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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TableTableTableTable ofofofof ContentsContentsContentsContents

Page

List of tables and appendices 4

List of acronyms 5

Abstract 7

Acknowledgments 8

1.0 Introduction 9

2.0 Literature review 11

3.0 Methodology 17

4.0 Results 19

5.0 Analysis 32

6.0 Conclusion 35

References 36

Appendices 39

4

ListListListList ofofofof TablesTablesTablesTables andandandand AppendicesAppendicesAppendicesAppendices

Page

Table 1. Demographics of farm managers interviewed 19

Table 2. Factors involved in sourcing farmland 20

Table 3. Land Preparation and Planting Information 21

Table 4. Area planted breakdown by crops 22

Table 5. Maize intercropping information 23

Table 6. Agricultural water management adoption 24

Table 7. Agricultural water management adoption by factors 25

Table 8. Crop performance during long rains season (Oct-Nov-Dec) of 2011 26

Table 9. Farmland condition and desired productivity improvementmethods

28

Table 10. Soil fertility results from representative soil sampling 29

Table 11. Perceptions of the opposite gender on their farming ability 31

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

39

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ListListListList ofofofof AcronymsAcronymsAcronymsAcronyms

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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AbstractAbstractAbstractAbstract

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 AWMwas compared between male and female-

managed 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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AcknowledgmentsAcknowledgmentsAcknowledgmentsAcknowledgments

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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1.01.01.01.0 IntroductionIntroductionIntroductionIntroduction

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).

10

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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2.02.02.02.0 LiteratureLiteratureLiteratureLiterature ReviewReviewReviewReview

The regions of the world with the highest percentage of least developed countries are Sub-

Saharan 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 present-

day 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 gender-

aware 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 able-

bodied 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 female-

managed 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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3.03.03.03.0MethodologyMethodologyMethodologyMethodology

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

18

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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4.04.04.04.0 ResultsResultsResultsResults

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 FMF MMF Machakos MakinduAll All All FMF MMF All FMF MMF

Farm managers (count) 44 46 45 23 22 45 21 24Age

<= 60 yrs (%) 54 59 51 43 59 62 66 58> 60 yrs (%) 46 41 49 571 41 38 331 42

EducationNone (%) 45 33 33 39 27 44 52 38Primary (%) 32 30 33 39 27 29 24 33Secondary or more (%) 23 37 33 22 45 27 24 29

Marital statusSingle (%) 7 2 4 9 0 4 5 4Married (%) 55552 919191912 49 4 95 49 5 88Widow (%) 757575753 44443 38 70 5 40 81 4Others4 (%) 13 2 9 17 0 6 10 4

Source: data derived from survey conducted in study areaNote: highlighted data pairs tested for significant difference and bold indicates difference is significant1Chi-square p-value for difference is 0.123 = not significant2,3Chi-square p-value for difference is 0.000 = significant4Others: 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 factorsFMF MMF Machakos MakinduAll All All FMF MMF All FMF MMF

Farm managers (count) 44 46 45 23 22 45 21 24Land acquisition

Purchased (%) 26 43 36 27 43 33 24 42Inherited (%) 161616161 494949491 31 9 52 36 24 46Through marriage (%) 565656562 99992 33 64 4 29 48 13No info (%) 2 0 0 0 0 2 5 0

Inherited land shared howEqually among eligiblemembers (%)

88 80 89 100 87 75 80 73

Able members get more (%) 0 7 6 0 7 5 0 7Male members get more (%) 13 10 6 0 7 15 20 13Female members get more (%) 0 3 0 0 0 5 0 7

Divorce/widow affect allocation?Not applicable (%) 27 85 57 38 69 71 14 100Yes, area reduced (%) 0 7 10 0 15 0 0 0No (%) 73 7 33 63 15 29 86 0

Gender preference in land sharingYes (%) 13 54 15 0 25 62 29 79No (%) 87 46 85 100 75 38 71 21

Land sharing practices fairYes (%) 92 90 91 88 93 89 100 87No (%) 8 10 9 13 7 11 0 13

Source: data derived from survey conducted in study areaNote: highlighted data pairs tested for significant difference and bold indicates difference is significant1Chi-square p-value for difference is 0.001 = significant2Chi-square p-value for difference is 0.000 = significant

21

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 factorsFMF MMF Machakos MakinduAll All All FMF MMF All FMF MMF

Farm managers (count) 44 46 45 23 22 45 21 24Farm size

<= 2 ha (%) 73 89 89 87 91 73 86 62> 2 ha (%) 27 11 11 13 9 27 14 38

Land preparationManual (%) 11 17 18 9 271 11 14 81Animal (bullocks) (%) 86 72 82 91 73 76 81 71Tractor (%) 2 11 0 0 0 13 52 212

No of ploughings<2 (%) 38 34 39 40 39 31 35 28>2 (%) 62 66 61 60 61 69 65 72

Planting typeDry planted (%) 40 37 45 50 40 31 27 35Planted with rain (%) 60 63 55 50 60 69 73 65

Seed sourceLow quality seeds3 (%) 49 37 41 42 40 46 59 34High quality seeds4 (%) 51 63 59 58 60 54 41 66

Seed type plantedPrimed seed (%) 3 6 5 5 6 4 2 7Dry seed (%) 97 95 95 95 94 96 98 93

Source: data derived from survey conducted in study areaNote: highlighted data pairs tested for significant difference and bold indicates difference is significant1Chi-square p-value for difference is 0.090 = not significant2Chi-square p-value for difference is 0.114= not significant3Low quality seeds = recycled seeds (owned, borrowed or bought from local market)4High quality seeds = hybrid and improved varieties (bought from agrovet, CBO, KARI and donated by government andKARI)

22

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 frommaize to sorghum (Beddington et al 2011).

Table 4. Area planted breakdown by crops

Crop area planted FMF MMF Machakos MakinduAll All All FMF MMF All FMF MMF

Farm managers (count) 44 46 45 23 22 45 21 24Total area planted (ha) 206.1 189.0 143.0 81.5 61.6 252.4 124.9 127.7

Maize (ha) 72.0 86.7 67.2 41.3 26.0 91.6 30.8 60.9(%) (35) (46) (47) (51) (42) (36) (25) (48)

SDL1 (ha) 107.5 76.5 32.1 15.9 16.2 151.9 91.7 60.2(%) (52) (41) (22)(22)(22)(22)2 (20) (26) (60)(60)(60)(60)2 (73)3 (47)3

Pigeon pea (ha) 25.3 18.9 43.2 24.3 18.9 1.1 1.1 0.0(%) (12) (10) (30)(30)(30)(30)4 (30) (31) (0)(0)(0)(0)4 (1) (0)

Sorghum (ha) 1.0 1.9 0.5 0.0 0.5 2.6 1.0 1.6(%) (1) (1) (0) (0) (1) (1) (1) (1)

Vegetables5 (ha) 0.3 5.0 0.0 0.0 0.0 5.3 0.3 5.0(%) (0) (3) (0) (0) (0) (2) (0) (4)

Source: data derived from survey conducted in study areaNote: highlighted data pairs tested for significant difference and bold indicates difference is significant1SDL = short duration legumes (green grams, beans, cow peas and dolichos)2,4Chi-square p-value for difference is 0.000 = significant3Chi-square p-value for difference is 0.083 = not significant5Vegetables = tomatoes and brinjals

23

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 FMF MMF Machakos MakinduAll All All FMF MMF All FMF MMF

Farm managers (count) 44 46 45 23 22 45 21 24Maize intercrop (IC), yes (%) 32 42 454545451 34 58 242424241 26 23

IC w/ SDL2 (%) 54 48 343434343 37 32 898989893 88 90IC w/ pigeon peas (%) 42 46 63 63 64 0 0 0IC w/ sorghum (%) 4 3 0 0 0 11 13 10IC w/ vegetables4 (%) 0 3 2 0 4 0 0 0

Source: data derived from survey conducted in study areaNote: highlighted data pairs tested for significant difference and bold indicates difference is significant1,3Chi-square p-value for difference is 0.000 = significant2SDL = short duration legumes (green grams, beans, cow peas and dolichos)4Vegetables = tomatoes and brinjals

24

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 FMF MMF Machakos MakinduAll All All FMF MMF All FMF MMF

Farm managers (count) 44 46 45 23 22 45 21 24No AWM (%) 8 10 4 8 0 14 7 20Terraces (%) 68 66 838383831 77 89 474747471 55 41Tied ridges (%) 22 18 101010102 143 63 323232322 33 31Conservation agriculture (%) 2 0 0 0 0 2 5 0Irrigation (%) 0 1 0 0 0 1 0 3Furrows (%) 0 3 2 0 5 0 0 0Rock bunds (%) 0 3 0 0 0 3 0 5At least 1 type of AWM on farm (%) 92 90 96 92 100 86 93 80AWM adoption w/o terraces4 (%) 24 25 121212125 14 11 383838385 38 39Source: data derived from survey conducted in study areaNote: highlighted data pairs tested for significant difference and bold indicates difference is significant1Chi-square p-value for difference is 0.000 = significant2Chi-square p-value for difference is 0.020 = significant3Chi-square p-value for difference is 0.317 = not significant4Terraces are primarily built to prevent soil erosion but inherently aid in water conservation (Mati 2005)5Chi-square p-value for difference is 0.003 = significant

25

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

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).

Possible AWM adoption factors AWM adoption rate P-value Significant Difference?All FFMs (%) 93 0.500 NoAll MFMs (%) 89Farmers in Machakos (%) 95 0.138 NoFarmers in Makindu (%) 87Farmers with no education (%) 91 0.933 NoFarmers with some education (%) 91FFM widows (%) 91 0.300 NoFFM non-widows (%) 100FFM widows (not incl terraces) (%) 33

0.291 NoFFM non-widows (not incl terraces) (%) 18

26

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

Crop performance factorsFMF MMF Machakos MakinduAll All All FMF MMF All FMF MMF

Farm managers (count) 44 46 45 23 22 45 21 24Crop plant timing with the rains

Early (%) 19 21 19 21 17 20 15 24Normal (%) 66 56 65 63 68 55 70 43Late (%) 15 24 15 15 15 25 15 33

Planting not timely, why?Poor weather advice (%) 38 55 282828281 30 26 646464641 50 70Physical disability (%) 212 62 232323233 27 17 33333 11 0Labor shortage (%) 29 23 25 30 17 26 28 26Seed problems (%) 13 17 252525254 13 39 77774 11 5

Crop statusGood (%) 27 19 32 39 24 12 10 13Average (%) 24 36 36 30 43 23 17 28Poor (%) 49 46 323232325 31 33 656565655 73 59

Crop problems facedLack of rains (%) 43 45 343434346 33 34 606060606 62 59Pests and diseases (%) 36 42 41 37 46 36 35 37Lack of inputs (%) 13 7 15 19 10 2 1 2Poor farm management (%) 1 2 2 1 3 0 0 0None (%) 7 5 8 10 7 3 2 3

Hired farm laborNo male labor (%) 70 78 73 64 83 75 78 72Male laborer = 1 (%) 13 8 12 17 7 9 8 9Male laborers > 1 (%) 17 14 15 19 10 16 13 19No female labor (%) 85 90 89 86 93 87 85 88Female laborer = 1 (%) 5 4 5 8 2 3 0 5Female laborers > 1 (%) 10 6 5 6 5 10 15 7

Productivity average7 (kg/ha) 498 525 6686686686688 629 707 3603603603608 361 359Source: data derived from survey conducted in study areaNote: highlighted data pairs tested for significant difference and bold indicates difference is significant1Chi-square p-value for difference is 0.001 = significant2Chi-square p-value for difference is 0.052 = not significant3Chi-square p-value for difference is 0.004 = significant4Chi-square p-value for difference is 0.020 = significant5Chi-square p-value for difference is 0.002 = significant6Chi-square p-value for difference is 0.011 = significant7Source: ASARECA water productivity baseline survey data, provided by KARI8Paired sample t-test p-value for difference is 0.008 = significant

27

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 fromMFMs

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 AWMwas the preferred method (51%). This is as expected because the

drier climate of Makindu could see yields increase if more AWMwas 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 AWMwill 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.

28

Table 9. Farmland condition and desired productivity improvement methods

Farmland conditionFMF MMF Machakos MakinduAll All All FMF MMF All FMF MMF

Farm managers (count) 44 46 45 23 22 45 21 24Satisfied with land quality

Yes (%) 86 81 73 82 656565651 93 90 969696961No (%) 14 19 27 18 35 7 10 4

Variations in land quality affectcrop choice2

Yes (%) 50 68 61 444444443 808080803 58 57 58No (%) 50 33 39 56 20 42 43 42

Productivity improve howImplement AWM (%) 49 36 334 41 26 514 58 45Increase inputs (%) 46 58 626262625 55 70 414141415 37 45Better farm management6(%) 5 7 4 5 4 7 5 9

Improvement not implement whyLack of labor (%) 25 17 19 27 13 22 23 21Lack of capital (%) 616161617 404040407 42 53 31 56 69 47Lack of other resources8 (%) 14 43 39 20 56 22 8 32

Source: data derived from survey conducted in study areaNote: highlighted data pairs tested for significant difference and bold indicates difference is significant1Chi-square p-value for difference is 0.006 = significant2Such as planting legumes in less fertile areas of the farm3Chi-square p-value for difference is 0.008 = significant4Chi-square p-value for difference is 0.088 = not significant5,7Chi-square p-value for difference is 0.035 = significant6Farm management: land preparation, crop rotation, timely planting, soil analysis, regular weeding, etc.8Other resources: manure, certified seeds, access to inputs, access to extension services, etc.

29

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 indicatorsFMF MMF Machakos MakinduAll All All FMF MMF All FMF MMF

Farm managers (count) 44 46 45 23 22 45 21 24Carbon (average) (%) 1.06 1.00 1.02 1.071 0.971 1.04 1.051 1.01

< 1.5% (%) 91 93 98 96 100 87 86 88Optimal (%) 9 7 22222 4 0 131313132 14 13> 3% (%) 0 0 0 0 0 0 0 0

Nitrogen (average) (%) 0.10 0.09 0.10 0.103 0.093 0.10 0.103 0.093< 0.12% (%) 77 87 87 78 95 78 76 79Optimal (%) 23 13 13 22 5 22 24 21> 0.25% (%) 0 0 0 0 0 0 0 0

Phosphorous (average) (ppm) 105 80 62 646464644 595959594 123 1501501501504 1001001001004< 36 ppm (%) 27 26 36 43 27 18 10 25Optimal (%) 14 22 22 17 27 13 10 17> 50 ppm (%) 59 52 42 39 45 69 81 58

Zinc (average) (ppm) 5.24 4.86 6.76 5.985.985.985.985 7.587.587.587.585 3.34 4.444.444.444.445 2.372.372.372.375< 4 ppm (%) 50 67 383838386 43 32 808080806 57 100Optimal (%) 30 17 31 26 36 16 33 0> 8 ppm (%) 20 15 31 30 32 4 10 0

Source: Crop Nutrition Laboratory, Nairobi, soil fertility analysis on soil sampling taken in study areaNote: highlighted data pairs tested for significant difference and bold indicates difference is significant1Welch test p-value for difference is 0.597 = not significant2Chi-square p-value for difference is 0.049 = significant3Welch test p-value for difference is 0.420 = not significant4Welch test p-value for difference is 0.008 = significant5Welch test p-value for difference is 0.000 = significant6Chi-square p-value for difference is 0.000 = significant

30

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 fromMFMs 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.

31

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

Gender perceptions FMF MMF Machakos MakinduAll All All FMF MMF All FMF MMF

Farm managers (count) 44 46 45 23 22 45 21 24Gender difference in farmmanagement

No differences (%) 33 47 36 23 48 44 43 45MMF better1 (%) 47 53 47 41 52 53 52 55FMF better (%) 212121212 00002 18 363636363 0 2 55553 0

Gender difference in crop preferenceNo difference (%) 81 89 89 81 96 82 81 83FFM more conservative4 (%) 7 6 5 5 4 9 10 8FFM more progressive5 (%) 10 2 5 10 0 7 10 4MFM more conservative (%) 2 0 2 5 0 0 0 0MFM more progressive (%) 0 2 0 0 0 2 0 4

Gender difference in farming skillNo differences (%) 50 66 55 42 65 62 57 67MFM better (%) 28 26 24 26 22 29 29 29FFM better (%) 23 9 21 32 13 9 14 4

Gender spending most time on farmWomen (%) 79 74 84 77 91 69 81 58Men (%) 21 21 13 23 4 29 19 38No difference (%) 0 4 2 0 4 2 0 4

Which gender-managed farm moreproductive6

FMF (%) 333333337 111111117 20 27 13 22 38 8MMF (%) 212121218 515151518 40 27 52 33 14 50No difference (%) 47 38 40 45 35 44 48 42

Perceived female farmer rolesPlanting and preparation9 (%) 28 24 28 31 26 24 25 23Crop management10(%) 29 27 26 25 27 30 33 27Harvesting (%) 43 49 46 44 48 46 42 50

Perceived male farmer rolesPlanting and preparation (%) 61 57 64 64 64 53 57 49Crop management(%) 28 33 27 26 28 34 29 38Harvesting (%) 12 10 9 10 8 13 14 13

Source: data derived from survey conducted in study areaNote: highlighted data pairs tested for significant difference and bold indicates difference is significant1Better as perceived by the farmers themselves (work harder, more active on farm, capacity to organize and learnnew techniques, more financially stable to access inputs, etc.)2Chi-square p-value for difference is 0.000 = significant3Chi-square p-value for difference is 0.014 = significant4Conservative: planting staples instead of cash crops, planting few varieties, etc.5Progressive: planting cash crops, planting many varieties, etc.6In terms of yield/hectare7Chi-square p-value for difference is 0.008 = significant8Chi-square p-value for difference is 0.003 = significant9Preparation: seed cleaning, ploughing, terracing, farrowing, acquiring inputs, etc.10Crop management: weeding, manuring, applying fertilizer, supervision, pest control, etc.

32

5.05.05.05.0 AnalysisAnalysisAnalysisAnalysis

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 AWMmore 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

33

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).

34

Irrigation is an obvious AWM technique that would certainly increase a farmer’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 farmer’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).

35

6.06.06.06.0 ConclusionConclusionConclusionConclusion

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.

36

7.07.07.07.0 ReferencesReferencesReferencesReferences

Alila P and Atieno R (2006) Agricultural Policy in Kenya: Issues and Processes. A paper for the FutureAgricultures Consortium Workshop, Institute of Development Studies, University of Nairobi, Kenya, 20-22March 2006.

Beddington J, Asaduzzaman M, Fernandez A, Clark M, Guillou M, Jahn M, Erda L, Mamo T, Van Bo N,Nobre CA, Scholes R, Sharma R and Wakhungu J (2011) Achieving food security in the face of climatechange: Summary for policy makers from the Commission on Sustainable Agriculture and Climate Change.CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Copenhagen,Denmark. Available online at: www.ccafs.cgiar.org/commission.

Bongaarts J (2001) Household size and composition in the developing world in the 1990s. PopulationStudies: A Journal of Demography 55(3):263–279.

Brown C and Hansen JW (2008) Agricultural Water Management and Climate Risk. Report to the Bill andMelinda Gates Foundation. IRI Tech. Rep. No. 08-01. International Research Institute for Climate andSociety, Palisades, New York, USA. 19 pp. Accessed fromhttp://iri.columbia.edu/publications/search.php?id=780

Chant S (2004) Dangerous Equations? How Female-headed Households Became the Poorest of the Poor:Causes, Consequences and Cautions. IDS Bulletin 35(4):19-26.

Cooper P, Rao KPC, Singh P, Dimes J, Traore PS, Rao K, Dixit P and Twomlow SJ (2009) Farming withcurrent and future climate risk: Advancing a 'Hypothesis of Hope' for rainfed agriculture in the semi-aridtropics. Journal of SAT Agricultural Research 7.

Deere CD and Leon M (2003) The Gender Asset Gap: Land in Latin America.World Development31(6):925-47.

Doss C (2002) Men’s Crops? Women’s Crops? The Gender Patterns of Cropping in Ghana.WorldDevelopment 30(11):1987-2000.

Doss C and Morris ML (2001) How does gender affect the adoption of agricultural innovations? The caseof improved maize technology in Ghana. Agricultural Economics 25(1):27-39.

Drechsel P, Giordano M and Gyiele L (2004) Valuing nutrition in soil and water: concepts and techniqueswith examples from IWMI studies in developing world. IWMI Research Paper 82. International WaterManagement Institute (IWMI), Colombo, Sri Lanka.

Engel-Di Mauro S (2003) Disaggregating local knowledge: the effects of gendered farming practices on soilfertility and soil reaction in SW Hungary. Geoderma 111(3-4):503-520. Accessed fromhttp://dx.doi.org/10.1016/S0016-7061(02)00279-3

FAOSTAT (1998) Gender and Food Security Statistics. Accessed fromwww.fao.org/Gender/stats/genstats.htm

Farnworth CR and Munachonga M (2010) Gender Aware Approaches in Agricultural Programmes - ZambiaCountry Report - UTV Working Paper 2010:08. A report to the Swedish International DevelopmentCooperation Agency. Accessed from http://www.oecd.org/dataoecd/11/34/46150642.pdf

37

Feed the Future FY 2011-2015 Multi-Year Strategy for Kenya (2011), United States Government's GlobalHunger and Food Security Initiative. Accessed fromhttp://feedthefuture.gov/sites/default/files/country/strategies/files/KenyaFTFMulti-YearStrategy.pdf

Fletschner D and Kenney L (2011) Rural women’s access to financial services. ESA Working Paper No. 11-07, The Food and Agriculture Organization of the United Nations.

Gichuki FN (1991) Environmental Change and Dryland Management in Machakos district, Kenya 1930-90.Working Paper 56, Overseas Development Institute, London.

Giller KE, Witter E, Corbeels M and Tittonell P (2009) Conservation agriculture and small holder farming inAfrica: the heretics view. Field Crops Research 114(1):23-34.

Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, Muir JF, Pretty J, Robinson S, Thomas SMand Toulmin C (2010) Food security: the challenge of feeding 9 billion people. Science 327:812–818.(doi:10.1126/science.1185383)

Gopal G and Salim M (1998) Gender and Law, Eastern Africa Speaks, The World Bank, Washington DC.

Harris D (1996) The effects of manure, genotype, seed priming, depth and date of sowing on theemergence and early growth of Sorghum bicolor (L.) Moench in semi-arid Botswana. Soil and TillageResearch 40:73–88.

Hobbs PR (2007) Conservation agriculture: what is it and why is it important for future sustainable foodproduction? Journal of Agricultural Science 145:127–137.

IFAD (1999) Human Enterprise Ecology: Supporting the Livelihoods of the Rural Poor in East and SouthernAfrica, Main Report and Working Paper No. 2, Rome.

Indeje M, Semazzi HFM, Ogallo LJ (2000) ENSO signals in East African rainfall seasons. InternationalJournal of Climatology 20:19–46.

Joshi PK, Tewari L and Birthal PS (2006) Diversification and its impact on smallholders: Evidence from astudy on vegetable production. Agricultural Economics Research Review 19(2):219-236

Karanja PW (1991) Women's Land Ownership Rights in Kenya, Third World Legal Studies 10:6. Accessedfrom http://scholar.valpo.edu/twls/vol10/iss1/6

Kevane M (2011) Gendered production and consumption in rural Africa. Proceedings of the NationalAcademy of Sciences. Accessed from http://www.pnas.org/cgi/doi/10.1073/pnas.1003162108

Kumar SK (1994) Adoption of hybrid maize in Zambia: effects on gender roles, food consumption, andnutrition. Research Report No. 100, International Food Policy Research Institute, Washington, D.C.

Mati BM (2005) Overview of water and soil nutrient management under smallholder rain-fed agriculturein East Africa.Working Paper 105. Colombo, Sri Lanka: International Water Management Institute (IWMI).

Moock P (1976) The Efficiency of Women as Farm Managers: Kenya. American Journal of AgriculturalEconomics 58(5):831-835.

Mortimore M, Tiffen M and Gichuki F (1993) Sustainable growth in Machakos, ILEIA Newsletter 9:4,Overseas Development Institute, London.

38

O’Laughlin B (2007) A Bigger Piece of a Very Small Pie: Intrahousehold Resource Allocation and PovertyReduction in Africa. Development and Change 38(1):21–44.

Posel DR (2001) Who are the heads of household, what do they do, and is the concept of headship useful?An analysis of headship in South Africa. Development Southern Africa 18(5):651–670.

Rosenow DT, Quisenberry JE, Wendt SW and Clark LE (1983) Drought tolerant sorghum and cottongermplasm. Agricultural Water Management 7(1-3):207-222.

Sagardoy JA (2008) Overview of Module 6: Gender Mainstreaming in Agricultural Water Management.Gender in Agriculture Sourcebook, Conference Edition: 229-256. IBRD/World Bank/FAO/IFAD. Accessedfrom 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-SaharanAfrica, Africa Technical Department Paper Series 230, World Bank, Washington DC.

SEI (Stockholm Environment Institute) (2005) Sustainable pathways to attain the millennium developmentgoals – 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 WaterManagement Options in Smallholder Agriculture: Synthesis of Lessons and Experiences. In: Rainfedagriculture: unlocking the potential. Comprehensive Assessment of Water Management in AgricultureSeries 7:258-275. CAB International, Wallingford, Oxon, UK. ISBN 978-1-84593-389-0.

Singh HP, Venkateswarlu B, Vittal KPR and Ramachandran K (2000) Management of rainfed agro-ecosystem. Proceedings of the International Conference on Managing Natural Resources for SustainableAgricultural Production in the 21st Century:669-774, 14–18 February, New Delhi, India.

Udry C (1996) Gender, Agricultural Production and the Theory of the Household. Journal of PoliticalEconomy 104(5): 1010–46.

Udry C, Hoddinott J, Alderman H and Haddad L (1995) Gender Differentials in Farm Productivity:Implications for Household Efficiency and Agricultural Policy, Food Policy 20:407-23.

AppendixAppendixAppendixAppendix 1:1:1:1: GenderGenderGenderGender DisaggregatedDisaggregatedDisaggregatedDisaggregated SurveySurveySurveySurvey ofofofof AgriculturalAgriculturalAgriculturalAgricultural WaterWaterWaterWaterManagementManagementManagementManagement AdoptionAdoptionAdoptionAdoption inininin EasternEasternEasternEastern KenyaKenyaKenyaKenya

1.1.1.1. GeneralGeneralGeneralGeneral InformationInformationInformationInformationEnumerator Date (dd/mm/yy) District Village HouseHold No.

Respondent Relation toHH Head Marital Status of HHH Gender HHH No. of On-farm Working HH Members

M: F:

HH Head’s Name HHH’s Age HHH’s Year of Birth HHH Active onFarm?

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

2.2.2.2. CropCropCropCrop ProductionProductionProductionProduction PracticesPracticesPracticesPractices AdoptedAdoptedAdoptedAdoptedCrop 1 ( ) Crop 2 ( ) Crop 3 ( ) Crop 4 ( )

Variety Planted

Total Area Planted (AC or HA)Land Preparation Methods Manual/Animal/Tractor Manual/Animal/Tractor Manual/Animal/Tractor Manual/Animal/TractorNumber of PloughingsIs this Optimum? Less/Optimum/More Less/Optimum/More Less/Optimum/More Less/Optimum/MoreIf Less or More, Why?Type of Planting Dry/Planting with Rain Dry/Planting with Rain Dry/Planting with Rain Dry/Planting with RainWater Conservation Practices Used(see codes)Source of Seed (see codes)Date of Planting (dd/mm/yy)How do you rate the time ofplanting? Early/Normal/Late Early/Normal/Late Early/Normal/Late Early/Normal/Late

If Planted Early or Late, Why?Planting Method Row planting/Broadcast Row planting/Broadcast Row planting/Broadcast Row planting/BroadcastSeed Type Primed Seed/Dry Seed Primed Seed/Dry Seed Primed Seed/Dry Seed Primed Seed/Dry SeedManure Type (see codes)Manure Quantity Applied (Kg)

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Crop 1 ( ) Crop 2 ( ) Crop 3 ( ) Crop 4 ( )Value of the Manure(Estimate per AC)Fertilizer Type (see codes)Fertilizer Quantity AppliedAmount Invested (Estimate per ac)No. of Hired Labour (Men)Operations for Which Hired LabourWas Used (Men)Area Covered (ac) (Men)Labor Rate (kSh/day) (Men)No. of Hired Labour (Women)Operations for Which Hired LabourWas 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.

Current Status of Crop (see codes)

If Poor, Why?

1. ________________2. ________________3. ________________4. ________________5.

1. ________________2. ________________3. ________________4. ________________5.

1. ________________2. ________________3. ________________4. ________________5.

1. ________________2. ________________3. ________________4. ________________5.

Water Conservation Practices: 1 = Tied Ridges; 2 = Terraces; 3 = Conservation Farming; 4 = Mulching; 5 = IrrigationSeed 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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3.3.3.3. SourceSourceSourceSource ofofofof LandLandLandLandHow was Land Acquired?1 = Purchased; 2 = Inherited; 3 = Marriage/Gift; 4 = LeasedIn 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 = Femalemembers 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 moreHow is the Inherited Land Shared (How was Decision Made)?1 = Village head decides; 2 = Elder family members (men and women) decide; 3 = Elder malemembers of family decide; 4 = Elder female members of family decide; 5 = allocated bylottery; 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 = NoIs 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?

4.4.4.4. LandLandLandLand ConditionConditionConditionConditionSatisfied with quality of land? (Y/N)If No, Why?1 = Infertile Soil; 2 = Too Shallow; 3 = Too Steep; 4 =Too Stony/Rocky; 5 = Too FarAway 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.

5.5.5.5. PerceptionsPerceptionsPerceptionsPerceptions AboutAboutAboutAbout DifferencesDifferencesDifferencesDifferences ininininMenMenMenMen andandandandWomen-HeadedWomen-HeadedWomen-HeadedWomen-Headed FarmsFarmsFarmsFarms

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?

1. _____________________________________________2. _____________________________________________3. _____________________________________________4. _____________________________________________5.

Operations mainly Carried out by Men?

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 bywomen have higher productivity; 3 = No major difference

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