Gender-disaggregated analysis of adoption of agricultural water management technologies in Kenya

Gender-disaggregated analysis of adoption of agricultural water management technologies in lower eastern Kenya Jayanth Kannaiyan

Research report submitted in partial fulfilment of the requirements for the MSc in Sustainable Development for Distance Learning Students of the University of London, Centre for Development, Environment and Policy (CeDEP), School of Oriental and African Studies (SOAS)

17 August, 2012 Nairobi, Kenya

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Table of Contents

Page List of tables and appendices

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List of acronyms

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Abstract

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Acknowledgments

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1.0 Introduction

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2.0 Literature review

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3.0 Methodology

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4.0 Results

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5.0 Analysis

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6.0 Conclusion

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References

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Appendices

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List of Tables and Appendices

Page Table 1. Demographics of farm managers interviewed

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Table 2. Factors involved in sourcing farmland

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Table 3. Land Preparation and Planting Information

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Table 4. Area planted breakdown by crops

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Table 5. Maize intercropping information

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Table 6. Agricultural water management adoption

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Table 7. Agricultural water management adoption by factors

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Table 8. Crop performance during long rains season (Oct-Nov-Dec) of 2011

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Table 9. Farmland condition and desired productivity improvement methods Table 10. Soil fertility results from representative soil sampling

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Table 11. Perceptions of the opposite gender on their farming ability

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Appendix 1: Gender Disaggregated Survey of Agricultural Water Management Adoption in Eastern Kenya

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List of Acronyms

ASARECA

The Association of Strengthening Agricultural Research in Eastern and Central Africa

AWM

Agricultural water management

CA

Conservation Agriculture

CCAFS

CGIAR Research Program on Climate Change, Agriculture and Food Security

CGIAR

Consultative Group on International Agricultural Research

ECA

Eastern and Central Africa

ICRISAT

International Crops Research Institute for the Semi-Arid Tropics

KARI

Kenya Agricultural Research Institute

FFM

Female farm managers

FMF

Female-managed farms

MFM

Male farm managers

MMF

Male-managed farms

SDL

Short duration legumes

SOAS

School of Oriental and African Studies, University of London

SSA

Sub-Saharan Africa

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Abstract

As climate change predictions of higher temperatures and more erratic rainfall come true for districts in Lower Eastern Kenya, adopting agricultural water management (AWM) technologies are going to be crucial to ensure future food security. In light of the highly gendered farming practices of rural Africa, the adoption rate of AWM was compared between male and femalemanaged farms, with the presumption that the women farmers would be lagging behind. The results showed that there is actually no difference in the adoption rate between male and female-managed farms in the two watersheds studied. Other revelations were that there was no difference in farm productivity or soil fertility between farms managed by either gender.

However, male-managed farms were more likely to use capital-intensive technologies such as irrigation whilst female-managed farms adopted labor and capital-reductive technologies such as conservation agriculture, highlighting the need to provide more access to gender-friendly AWM technologies.

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Acknowledgments

I would like to thank Dr. KPC Rao at ICRISAT in Nairobi, Kenya, for facilitating this research project and supervising my fieldwork in Kenya. Funds for this project were provided by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the ASARECA supported project "Integrated management of water for productivity and livelihood security under variable and changing climatic conditions in ECA."

I would also like to thank Mr. Kizito Kwena at KARI Katumani for his considerable help in organizing transport and accommodation during the actual fieldwork and providing guidance where needed.

And last, but not least, I would like to thank my dissertation advisor, Dr. Rebecca Kent at the Centre for Development, Environment and Policy, SOAS, for her guidance throughout this research project.

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1.0 Introduction

Agriculture is a dominant sector of the Kenyan economy, contributing approximately 51% of its Gross Domestic Product (GDP) and employing 75% of the workforce (Feed the Future 2011).

Most of Kenya's population, around 80%, live in rural areas and depend on agriculture for their livelihood (Alila and Atieno 2006) and with 50% of the population living below the poverty line, increases in agricultural productivity will help reduce the state of poverty in Kenya (Feed the Future 2011). However, agricultural productivity on small-scale farms in Kenya is decreasing (Alila and Atieno 2006). This is affecting rural peoples' climate change resilience as their food security needs are not met (Beddington et al 2011).

In the semi-arid regions of eastern Kenya, rainfed agriculture is the primary source of food and water availability is the key limiting factor affecting crop growth (SEI 2005). For the whole of Kenya, 70% of the agricultural output comes from 11% of the land that receives high rainfall with 20% of the output coming from semi-arid regions, that are characterized by highly-variable rainfall patterns (Feed the Future 2011). Application of agricultural water management practices in the semi-arid regions could increase water availability and thereby facilitate adoption of other agricultural innovations that enhance soil fertility, productivity and long-term sustainability, leading to more secure livelihoods (Shiferaw et al 2009).

Farming is highly gendered in Sub-Saharan Africa (SSA) with specific roles for men and women farmers (Sagardoy 2008). On a typical farm in SSA (such as Ghana), a man will be the household head, responsible for cash crop production while women in the household will be responsible for producing subsistence crops (Doss 2002). However, there are an increasing number of women who manage their own land, without the presence of a man (IFAD 1999) and this research will be comparing and analyzing the differences between these female-managed farms (FMF) and the more traditional male-managed farms (MMF).

While there might not be a significant productivity differential between FMFs and MMFs (Moock 1976), FMFs are said to be more risk-averse when it comes to adopting new agricultural technologies such as improved crop varieties (Doss and Morris 2001) and this could affect their resilience to climate change (Beddington et al 2011).

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Agricultural water management (AWM) technologies, such as tied ridges and conservation agriculture (CA), are a crucial component in adapting to climate variability (Brown and Hansen 2008). However, FMFs face specific constraints, such as a lack of access to land that results in difficulties in obtaining credit and inputs, which might limit their adoption of AWM (Gopal and Salim 1998). Low adoption may negatively affect the future productivity of FMFs, in light of the predicted changes in climate for this region (Beddington et al 2011). Efficient management of rain water has the potential to buffer agriculture from the negative effects of increased temperature and erratic rainfall patterns.

This study aims at understanding the gender differences in the adoption of agricultural water management (AWM) technologies and identifying interventions to increase uptake of said technologies. The primary research questions to be addressed are as follows: 1) what are the differences in the type and level of adoption of AWM technologies between MMFs and FMFs? 2) What are the key drivers and constraints for the adoption of AWM technologies and how does this differ between MMFs and FMFs? 3) Is there a difference in the productivity, soil fertility and soil quality of farms lead by either gender? 4) Is there a relation between how a farm became female-managed and its adoption of AWM technologies? 5) What is the perception of farmers regarding their quality of land and water availability and how do they perceive farm managers from the opposite gender and 6) What changes to current AWM technologies would increase adoption by both genders?

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2.0 Literature Review

The regions of the world with the highest percentage of least developed countries are SubSaharan Africa (SSA) and South Asia. Most of the inhabitants of these regions depend on agriculture for their livelihood. While the countries of South Asia were able to harness the benefits of the Green Revolution and build infrastructure and institutions to support irrigated farm land, farming in SSA is still primarily (93%) rain-fed. Only 9 million hectares out of 183 million is under some form of water management in SSA (Brown and Hansen 2008). The plight of this situation is highlighted when climate change and its variation in seasonal rainfall are predicted to affect tropical countries more severely than temperate ones. This draws attention to the urgent need to increase water management, especially on small-scale farms, to help farmers cope with abnormal rainfall patterns. With the recognition that women farmers make up 48% of the global agricultural workforce, agricultural water management (AWM) practices should be designed to enable adoption irrespective of the gender of the farmer (FAOSTAT 2000).

The following literature review will first discuss the importance of AWM and then introduce relevant publications that tie in gender with AWM. The drive to increase agricultural productivity and efficiency will then be discussed with cautions on drawing unrealistic conclusions. The review will end by looking at influential papers on the productivity of female farm managers and draw attention to the social construct of the gender division of labor.

As Brown and Hansen (2008) state in a report to development investors, there is a strong correlation between the increase in regional climatic variability and the risks faced by the rural people of SSA and South Asia, particularly in semi-arid and arid regions. Since the people of these regions depend on agriculture for their livelihood, variations in climate and rainfall have a direct impact on their food security. Brown and Hansen (2008) identify AWM practices as a fundamental strategy for helping farmers in dryland areas to cope with the expected increases in hydrometerological changes. However, they insist that AWM alone cannot provide the necessary protection to farmers from climate risks. They argue for a multi-level approach to climate-risk mitigation and adaptation to ensure farmers can manage their agricultural water needs. My research will contribute to Brown and Hansen's smallest-scale strategy, which involves increasing AWM capacity on farms to increase farmers' resilience and to direct this investment based on sound climatic information. Other strategies involve deploying climate

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information services for seasonally-adaptive management of water and early warning systems for climatic shocks such as severe droughts and floods.

Gender is undoubtedly becoming an integral part of all aspects of development planning and Kevane (2011) reviews the current status of gender-based development in rural Africa and the prospectus for the future. He has a clean and simple definition of gender, describing it "as a set of discursive habits relating to males and females." All too often, gender is equated to women, but Kevane (2011) correctly defines it as the roles that each sex takes on in accordance with local customs. Kevane (2011) goes on to say that gender roles that are defined by social norms, such as men farming cash crops and women farming food for the family, are the most difficult to influence since women in these roles might not see their gender as being disadvantaged due to the reinforcement of gender norms through shared discursive habits. However, Kevane (2011) highlights how the affordable spread of mobile communication is putting information and access directly in the hands of women, helping to raise their position among men. This and other strategies targeted at gender balance help influence the evolution of gender norms.

According to Sagardoy (2008), women have traditionally been excluded from water management decisions at all levels from farm-scale to watershed-scale. He argues for equity's sake that participation of rural women in water management should increase, but recognizes the challenges posed by the social organization of agricultural production and the gender division of labor in agriculture. This human rights-based approach of gender mainstreaming underlies the current justification for gender-focused development. However, prior to equity, economic efficiency was the driver for gender-focused development (Udry 1995, Saito et al 1994). This line of thinking continues today as the drive to increase global agricultural productivity (kg/ha) is accelerating due to the instability of future food security (Godfray 2010).

The idea that targeting gender imbalance in rural Africa would lead to increased agricultural efficiency and thereby, poverty reduction, was initiated by an often-cited work by Udry (1996).

He calculated the efficiency loss, on small-scale farms in Burkina Faso in the early 1980s, from the unequal distribution of productive resources among women's and men's plots. He suggested that by moving labor and manure from men's plots to women's plots, household agricultural yield would increase, in this case by around 6%. From this, he claims that gender inequality leads

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to agricultural inefficiency. At the time of publication, major donors of development projects, such as the World Bank, were looking for neo-classical economic justifications for why productivity was decreasing in SSA and relied on Udry's study, among others, to support the strategy of targeting women to achieve greater gains in poverty reduction and thus economic growth (O'Laughlin 2007). While greater attention to gender inequality is welcomed by all who have been raising awareness of this topic, the propagation of Udry's study has created unrealistic expectations that women farmers are the key to Africa's agricultural productivity (O'Laughlin 2007).

O'Laughlin (2007) counters the importance of Udry's study by emphasizing how the greater social context of the gender division of labor is a more important factor in agricultural productivity than simply the allocation of farm resources. She states how a colonial-era forced labor system initiated changes in the rural livelihoods of Burkina Faso that underlie the presentday gendered division of labor there. She agrees with Udry that social justice will lead to a better use of resources, but disagrees with the market-oriented idea of individualization and commodification of resources, because they will reinforce the current global wealth gaps and not achieve real poverty reduction. Her voice adds to the argument that "gender equality should be valued for itself, not simply because it increases output" (O'Laughlin 2007). With the greater goal of global development being poverty eradication, gender balance is rightly seen as one strategy in achieving that goal. O’Laughlin (2007) encourages policy makers and donors to look at the deeper reasons for poverty and gender imbalance than simply seeking market-oriented gains.

A report to the Swedish International Development Cooperation Agency (SIDA) on its genderaware agricultural support program (ASP) in Zambia praises the multiple successes in rural livelihoods due to its household approach for gender empowerment. The authors, Farnworth and Munachonga (2010), ascribe ASP's strategy of involving all the members of a family in farm planning to the improved livelihoods of targeted households, compared to un-targeted households. Gains have been noted in faster-than-expected change in gender roles and the possibility for influencing other gender-based development. By training the entire family (household head, spouse and children) to treat the farm as a business, household output has increased along with soft benefits such as increased health and reduced tension among family

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members. The report acknowledges that while ASP was able to cause desired change in intrahousehold gender relations, it still hasn't been able to influence wider social structures that govern gender norms outside the household. A particular issue that is often overlooked by gender-focused development projects is the attitude of the men when the women are empowered. The report states that men in targeted households did not feel disempowered and were actually encouraged to continue along the path of gender role evolution, with men taking part in household reproductive activities, due to the admiration of women in non-targeted households (Farnworth and Munachonga 2010). Being seen as a boost in social status is a desired effect that gender balance should bring to household members, with men seen as more equitable and women seen as empowered. This creates for a resilient community that is better able to adapt to changing conditions or unforeseen shocks, which can easily reverse slow developmental progress. In this report, gender equity was sought through a focus on increasing household productive efficiency and thus reinforces the importance of relating gender balance to productivity.

While the literature above has focused on gender relations between family members and the general concept of gender equity, this thesis is interested in analyzing the differences between male-managed and female-managed farms. Farming is highly gendered in SSA with specific roles for men and women farmers (Sagardoy 2008). On a typical farm in SSA (such as Ghana), a man will be the household head, responsible for cash crop production while women in the household will be responsible for producing subsistence crops (Doss 2002). However, in developing countries, there is a growing trend for households to be headed by females that lack an ablebodied male (Bongaarts 2001). The primary factor is the out-migration of males to urban areas to seek higher wages (Posel 2001). Other female-headed households (FHH) might be created through divorce, death of husband or single parenthood (Fletschner and Kenny 2011). Chant (2004) cautions against grouping all FHH in a survey under one grouping and for generalizations to be drawn due to the fact that not all FHH are created equally. Simply targeting FHH for rapid returns in poverty reduction investment can lead down a slippery slope where perverse incentives can arise, such as females artificially being made household heads in order to reap development benefits. Chant (2004) encourages against viewing all FHH as being poorer than male-headed households and to instead look at the greater social structures that lead to poverty and gender inequality.

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Besides the effects of (male) labor shortage on a FMF, the bigger burden is in access to credit, inputs and knowledge (Fletschner and Kenny 2011). Fletschner and Kenny (2011) report that in order to access credit to purchase modern inputs and implement new technologies, collateral is needed, usually in the form of land. However, agricultural land ownership by women is less common than that by men due to factors such as cultural norms that dictate that only men are entitled to inherited land or state programs that are biased towards redistributing land to men (Deere and Leon 2003). Without adequate land ownership (and access to labor), studies by Kumar (1994) have shown how FHHs in Zambia have been less likely to adopt new technologies, such as improved seeds and fertilizers.

While recognizing that not all FHH are formed similarly, this thesis will be comparing femalemanaged farms (FMF) against male-managed farms (MMF) in regards to their adoption of AWM practices and its effect on their productivity (measured as yield per hectare of farmland). A frequently cited study by Moock (1976) investigates whether there is any difference in agricultural productivity between MFMs and FFMs from an area in Western Kenya. Particularly, he analyzes the technical efficiency of the farmers, which is their ability to combine inputs to increase output. Controlling for various variables, he concludes that FMFs are technically more efficient and highlights how they achieve this by using less inputs compared to MMFs, but use more labor to increase productivity of the few inputs that they do use. Moock (1976) assumes that most female farm managers acquired their position due to out-migration of their male head and does not comment on whether the higher technical efficiency seen on FMFs might be due to financial support from remittances. This financial support could account for the higher use of wage labor on FMF. He states that FMF benefited less from agricultural extension services compared to MMF and attributed this to the predominantly male orientation of the extension service.

Taking into account the social constructs of the gender division of labor in southwest Hungary, Mauro (2003) draws a relation between the gender of the farmer and the soil management practices in that region. He states that plots controlled by men, usually used for cash-cropping that demand high levels of inputs, show low pH, soil acidification and depleted organic matter content. This is compared to plots controlled by women, usually used for subsistence food

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production, with crops that demand lower inputs than cash-crops, which show a higher pH value and generally higher soil fertility. He is quick to point out that given the chance to cash-crop women's plots would resemble those of men. Thus, caution should be taken in attributing observed or measured differences in farm productivity directly to the gender of the farmer.

Instead, the social situation of the particular farmer, their access to inputs and their ability to efficiently combine these inputs should be accounted for when measuring agricultural productivity.

In order to build resilience against predicted variation in rainfall patterns in SSA, AWM capacity needs to increase on small-scale farms. This should be done by tailoring adoption solutions to the specific needs of farmers, which arise due to their gender, financial ability or particular social situation. The papers above encourage caution in seeking out the weakest link, female-headed farms, and expecting unrealistic returns for development investment. However, helping female farmers to adopt the technical solutions (AWM) that they would like to implement on their farm is one part of the strategy of increasing rural food security. This thesis aims to understand the gender differences in the adoption of AWM in Eastern Kenya.

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3.0 Methodology

To determine whether the gender of the farm manager affects agricultural water management adoption and thus, farm productivity, this research project conducted interviews with select farmers and analyzed soil samples from their farms. The research design was a cross-sectional comparison between the gender of the farm manager, adoption of AWM and soil fertility. The data collection strategy was an instrumental case study of farmers in two districts of Lower Eastern Kenya. The districts of Machakos and Makindu were chosen for this study as they were covered by a larger baseline survey (conducted in June, 2011) for a long-term water productivity improvement project managed by ASARECA. This baseline survey data was used as a secondary source and access was provided through ICRISAT and KARI. The water productivity project compares farmers in watersheds, with one being in a drier climate than the other. The drier of the two watersheds, Makindu, provides an analogy to how the climate of the wetter watershed, Machakos, will be like if the predicted drying out of the regional climate in the next 30 years comes true. This case study compared multiple cases of farmers: pairing up a female farm manager (FFM) with a male farm manager (MFM) from the same immediate location (hillside or village) and with similar education levels.

The water productivity baseline survey covered 175 households in Machakos and 209 households in Makindu. FFMs accounted for 22 of those households in Machakos and 23 in Makindu. In order to have a large enough sample size to be able to help develop theory, all FFMs have been purposively selected to be in the sample. However, since there are many more MFMs that meet the basic criteria of location and education level, they have been selected through quota sampling. Once the criteria were met for each type of FFM, the MFM was then randomly selected, usually out of four to six options. The population was sampled as stated due to time constraints.

The definition of a farm manager used in this study is that of a person who is responsible for the daily to seasonal operations of a farm. This person is usually also the household head, but not always, as the household head might be in absentia due to employment in urban areas. In these situations, the spouse becomes the farm manager. The household heads in rural Sub-Saharan Africa are traditionally male (Doss 2002). A woman might become a farm manager either from

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urban migration of her husband, divorce, single-womanhood or by becoming a widow (Fletschner and Kenny 2011). The definition of a farm is the combined plots of land that are under the responsibility of one farm manager.

Data collection comprised of a questionnaire survey (Appendix 1) and soil sampling. Due to a language barrier between the researcher and the respondents, local enumerators conducted the interviews. Their familiarity with the local language helped in conveying the intended meaning of the interview questions. The enumerators were encouraged to conduct the interview in a conversational style to increase reliability of the answers and total interview time was kept to under an hour to reduce disruption to farmer activities. Soil core samples were taken up to a depth of 20 cms from the surface using an auger. In order to see whether there is a relation between soil fertility and the gender of the farm manager, a representative soil sample was taken from each farm. Soil samples were taken along a transect and two boundaries (Z-shaped) of the farmland under a farm manager and crossed various crop types. This was done to balance the variation in soil quality that can be found on farms on hill slopes, such as those in Machakos. The soil samples from across a farm were then mixed on site and a 1 kg representative sample was taken for analysis. The soil samples were analyzed by the certified soil laboratory at the Crop Nutrition Laboratory in Nairobi. Farm productivity data was generated from the baseline survey as it wasn't practical to make direct field measurements during the fieldwork.

Due to the informal, non-probability sampling of the farmers in this study, non-parametric statistics were used in the analysis. For the data that was generated as counts, such as the number of water conservation techniques in use by FFMs and MFMs, a distribution free test was applied to see if there was a relation to the categorical variable of gender. This test is also known as the chi-square test of independence and this analysis was conducted using SPSS software.

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4.0 Results

4.1 Demographics Key findings from the data generated by the questionnaire is presented in the following tables.

Table 1 shows that there is a significant difference in the marital status of the farm managers that were surveyed in the study area. 91% of MFMs were married contrasting to 75% of FFMs who were widowed. This highlights a possible labor shortfall on farms managed by women since married men will have at least their wives to help in farm labor, while widowed women have lost an able-bodied male in terms of farm labor.

Table 1. Demographics of farm managers interviewed Demographics Farm managers (count) Age <= 60 yrs (%) > 60 yrs (%) Education None (%) Primary (%) Secondary or more (%) Marital status Single (%) Married (%) Widow (%) Others4 (%)

FMF All 44

MMF All 46

All 45

Machakos FMF MMF 23 22

54 46

59 41

51 49

43 571

59 41

62 38

66 331

58 42

45 32 23

33 30 37

33 33 33

39 39 22

27 27 45

44 29 27

52 24 24

38 33 29

7 52 753 13

2 912 43 2

4 49 38 9

9 4 70 17

0 95 5 0

4 49 40 6

5 5 81 10

4 88 4 4

All 45

Makindu FMF MMF 21 24

Source: data derived from survey conducted in study area Note: highlighted data pairs tested for significant difference and bold indicates difference is significant 1 Chi-square p-value for difference is 0.123 = not significant 2,3 Chi-square p-value for difference is 0.000 = significant 4 Others: Spouse away, divorced, polygamous marriage (not first wife)

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4.2 Farmland acquisition A telling sign about gender equality in lower eastern Kenya is the method in how the farm managers surveyed in the study area acquired their farmland. Table 2 shows that 49% of MFMs compared to 16% of FFMs acquired their land through inheritance with 56% of FFMs acquiring their land through marriage compared to 9% of MFMs. This significant difference implies that women are not favored when it comes to allocating inherited land within a family even though 88% of FFMs responded that inherited land was shared equally among eligible (adult) family members.

Table 2. Factors involved in sourcing farmland Land source factors Farm managers (count) Land acquisition Purchased (%) Inherited (%) Through marriage (%) No info (%) Inherited land shared how Equally among eligible members (%) Able members get more (%) Male members get more (%) Female members get more (%) Divorce/widow affect allocation?

Not applicable (%) Yes, area reduced (%) No (%) Gender preference in land sharing Yes (%) No (%) Land sharing practices fair Yes (%) No (%)

FMF All 44

MMF All 46

All 45

Machakos FMF MMF 23 22

26 161 562 2

43 491 92 0

36 31 33 0

27 9 64 0

43 52 4 0

33 36 29 2

24 24 48 5

42 46 13 0

88

80

89

100

87

75

80

73

0 13 0

7 10 3

6 6 0

0 0 0

7 7 0

5 15 5

0 20 0

7 13 7

27 0 73

85 7 7

57 10 33

38 0 63

69 15 15

71 0 29

14 0 86

100 0 0

13 87

54 46

15 85

0 100

25 75

62 38

29 71

79 21

92 8

90 10

91 9

88 13

93 7

89 11

100 0

87 13

All 45

Makindu FMF MMF 21 24

Source: data derived from survey conducted in study area Note: highlighted data pairs tested for significant difference and bold indicates difference is significant 1 Chi-square p-value for difference is 0.001 = significant 2 Chi-square p-value for difference is 0.000 = significant

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4.3 Land preparation Table 3 shows that land holding size was similar among FMFs and MMFs with the majority farming on less than two hectares. In preparing the land for planting, animal draught power was the most common response by all farmers. The flatter terrain of Makindu permitted the use of tractors by some farmers there compared to no tractor use in hilly Machakos.

Table 3. Land Preparation and Planting Information Planting factors Farm managers (count) Farm size <= 2 ha (%) > 2 ha (%) Land preparation Manual (%) Animal (bullocks) (%) Tractor (%) No of ploughings <2 (%) >2 (%) Planting type Dry planted (%) Planted with rain (%) Seed source Low quality seeds3 (%) High quality seeds4 (%) Seed type planted Primed seed (%) Dry seed (%)

FMF All 44

MMF All 46

All 45

Machakos FMF MMF 23 22

73 27

89 11

89 11

87 13

91 9

73 27

86 14

62 38

11 86 2

17 72 11

18 82 0

9 91 0

271 73 0

11 76 13

14 81 52

81 71 212

38 62

34 66

39 61

40 60

39 61

31 69

35 65

28 72

40 60

37 63

45 55

50 50

40 60

31 69

27 73

35 65

49 51

37 63

41 59

42 58

40 60

46 54

59 41

34 66

3 97

6 95

5 95

5 95

6 94

4 96

2 98

7 93

All 45

Makindu FMF MMF 21 24

Source: data derived from survey conducted in study area Note: highlighted data pairs tested for significant difference and bold indicates difference is significant 1 Chi-square p-value for difference is 0.090 = not significant 2 Chi-square p-value for difference is 0.114= not significant 3 Low quality seeds = recycled seeds (owned, borrowed or bought from local market) 4 High quality seeds = hybrid and improved varieties (bought from agrovet, CBO, KARI and donated by government and KARI)

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4.4 Crops planted Farmers planted a variety of crops on their land, ranging from maize, short duration legumes (SDLs), pigeon pea, sorghum and vegetables (tomatoes and brinjals). Table 4 shows that there was a significant difference in the percentage of land given to farming SDLs in the two locations with 60% growing it in Makindu compared to 22% in Machakos. This is related to the fact that almost all the pigeon pea was farmed in Machakos. SDLs are a drought-tolerant crop and grow better in drier climates, like in Makindu, and pigeon pea, a long-duration legume, needs a wetter climate, like in Machakos (Singh et al 2000). While farmers demonstrate awareness of how climate affects crop yields, there is room for further development since sorghum, a better drought-tolerant cereal compared to maize (Rosenow et al 1983), is only being grown by 1% of farmers. There is a need to promote higher uptake of drought-tolerant crops to increase resilience to climate change factors, but practical issues need to be solved first, such as creating a market and encouraging change in local taste from maize to sorghum (Beddington et al 2011).

Table 4. Area planted breakdown by crops Crop area planted Farm managers (count) Total area planted (ha) Maize (ha) (%) SDL1 (ha) (%) Pigeon pea (ha) (%) Sorghum (ha) (%) Vegetables5 (ha) (%)

FMF All 44 206.1 72.0 (35) 107.5 (52) 25.3 (12) 1.0 (1) 0.3 (0)

MMF All 46 189.0 86.7 (46) 76.5 (41) 18.9 (10) 1.9 (1) 5.0 (3)

All 45 143.0 67.2 (47) 32.1 (22)2 43.2 (30)4 0.5 (0) 0.0 (0)

Machakos FMF 23 81.5 41.3 (51) 15.9 (20) 24.3 (30) 0.0 (0) 0.0 (0)

MMF 22 61.6 26.0 (42) 16.2 (26) 18.9 (31) 0.5 (1) 0.0 (0)

All 45 252.4 91.6 (36) 151.9 (60)2 1.1 (0)4 2.6 (1) 5.3 (2)

Makindu FMF 21 124.9 30.8 (25) 91.7 (73)3 1.1 (1) 1.0 (1) 0.3 (0)

MMF 24 127.7 60.9 (48) 60.2 (47)3 0.0 (0) 1.6 (1) 5.0 (4)

Source: data derived from survey conducted in study area Note: highlighted data pairs tested for significant difference and bold indicates difference is significant 1 SDL = short duration legumes (green grams, beans, cow peas and dolichos) 2,4 Chi-square p-value for difference is 0.000 = significant 3 Chi-square p-value for difference is 0.083 = not significant 5 Vegetables = tomatoes and brinjals

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4.5 Intercropping Farmers are aware of the benefits of intercropping. However there was a significant difference between the locations, with 45% intercropping their primary crop of maize in Machakos compared to 24% in Makindu (Table 5). This could be related to the fact that farms in Makindu are larger (Table 3) than those in Machakos and perhaps the pressure to intercrop isn't as high compared to Machakos, where hilly terrain reduces the amount of arable land. While intercropping is advocated to ensure a balance in soil fertility between nitrogen-consuming crops, such as maize, with nitrogen-fixing crops, such as SDLs, (Singh et al 2000) the benefit of growing more produce on the same amount of land is also a factor as was revealed during casual conversations with farmers in Machakos during the fieldwork.

Table 5. Maize intercropping information Intercropping information Farm managers (count) Maize intercrop (IC), yes (%) IC w/ SDL2 (%) IC w/ pigeon peas (%) IC w/ sorghum (%) IC w/ vegetables4 (%)

FMF All 44 32 54 42 4 0

MMF All 46 42 48 46 3 3

All 45 451 343 63 0 2

Machakos FMF MMF 23 22 34 58 37 32 63 64 0 0 0 4

All 45 241 893 0 11 0

Makindu FMF 21 26 88 0 13 0

MMF 24 23 90 0 10 0

Source: data derived from survey conducted in study area Note: highlighted data pairs tested for significant difference and bold indicates difference is significant 1,3 Chi-square p-value for difference is 0.000 = significant 2 SDL = short duration legumes (green grams, beans, cow peas and dolichos) 4 Vegetables = tomatoes and brinjals

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4.6 Agricultural water management adoption Table 6 shows the AWM techniques that were employed on farms in the study area. While terraces are primarily built to prevent soil erosion, they inherently aid in rain water conservation (Mati 2005) and were considered an AWM technique in this study. 83% of farms in hilly Machakos reported the use of terraces compared to 47% in the flatter farms of Makindu. Since terraces are required for farming on hilly slopes, it is not surprising that there's a significant difference in their use between the locations. Terrace farming has been a feature of the hillsides in Machakos since the 1930s when government intervention plans were initiated to stop the degradation of soils (Gichuki 1991). Due to this fact, terracing should not be considered an AWM adoption method by the current farmers in Machakos, since they inherited already terraced farmland (Mortimore et al 1993). Tied ridges are specifically an AWM technique (Shiferaw et al 2009) and farms in the drier location of Makindu reported a significantly higher use (32%) compared to those in Machakos (10%). When terraces are not counted as an AWM technique, the data shows that only 12% of farmers in the relatively wetter climate of Machakos are implementing an AWM technique on their farms compared to 38% in Makindu. This significant difference could imply that farmers are more keen to implement AWM as the climate gets drier, which is predicted to happen in Machakos over the next 30 years (Indeje et al 2000).

Table 6. Agricultural water management adoption AWM practiced on farms Farm managers (count) No AWM (%) Terraces (%) Tied ridges (%) Conservation agriculture (%) Irrigation (%) Furrows (%) Rock bunds (%) At least 1 type of AWM on farm (%) AWM adoption w/o terraces4 (%)

FMF All 44 8 68 22 2 0 0 0 92 24

MMF All 46 10 66 18 0 1 3 3 90 25

All 45 4 831 102 0 0 2 0 96 125

Machakos FMF 23 8 77 143 0 0 0 0 92 14

MMF 22 0 89 63 0 0 5 0 100 11

All 45 14 471 322 2 1 0 3 86 385

Makindu FMF MMF 21 24 7 20 55 41 33 31 5 0 0 3 0 0 0 5 93 80 38 39

Source: data derived from survey conducted in study area Note: highlighted data pairs tested for significant difference and bold indicates difference is significant 1 Chi-square p-value for difference is 0.000 = significant 2 Chi-square p-value for difference is 0.020 = significant 3 Chi-square p-value for difference is 0.317 = not significant 4 Terraces are primarily built to prevent soil erosion but inherently aid in water conservation (Mati 2005) 5 Chi-square p-value for difference is 0.003 = significant

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Table 7 shows that there is no significant difference between farmers regarding the factors of gender, location, education level and widowhood when in comes to practicing AWM (including and not including terracing) on their farms.

Table 7. Agricultural water management adoption by factors Possible AWM adoption factors All FFMs (%) All MFMs (%) Farmers in Machakos (%) Farmers in Makindu (%) Farmers with no education (%) Farmers with some education (%) FFM widows (%) FFM non-widows (%) FFM widows (not incl terraces) (%) FFM non-widows (not incl terraces) (%)

AWM adoption rate 93 89 95 87 91 91 91 100 33 18

P-value

Significant Difference?

0.500

No

0.138

No

0.933

No

0.300

No

0.291

No

Source: data derived from survey conducted in study area

4.7 Crop performance This study was conducted in the early part of 2012 and information on crop performance from the previous season (October to December, 2011) is presented in Table 8. Since farming in the semi-arid regions of lower eastern Kenya is rain-fed (FAOSTAT 2011), timing the planting of crops with the arrival of the rains is highly crucial to a good yield (SEI 2005). 66% of FFMs and 56% of MFMs planted timed their planting with the rains. Of those remaining farmers that did not plant timely, a significant percentage (64%) of farmers in Makindu blamed poor weather advice compared to those in Machakos (28%). This is not surprising as the climate in Makindu is considered more erratic than that of Machakos, making accurate weather forecasting a challenge (Cooper et al 2009). Other reasons stated for untimely planting were physical disabilities and seed problems. Regarding crop status, a significant portion of farmers (65%) in Makindu reported a poor status compared to those in Machakos (32%). This could be attributed to the complaint of a lack of rains by 60% of farmers in Makindu compared to 34% in Machakos.

Farm productivity (yield) was significantly higher in Machakos (668 kg/ha) compared to Makindu (360 kg/ha).

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Table 8. Crop performance during long rains season (Oct-Nov-Dec) of 2011 Crop performance factors Farm managers (count) Crop plant timing with the rains Early (%) Normal (%) Late (%) Planting not timely, why?

Poor weather advice (%) Physical disability (%) Labor shortage (%) Seed problems (%) Crop status Good (%) Average (%) Poor (%) Crop problems faced Lack of rains (%) Pests and diseases (%) Lack of inputs (%) Poor farm management (%) None (%) Hired farm labor No male labor (%) Male laborer = 1 (%) Male laborers > 1 (%) No female labor (%) Female laborer = 1 (%) Female laborers > 1 (%) Productivity average7 (kg/ha)

FMF All 44

MMF All 46

All 45

Machakos FMF MMF 23 22

19 66 15

21 56 24

19 65 15

21 63 15

17 68 15

20 55 25

15 70 15

24 43 33

38 212 29 13

55 62 23 17

281 233 25 254

30 27 30 13

26 17 17 39

641 33 26 74

50 11 28 11

70 0 26 5

27 24 49

19 36 46

32 36 325

39 30 31

24 43 33

12 23 655

10 17 73

13 28 59

43 36 13 1 7

45 42 7 2 5

346 41 15 2 8

33 37 19 1 10

34 46 10 3 7

606 36 2 0 3

62 35 1 0 2

59 37 2 0 3

70 13 17 85 5 10 498

78 8 14 90 4 6 525

73 12 15 89 5 5 6688

64 17 19 86 8 6 629

83 7 10 93 2 5 707

75 9 16 87 3 10 3608

78 8 13 85 0 15 361

72 9 19 88 5 7 359

All 45

Makindu FMF MMF 21 24

Source: data derived from survey conducted in study area Note: highlighted data pairs tested for significant difference and bold indicates difference is significant 1 Chi-square p-value for difference is 0.001 = significant 2 Chi-square p-value for difference is 0.052 = not significant 3 Chi-square p-value for difference is 0.004 = significant 4 Chi-square p-value for difference is 0.020 = significant 5 Chi-square p-value for difference is 0.002 = significant 6 Chi-square p-value for difference is 0.011 = significant 7 Source: ASARECA water productivity baseline survey data, provided by KARI 8 Paired sample t-test p-value for difference is 0.008 = significant

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4.8 Farmland satisfaction and desired improvements Table 9 shows the satisfaction of farmers with their land quality and what they would like to do to improve its productivity. 86% of FFMs and 81% of MFMs were satisfied with their land quality.

However, among the MFMs, there was a significant difference between the locations with 65% satisfied in Machakos, compared to 96% in Makindu. The responses of high satisfaction with land quality contrasts with the biophysical data in Table 10, which shows that most of the soils are poor in quality. This could either be due to a lack of awareness among farmers as to what constitutes healthy soils or a lack of resources needed to maintain healthy soils. In Machakos, there was a significant difference among the genders (44% of FMFs to 80% of MMFs) regarding whether variations in the quality of the land within a farm affected crop choice (i.e. planting legumes in less fertile areas and maize in more fertile areas). The higher response from MFMs could be a result of men having better access to extension services and being more aware about how crop choice should be determined by the quality of land available. When asked how they would like to improve the productivity of their farms, a significantly higher portion of farmers in Machakos (62%) said they would like to increase inputs compared to farmers in Makindu (41%), where implementing AWM was the preferred method (51%). This is as expected because the drier climate of Makindu could see yields increase if more AWM was implemented, while in the wetter Machakos, increasing inputs are perceived to be the better method for improving productivity. This brings about the differences in various agroecologies and their tendency to implement AWM. The climate change implication is that as the environment becomes drier, investment in AWM will become more important. When asked to state the reasons why their stated productivity improvement methods were not implemented, a significant difference is evident among the genders with 61% of FFMs stating a lack of capital compared to 40% of MFMs. This might be indicative of the lack of access to capital for FFMs or the lack of collateral (land titles in their name) that could be used to secure capital.

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Table 9. Farmland condition and desired productivity improvement methods Farmland condition Farm managers (count) Satisfied with land quality Yes (%) No (%) Variations in land quality affect crop choice2 Yes (%) No (%) Productivity improve how Implement AWM (%) Increase inputs (%) Better farm management6(%) Improvement not implement why Lack of labor (%) Lack of capital (%) Lack of other resources8 (%)

FMF All 44

MMF All 46

All 45

Machakos FMF MMF 23 22

86 14

81 19

73 27

82 18

651 35

93 7

90 10

961 4

50 50

68 33

61 39

443 56

803 20

58 42

57 43

58 42

49 46 5

36 58 7

334 625 4

41 55 5

26 70 4

514 415 7

58 37 5

45 45 9

25 617 14

17 407 43

19 42 39

27 53 20

13 31 56

22 56 22

23 69 8

21 47 32

All 45

Makindu FMF MMF 21 24

Source: data derived from survey conducted in study area Note: highlighted data pairs tested for significant difference and bold indicates difference is significant 1 Chi-square p-value for difference is 0.006 = significant 2 Such as planting legumes in less fertile areas of the farm 3 Chi-square p-value for difference is 0.008 = significant 4 Chi-square p-value for difference is 0.088 = not significant 5,7 Chi-square p-value for difference is 0.035 = significant 6 Farm management: land preparation, crop rotation, timely planting, soil analysis, regular weeding, etc.

8 Other resources: manure, certified seeds, access to inputs, access to extension services, etc.

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4.9 Soil fertility indicators Four soil fertility indicators were chosen to represent the overall quality of the soils (Table 10).

The low levels of carbon content in the soil are highlighted by the fact that only 2% of farmers in Machakos were in the optimal range along with 13% of those in Makindu. Likewise, nitrogen content was also generally below optimal. Regarding phosphorous content, there was a significant difference between the average values of farmers in Makindu compared to those in Machakos. In contrast, 80% of farmers in Makindu had below optimal readings of zinc, compared to 38% of those in Machakos.

Table 10. Soil fertility results from representative soil sampling Soil fertility indicators Farm managers (count) Carbon (average) (%) < 1.5% (%) Optimal (%) > 3% (%) Nitrogen (average) (%) < 0.12% (%) Optimal (%) > 0.25% (%) Phosphorous (average) (ppm) < 36 ppm (%) Optimal (%) > 50 ppm (%) Zinc (average) (ppm) < 4 ppm (%) Optimal (%) > 8 ppm (%)

FMF All 44 1.06 91 9 0 0.10 77 23 0 105 27 14 59 5.24 50 30 20

MMF All 46 1.00 93 7 0 0.09 87 13 0 80 26 22 52 4.86 67 17 15

All 45 1.02 98 22 0 0.10 87 13 0 62 36 22 42 6.76 386 31 31

Machakos FMF 23 1.071 96 4 0 0.103 78 22 0 644 43 17 39 5.985 43 26 30

MMF 22 0.971 100 0 0 0.093 95 5 0 594 27 27 45 7.585 32 36 32

All 45 1.04 87 132 0 0.10 78 22 0 123 18 13 69 3.34 806 16 4

Makindu FMF 21 1.051 86 14 0 0.103 76 24 0 1504 10 10 81 4.445 57 33 10

MMF 24 1.01 88 13 0 0.093 79 21 0 1004 25 17 58 2.375 100 0 0

Source: Crop Nutrition Laboratory, Nairobi, soil fertility analysis on soil sampling taken in study area Note: highlighted data pairs tested for significant difference and bold indicates difference is significant 1 Welch test p-value for difference is 0.597 = not significant 2 Chi-square p-value for difference is 0.049 = significant 3 Welch test p-value for difference is 0.420 = not significant 4 Welch test p-value for difference is 0.008 = significant 5 Welch test p-value for difference is 0.000 = significant 6 Chi-square p-value for difference is 0.000 = significant

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4.10 Gender perceptions The questionnaire ended with a section on gauging the gender perceptions among the farm managers (Table 11). As can be expected in the male-dominated society of rural SSA (Saito et al 1994), no men stated that FFMs were better at farm management than them while 47% of FFMs stated that men were better in farm management. Between the locations, there exists a significant difference among the FFMs on whether they are 'better' in farm management compared to MFMs. 36% of FMFs in Machakos responded that FMFs were better compared to 5% in Makindu. When farmers were asked which gender-managed farms were perceived to be more productive, it is not surprising that a significant difference exists for each gender siding with themselves. 33% of FMFs stated that they were more productive compared to 11% of MMFs. In direct contrast, 51% of MMFs stated that they were the more productive gender with 21% stating that FMFs were more productive. These responses might be a case of gender pride where if asked, proud FFMs will say they are better than MFMs whether the data agrees with them or not. However, the lack of responses from MFMs in saying that FFMs are better farmers could be an issue of male chauvinism where the MFMs think FFMs could never be better than them.

There were no differences between the genders on perceived roles on the farm with both genders saying that MFMs were primarily involved in land preparation while FFMs were primarily involved in harvesting. This highlights the need for more gender-friendly land preparation techniques, such as CA (Hobbs 2007), because the conventional method of tilling requires more physical work and investment and is primarily done by men. A word of caution though on CA, as Giller et al (2009) found that without the use of herbicides, CA would not result in net labor savings for women.

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Table 11. Perceptions of the opposite gender on their farming ability Gender perceptions Farm managers (count) Gender difference in farm management No differences (%) MMF better1 (%) FMF better (%) Gender difference in crop preference No difference (%) FFM more conservative4 (%) FFM more progressive5 (%) MFM more conservative (%) MFM more progressive (%) Gender difference in farming skill No differences (%) MFM better (%) FFM better (%) Gender spending most time on farm Women (%) Men (%) No difference (%) Which gender-managed farm more productive6 FMF (%) MMF (%) No difference (%) Perceived female farmer roles Planting and preparation9 (%) Crop management10(%) Harvesting (%) Perceived male farmer roles Planting and preparation (%) Crop management(%) Harvesting (%)

FMF All 44

MMF All 46

All 45

Machakos FMF MMF 23 22

33 47 212

47 53 02

36 47 18

23 41 363

48 52 0

44 53 2

43 52 53

45 55 0

81 7 10 2 0

89 6 2 0 2

89 5 5 2 0

81 5 10 5 0

96 4 0 0 0

82 9 7 0 2

81 10 10 0 0

83 8 4 0 4

50 28 23

66 26 9

55 24 21

42 26 32

65 22 13

62 29 9

57 29 14

67 29 4

79 21 0

74 21 4

84 13 2

77 23 0

91 4 4

69 29 2

81 19 0

58 38 4

337 218 47

117 518 38

20 40 40

27 27 45

13 52 35

22 33 44

38 14 48

8 50 42

28 29 43

24 27 49

28 26 46

31 25 44

26 27 48

24 30 46

25 33 42

23 27 50

61 28 12

57 33 10

64 27 9

64 26 10

64 28 8

53 34 13

57 29 14

49 38 13

All 45

Makindu FMF MMF 21 24

Source: data derived from survey conducted in study area Note: highlighted data pairs tested for significant difference and bold indicates difference is significant 1 Better as perceived by the farmers themselves (work harder, more active on farm, capacity to organize and learn new techniques, more financially stable to access inputs, etc.)

2 Chi-square p-value for difference is 0.000 = significant 3 Chi-square p-value for difference is 0.014 = significant 4 Conservative: planting staples instead of cash crops, planting few varieties, etc.

5 Progressive: planting cash crops, planting many varieties, etc.

6 In terms of yield/hectare 7 Chi-square p-value for difference is 0.008 = significant 8 Chi-square p-value for difference is 0.003 = significant 9 Preparation: seed cleaning, ploughing, terracing, farrowing, acquiring inputs, etc.

10 Crop management: weeding, manuring, applying fertilizer, supervision, pest control, etc.

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5.0 Analysis

The primary focus of this research is to determine whether the gender of the farmer affects their ability to adopt AWM practices to be more climate change resilient. The hypothesis is that FFMs are traditionally disadvantaged compared to MFMs in terms of access to new agricultural technologies due to gender norms and other means, such as access to credit and resources for adopting these new technologies aimed at improving farm productivity. The results in Table 7 show that there is no relation between the gender of the farm manager and their adoption of AWM practices. This might be because agricultural extension services are now aware that women farmers should be actively targeted when disseminating information on new farm technologies.

Under the research question of what is the current level of AWM adoption, a hypothesis can be constructed that farmers in the drier climate of Makindu would adopt AWM more than farmers in the wetter climate of Machakos. However, the results in Table 7 show that there is no relation between the location of the farmer and their adoption of AWM. But removing terraces as an AWM practice, then it is evident that farmers in Makindu (38%) are implementing more AWM than farmers in Machakos (12%) (Table 6).

Another hypothesis is that the education of the farmer influences their ability to adopt new technologies. The hypothesis is that farmers who received no education would not adopt AWM due to their inability to understand new knowledge. Again, the results show that there is no relation between the education level of the farmer and their adoption of AWM (Table 7), which is driven more by climatic conditions than any other factor.

The demographic results in Table 1 show that a majority (75%) of the FFMs are widows, while the majority (91%) of MFMs are married. This is because most women only get the opportunity to become a farm manager on the passing of their husband (Fletschner and Kenny 2011). The other 25% of FFMs indicate that women are slowly taking on the task of being a farm manager even without the passing of their husband. However, most of this remaining group became the farm manager due to a lack of presence of their husband, either because he was employed in an

32

urban setting and not present on the farm or because he was incapacitated due to old age (Posel 2001).

One of the primary questions of this research is to check for a relation between how a farm became female-managed and its adoption of AWM. The hypothesis is that FFMs who got to that position through a passing of their husband would face higher constraints than FFMs whose husbands are still alive or who are not associated with a spouse and thus widowed FFMs would not be able to adopt new farm technologies. Constraints on widowed FFMs could be emotional stress, financial stress due to losing an earning member of the family, reproductive stress as the widow has to manage the farm and continue to support any children and family members and legal stress as some women in Kenya do not hold the right to their husband's land after his death (Karanja 1991). These added constraints would reduce her ability to seek out new knowledge, such as AWM.

The null hypothesis is that there is no relation between how a farm became female-managed and its adoption of AWM. The results in Table 7 show that there is no statistical evidence that widowed FFMs are less able to adopt AWM than non-widowed FFMs.

Another primary question of this research was to check for any significant differences in farm productivity and soil fertility of farms lead by either gender. The results in Table 8 show that farm productivity is more dependent on the agroecology of the farm's location than the gender of the farm manager. Regarding soil fertility, again significant differences were only visible when comparing agroecologies but not gender (Table 10).

The main goal of AWM is soil moisture conservation, which, besides the popular practices of terraces and tied ridges, can be achieved by practicing CA, which was done by one FFM in Makindu (Table 6). CA involves reducing tillage of the soil to preserve its fertility and moisture, mulching to provide a soil cover and crop rotation (Hobbs 2007). Although only a small percentage of FFMs identified CA as their AWM practice, it is a promising sign because CA requires less labor than conventional farming and also uses less external inputs, two factors that should favor FFMs because a lack of labor and capital were identified as the major impediments to AWM adoption (Table 9).

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Irrigation is an obvious AWM technique that would certainly increase a farmerâ&#x20AC;&#x2122;s resilience to climate change factors such as erratic rainfalls but implementation is low due to the high capital costs involved (Brown and Hansen 2008). Only 3% of MFMs in Makindu and no other farmer group in the study identified irrigation as their AWM practice (Table 6). If farmers had better access to water, they are willing to diversify their crop choice as is shown by MFMs in Makindu who planted vegetables on 4% of their land instead of cereal crops (Table 4). Vegetables provide higher profits than cereal crops and increase a farmerâ&#x20AC;&#x2122;s resilience to drought shocks that can damage cereal crops but they require irrigation (Joshi 2006).

Another factor in increasing climate change resilience is the usage of high quality seeds. There was no significant difference between the genders or locations but a trend was noticed where 59% of FFMs in Makindu were using low quality seeds compared to 42% in Machakos. The implication is that since Makindu is a climate analogue to Machakos, as the effects of climate change become more pronounced, there could be a tendency for FFMs to shift to lower quality seeds as the climate dries out and as farmers in the area are risk averse, they will try to minimize investment in high quality seeds, which will lead to reduced profitability and resilience. To be more climate resilient, there is a need to encourage more use of high quality seeds and a need to provide better access to them at affordable prices (Table 3).

In Makindu, even though FFMs appear more constrained in terms of inputs (such as less use of tractors) compared to MFMs there, 85% of FFMs timed their cropping properly with the rains compared to 67% MFMs (Table 8). This demonstrates a trend in the willingness of FFMs to adopt good farming practices in the hopes of raising productivity even in the face of less access to inputs and capital (Table 9) and highlights the need for more awareness of gender-friendly farming practices, such as CA.

There is a need also to raise awareness about soil fertility as 84% of farmers said they were satisfied with the quality of their land even when the soil analysis revealed that 92% of farms are deficient in organic carbon and 82% are deficient in nitrogen, which is considered as the most important factor in limited productivity increases on small-scale farms in SSA (Drechsel et al 2004) (Table 10).

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6.0 Conclusion

This case study revealed that there were no significant differences between the FMFs and MMFs of lower eastern Kenya in the adoption of AWM technologies, farm productivity, soil fertility and other factors. The general presumption that FMFs would be less productive and adopt AWM less than MMFs was found not to be true. The results from this research only apply directly to the two watersheds studied in Machakos and Makindu, but perhaps it will be part of a trend showing that gender parity is being achieved by programs focusing on gender in agriculture.

The search for relations between age, marital status, education or location and AWM adoption showed that there were no significant differences between the genders. However, some presumed trends did show through such as more FFMs stating a lack of capital as an impediment for implementing productivity improvements and more FFMs having no education compared to MFMs, but this did not affect the rate of AWM adoption by FFMs, which was actually higher than MFMs.

The study did reveal some predicted gender trends in that MFMs were expected to have access to more capital and technologies and this showed through by the higher use of irrigation and tractors for ploughing compared to FFMs. On the other hand, FFMs were expected to have less access to capital and this showed through in one woman's use of CA with its reduced or no tilling of the land requiring less capital and labor. CA could be the most gender-friendly AWM technology if herbicides are used in conjunction (Giller et al 2009). Its use should be encouraged through agricultural extension services.

The study also revealed the expected trend that as the climate gets drier and the rains more erratic, farmers will chose to invest in AWM to increase their productivity. This highlights the need to make AWM technologies easier to access and implement for farmers of both genders if increasing climate change resilience and food security are sought.

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Gender in Agriculture Sourcebook, Conference Edition: 229-256. IBRD/World Bank/FAO/IFAD. Accessed from http://www.wemanglobal.org/documents/Gender_in_Agriculture_Sourcebook_conf_ed.pdf Saito KA, Mekonnen H and Spurling D (1994) Raising the Productivity of Women Farmers in Sub-Saharan Africa, Africa Technical Department Paper Series 230, World Bank, Washington DC. SEI (Stockholm Environment Institute) (2005) Sustainable pathways to attain the millennium development goals – assessing the role of water, energy and sanitation. Document prepared for the UN World Summit, 14 September 2005, New York, USA, SEI, Stockholm, Sweden. Accessed from http://www.sei.se/mdg.htm Shiferaw B and Okello J and Reddy VR (2009) Challenges of Adoption and Adaptation of Land and Water Management Options in Smallholder Agriculture: Synthesis of Lessons and Experiences. In: Rainfed agriculture: unlocking the potential. Comprehensive Assessment of Water Management in Agriculture Series 7:258-275. CAB International, Wallingford, Oxon, UK. ISBN 978-1-84593-389-0.

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Appendix 1: Gender Disaggregated Survey of Agricultural Water Management Adoption in Eastern Kenya 1. General Information Enumerator

Date (dd/mm/yy)

Respondent

HH Head’s Name

Relation to HH Head HHH’s Age

District

Village

HouseHold No.

Marital Status of HHH

Gender HHH

No. of On-farm Working HH Members

HHH’s Year of Birth

HHH Active on Farm?

M: F: Education Level of HH Head (None, Primary, Secondary, Tertiary)

2. Crop Production Practices Adopted Crop 1 (

)

Crop 2 (

)

Crop 3 (

)

Crop 4 (

)

Variety Planted Total Area Planted (AC or HA) Land Preparation Methods Number of Ploughings Is this Optimum?

If Less or More, Why? Type of Planting Water Conservation Practices Used (see codes) Source of Seed (see codes) Date of Planting (dd/mm/yy) How do you rate the time of planting?

If Planted Early or Late, Why? Planting Method Seed Type Manure Type (see codes) Manure Quantity Applied (Kg)

Manual/Animal/Tractor

Manual/Animal/Tractor

Manual/Animal/Tractor

Manual/Animal/Tractor

Less/Optimum/More

Less/Optimum/More

Less/Optimum/More

Less/Optimum/More

Dry/Planting with Rain

Dry/Planting with Rain

Dry/Planting with Rain

Dry/Planting with Rain

Early/Normal/Late

Early/Normal/Late

Early/Normal/Late

Early/Normal/Late

Row planting/Broadcast Primed Seed/Dry Seed

Row planting/Broadcast Primed Seed/Dry Seed

Row planting/Broadcast Primed Seed/Dry Seed

Row planting/Broadcast Primed Seed/Dry Seed

Crop 1 (

)

Crop 2 (

)

Crop 3 (

)

Crop 4 (

)

Value of the Manure (Estimate per AC) Fertilizer Type (see codes) Fertilizer Quantity Applied Amount Invested (Estimate per ac) No. of Hired Labour (Men) Operations for Which Hired Labour Was Used (Men) Area Covered (ac) (Men) Labor Rate (kSh/day) (Men) No. of Hired Labour (Women) Operations for Which Hired Labour Was Used (Women) Area covered (ac) (Women) Labor rate (kSh/day) (Women)

Problems faced in this season? (specify)

1. ________________ 2. ________________ 3. ________________ 4. ________________ 5.

1. ________________ 2. ________________ 3. ________________ 4. ________________ 5.

1. ________________ 2. ________________ 3. ________________ 4. ________________ 5.

1. ________________ 2. ________________ 3. ________________ 4. ________________ 5.

1. ________________ 2. ________________ 3. ________________ 4. ________________ 5.

1. ________________ 2. ________________ 3. ________________ 4. ________________ 5.

1. ________________ 2. ________________ 3. ________________ 4. ________________ 5.

1. ________________ 2. ________________ 3. ________________ 4. ________________ 5.

Current Status of Crop (see codes)

If Poor, Why?

Water Conservation Practices: 1 = Tied Ridges; 2 = Terraces; 3 = Conservation Farming; 4 = Mulching; 5 = Irrigation Seed Source: 1 = Own; 2 = Neighbour; 3 = Agrovet; 4 = Other (Specify) Manure type: 1 = Cattle Manure; 2 = Poultry Manure; 3 = Compost; 4 = Other (Specify) Fertilizer type: 1 = CAN; 2 = Urea; 3 = DAP; 4 = Complex 28:28; 5 = Other (Specify) Crop Status: 1 = Good, 2 = Average, 3 = Poor

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3. Source of Land How was Land Acquired?

1 = Purchased; 2 = Inherited; 3 = Marriage/Gift; 4 = Leased In Case of Inheritance, Who is Eligible for a Share in the Family Land?

1 = Male members of family; 2 = Both male and female members of family; 3 = Female members of the family; 4 = Others (specify) How is the Land Shared?

1 = Equally among the eligible members; 2 = Elders get less; 3 = Able members get more How is the Inherited Land Shared (How was Decision Made)?

1 = Village head decides; 2 = Elder family members (men and women) decide; 3 = Elder male members of family decide; 4 = Elder female members of family decide; 5 = allocated by lottery; 6 = Other (Specify) If Divorced or Widowed, did this Event Affect Land Allocation?

1 = N/A; 2 = Yes, Area Reduced; 3 = Yes, Quality Reduced; 4 = No Is there any Preference given to Men or Women Members of the Family in Sharing the Land?

Do you Think the Land Sharing Methods are Fair? (Y/N) If No, What in your Opinion will Improve It? 4. Land Condition Satisfied with quality of land? (Y/N) If No, Why? 1 = Infertile Soil; 2 = Too Shallow; 3 = Too Steep; 4 =Too Stony/Rocky; 5 = Too Far Away From Home; 6 = Low Security; 7 = Other (Specify) Are there Differences in Quality of the Land Within your Farm? (Explain) Do you Consider these Differences in Selecting the Crop to be Grown?

If Yes, Which Crop is Preferred Where?

What can be Done to Improve the Productivity of your Farm?

1. _____________________________________________ 2. _____________________________________________ 3. _____________________________________________ 4. _____________________________________________ 5.

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Which of these Practices are Implemented on your Farm?

If Not Implemented, Why?

1. _____________________________________________ 2. _____________________________________________ 3. _____________________________________________ 4. _____________________________________________ 5.

5. Perceptions About Differences in Men and Women-Headed Farms Are there any Differences in the Way and Men and Women do Farming? (Explain) Are there any Differences in the Crops Preferred by Men and Women? (Explain) Are there any Differences in Access to Inputs Between Men and Women? (such as in getting loans, in accessing seed, in accessing fertilizer, etc.)

Are there any Differences in Access to Information? (Explain) Are there any Differences in Farming Skills Between Men and Women? (Explain)

Operations mainly Carried out by Women?

Operations mainly Carried out by Men?

1. _____________________________________________ 2. _____________________________________________ 3. _____________________________________________ 4. _____________________________________________ 5.

1. _____________________________________________ 2. _____________________________________________ 3. _____________________________________________ 4. _____________________________________________ 5.

Who Spends More Time on Farm?

Are there Differences in Productivity of Farms Managed by Men and Women?

1 = Farms managed by men have higher productivity; 2 = Farms managed by women have higher productivity; 3 = No major difference

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