety and Magilis tomato variety.

The optimal planting dates for rice were constructed based on rainfall probabilities and yield probabilities of IR72 variety. Although the two methods generally gave different planting dates, they both recommended the last week of August as the optimal planting date of rice in Banaue for a normal year. The optimal planting dates derived based on yield probabilities are more plausible, and therefore, recommended. This is because of the fact that estimated crop yields using eco-physical crop simulation model accounts more for the agro-climatic environment and crop management strategies.

The optimal planting dates for tomato were obtained based on the yield probabilities of Magilis variety. Results showed that generally the dates are different for the two adjacent locations namely, Baguio City and La Trinidad except during wet years. These differences may be due to the fact that the number of historical years available for the two locations differs. Since the classification of years into the three climatic conditions is highly dependent on the available historical data, some normal years in Baguio City are classified wet years in La Trinidad. Thus, these differences in the groupings of the historical years resulted in different recommended planting dates.

To understand the weather scenarios for 2020 and 2050 in Baguio City, the historical analogues of these years were determined using four methods. The first two methods which involved the cluster analysis of years failed to find immediate historical analogue. Hence, two methods involving the use of the least average absolute deviation were explored. Results showed that among the 30 historical years considered, 1995 has the closest weather scenario to 2020 and 2050. This implies that during these years, Baguio City is expected to experience an average daily temperature similar to 1995, when highest values of temperatures were observed among the 30 historical years. Thus, the recommended planting dates for tomato in 2020 and 2050 will be based from the optimal planting dates of year 1995. It is week 51, that is, during the third week of December.

However, given data limitations such as: a) incomplete historical weather data and b) unavailability of crop genetic efficients for varieties commonly planted in Ifugao and Benguet; the construction of optimal planting dates for selected varieties was carried out more as a methodological exercise to illustrate its usefulness as a tool for climate change adaptation planning—granted that there is access to complete historical data and available genetic coefficients for local varieties. The optimal/recommended planting dates in this report are not in any way endorsed by FAO and DA for inclusion or adoption into the current agricultural production systems of Ifugao and Benguet.

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REFERENCES: Aldenderfer, M.S., Blashfield, R.K, Cluster Analysis, (1984), Newbury Park (CA) Allen Consulting (2005). Climate Change Risk and Vulnerability. Canberra: Australian Greenhouse Office, Department of Environment and Water Resources.

Amador, J.P.Y. 2000. The effect of Climate Variability on the Yield of Yellow Hybrid Corn.

Undergraduate Special Problem, UP Los Baños.

Asian Rice Foundation (2003) Rice: The Grass that Feeds Millions. The Manila Times.

Briones, ND., Garcia, JNM, Wagan, WM., Alcantara, EL., and Alaira, SA. 2010. Review and Screening of Available Vulnerability Assessment Tools for Their Application in the Agricultural Sectors in Benguet and Ifugao. Component 1A Project Progress Report, Strengthening Philippine Institutional Capacity to Adapt to Climate Change Outcome 3.1 Activity 3.3. University of the Philippines Los Banos, College, Laguna Corn Planting Dates Research. Iowa State University. 2006. http://www.agronext.iastate.edu/corn/production. de Dios, J.L., Javier, E.F., Malabayabas, M.D., Casimero, M.C., Espiritu, A.J., (2005), An Overview on Direct Seeding for Rice Crop Establishment in the Philippines, Philippine Rice Research Institute, Science Cityof Muñoz, Nueva Ecija Garcia, A.G. 1979. A model for predicting grain yield of corn. Masters’ Thesis, UP Los Baños.

Gumbel, E. J. 1958. Statistics of Extremes. New York: Columbia University Press.

IFRC & RCS. 2007. How To Do A VCA, International Federation of Red Cross and Red Crescent Societies, Geneva, Switzerland. www.ifrc.org Ikisan Limited (2000), Rice: Water Management. Nagarjuna Hills, India.

Intergovernmental Panel on Climate Change (2001). In: McCarthy, J., Caziani, O., Leary, N., Dokken, D. & White, K. (eds.) Climate change 2001: Impacts, adaptation, and vulnerability. Cambridge: Cambridge University Press.

Jolliffe, I. T. (1986). Principal Component Analysis. Springer-Verlag. pp. 487 Knuuttila, K. (2000) Rice in the Philippine, Northern Illinois University.

Lansigan, F.P. and Salvacion, A.R. (2007) Assessing the Effect of Climate Change on Rice and Corn Yields in Selected Provinces in the Philippines. In Proceedings of the 10th National Convention on Statistics, 1-2 October, EDSA Shangri-La Plaza Hotel, Mandaluyong City.

Macatangay, M.C. 1992. Forecasting of Rice Production using Rainfall Data: Comparison between the Ordinary Least Squares and Ridge Regression Methods. Undergraduate Special Problem, UP Los Baños.

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McKee, T.B., Doesken, N. J., and Kliest, J., (1993) The Relationship of Drought Frequency and Duration to Time Scales. In Proceedings of the 8th Conference of Applied Climatology, 17-22 January, Anaheim, CA. American Meterological Society, Boston, MA. 179-184.

National Climatic Data Center (2009), Climate of 2009 - June U.S. Standardized Precipitation Index http://www.ncdc.noaa.gov/oa/climate/research/prelim/ drought/spi.html.

Official Website of the Philippine Atmospheric, Geophysical and Astronomical Services Administration. (2004) PAGASA <http://www.pagasa.dost.gov.ph> PRRM & DENR. 2009. Vulnerability and Adaptation (V & A) Assessment Toolkit. Philippine Rural Reconstruction Movement (PRRM) and Department of Environment and Natural Resources (DENR), Quezon City, Philippines.

Rebancos, CM. and JO. Coladilla et al. 2010. Local Knowledge and Tools for Assessing Vulnerability of the Agricultural Systems to Changing Climate: The Case of Ifugao and Benguet Provinces (Formal Field Survey). 1st Mid-Term Progress Report, Strengthening Philippine Institutional Capacity to Adapt to Climate Change Outcome 3.1 Activity 3.3. University of the Philippines Los Banos, College, Laguna.

Saseendran, S.A., l. Ma, D.C. Nielsen, M.F. Vigil and L.R. Ahuja. 2004. Simulating Planting Date Effects on Corn Production Using RZWQM and CERES-Maize Models.

Sarmientio, R. O. 1981. Characterization of Climatic and Soil Moisture variable as they influence corn yield. Graduate Thesis, UP Los Ba単os.

Semenov, M.A., (2002) LARS-WG: A Stochastic Weather Generator for Use in Climate Impact Studie,s Rothamsted Research, Harpenden, Hertfordshire, AL5 2JQ, UK. Smith, T. F. 2010. Towards Enhancing Adaptive Capacity for Climate Change Response in South East Queensland. The Australian Journal of Disaster and Trauma Studies.

ISSN: 1174-4707, Vol. 1010-1. http://www.massey.ac.nz/~trauma/issues/20101/tsmith.htm Tandang, NA, AB. Cosico, RIZA. Morantte, LE. Rotairo, RC. Abo-abo, JRS. Reyes, OA. Tumolva, 2010. Local Knowledge and Tools for Assessing Vulnerability of the Agricultural Systems to Changing Climate: The Case of Ifugao and Benguet Provinces (Formal Field Survey). 2nd Mid-Term Progress Report, Strengthening Philippine Institutional Capacity to Adapt to Climate Change Outcome 3.1 Activity 3.3.

University of the Philippines Los Banos, College, Laguna.

Tsuji, G.Y., Uehara, G., Balas, S. (Eds.). 1994. Decision Support System for Agrotechnology Transfer (DSSAT) Version 3. University of Hawaii, Honolulu, Hawaii.

Yoshida, S. 1981. Fundamentals of Rice Crop Science. International Rice Research Institute.

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Yoshida, S. (1991). Fundamentals of Rice Crop Science, International Rice Reseach Institute, Los Ba単os, Laguna, Philippines. Acaso, J. C. (2004, June). Rice is life to Filipinos and other Asians. Manila Times .

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ANNEXES Annex 1 . Consistency of the reported crop sensitivity to climate-related hazards between Benguet and Ifugao.

Climate Hazard

Benguet Strongly

Extreme cold Consistent Cabbage Inconsistent

Not common crop

Corn, Bell pepper, Lettuce Celery, Sayote, Chinese pechay, Green onion

Moderately

Ifugao No Effect

Carrots, Tomato Potato

Squash

Radish, Peanut, Pipino, Pineapple, Brocolli, Tiger grass

Palay, Camote, Ginger, Cassava, Beans, Banana, Mums

Strongly

Moderately

No Effect

Squash

Potato

Carrots, Tomato Corn

Beans

Palay

Cabbage

Bell pepper, Lettuce Camote, Peanut, Gabi, Chinese Pechay, Ginger, Eggplant

Consistent: Inconsistent ratio

4/5

Extreme hot Consistent

Carrots, Potato, Cabbage, Corn, Chinese pechay, Beans, Lettuce

Inconsistent

Bell pepper,

Not common crop

Pipino, Sayote, Green onion

Camote, Peanut, Squash, Palay

Carrots, Potato, Cabbage, Corn, Chinese Pechay, Beans, Lettuce

Palay, Camote, Peanut, Squash

11/1

Bell pepper Celery, Radish, Tomato, Cassava

Pineapple, Ginger, Tiger grass, Banana, Mums

Ginger, Eggplant

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Gabi

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Frequent heavy rain Consistent

Carrots, Potato, Cabbage, Chinese pechay, Lettuce

Inconsistent

Corn, Bell Pepper

Not common crop

Celery, Sayote, Ginger, Green onion

Palay, Tomato, Peanut

Radish, Pipino, Pineapple, Brocolli, Tiger grass, Banana, Beans

Ginger

Carrots, Potato, Cabbage, Chinese Pechay, Lettuce

Squash

Palay, Tomato, Peanut

Ginger

Corn, Squash, Bell pepper

6/7

Camote, Cassava, Mums

Camote, Beans, Eggplant

Gabi,

Camote, Corn, Bell Pepper,

Chinese Pechay

Camote, Corn, Bell pepper

Palay, Beans

Carrots, Potato, Cabbage

Eggplant

Peanut, Gabi, Ginger, Squash, Beans

Frequent flooding Consistent

Chinese pechay,

Inconsistent

Carrots, Potato, Cabbage

Palay, Beans

Celery, Radish, Pineapple, Brocolli, Tiger grass, Banana

Tomato, Peanut, Green onion, Mums

Not common crop

Pipino, Sayote, Lettuce

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4/5

Frequent landslide Consistent

Camote

Tomato

Inconsistent

Peanut, Chinese pechay, Ginger, Squash, Beans

Palay

Pipino, Sayote, Pineapple, Cassava, Mums

Potato, Tiger grass

Not common crop

Carrots, Cabbage, Corn, Bell pepper

Camote

Tomato

Palay

Celery, Radish, Banana, Lettuce, Green onion

Potato

Carrots, Cabbage, Corn, Bell pepper Peanut, Chinese Pechay, Ginger, Squash, Beans

6/6

Gabi, Eggplant

Frequent thunder Consistent

Carrots, Potato, Cabbage, Palay, Tomato, Camote, Corn, Peanut, Chinese pechay, Ginger, Squash, Beans, Bell pepper

Carrots, Potato, Cabbage, Palay, Tomato, Camote, Corn, Peanut, Chinese Pechay, Ginger, Squash, Beans, Bell pepper

Inconsistent

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13:0

Not common crop

Banana

Celery, Radish, Pipino, Sayote, Pineapple, Brocolli, Tiger grass, Lettuce, Green onion, Mums

Gabi, Eggplant

Strong quake Consistent

Lettuce

Inconsistent

Chinese pechay, Bell pepper

Not common crop

Green onion

Carrots, Palay, Tomato, Corn, Peanut

Lettuce

Carrots, Palay, Tomato, Corn, Peanut Chinese Pechay, Bell pepper 6/2

Pipino, Pineapple, Ginger, Beans, Banana, Mums

Potato, Cabbage, Celery, Radish, Camote, Sayote, Brocolli, Tiger grass

Potato, Cabbage, Ginger

Camote, Squash

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Gabi, Eggplant

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Long dry season Consistent

Carrots, Potato, Cabbage, Palay, Camote, Corn, Peanut, Chinese pechay, Beans, Bell pepper, Lettuce

Squash

Carrots, Potato, Cabbage, Palay, Camote, Corn, Peanut, Chinese Pechay, , Beans, Bell pepper, Lettuce

Squash

Inconsistent

Tomato

Ginger

Ginger

Tomato

Not common crop

Celery, Pipino, Sayote, Green onion

Radish, Pineapple, Cassava

Brocolli, Tiger grass, Banana, Mums

12/2

Eggplant

Gabi

Long wet season Consistent

Carrots, Potato, Cabbage, Corn, Chinese pechay Bell pepper, Lettuce

Inconsistent

Peanut,

Not common crop

Celery, Radish, Sayote, Tiger grass, Green onion

Tomato, Camote

Pineapple, Beans, Banana

Carrots, Potato, Cabbage, Corn, Chinese Pechay, Bell pepper, Lettuce

Tomato, Camote

Palay, Squash,

Palay, Squash,

Peanut

Ginger, Cassava, Mums

Ginger, Beans, Eggplant

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9/3

Gabi

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Erratic weather Consistent

Cabbage, Peanut, Chinese Pechay, Bell pepper, Lettuce

Potato, Palay, Camote

Cabbage, Peanut, Chinese Pechay, Bell pepper, Lettuce

Potato, Palay, Camote

Inconsistent

Corn

Carrots, Tomato, Ginger

Carrots, Ginger

Corn

Eggplant

Squash, Beans

Not common crop

Celery, Sayote, Pineapple, Beans, Banana, Green onion

Radish, Pipino, Brocolli, Tiger grass

Mums

Tomato 8/4

Gabi

Stronger and frequent typhoons Consistent

Inconsistent

Carrots, Cabbage, Palay, Tomato, Camote, Corn, Peanut, Chinese pechay, Squash, Beans, Bell pepper, Lettuce Potato,

Carrots, Cabbage, Palay, Tomato, Camote, Corn, Peanut, Chinese Pechay, Squash, Beans, Bell pepper, Lettuce Ginger

Ginger

12/2

Potato

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Not common crop

Celery, Radish, Pipino, Sayote, Pineapple, Brocolli, Cassava, Tiger grass, Green onion

Banana, Mums

Eggplant

Gabi

Source: Tandang, N.A. et al. (2010)

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Annex 2 . Comparison of responses from different focus groups on climate change questions based on survey conducted in Bengeut and Ifugao (Rebancos, C.M. et al.

(2010) Climate Change Questions Observed climatic phenomena

Observed impacts/effects

Vulnerable groups and areas

Farmer

• Extremely warm temperature (e.g. in Mayoyao) • Extremely cold temperature (e.g. in Banaue) • Longer dry season followed by longer wet season • Occurrence of typhoons even during summer • Longer and intense rains coupled with thunders • More thunderstorms • Proliferation of new varieties of plants and species of insects and worms • Increase in rice yield/production in some parts of Mayoyao • Frosting of plants in some parts of Banaue • Physiologic changes in plants and insects (excessive production) • Change in size of earthworm • Surge of insects • Landslide • Flooding • Decreasing rice productivity • They can no longer follow their cropping calendar which was developed hundreds of years ago because of climatic changes in the last 5 to 10 years • Places without cover • Farms in steep slopes and high elevations (Banaue, Mayoyao) • Areas without good road network (Tinoc) • High elevation areas (Mayoyao) • Denuded forest (Alfonso Lista and Banaue) • Area where “muyong” no

Gov’t officials

MPDO/MAO

• Increasing day and night temperature • Longer dry and wet season • More frequent El Niño and La Niña events

• More frequent El Niño and La Niña

• Change in cropping schedules • Destruction of paddies due to El Niño followed by La Niña events that resulted to landslides of paddies • Positive effect in Mayoyao rice production systems.

Increasing temperature resulted to increase in rice yield in some parts of Mayoyao • Lowland/flat lands, in lower elevation rice lands are last to dried-up during dry season.

• Yield loss

• Battad, Banaue is the most vulnerable place to drought because of its current status.

The communal forest in this municipality is already deforested hence irrigation supply of water is decreasing especially during dry season • Rice fields in steep slope and in high elevation because of natural flow of

• Farmers

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Climate Change Questions

Farmer

longer exist • Lowland areas vulnerable to drought • Banaue vulnerable to landslides • Lamut is vulnerable to high temperature based from its location

Gov’t officials

•

• • •

Local coping strategies

• Timing of planting, they wait for the “Tumona” to start planting. The Tumona is the community elder, also farmer leader, who determine when is the best time for planting to minimize risk based on behavior • They used to plant rice from Dec to January. Now they plant as late as April because of unavailability of rainfall or longer dry season • Planting of other crops, some crops are not suitable for longer dry season or for longer wet season so they chose crops appropriate for the season • Raising of livestock/animals, this will serve as additional source of food in times of famine associated with drought • Reviving the “bayanihan” system to repair irrigation systems if the damage of typhoon are minimal • Crop diversification, some crops can survive at extreme condition so this will ensure food availability in times of crisis.

• Planting of additional crops

• •

• •

•

•

•

•

water, these areas dried up during extended summer or longer dry season (El Nino) Communities who are dependent on agricultural produce for their income are vulnerable to CC Those with other sources of income are less vulnerable to CC Subsistence farmers Tourism industry is vulnerable to the impacts of CC on the agricultural production specifically on impacts of CC on the rice terraces.

Pumping of water from the river to the irrigation sites during drought Take advantage of the positive impact of high temperature to rice production by planting rice twice a year Intercropping of onions and vegetables with upland rice Revive the “chiaowa” arrangement as LGU initiative to cultivate the abandoned fields Have a database/census of farmer/owner of abandoned rice fields for acceptable sharing arrangement Revive traditional practices/rules on tree cutting within the communal forest or “muyong” Enhance eco-tourism activity within the Banaue area to keep the cultural heritage, i.e. The Banaue rice terraces in spit of climate change Tapping of local materials for irrigation system, e.g. bamboo as pipe for irrigation water

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MPDO/MAO

• Providing seed subsidy • Swine dispersal • Value formation • Reviving the muyong or the community system • Reviving the “hinuop” or the family forest system/practic es • Follow/observe the “tumonas” for time of planting and other activities.

The “tumona” know how to predict/forecast the weather and climate pattern based from his observation of plants and animal behavior as well as reading of moon and sky.

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Climate Change Questions

Farmer

•

• • • • •

Vulnerability assessment tools/indicators

• •

•

•

•

•

non-traditional crops for exploration and possible sources of income Reforestation using fruitbearing trees to protect the soil and have other sources of food Reviving the “muyong” system Reviving the traditional/organic rice culture Reviving the “tinawon” rice cropping system Value formation Tapping of government assistance for unmanageable (within the community level) damage Land uses, some land use are more vulnerable than the others Road network accessibility, those areas without good road network are cut-off from communication every time there is typhoon like the municipality of Tinoc Location, Lamut and those areas in the lowland are more vulnerable to increasing temperature, some areas already experience drought even without climate change.

Further increase in temperature will affect crop yield Land covers, denuded forest are prone to landslide if typhoon occurs after long dry season because of soil cracks High elevation areas, these are prone to landslides and erosion, especially if open to vegetable gardening Younger generations who no longer observe their environment are prone to climate change because

Gov’t officials

MPDO/MAO

• Vulnerable areas/people/crops etc were identified based from reported damage in times of extreme events • Based from people’s report on affected areas, crops, places • Based from the report of MSWD re: affected group • Based from report of local officials, barangay leaders during time of disasters • Changing attitudes towards ritual practices (elder in the group noted that younger generation no longer observed their environment for the signs of times, and depends instead on the advice of technicians who are not immersed in the field/nature /environment, hence, they are often caught unaware and vulnerable to impacts of climate change) • Traditional/cultural practices associated to production systems are eroding. These practices evolved from hundreds of years of living with nature and observing its

• Low lying areas as vulnerable to longer drought period • Landslides prone areas

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Climate Change Questions

Farmer

they are dependent on forecasts which are not reliable. Younger generation can no longer read the environment (no longer observe the sky, the moon, the plants, the trees, etc) they don’t even observe the behavior of animals which are good indicators of changes in climatic pattern

Gov’t officials

MPDO/MAO

behavior, then handing it down from generation to generation. Farmers unaware of the traditional practices are more vulnerable to climate change • Ignorance of environmental behavior – farmers who are not in tune with nature are more vulnerable to climate change • Farms of part-time farmers are more vulnerable to impacts of climate change because of their absences in the farm

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Annex 3. Adaptation Strategies of Benguet and Ifugao to drought Benguet

Percent of Respondents

Ifugao

Percent of Respondents

Adjustment of planting dates

27.49

Adjustment of planting dates

33.75

Exercise zero tillage in the farm

21.87

Find other water source, set up irrigation

24.38

Change of crop/crop rotation

14.37

Change of variety

12.50

Change of variety

13.12

Deforestation

8.75

Kaingin system

11.87

Building of dikes

8.13

Staggered cropping

11.25

Finding other source of income

8.13

Use organic fertilizer in the farm

10.62

Applying the barangay disaster management plan

7.50

Establish catch basin

10.00

Change of crop/crop rotation

7.50

Finding other source of income

9.37

Establish catch basin

7.50

Lime application

9.37

Use of organic fertilizers in the farm

6.25

Building of dikes

8.75

Exercise zero tillage in the farm

4.38

Converted the feces of animals as organic fertilizers

3.75

Kaingin system

4.38

Seed selection

3.75

Increasing farm inputs (fertilizer, chicken dung, etc)

3.75

Applying the barangay disaster management plan

3.12

Seed selection

3.75

Increasing farm inputs (fertilizer, chicken dung, etc)

3.12

Terrace construction

3.75

Recropping/replanting of same crop

3.12

Avoid burning plant residues

3.13

Migration of farmers to other farm areas

2.50

Multicropping system

3.13

Find other water source, set up irrigation

1.87

Recropping/replanting of same crop

3.13

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Sacrificing animals

1.87

Converted feces of animals as organic fertilizers

1.88

Safeguarded the forests

1.25

Food offering

1.25

Avoid burning plant residues

0.62

Safeguarded the forest

1.25

Bahala na system

0.62

Staggered cropping

1.25

Deforestation

0.62

Plant fruit trees

0.63

Greenhouse

0.62

Planting water succulent trees

0.63

Planting of water succulent trees

0.62

Sacrificing of animals

0.63

Putting a screen on the pigpen

0.62

Stop cropping

0.63

Rip rapping

0.62

Water pump

0.63

Rotation of supply of water

0.62

Wood carving

0.63

Source: Tandang, N.A. et al. (2010)

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Annex 4 Adaptation Strategies of Benguet and Ifugao to flooding.

Benguet

Building of dikes

Percent of Respondents 16.25

Ifugao

Percent of Respondents

Applying the barangay disaster management plan

7.50

Building of dikes

5.00

Drainage construction

1.88

Exercise zero tillage in the farm

1.25

Rip rapping

1.25

Terrace construction

9.38

Applying the barangay disaster management plan

8.75

Kaingin system

8.13

Use organic fertilizer in the farm

7.50

Rip rapping

5.63

Terrace construction

1.25

3.75

Find other water source, set up irrigation

0.63

Putting sticks in between corns

0.63

Widening outlets of rice paddies

0.63

Adjustment of planting dates

Exercise zero tillage in the farm

2.50

Greenhouse

1.25

Avoid burning plant residues

0.63

Drainage system

0.63

Finding other source of income

0.63

Increasing farm inputs (fertilizer, chicken dung, etc)

0.63

Source: Tandang, N.A. et al. (2010)

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Annex 5. Adaptation Strategies of Benguet and Ifugao to landslide.

Benguet

Percent of Respondents

Ifugao

Percent of Respondents

Rip rapping

16.21

Rip rapping

20.63

Terrace construction

13.71

Terrace construction

19.38

Deforestation

6.23

Finding other source of income

8.13

Building of dikes

5.61

Building of dikes

5.00

Finding other source of income

3.12

Exercise zero tillage in the farm

2.50

Migration of farmers to other farm areas

1.87

Find other water source, set up irrigation

2.50

Safeguarded the forest

1.87

Kaingin system

1.25

Kaingin system

1.25

Migration of farmers to other farm areas

1.25

Use of organic fertilizers in the farm

1.25

Deforestation

0.63

Adjustment of planting date

0.62

Drain water from rice paddies

0.63

Avoid burning the plant residues

0.62

Drainage system

0.63

Greenhouse

0.62

Planting of pineapple

0.63

Sacrificing of animals

0.62

Safeguarded the forests

0.63

Use organic fertilizers in the farm

0.63

Using cassava to fence the corn and palay and avoid soil erosion

0.63

Source: Tandang, N.A. et al. (2010)

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Annex 6 Adaptation Strategies of Benguet and Ifugao to pests and diseases.

Benguet

Percent of Respondents

Ifugao

Percent of Respondents

Increasing farm inputs (fertilizer, chicken dung, etc)

18.70

Applying the barangay disaster management plan

2.50

Use organic fertilizers in the farm

16.21

Increasing farm inputs (fertilizer, chicken dung, etc)

2.50

Spraying of insecticides

15.58

Converted feces of animals as organic fertilizers

1.88

Lime application

5.61

Change of variety

1.25

Change of crop/crop rotation

4.99

Use organic fertilizers in the farm

1.25

Converted the feces of animals as organic fertilizer

4.99

Adjustment of planting dates

0.63

Recropping/replanting of same crop

3.12

Change of crop/crop rotation

0.63

Greenhouse

1.87

Change of food preference

0.63

Convert plants as organic fertilizer

1.25

Finding other source of income

0.63

Applying the barangay disaster management plan

0.62

Sacrificing of animals

0.63

Bahala na system

0.62

Spray of pesticides

0.63

Change of variety

0.62

Finding other source of income

0.62

Kaingin system

0.62

Mix kuhol with watwat

0.62

Seed selection

0.62

Terrace construction

0.62

Source: Tandang, N.A. et al. (2010)

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Annex 7. Adaptation Strategies of Benguet and Ifugao to continuous rain.

Benguet

Percent of Respondents

Ifugao

Percent of Respondents

Building of dikes

21.19

Building of dikes

21.25

Change of crop/crop rotation

16.21

Adjustment of planting dates

13.13

Adjustment of planting dates

13.71

Avoid burning the plant residues

8.13

Use organic fertilizers in the farm

12.47

Use organic fertilizers in the farm

8.13

Rip rapping

11.22

Kaingin system

7.50

Kaingin system

9.35

Rip rapping

7.50

Avoid burning the plant residues

8.73

Drain water from rice paddies

6.88

Increasing farm inputs (fertilizer, chicken dung, etc)

8.10

Change of crop/crop rotation

6.25

Lime application

7.48

Applying the barangay disaster management plan

5.63

Change of variety

6.23

Recropping/replanting of same crop

5.00

Terrace construction

4.99

Change of variety

4.38

Multicropping system

3.74

Seed selection

4.38

Staggered cropping

3.74

Terrace construction

4.38

Exercise zero tillage in the farm

2.49

Exercise zero tillage in the farm

3.75

Seed selection

2.49

Increasing farm inputs (fertilizer, chicken dung, etc)

3.13

Greenhouse

1.87

Multicropping system

3.13

Spraying of insecticides

1.87

Drainage construction

2.50

Converted the feces of animals as organic fertilizer

1.25

Find other water source, set up irrigation

1.88

Finding other source of

1.25

Converted the feces of animals

1.25

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income

as organic fertilizers

Recropping/replanting of same crop

1.25

Finding other source of income

1.25

Sacrificing of animals

1.25

Sacrificing of animals

1.25

Bahala na system

0.62

Build water outlets on rice paddies

0.63

Composting

0.62

Establish catch basin

0.63

Potting

0.62

Food offering

0.63

Radio/TV news

0.62

Migration of farmers to other farm areas

0.63

Spraying of insecticides

0.63

Staggered cropping

0.63

Source: Tandang, N.A. et al. (2010)

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Annex 8 Adaptation Strategies of Benguet and Ifugao to typhoon.

Benguet

Building of dikes

Percent of Respondents 11.22

Ifugao

Percent of Respondents

Applying the barangay disaster management plan

8.75

Adjustment of planting dates

4.36

Building of dikes

5.63

Kaingin system

4.36

Adjustment of planting dates

4.38

Terrace construction

4.36

Drain water from rice paddies

3.13

Rip rapping

3.74

Find other water source, set up irrigation

3.13

Change of crop/crop rotation

3.12

Terrace construction

2.50

Finding other source of income

3.12

Drainage construction

1.88

Sacrificing of animals

2.49

Rip rapping

1.88

Greenhouse

1.87

Building fences

1.25

Recropping/replanting of same crop

1.87

Finding other source of income

1.25

Avoid burning plant residues

1.25

Build storage

0.63

Stays at home

1.25

Establish catch basin

0.63

Praying

0.63

Recropping/replanting of same crop

0.63

Sacrificing of animals

0.63

Stop planting/fishing

0.63

Source: Tandang, N.A. et al. (2010)

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Annex 9. Agri System Vulnerability and Adaptive Capacity Assessment Tool used in the Pretesting of the Vulnerability Assessment Tool.

A 1 2

Exposure

What proportion of the communityâ&#x20AC;&#x2122;s land area is related to agriculture?

What proportion of the agricultural area could be directly affected by the hazard?

Score

Data Source

0 - 10

MAO

0 - 10

MAO MAO Weather Data Weather Data

3

What proportion of the community is dependent on agriculture?

0 - 10

4

How frequent does the community experiences this hazard in a 10-year period?

0 - 10

5

How long does this hazard affect the agricultural sector to cause damage?

B 1

Risk/Impact

0 - 10

What proportion of profit in agricultural production may be lost?

0 - 10

Data Source Farmer

What proportion of the agricultural assets of the community was damaged?

What is the opportunity cost from the hazard? (not able to market, low growth rate of plants and animal)

0 - 10

Barangay

0 - 10

Farmer

What is the proportion of subsistence farmers?

What proportion of agriculture contributes to the community's income?

0 - 10

MAO

0 - 10

Barangay

Adaptive Capacity

Score

Data Source

1

How much agricultural area are isolated during the hazard?

0 - 10

Farmers

2

How much is the need for support systems during the hazard? (Ex. Bayanihan, Government and NGO support Credit facility, etc.)

0 - 10

Farmers

0 - 10

Barangay

0 - 10

Barangay

0 - 10

MAO

2 3 4 5

C

3 4

5

Score

What proportion of your community cannot afford to spend for adaptation cost?

What proportion of people in your community has no other sources of income?

What is the need of your community's technological adaptation (Knowledge of adaptation techniques both scientific and indigenous practices?)

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