Livelihood Analysis of Households in Manafwa and Kapchorwa

LIVELIHOOD ANALYSIS OF HOUSEHOLDS IN MANAFWA AND KAPCHORWA

DEVELOPING VALUE CHAIN INNOVATION PLATFORMS TO IMPROVE FOOD SECURITY IN EAST AND SOUTHERN AFRICA (VIP4FS) PROJECT (FST/2014/093)

JULY 2017

CONTRIBUTORS

Joan Kimaiyo1, Evelyne Kiptot1, Joseph Tanui1, Judith Oduol1, Hilda Kegode1, Prossy Isubikalu2, Joel Buyinza3, Awadh Chemangei4, Simon Nyangas4 and Clement Okia5

1World Agroforestry Centre (ICRAF), P .O. Box 30677-00100 Nairobi, Kenya 2Makerere University, P.O. Box 7062. Kampala, Uganda 3National Forestry Resources Research Institute (NaFORRI), P O Box 1752, Kampala, Uganda 4Kapchorwa District Landcare Chapter (KADLACC), P.O box 127, Kapchorwa, Uganda 5World Agroforestry Centre (ICRAF), Uganda Country Office, P .O. Box 26416, Kampala, Uganda

Correct citation:

Kimaiyo J, Kiptot E, Tanui J, Oduol J, Kegode H, Isubikalu P, Buyinza J, Chemangei A, Nyangas S and Okia C 2017. Livelihood Analysis of Households in Manafwa and Kapchorwa. Research Report. World Agroforestry Centre, Nairobi, Kenya, 81pp.

ACKNOWLEDGEMENTS The Value Chains Innovations Platform for Food Security (VIP4FS) project is generously funded by the Australian government through the Australian Centre for International Agricultural Research (ACIAR). The project team is grateful to all the people who contributed in one way or another in data collection and analysis of the VIP4FS baseline data in Manafwa and Kapchorwa districts.

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TABLE OF CONTENTS

Contributors ...... ii Acknowledgements ...... iii List of Tables ...... v List of Figures ...... vi Acronyms ...... viii Executive summary ...... ix 1.0 Background ...... 1

1.1 Livelihood analysis...... 3

2.0 Methodology ...... 5

2.1 Site description ...... 5

2.2 Sampling and data collection ...... 7

2.2 Data Analysis ...... 8

3.0 Results and discussion ...... 10

3.1 Demographic Characteristics of households in Kapchorwa and Manafwa district ...... 10

3.2 Agriculture and livestock production ...... 13

3.2.1 Land ownership ...... 13

3.2.2 Main and secondary occupation ...... 14

3.2.3 Crop enterprises ...... 15

3.2.4. Livestock production ...... 20

3.3 Institutions and farmer groups ...... 26

3.3.1 Participation in farmer groups ...... 26

3.4 Household income ...... 32

3.4.1Off farm Income ...... 35

3.5 Dietary diversity ...... 36

3.5.1 Comparison in consumption of different food categories ...... 39

3.5.2 Determining total consumption score for different households ...... 42

3.6 Asset endowments ...... 45

3.6.1 Wealth index ...... 50

3.7 Infrastructure ...... 61

3.7.1 Transport services, road systems ...... 61

3.7.2 Market Infrastructure and other facilities ...... 63

4.0 Conclusion and recommendations ...... 65 5.0 References ...... 69

LIST OF TABLES

Table 1: Site description of the different sub counties ...... 7 Table 2: Categorization of different Food types ...... 9 Table 3: Household types in Manafwa and Kapchorwa ...... 11 Table 4: Age of household members ...... 11 Table 5: Education level of household head in each subcounty ...... 12 Table 6: Land ownership ...... 13 Table 7: Land ownership in season 2014/2015 ...... 13 Table 8: Land tenure in Uganda ...... 14 Table 9: Main occupation ...... 14 Table 10: Secondary household occupation ...... 15 Table 11: Crop enteprises in Manafwa and Kapchorwa ...... 16 Table 12: Coffee production ...... 17 Table 13: Main source of seedlings ...... 17 Table 14: Input use in coffee production ...... 20 Table 15: Livestock enterprises ...... 20 Table 16: Livestock types ...... 21 Table 17: Main purpose of livestock enterprise ...... 22 Table 18: Ownership of dairy cows ...... 22 Table 19: Mode of acquisition of the dairy cows ...... 23 Table 20: Fodder grown by smallholder farmers ...... 24 Table 21:Reasons for not growing fodder ...... 25 Table 22: Apiary locations ...... 25 Table 23: Main source of bee hives ...... 26 Table 24: Reasons farmers did not join groups ...... 28 Table 25: Main reason for joining groups ...... 28 Table 26: Farmer group characteristics ...... 30 Table 27: Challenges faced by groups ...... 31 Table 28: Income ratings from different sources in Manafwa district ...... 33

Table 29: Income ratings from different sources in Kapchorwa District ...... 34 Table 30: Off farm income sources in Manafwa and Kapchorwa ...... 35 Table 31: Household consumption of different food types ...... 37 Table 32: Food type consumption across subcounties ...... 38 Table 33: Food types in different household types ...... 39 Table 34: Count of food categories consumed by households ...... 40 Table 35: Household dietary diversity ...... 40 Table 36: Number of times food category was consumed ...... 41 Table 37: Patterns of dietary diversity ...... 42 Table 38: Food consumption score between Manafwa and Kapchorwa ...... 42 Table 39: Food categories consumed by different food consumption clusters ...... 44 Table 40: Farm assets owned by households ...... 47 Table 41: Household asset ownership ...... 48 Table 42: Other household assets owned by households in Manafwa and Kapchorwa ...... 49 Table 43: Summary of assets used to compute wealth index ...... 52 Table 44: Difference in wealth score between Manafwa and Kapchorwa ...... 54 Table 45: Summary statistics of wealth categories ...... 55 Table 46: Wealth categories of households in different subcounties ...... 57 Table 47: Asset ownership of households in different subcounties ...... 58 Table 48: Ownership of household assets by different wealth categories ...... 60 Table 49: Access to transport and road systems ...... 62 Table 50: Distance and time to different roads in the community ...... 63 Table 51: Market and inputs infrastrucre ...... 64 Table 52: Distance and time to different markets ...... 65

LIST OF FIGURES

Figure 1: The sustainable livelihood framework (DFID 2000) ...... 4 Figure 2: Map of Kapchorwa and Manafwa and locations of households interviewed ...... 6 Figure 3: Gender of household members ...... 12 Figure 4: Form which coffee is sold ...... 18 Figure 5: Challenges in Coffee production ...... 19 Figure 6: Breeding methods for dairy cows ...... 23 Figure 7: Membership in groups ...... 27 Figure 8: Farmer group registration ...... 29 Figure 9: Farmer group composition ...... 29

Figure 10: Proportion of households with household incomes ...... 32 Figure 11: Food consumption categories ...... 43 Figure 12: Food consumption clusters in Manafwa and Kapchorwa ...... 44 Figure 13: Food consumption clusters between different household types ...... 45 Figure 14: Wealth categories proportions in Manafwa and Kapchorwa ...... 54 Figure 15: Wealth categories by gender ...... 55 Figure 16: Wealth categories of different household types ...... 56

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ACRONYMS ACIAR Australian Centre for International Agricultural Research AI Artificial insemination BCU Bugisu Cooperative Union ICRAF World Agroforestry Centre ICT Information and Communication Technology KACODA Kapchorwa Community Development Association KADLACC Kapchorwa District Landcare Chapter NAADS National Agricultural Advisory Services UCDA Uganda Coffee Development Authority UWA Uganda Wildlife Authority VIP4FS Value Chains Innovation Platforms for Food Security

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EXECUTIVE SUMMARY The Value chain Innovation Platforms Project for Food Security (VIP4FS) aims to identify principles and drivers that support scalable establishment of effective and equitable innovation platforms that enhance food security through greater engagement of smallholder farmers with markets. As a starting point, the project needed to understand the context in which smallholder farmers operate in order to be able to propose interventions that will improve food security and hence enhance their livelihoods. A household survey was carried out in Uganda project sites, Manafwa and Kapchorwa, to understand the livelihood status of communities living in the Mt. Elgon ecosystem. This report presents a livelihood analysis by focusing on household characteristics, institutions, income, assets, dietary diversity, wealth status and infrastructure.

Data was collected from a total of 306 and 321 farmers from Manafwa and Kapchorwa districts respectively. The farmers interviewed spread across three sub-counties from each of the project sites: Mukoto, Namabya and Butiru sub-counties in Manafwa and Kapchesombe, Tegeres and Kabeywa in Kapchorwa. The sub counties were selected based on their representation of different agro-ecological zones of the Mt Elgon ecosystem: highland, midland and lowland zones and also based on availability of farmers practicing the different value chains of interest: coffee, dairy and honey. Of the respondents interviewed, 55.9% were male and 44.1 % female in Manafwa while 50.8% of farmers were male and 49.2 % female in Kapchorwa. Data analysed was on demographic characteristics of households, education, land ownership, crop enterprises, household assets, income, institutions, agricultural and livestock production with a focus on coffee, dairy and bee keeping. Dietary diversity and wealth index was computed as a proxy for food security and poverty levels of households in the area respectively.

Demographics, agricultural production and institutions From the study, basic characteristics of households showed that households consisted of mainly the Sebei and Bagisu in Kapchorwa and Manafwa respectively. These two tribes have distinct language and culture and accounted for the highest inhabitants of the Mt

Elgon ecosystem. The majority of households were male headed. Agriculture was found to be the main income generating activity in both sites with maize, beans and bananas being the main crops grown by farmers. Coffee came in fourth produced by 43.1% and 67.5% of farmers in Manafwa and Kapchorwa respectively. Farming was also considered a major income generating activity with the highest percentage of household heads listing farming as their main occupation. More than half of the households did not have alternative occupation or income sources. Livestock production was also highly prevalent in both sites where farmers reared an average number of 2 and 3 animals in Manafwa and Kapchorwa respectively. Chicken rearing was the most preferred livestock enterprise in both Manafwa and Kapchorwa district with dairy cattle and goats for meat coming in second and third respectively.

Although farmer groups have been widely recommended for high level impacts in smallholder farmer livelihoods, only a few farmers in the study sites belonged to groups. Only 22.9% of members of households in Manafwa and 35.2% in Kapchorwa ever belonged to groups in the past. The main reason given was that there were no groups to join in the area with a few farmers indicating to having no time for group activities and the benefits obtained from group were unseen. In both areas, farmer groups were mainly mixed with a few male only and women only groups. There were barely any youth groups in both areas. Most groups mainly engaged in agricultural related production. Other activities included savings and credit, input purchases, joint extension services, marketing, welfare and advocacy.

Dietary diversity Dietary diversity was used as a proxy indicator of food security. From the analysis, more than 90% of households in both Manafwa and Kapchorwa districts consumed cereals, sugars, beverages, vegetables and oils with cereals being the highest consumed food item in both areas. The consumption of the food types did not significantly differ between sub- counties in both districts as well as in the different household types. Consumption of all food types did not significantly differ between household types with exception of eggs and oils where a higher proportion of male headed households consumed them more than

x proportion of female headed households. In Kapchorwa and Manafwa, all households consumed at least three different food categories with a higher proportion of households consuming all five food categories within a seven day period. Food types were categorized into 6 distinct types: proteins, vitamins A rich, pulses, staples, sugars and oils. Categorization was important to avoid duplication of food items with similar nutrients counted as different food types. From the analysis, almost all of the households could be considered diet diverse as over 50% of the households consumed all the food categories within a 7 day period before the study. An analysis of the number of times households consumed the different food varieties, showed that proteins were consumed most than any other food category. Households consumed proteins an average of 24.05 and 29.73 times in a 7 day period for Manafwa and Kapchorwa respectively. The patterns of dietary diversity between different households did not significantly differ between male and female headed households. The total number of different food types taken within the 7 day period by each of the food consumption categories between poor, borderline and acceptable categories also differed. Households in “acceptable” category consumed all food categories in both districts while consumption of food in “poor” category varied.

Wealth index Wealth index provides a stable and understandable yardstick for evaluating and comparing the economic situation of households, social groups and societies across regions. To compute the wealth index, assets that contribute to material well-being were used. Three categories of wealth were used; low income, middle income and high income. From the analysis, households in Kapchorwa district had higher scores than in Manafwa, and the difference in the mean score was significant (p value<0.000). Kapchorwa households could therefore be considered wealthier than households in Manafwa on average. Wealth status also significantly differed between male and female producers. A higher proportion of female farmers were in the middle and high income category compared to their male counterparts. Households in high income categories owned televisions, radios, mobile phones, bicycles, solar panels. Some even had internet access, owned computers, motor vehicles and electricity in their houses.

Infrastructure At least 70% of households in both Manafwa and Kapchorwa had access to some form of road especially feeder and community roads. However households in Kapchorwa also accessed other types of roads such as murram and tarmac than households in Manafwa. The distance travelled to different roads was shorter, both in minutes and in kilometers, and were mostly accessed through walking by households in Kapchorwa than those in Manafwa. Despite having slightly less access to different types of roads, Manafwa households had readily available market for crops and livestock and even agrovet shops. About 38%, 42% and 37% of farmers indicated to be aware of markets for crops, livestock and agrovet shops respectively in Manafwa compared to 16%, 13% and 21% of farmers in Kapchorwa. Manafwa’s proximity to Mbale town provides a good avenue for market and information accessibility.

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1.0 BACKGROUND

Agriculture is a core sector of Uganda’s economy, and contributes about 23% of GDP with 60 percent of the population engaged in agriculture, forestry and fishing. Agriculture in the country has had a steady growth over the years. It also presents immense opportunities for growth in other sectors like manufacturing especially agro-processing. Out of 3.95 million agricultural households in Uganda, 28.1% of the households are found in the Eastern region of Uganda with over 70% of these households headed by males. Uganda's key agricultural products can be divided into cash crops, food crops, and horticultural produce. Uganda’s most important traditional cash crops are coffee, tea, cotton, tobacco, and cocoa. Other non-traditional cash crops include: maize, rice, beans, soya beans, palms and horticulture produce. Suggestion on developments for future growth focuses on increasing production and productivity, improving household food security, increasing farmers’ income and increasing the value of exports.

The Mt. Elgon Sub-region in particular, which constitutes the Value Chain Innovation Platforms for Food Security (VIP4FS) project sites in Manafwa and Kapchorwa districts has a high population density, ranging from 295 persons per km² in Kapchorwa to 586 persons per km² in Manafwa making it the second most densely populated sub-region in Uganda. With annual growth rate of 3.0% (UBOS, 2014), communities in the Mt. Elgon sub-region depend largely on smallholder agriculture and natural resource-based commodities obtained in the Mt Elgon ecosystem for their livelihoods. Farmers are constrained by factors such as the remoteness of urban market outlets, poor infrastructure, limited range of processing opportunities, access to market information, lack of collective institutional arrangements and limited land holdings.

A key challenge facing the management of the Mt Elgon ecosystem is to maintain and develop its natural resource base to meet the increasing demands for goods and services while maintaining the ecosystem’s ecological integrity. This challenge is largely attributed to the fact that local livelihoods are primarily based on smallholder subsistence agriculture, hence directly dependent on the natural resource endowment. High population density coupled with small land landholdings and declining agricultural production builds pressure

1 on protected areas such as Mt. Elgon National Park. For instance, wood production for fuel, timber and construction has significantly decreased in the farming system over the years forcing many households to source it from the protected areas. The role of trees in the landscape and their contribution to soil conservation, soil fertility maintenance, the generation of goods and services essential to livelihoods and the regulation of ecosystem processes have gained increased recognition over the years. In order to tackle some of the challenges facing smallholder farmers in the Mt. Elgon ecosystem, the VIP4FS project was initiated. The main aim of the project is to identify principles and drivers that support scalable establishment of effective and equitable innovation platforms that enhance food security through greater engagement of smallholder farmers with markets. The project has a particular focus on enabling women and young people to improve their livelihoods. There are five specific objectives.

1. To assess smallholder livelihoods, institutional arrangements across scales, and identify drivers that enable value chain IP development for sustainable agricultural commercialization.

2. To identify best fit value chain development strategies and market information delivery systems, and examine their influence on the success of value chain innovation platforms in enhancing rural enterprise development.

3. To develop and evaluate scalable approaches for promoting value chain innovation platforms among smallholders and other stakeholders in ways that generate inclusive and sustainable economic benefits.

4. To engage with and strengthen the capacity of key stakeholder groups to both enhance the research process and promote the widespread scaling up of approaches generated by the project.

5. To systematically monitor and review project implementation and evaluate its outcomes and impacts

The project’s objectives are being realized through the use of a participatory action research process involving different stakeholders to improve income and food security in the project sites.

As a starting point, the project needed to understand the context in which smallholder farmers operate in order to be able to propose interventions that will improve food security and hence enhance their livelihoods. A household survey was therefore carried out

2 in order to understand the livelihood status of communities living in the Mt. Elgon ecosystem. This report presents livelihoods analysis by focusing on household characteristics, agricultural production, institutions, income, assets, dietary diversity, wealth status and infrastructure.

1.1 LIVELIHOOD ANALYSIS

The project adapted the sustainable livelihoods framework (Figure 1) by (DFID, 2000) and the five capitals by (Donovan and Stoian, 2012) to identify opportunities for inclusive and sustainable value chain development to achieve balanced improvement of key livelihood assets (human, social, natural, physical and financial) as elaborated in the 5Capitals tool. This links household access to livelihood assets with greater well-being and resilience. Likewise, the economic viability and performance of smallholder enterprises is linked to their access to business assets. We used this framework to assess the extent to which existing asset endowments determine the outcomes of value chain development, relationships between asset building at enterprise and household levels, and the role of market, political and institutional factors in facilitating or hindering favourable outcomes, separating the changes caused by interactions and interventions in value chains from those induced by the overall context. Trade-offs and synergies amongst natural, social and financial assets are explicitly considered. Livelihood is the material means whereby people live and involves a myriad of activities that people partake to provide for their basic needs. Livelihood is a concept of research and development and includes what people do (given their resources and assets) and what they achieve by doing it. Livelihood analysis investigates people, their capabilities and their means of living including food, income, and properties one owns. According to (DFID, 2000), a livelihood is considered sustainable when it can cope with and recover from stresses and shocks and maintain or enhance its capabilities and assets now and in future, while not undermining the natural resource base. Livelihood strategies consist of a set of activities that an individual undertakes in order to meet basic needs. Understanding livelihood strategies will assist the VIP4FS project identify interventions that can be acted upon in order to improve livelihood prospects which is a prerequisite to reduction of rural poverty. According to the World Bank group, strategies seek patterns that can be acted upon in order to improve the livelihood

3 prospects of the poor through discovering alternatives and increasing options. In order to adequately address rural poverty, farmers are required to adopt sustainable livelihood strategies.

Using the DFID framework we conceptualize how households in Uganda operate within a vulnerability context that is shaped by different factors and opportunities and how they draw on different types of livelihood assets or capitals which may be influenced by the vulnerability contexts, institutions and processes and how they use their asset base to develop a range of livelihood strategies to achieve desired livelihood outcomes (de Satge et al., 2002)

FIGURE 1: THE SUSTAINABLE LIVELIHOOD FRAMEWORK (DFID 2000)

The study aims at providing useful information for understanding initial livelihood status of households in the area. The assessment was guided by the five capitals; human capital, natural capital, financial capital, physical capital and social capital. The three value chains of interest (coffee, honey and dairy) were preselected based on agreed upon nine point criteria by the project team after extensive consultation with the implementing partners. The nine-point criteria included (i) potential for large impact, particularly for women and the youth, (ii) prospects for tractable interventions that could yield useful results from

4 planned comparisons, (iii) existence of the private sector actor who could be approached to co-finance planned comparisons, (iv)existence of the development partners who are already working on the value chains to effect interventions, (v)co-benefits to smallholder livelihood systems, (vi)availability of resource persons within the project team, (vii)clear institutional access necessary to effect change and (viii) supportive policy context within which the interventions can be developed.

2.0 METHODOLOGY

2.1 SITE DESCRIPTION

The data for this study was collected from Eastern Uganda in two districts: Manafwa and Kapchorwa. The two sites are located at the slopes of the Mt Elgon. Mt Elgon ecosystem consists of forests, farm land and Mt Elgon national park. The Mt. Elgon Sub-region has a high population density, ranging from 295 persons per km² in Kapchorwa to 586 persons per km² in Manafwa making it the second most densely populated sub-region in Uganda and with annual growth rate of 3.0% (UBOS, 2014). The number of households for Kapchorwa and Manafwa are 21,652 and 72,740, respectively both with an average of 4.8 persons per household (UBOS, 2014). The majority of people are engaged in smallholder agriculture as the main economic activity. Crops grown include maize, Arabica coffee, bananas, sorghum, potatoes, beans, tomatoes, cabbage, and passion fruits in a dominantly coffee-banana system. Most households also own livestock, usually kept in zero grazing units or in combination with partial grazing. The main animals kept include cattle, goats, sheep, pigs and chicken.

Kapchorwa district is divided into three agro-ecological zones, namely, Mt. Elgon high farmlands, Kapchorwa farm forest and North East short grass plains with clay soils. The average altitude in the three zones is 1466 m, 1455 m, and 1093 m respectively. Rainfall varies from less than 1000 mm in the north increasing to 2000 mm towards Mt. Elgon Kapchorwa district is divided into 11 sub counties; Kaptanya, Kapchorwa town council,

Kapchesombe, Kapteret, Tegeres, Chema, Sipi, Chepterech, Kawowo, Amukol and Kaserem. Kapchorwa district is bordered by Kween district to the northeast and east, Sironko district to the south, and Bulambuli district to the west and northwest (Figure 2). The priority cash crops for Manafwa district are coffee, maize, beans, banana and potatoes. Coffee is mainly marketed through farmer primary societies which are linked to the Bugisu Cooperative Union (BCU) which undertakes processing and marketing under the brand “Elgon Coffee”. On the other hand, the main cash crops in Kapchorwa are maize, coffee, barley, wheat, beans, banana, potatoes, sesame, sunflower, onions and cabbage. The main coffee marketing agency is Kawacom which also undertakes processing and export. Other coffee dealers include; Kapchorwa-Bukwa Marketing Association. Kapchorwa Community Development Association (KACODA) specializes in the marketing of milk and honey.

FIGURE 2: MAP OF KAPCHORWA AND MANAFWA AND LOCATIONS OF HOUSEHOLDS INTERVIEWED

2.2 SAMPLING AND DATA COLLECTION

Three sub-counties from each district were selected for this exercise: Mukoto, Namabya and Butiru sub-counties in Manafwa and Kapchesombe, Tegeres and Kabeywa (Table 1). The sub counties were selected based on representation of the different agro-ecological zones of the Mt Elgon ecosystem: highland, midland and lowland zones in the district and also based on availability of farmers practicing the different value chains of interest: coffee, dairy and honey. In Manafwa district, Mukoto, Namabya and Butiru sub-counties were selected to represent highland, midland and lowland zones. In Kapchorwa district, Kapchesombe was selected to represent high altitude while Tegeres and Kabeywa were selected to represent high to mid altitudes (Table 1). Sub-counties in the lowlands were dropped from the sampling frame because the three enterprises were not predominantly undertaken by the farmers. Kapchesombe and Kabeywa were selected for dairy and apiary while Tegeres was prioritised for dairy. All the three sub-counties were predominantly coffee growing zones although Kabeywa was reported to be the main coffee producing zone. A total of 18 and 30 villages were selected in Manafwa and Kapchorwa district respectively.

TABLE 1: SITE DESCRIPTION OF THE DIFFERENT SUB COUNTIES

District Sub-county n Percentage Predominant crop in the area Manafwa Mukoto 70 22.9 Coffee, dairy and apiary Namabya 98 32.0 Coffee and dairy Butiru 138 45.1 dairy Total 306 Kapchorwa Kapchesombe 105 32.7 Coffee and apiary Tegeres 126 39.3 Dairy Kabeywa 90 28.0 Coffee Total 321 Grand Total 627

Of the respondents interviewed, 55.9% were male and 44.1 % female in Manafwa while 50.8% of farmers were male and 49.2 % female in Kapchorwa. The sampling frame for the

7 households was constructed during the surveys, because the lists were not available at the government offices. The sampled households were allocated to the six sub-counties proportionately based on the total number of households in a given sub-county. A total of 306 and 321 farmers were selected for interviews in Manafwa and Kapchorwa districts respectively (Table 1).

2.2 DATA ANALYSIS

Variables from households and individual respondent characteristics were assessed to capture relevant information from respondents. Descriptive statistics such as frequency counts, percentages, mean and standard error of mean were used to display the data. Data analysed was on demographic characteristics of households, education, land ownership, crop enterprises, household assets, income, institutions, agricultural and livestock production with a focus on coffee, dairy and bee keeping. Dietary diversity was computed as a proxy of food security. Dietary scores and percentage of households consuming each food group was used as a one-time measure. The dietary score in this study was measured by the following criteria:

i) Creating food group variables for each of the food groups and aggregations done by the food group category. For the purposes of the study the categorization in Table 2 was used. ii) Generating a combined variable for all food groups falling under each of the defined categories in Table 2. The combination was defined to be 1 if a household consumed at least one of the food items iii) Dietary diversity was computed by summing all food groups consumed by the household within a 7 day period. iv) Food consumption score was computed as a factor of the household consuming the food category and the number of days the households have consumed the food item in a period of seven days multiplied by the assigned food consumption score v) Summation of the total household consumption score for each household

vi) Categorization of households in different food consumption categories using percentiles. Those households with less than 25th percentile were considered having ‘poor’ dietary diversity, households with greater that 25th and less than 75th percentile were considered to have “borderline” diversity while those with greater than 75th percentile were considered to have “acceptable” diversity and food consumption.

TABLE 2: CATEGORIZATION OF DIFFERENT FOOD TYPES

Food Category Types of foods Food consumption score Proteins Meat, milk, fish and eggs 4 Vitamins A rich Fruits and vegetables 1 Pulses Beans and peas 3 Staples Tubers, roots, cereals and grains 2 Sugars Sugars and beverages 0.5 Oils Oils 0.5

Wealth index The wealth index which is a composite measure of a household cumulative living standard was calculated using household ownership of different items such as television, bicycles and cars. Type of roofing materials, type of drinking water sources, toilet facility and other characteristics related to wealth status were also used. Each of the assets was assigned a weight or factor score generated through principal component analysis. The scores were then standardized in relation to standard normal distribution with a mean of zero and standard deviation of one. The standardized scores were then used to create the break points that define wealth quintiles: low, middle and high income households. Asset index has replaced previous popular income and consumption data and depicts an individual or a

9 household’s long-run economic status and therefore do not necessarily account for short- term fluctuations in economic wellbeing (Filmer and Pritchett, 2001). The wealth index of a given household, i, is a linear combination of assets owned.

The wealth index, yi, calculated as below:

푥1 − 푥̅̅1̅ 푥2 − 푥̅̅2̅ 푥푘 − 푥푘̅̅̅ 푦 = 훼 ( ) + 훼 ( ) + ⋯ … . + 훼 ( ) 푖 1 훿1 2 훿2 푘 훿푘

Where, 푥̅ and k are mean and standard deviations of assets 푥푘 and α represents the weight for each variable 푥푘 for the first principal component. The first principal component, y, yields a wealth index that assigns a larger weight to assets that vary the most across households so that an asset found in all households is given a weight of zero (McKenzie, 2005). The first principal component or wealth index can take positive as well as negative values.

3.0 RESULTS AND DISCUSSION

3.1 DEMOGRAPHIC CHARACTERISTICS OF HOUSEHOLDS IN KAPCHORWA AND

MANAFWA DISTRICT

From the survey, 78.1%of the respondents in Manafwa were from Bagisu community followed by the Teso tribe (19.9%). In Kapchorwa, 73.8% of the respondents were from Sebei community with the rest 26.2% being from the Bagisu tribe. Households in Manafwa and Kapchorwa were mostly male headed with more than 85 % headed by males in both districts. Of this, most were male headed (monogamous) households, 71.7% and 69.2% for Manafwa and Kapchorwa respectively (Table 3). Female headed households (12.2%) were few in both sites. More than 95.1% of the respondents in both districts had at least one person living with them in the household. Households in Manafwa and Kapchorwa had an average of five people with few households having up to 12 and 11 household members in Manafwa and Kapchorwa respectively. Highest percentage of households members in both

10 districts were below 18 years with a number of members in the young adults category. The minorities were older members above 50 years old (Table 4).

TABLE 3: HOUSEHOLD TYPES IN MANAFWA AND KAPCHORWA

Manafwa (n=306) Kapchorwa (n=321) What is the type of household? Percent Percent Male headed (monogamous) 71.2 69.2 Male headed (polygamous) 9.5 13.4 Female headed (spouse living in another town) 1.3 0.3 Female headed (widowed) 9.8 8.4 Female headed (divorced/separated) 2.3 1.6 Female headed (single-never been married) 0.6 Male headed (single-never been married) 0.7 0.9 Male headed (divorced/separated) 2.3 4 Male headed (widowed) 2.3 1.2 Child headed 0.3 Male headed (spouse living in another town) 0.3 0.3

TABLE 4: AGE OF HOUSEHOLD MEMBERS

Manafwa Kapchorwa Percentage of Percentage of HH HH members characteristics HH members members (n=306) (n=321) YOUTH BELOW 18 below 18 71.6 65.4 YOUNG ADULTS Between 18> years>35 17.6 20.6 ADULTS 35> years>50 7.0 7.8 OLDER MEMBERS Above 50 3.9 6.1

The gender of the different household members is presented in Figure 3. Manafwa district has more females in the households than males while Kapchorwa had more males than females in the household.

GENDER COMPOSITION OF HH MEMBERS

Male Female

52.3%

51.6%

48.4% 47.7%

MANAFWA KAPCHORWA

FIGURE 3: GENDER OF HOUSEHOLD MEMBERS

The highest education level of the respondents varied between and within sites. Most of the farmers in Manafwa and Kapchorwa had primary level education levels with less having attained secondary, college and university education (Table 5). In Kapchorwa more farmers had secondary level of education than Manafwa but still few had college and university education. Majority of household heads are males in both districts (Kapchorwa 88%; Manafwa 85 %).

TABLE 5: EDUCATION LEVEL OF HOUSEHOLD HEAD IN EACH SUBCOUNTY

Highest level of Manafwa Kapchorwa education of Mukoto Namabya (n=98) Butiru (n=138) Total (N=306) Kapchesombe Tegeres Kabeywa Total Household head (n=70) (n=105) (n=126) (n=90) (N=321) None (%) 12 15 11 12 11 14 17 14 Primary (%) 74 52 68 64 39 51 63 51 Secondary (%) 14 24 18 19 29 31 16 26 Tertiary (%) 0 9 4 4 21 4 4 10 Gender of household 16 18 12 15 10 12 15 12 head (%female)

3.2 AGRICULTURE AND LIVESTOCK PRODUCTION

3.2.1 LAND OWNERSHIP Agriculture is the backbone of Uganda’s economy and almost all households in rural areas practice agriculture. Of the respondents interviewed almost all households owned land. More than 95% of the households owned land in both districts as shown in Table 6. Average sizes of land owned in the two sites were 1.90 and 2.11 acres in Manafwa and Kapchorwa respectively (Table 6).

TABLE 6: LAND OWNERSHIP

District Percent Average land Stand size (acres) Dev Manafwa (N=306) No 1.6

Yes 98.4 1.90 1.91 Kapchorwa No 3.1

(N=321) Yes 96.9 2.11 2.31

The total land owned and land under cultivation in Manafwa and Kapchorwa did not significantly differ. This is due to the fact that land owned is very small. Farmers in Kapchorwa cultivated more land than farmers in Manafwa. These difference was however not significant (Table 7).

TABLE 7: LAND OWNERSHIP IN SEASON 2014/2015

Land ownership in Season 2014/2015 Mean SE (acres) Size of land owned in Manafwa 1.78 0.11 previous season Kapchorwa 2.05 0.13

Total land under Manafwa 1.83 0.10 cultivation Kapchorwa 1.95 0.11 Manafwa 1.04 0.12 Total land rented in Kapchorwa 1.10 0.14

Total land rented out Manafwa 0.90 (leased) Kapchorwa 1.17

The land owned in both districts was either freehold and/or customary (Table 8). Customary tenure is whereby access to land is governed by customs, rules, and regulation of the community. The holders of land do not have formal titles to the land they use. Free hold system is where owners of the land have a deed to their land which allows them to hold the registered land indefinitely. The land owner has a right to use, sell, lease, transfer, subdivide, mortgage and give as they see fit. The rights are well respected by the government.

TABLE 8: LAND TENURE IN UGANDA

Land tenure Manafwa (%) Kapchorwa (%) (n=306) (321)

Freehold 49.3 47.7 Leasehold 0.0 0.6 Customary 49.3 48.3 None 1.6 3.4

3.2.2 MAIN AND SECONDARY OCCUPATION

Farming is a major income generating activity in both sites. More than 85% of household heads mentioned farming as their main occupation (Table 9.)

TABLE 9: MAIN OCCUPATION

Manafwa Kapchorwa Mukoto Namaby Butiru Total Kapchesombe Tegeres Kabeywa Total Variable (n=70) a (n=98) (n=138) (N=306) (n=105) (n=126) (n=90) (N=321) Main occupation Farming (%) 99 84 92 91 74 89 84 83 Regular employment (%) 1 4 2 3 10 5 6 7 Business (%) 0 5 1 2 5 2 6 4 Casual labourer (%) 0 3 4 3 5 1 2 3 Others 0 3 1 1 7 3 2 4

Farming is relied on as an income generating activity as most farmers are not employed nor do they have other activities that can bring income. Only a few farmers got involved in small scale businesses while others served as casual laborers (Table 9). The majority of farmers do not have a secondary occupation (Table 10).

TABLE 10: SECONDARY HOUSEHOLD OCCUPATION

Secondary Job or Occupation District (%) Total Manafwa Kapchorwa (n=627) (n=306) (n=321) None 76.1 51.4 63.5 Not involved in productive work due to age or health reasons 0.0 2.2 1.1 Farmer (crop and/or livestock) 9.2 19.0 14.2 Runs self-owned off-farm business 9.8 9.3 9.6 Regular employment 0.7 2.5 1.6 Casual off-farm employment like construction labourer 2.3 2.2 2.2 Agricultural casual labourer 1.6 11.8 6.9 Student 0.3 0.3 0.3 Other specify 0.0 1.2 0.6

3.2.3 CROP ENTERPRISES Households in Kapchorwa and Manafwa had an average of four and five enterprises respectively, with a small number of farmers having up to 11 crop enterprises in their farm in the cropping season 2014-2015. The major crops practiced by households in Manafwa and Kapchorwa were: maize, beans and bananas (Table 11). Maize and beans are major staple foods in East Africa and highly contributes to the household food security. Coffee, a value chain of interest to the VIP4FS project, comes in fourth with only 43.1% and 67.6% of households in Manafwa and Kapchorwa growing it respectively (Table 11).

TABLE 11: CROP ENTEPRISES IN MANAFWA AND KAPCHORWA

Crop enterprises on the farm in the last cropping District (%) season of 2015/2016 Manafwa Kapchorwa Total (n=306) (n=321) (n=627) Maize 90.8 81.6 86.1 Beans 92.5 73.2 82.6 Bananas (matooke) 58.5 65.1 61.9 Coffee 43.1 67.6 55.7 Cassava 57.5 5.6 30.9 Irish potato 2.3 56.7 30.1 Onions 27.1 8.4 17.5 Sweet potato 28.8 3.1 15.6 Groundnuts 22.5 0.3 11.2 Finger millet 18.3 0.0 8.9 Cabbage 4.6 11.5 8.1 Passion fruits 3.3 11.2 7.3 Tomato 12.1 2.5 7.2 Sorghum 11.8 0.0 5.7 Yams 3.6 4.4 4.0 Soya beans 7.2 0.0 3.5 Kales 0.3 5.6 3.0

3.2.3.1 COFFEE PRODUCTION Coffee is the main export crop in Uganda together with tea, cotton and tobacco. Coffee accounts for the highest export in tons for the country. The crop is relatively important to the household livelihoods. In the study sites, more farmers produced coffee in Kapchorwa (71%) than in Manafwa (40%). Although land acreage under coffee production was not significantly different between Manafwa and Kapchorwa sites, the results show that farmers in Kapchorwa allocate relatively more land to coffee than those in Manafwa. The production yields in kgs between the two sites were however different: farmers in Kapchorwa harvested significantly more coffee, mean 220kgs, than farmers in Manafwa in year 2014/2015 who sold an average of 134kgs (Table 12).

The numbers of farmers that sold coffee in Manafwa also significantly decreased from the farmers that harvested coffee in year 2014/2015 season with those that sold coffee in the same period. The amount of coffee harvested significantly decreased between years 2014/2015 to 2015/2016 season. The amount sold between Kapchorwa and Manafwa also differed significantly with farmers in Kapchorwa selling more coffee, 218kgs than farmers in Manafwa that sold 129 kgs of coffee per household in the 2015/2016 season (Table 12)

TABLE 12: COFFEE PRODUCTION

District n Mean SE T test

Coffee area in (acres) Manafwa 121 1.62 0.154 0.129 Kapchorwa 228 1.95 0.133 Amount of coffee Manafwa 152 134.57 9.709 harvested in 2014/2015 0.000** Kapchorwa 233 220.86 15.052 in kg Amount of coffee sold in Manafwa 91 226.63 109.176 0.497 kg in 2014/2015 Kapchorwa 221 299.75 52.350 Total quantity of coffee Manafwa 91 129.19 12.56 0.000** sold in 2015/2016 in kg Kapchorwa 221 218.08 15.752

During planting, almost all farmers in Manafwa (95%) and Kapchorwa (100%) plant Arabica coffee in their farms. A few households in Manafwa plant Robusta coffee. Slightly over half of the farmers in Manafwa (58.7%) and 45.2% in Kapchorwa establish their own seedlings. A few source them from a private trader in the village (Table 13 )

TABLE 13: MAIN SOURCE OF SEEDLINGS

District Main source of coffee seedlings Manafwa (%) Kapchorwa (%) (n=121) (n=228) Own 58.7 45.2

Private trader in the local/village market 16.5 34.2

Government 13.2 3.9

Fellow farmer 5.8 3.9

Neighbor/ Relative 2.5 3.1

Farmer Group 1.7 1.3

NGO 0.8 1.8

Other (specify) 0.8 1.8

Private trader in the district market 0.0 1.8

Cooperative 0.0 3.1

Coffee value addition

There was no value addition to the coffee sold. Harvested coffee was mainly sold as fresh unprocessed or dry processed coffee beans (Figure 4). This presents an opportunity as farmers would fetch more income if they sold processed coffee.

Form in which coffee is sold

Dry processed (for Arabica - pulped, washed and 30.6% dried) 30.8%

Dry processed beans (for Robusta - red cherries 0.9% floated to remove insects, then dried) 2.2%

Fresh processed (pulped and washed, sold before 5.4% drying, mainly Arabica) 2.2%

63.1% Fresh unprocessed beans (red cherries) 64.8%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0%

Kapchorwa Manafwa

FIGURE 4: FORM WHICH COFFEE IS SOLD

Constraints faced by coffee producers

Even with the high level of production of coffee in Kapchorwa and Manafwa, farmers faced a number of constraints during its production (Figure 5). A higher proportion of households in Kapchorwa experienced challenges than coffee farmers in Manafwa. More than 20% of farmers in Kapchorwa experienced low productivity of coffee, high incidence of pests and diseases, limited knowledge on coffee production and lack of proper storage facilities (Figure 5). Other challenges include limited access to extension and market information. Lack of storage was mentioned by over 30% of producers in Kapchorwa.

Coffee production challenges

Adulterated inputs

High transport cost to the main market/ point of sale

Unavailability of clean planting material

Unstandardized packaging

Low demand (poor prices)

Low productivity (limited surplus for sale)

High incidence of pests

High incidence of diseases

Lack of reliable buyers

Limited access to extension and market information

Lack of storage facilities

0 5 10 15 20 25 30 35 40

Kapchorwa Manafwa

FIGURE 5: CHALLENGES IN COFFEE PRODUCTION

Despite challenges facing farmers in both districts, only a few of the coffee producing households used inputs such as fertilizer and pesticides (Table 14). Chemical fertilizer used was sourced from private traders in the local market and village while a few farmers had own pesticides for coffee.

TABLE 14: INPUT USE IN COFFEE PRODUCTION

Input use Manafwa (%) Kapchorwa (%) (n=121) (n=228) Use of chemical fertilizer 5.8 3.1

Use of pesticides in coffee 7.4 16.2 production

Use of hired labour 19.8 31.6

3.2.4. LIVESTOCK PRODUCTION Livestock production in Uganda also forms an integral part of daily livelihoods. Although crop production is highly prevalent in Manafwa and Kapchorwa, there were a number of farmers practicing livestock farming. Only 14% of farmers did not have any livestock enterprises. Farmers reared an average number of two and three animals in Manafwa and Kapchorwa respectively. Chicken rearing was the most preferred livestock enterprise in both Manafwa and Kapchorwa district (Table 15). The second most practiced livestock enterprise was dairy cattle and rearing of goats for meat.

TABLE 15: LIVESTOCK ENTERPRISES

Livestock enterprises in the Manafwa (%) Kapchorwa (%) households (2015/2016 ) (n=306) (n=321) season

Local chicken 40.8 32.0 Dairy cattle 20.6 32.1 Goats (meat) 14.6 20.5 Pigs 10.3 4.3 Goats (milk) 4.9 1.5 Beef cattle 4.9 0.5 Others 2.3 1.5 Sheep 1.4 5.1 Bee keeping 0.2 2.6

The livestock reared were mainly of local breed except dairy animals in Kapchorwa where more farmers had improved breeds than local breeds (Table 16). A higher percentage of farmers in Kapchorwa had improved dairy cattle than Manafwa.

TABLE 16: LIVESTOCK TYPES

Livestock enterprises in the Manafwa (%) Kapchorwa (%) last cropping season of 2015/2016 Livestock type N Livestock type N Improved Local Improved Local Dairy cattle 45.6 54.4 114 66.8 33.2 196 Sheep 0.0 100.0 8 3.2 96.8 31 Pigs 0.0 100.0 57 15.4 84.6 26 Goats (milk) 7.4 92.6 27 11.1 88.9 9 Goats (meat) 0.0 100.0 81 0.8 99.2 125 Local chicken 0.9 99.1 226 1.5 98.5 195 Bee keeping 0.0 100.0 1 0.0 100.0 16 Beef cattle 48.1 51.9 27 0.0 100.0 3

Most of the livestock enterprises were kept for commercial purposes in Manafwa. Farmers sold dairy products, sheep, pigs, goats, honey products and beef cattle. Only local chicken was mainly reared for household consumption. In Kapchorwa, dairy products and local chicken were mostly for subsistence and household consumption. Other livestock enterprises in Kapchorwa were for commercial purposes (Table 17).

A farmer in Kapchorwa feeding her dairy cow

TABLE 17: MAIN PURPOSE OF LIVESTOCK ENTERPRISE

MAIN purpose of Manafwa (%) Kapchorwa (%) livestock Subsistence/ Commercial n Subsistence/ Commercial n enterprise Consumption /Sale Consumption /Sale Dairy cattle 41.2 58.8 114 54.1 45.9 196 Sheep 37.5 62.5 8 19.4 80.6 31 Pigs 10.5 89.5 57 23.1 76.9 26

Goats (milk) 48.1 51.9 27 55.6 44.4 9

Goats (meat) 18.5 81.5 81 28.8 71.2 125

Local chicken 68.6 31.4 226 69.7 30.3 195

Bee keeping 0.0 100.0 1 18.8 81.3 16 Beef cattle 7.4 92.6 27 33.3 66.7 3

Other (specify) 38.5 61.5 13 77.8 22.2 9

3.2.4.1 DAIRY PRODUCTION Farmers in Kapchorwa have embraced dairy farming more than farmers in Manafwa. About 29.7% and 54.8% of farmers in Manafwa and Kapchorwa respectively owned dairy cattle within the agricultural year preceding the interview. Farmers owned an average of one dairy cow in Manafwa and two in Kapchorwa (Table 18). Kapchorwa had more dairy cattle of improved breeds than in Manafwa. There was no significant difference in the number of local dairy breeds owned by farmers in the two sites (Table 18).

TABLE 18: OWNERSHIP OF DAIRY COWS

District n Mean SE Improved cows Manafwa 91 0.71 0.089 Kapchorwa 176 1.51 0.114 Local cows Manafwa 91 0.96 0.120 Kapchorwa 176 0.99 0.164 Total no. of dairy cows in Manafwa 67 1.52 0.109 the last agricultural year Kapchorwa 137 2.34 0.184

More than 80% of the farmers sold milk in Kapchorwa, while 43% sold milk in Manafwa. The dairy cattle were mostly purchased from other farmers in both Kapchorwa and Manafwa (Table 19)

TABLE 19: MODE OF ACQUISITION OF THE DAIRY COWS

Main mode of acquisition of the Manafwa (%) Kapchorwa (%) dairy cows (n=91) (n=176)

Purchased 25.8 46.1 Gift 1.6 0.0 Inherited 1.0 0.3 Born into the herd 0.7 5.9 Government programmes (e.g. 0.3 2.5 NAADs/Operation wealth creation) Other(specify) 0.3

A few households about 10 % in Manafwa and 16.3% in Kapchorwa lost at least one dairy cow due to accidents, diseases and/or theft. The average number of cows lost by households was one cow on average in both districts. In both districts, cow breeding was mostly by using locally shared bull from the village. A few farmers from Kapchorwa also use improved shared bull. Only a few households in Kapchorwa used improved methods for breeding such as artificial insemination (AI)(Figure 6).

MAIN BREEDING METHOD

Manafwa Kapchorwa

57.14

47.16

43.75

28.57

13.19

3.98

2.84

2.27

0 0

FIGURE 6: BREEDING METHODS FOR DAIRY COWS

Despite farmers using bulls within the village, the households paid for services rendered from the shared bulls. More than 60% of the farmers paid for services in both districts. The willingness of farmers to pay for breeding services shows that if such services are made accessible to farmers, they will be able to improve productivity.

Livestock feed and feeding practices

Most farmers in both sites planted their own forage (68% and 77% in Manafwa and Kapchorwa respectively). At least 20.3% and 42.7% of farmers in Manafwa and Kapchorwa had some kind of forages in their farm respectively. Forages included fodder crops, legumes and/or fodder trees. The different types of forages planted by farmers are shown in Table 20.

TABLE 20: FODDER GROWN BY SMALLHOLDER FARMERS

Fodder grown Manafwa % Kapchorwa % (N= 306) (N=321) Napier 19.6 44.1 Calliandra 2.0 0.6 Mucuna 0.0 0.3 Desmodium 0.0 0.3

Most smallholder dairy farmers in both sites planted Napier grass as the main source of forage. There were very few farmers planting calliandra, a fodder shrub. Improved milk production in both districts highly depends on the quality of feeds and one of the entry points for the VIP4FS project would be the promotion of improved feeds and forages. Reasons cited by farmers for not planting forages are: not having enough land to plant forages, unavailability of planting material, lack of technical knowledge and the high cost of planting material (Table 21).

TABLE 21:REASONS FOR NOT GROWING FODDER

Manafwa (%) Kapchorwa (%) Total (%) (n=306) (n=321) (n=627)

Not enough land 72 85 79

Unavailability of planting material 24 26 25 Lack technical knowledge 7 18 13 High cost of planting material 3 5 4 Not aware of the benefits 3 3 3

No interest 0 5 3 Lack of labour 0 18 10

3.2.4.2 BEE KEEPING AND HONEY PRODUCTION Bee keeping is practiced by very few smallholder farmers in districts; 1% and 13.1% of the farmers in Manafwa and Kapchorwa respectively (Table 22). In Kapchorwa, a higher number of bee hives were sited inside the national park. According to previous field work, Uganda Wildlife Authority (UWA) had allowed farmers living adjacent to the forest to site their beehives in the forest as long as the farmers followed rules set by the authority. Very few farmers sited their bee hives in their own land.

TABLE 22: APIARY LOCATIONS

Apiary location Manafwa (%) Kapchorwa (%) (n=306) (n=321)

Practicing 1.00 13.1 Own land 0.7 5.0 Forest Reserves 0.3 0.6 National Park 0 6.2 Communal land 0 0.3 Other (specify) 0.9

Of the farmers that practiced bee keeping, the bee hives were often produced by the farmers themselves using own materials and a few farmers purchased the bee hives from the market (Table 23). The hives are locally baited to attract bees in both sites.

TABLE 23: MAIN SOURCE OF BEE HIVES

Main source of bee hives Manafwa (%) Kapchorwa (%) (n=306) (n=321)

From own local materials 0.7 8.1 Purchased from the market 0.3 4.0 NGOs 0.3 Other (specify) 0.6

Households owned an average of 18 and three bee hives in Kapchorwa and Manafwa respectively. There were no improved bee hives in Manafwa. Households owned three improved bee hives on average in Kapchorwa. Farmers harvested an average of 2.78 litres of honey per hive in Kachorwa in the 2015/2016 period. There were no harvests indicated for Manafwa district during this period.

3.3 INSTITUTIONS AND FARMER GROUPS

3.3.1 PARTICIPATION IN FARMER GROUPS Farmer groups bring farmers together to obtain benefits collectively through mechanisms such as collective action that leverage on factors such as bargaining power. They have been seen as a way to reduce transaction costs by smallholders and of improving their levels of commercialization. Although farmer groups have been widely recommended for high level impacts in smallholder farmer livelihoods, only a few farmers in the study sites belonged to groups. Only 22.9% of members of households in Manafwa and 35.2% in Kapchorwa ever belonged to groups in the past (Figure 7).

MEMBERSHIP IN GROUPS

Manafwa Kapchorwa

35.2%

31.2%

22.9% 22.5%

EVER BELONGED TO GROUP BELONGED TO GROUP WITHIN 12 MONTHS OF SURVEY

FIGURE 7: MEMBERSHIP IN GROUPS

Only one member of the household belonged to groups in both sites. About 17.4% and 23.7% of households in Manafwa and Kapchorwa respectively had only one member belonging to groups. Only a few households had more than one member belonging to groups; 7.4% and 4.8% in Manafwa and Kapchorwa respectively.

Some farmers and households members did not join groups mainly because they thought that there were no groups to join (68.3% and 60.1%) for Manafwa and Kapchorwa respectively). Other reasons given include not having time for group activities and groups not seen as beneficial (Table 24)

Kabeywa bee keepers group , Kapchorwa

TABLE 24: REASONS FARMERS DID NOT JOIN GROUPS

Reasons for not joining groups Manafwa (%) Kapchorwa (%) (n=236) (n=208) No group to join 68.2 60.1 Groups are not beneficial 9.8 14.4 Do not have time for group activities 7.2 17.8 No money to pay for membership fee 7.2 1.9 No money to save or contribute in the group 2.1 0 Not able to find a group that matches interests 1.7 0.96 No information on appropriate group to join 1.3 1.4 Health condition is not good 0.85 0.48 Lack of trust among neighbours 0.85 1.9 Poor leadership of existing groups 0.85 0.48 No reason or need to join the group 0 0.48

Despite a higher number of farmers not joining groups in Kapchorwa and Manafwa, those farmers that joined groups thought that groups assisted them generate income for the household (Table 25).

TABLE 25: MAIN REASON FOR JOINING GROUPS

Main reason for joining a group Manafwa % Kapchorwa % (n=70) (n=113) Increased income generation for my house 75.71 64.6 Social (meeting people and support each other) 8.57 18.58 Access to information and technology 7.14 8.85 Access to benefits e.g. from donor/government 5.71 6.19 Access to labour 2.86 0 Savings accumulation 0 0.88 Other (specify) 0 0.88

Registration of groups in Manafwa was mostly done informally: this is where groups have internal member registrations but are not registered with formal government structures or

28 organizations working in the area. In Kapchorwa more groups were registered formally either with local government or with other governing authority (Figure 8).

90.0

80.0

70.0 60.0

50.0 Formally 48.2 40.0 84.2 Informally 30.0 51.8 20.0

10.0 15.8 0.0 Manafwa Kapchorwa

FIGURE 8: FARMER GROUP REGISTRATION

The groups in both sites were mixed, with a few male only and women only groups in Manafwa. There were barely any youth groups in both areas.

Manafwa Kapchorwa

85.5

78.9

13.2

9.1

6.6

2.7

1.8

1.3 0.9

M E N O N L Y W O M E N O N L Y WOMEN YOUTH MIXED MIXED YOUTH ONLY

FIGURE 9: FARMER GROUP COMPOSITION

The farmer groups in the study sites mainly focused on agriculture with the majority of groups in Manafwa focusing on coffee ( 33%) and dairy (17%) while in Kapchorwa focus

29 was on coffee (25%), dairy (27%) and honey (27%). Most of the groups in Manafwa concentrated on financial savings while Kapchorwa groups were more agricultural based groups (Table 26). Most of the groups in Manafwa are savings (52%) and credit groups (43%) while those in Kapchorwa offer varied services, ranging from savings (31%) and credit (30%) to input purchases (7%), joint extension services (11%) and marketing (16%). There were a few environmental, user associations and advocacy groups in both study sites.

TABLE 26: FARMER GROUP CHARACTERISTICS

Farmer group Manafwa Kapchorwa characteristics Mukoto Namabya Butiru Total Kapchesombe Tegeres Kabeywa Total (n=70) (n=98) (n=138) (N=306) (n=105) (n=126) (n=90) (N=321) Agricultural (%) 17 37 5 21 48 50 61 52 Coffee (%) 67 25 0 33 4 25 62 25 Dairy (%) 33 13 0 17 17 33 0 17

Honey (%) 0 0 0 0 30 17 31 27 Other enterprises 30 17 0 19 0 63 0 42 (%) Services received from farmer groups Credit/loan (%) 22 30 43 32 38 21 22 30 Produce 0 7 0 3 12 13 30 16 marketing (%) Input purchases 0 11 0 5 10 4 4 7 (%) Savings (%) 61 41 52 50 28 42 26 31 Joint extension 0 15 5 8 8 17 13 11 services (%) Market 6 0 0 2 6 0 4 4 information (%) Others (%) 11 11 5 9 14 8 13 12

Smallholder farmers received credit or loans from the groups which also provided an avenue for savings. Only a few groups in Kapchorwa provided marketing services and marketing information to its members. Even with the realization of the importance of farmer groups, many farmers in the project sites haven’t taken into consideration the benefits they would derive from membership in groups. Either the farmers are not aware of these benefits or the groups do not function to its maximum capacity. These results are quite unexpected as Kapchorwa was thought to be more advanced with institutions on the ground, with strong farmer groups that provide benefits to their members. Farmer groups, albeit their perceived importance, also fall short in some of the areas important to the overall performance of the group. Farmer groups faced challenges such as lack of commitment to group activities from members, poor leadership and lack of trust among members (Table 27).

TABLE 27: CHALLENGES FACED BY GROUPS

Main challenges faced by Manafwa (%) Kapchorwa (%) groups (n=76) (n=110) None 47.4 47.3 Embezzlement of funds 14.5 3.6

Lack of trust among 13.2 8.2 members Lack of commitment from 11.8 18.2 members Other (specify) 7.9 16.4 Poor leadership 5.3 6.4

To solve the challenges identified, a few solutions by members were suggested comprising: having clearly defined laws and rules to govern the group especially on property management owned by the group, training on membership roles and responsibilities, training on good leadership, putting measures to ensure proper accountability and transparency with group funds, having more linkages with governing structure such as the local government for training purposes and also punishment of offenders.

3.4 HOUSEHOLD INCOME

Smallholder farmers in both districts have diverse income sources. Among them sale of crops, sale of coffee, livestock and livestock products were frequently mentioned. Highest proportion of farmers obtained income from sale of crops in Manafwa (73.0%) and Kapchorwa (89.1%) (Figure 10

Proportion of households with on farm income sources

89.1 Sale of crops 73.0 65.1 Sale of coffee 35.7 40.8 Sale of livestock products 17.7 36.8 Sale of livestock 38.3 28.7 Sale of fruits 14.1 26.5 Sale of any agroforestry products 17.4 19.0 Sale of non-timber products 13.8 12.8 Sale of firewood 12.9 8.4 Sale of honey and honey products 0.6 4.7 Sale of other milk products 1.0 1.0 Sale of cow's milk 10.6 0.6 Sale of charcoal 2.9

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

Kapchorwa Manafwa

FIGURE 10: PROPORTION OF HOUSEHOLDS WITH HOUSEHOLD INCOMES

A higher proportion of smallholder farmers in Kapchorwa received income from more sources than farmers in Manafwa. The sources are: sale of crops, sale of coffee, sale of livestock products, sale of milk and milk products, sale of honey, sale of agroforestry products, sale of charcoal and sale of fruit (chi2 , p <0.05)(Figure 10).

The respondents were also asked to rate the importance of the different income streams to contribution to overall households livelihoods and well-being on a scale that ranged from no importance to very important in contributing to overall contribution to household income. These ranks were then assigned scores where rating of least importance was given a score of 1 while very important was given a score of 4. The scores were then weighted to obtain a standardized score (Table 28).

TABLE 28: INCOME RATINGS FROM DIFFERENT SOURCES IN MANAFWA DISTRICT

% of households Manafwa receiving income Income ratings from different sources average income Rank from this source scores N=306 Income from business 22.9 2.66 1.00 Sale of charcoal 2.6 2.63 2.00 Sale of crops 72.5 2.59 3.00 Income from salaries and wages 13.1 2.58 4.00 Sale of coffee 35.0 2.56 5.00 Sale of livestock 37.9 2.56 6.00 Income from casual work 31.4 2.41 7.00 Sale of any agroforestry products 17.0 2.38 8.00 Sale of cow's milk 10.8 2.33 9.00 Sale of other milk products 1.0 2.33 10.00 Sale of firewood 12.7 2.31 11.00 Sale of livestock products 17.6 2.28 12.00 Sale of non-timber products 13.7 2.26 13.00 Rent 6.9 2.10 14.00 Sale of fruits 14.1 2.07 15.00 Remittance 29.1 2.07 16.00 Sale of honey and honey products 0.7 2.00 17.00

In Manafwa, all income sources were considered somewhat important (average score of 2) by the households receiving the income. These ratings suggest that the income sources were thought to be intermittent and might not adequately cover household expenses (Table 28). There were barely any ratings on sale of honey in the district. This therefore suggests that farmers either do not practice bee keeping in Manafwa or that they do not

33 receive any income from this venture. Other practices with not enough ratings include sale of milk products such as ghee, cheese, yoghurt. Farmers in these areas do not add any value to their products and only sold milk in its raw form.

In Kapchorwa, sale of crops and coffee were considered important to the contribution of overall livelihoods of the household (average score of 3). The number of farmers selling coffee was also considerably higher than in Mnafwa. Income from sale of crops, coffee, livestock products, milk and milk products, honey and agroforestry products were considered reliable but not sufficient to meet the households needs (average score of 2) (Table 29).

TABLE 29: INCOME RATINGS FROM DIFFERENT SOURCES IN KAPCHORWA DISTRICT

% of households Kapchorwa receiving income Income ratings from different sources average income Ranks from this source scores N=321 Income from salaries and wages 17.1 3.49 1.00 Sale of coffee 65.1 3.03 2.00 Sale of crops 89.1 3.00 3.00 Income from business 22.7 2.93 4.00 Sale of livestock 36.8 2.84 5.00 Sale of cow's milk 36.4 2.82 6.00 Sale of livestock products 40.8 2.73 7.00 Sale of other milk products 4.7 2.67 8.00 Sale of any agroforestry products 26.5 2.66 9.00 Rent 10.0 2.59 10.00 Sale of honey and honey products 8.4 2.59 11.00 Income from casual work 42.1 2.57 12.00 Sale of non-timber products 19.0 2.51 13.00 Remittance 25.2 2.51 14.00 Sale of charcoal 0.6 2.50 15.00 Sale of fruits 28.7 2.48 16.00 Sale of firewood 12.8 2.44 17.00

Households in Manafwa and Kapchorwa greatly differed between scoring of the different incomes sources. Kapchorwa households significantly considered almost income sources to be of greater importance than those in Manafwa (Table 30).

TABLE 30: OFF FARM INCOME SOURCES IN MANAFWA AND KAPCHORWA

Income ratings from different sources Manafwa Kapchorwa P-Value Sale of crops 2.59 3.0 0.00*** Sale of coffee 2.56 3.03 0.00*** Sale of livestock 2.56 2.83 0.00*** Sale of livestock products 2.28 2.72 0.00*** Sale of cow's milk 2.33 2.82 0.00*** Sale of other milk products 2.33 2.67 0.47 Sale of honey and honey products 2.0 2.59 0.31 Sale of any agroforestry products 2.39 2.66 0.04** Sale of charcoal 2.62 2.50 0.88 Sale of firewood 2.31 2.44 0.49 Sale of fruits 2.07 2.48 0.00*** Sale of non-timber products 2.26 2.51 0.12 Income from casual work 2.41 2.57 0.16 Income from business 2.66 2.93 0.04** Income from salaries and wages 2.58 3.49 0.00*** Remittance 2.07 2.51 0.00*** Rent 2.10 2.59 0.01**

3.4.1OFF FARM INCOME In both Manafwa and Kapchorwa districts, only a few smallholder farmers received income from off-farm income such as casual work, business, wages, remittances and rent from commercial buildings. The proportion of farmers that received income from casual work in Manafwa and Kapchorwa varied significantly (p<0.05), where 42.1% and 31.4% of farmers received income from casual work in Kapchorwa and Manafwa respectively ( Table 28 and Table 29). Business was considered the highest contributor by households in Manafwa while wages and salaries was considered more important in Kapchorwa; these two activities were only practiced by 22.9% and 17.1% of households in Manafwa and Kapchorwa respectively. Agriculture, even if not ranked highly in importance, has the highest proportion of farmers dependent on it for general livelihood and well-being.

3.5 DIETARY DIVERSITY

Dietary diversity is a proxy indicator of household food security. Dietary diversity presents the number of unique foods consumed over a given period of time and is considered a good measure of household food access. Household food security as a measure of well-being encompasses three dimensions: availability (measure of food that is physically available in the relevant vicinity of a population: access (measure of the population ability to food during a given period and utilization (whether the population will be able to derive sufficient nutrition during a given period. A dietary diversity score can be created, which is the sum of the different food groups consumed. Dietary diversity aims to identify households that are food insecure, to characterize their insecurity, monitor changes in their circumstances and assess the impact of interventions. Varied diet is associated with improved birth weight and general health in the households.

Dietary diversity was presented by use of “count” which is the number of food categories consumed by a given household. Counting the number of food categories is more indicative of diversity than count of different food types as the types would be providing similar nutrients for instance a household that consumes proteins, vitamins and roots would be considered more diet diverse than a household that consumes different type of cereals. More than 90% of households in both Manafwa and Kapchorwa districts consumed cereals, sugars, beverages, vegetables and oils (Table 31). Consumption of cereals, beverages and vegetables significantly varied between the districts. A higher number of households in Manafwa significantly consumed roots & tubers and pulses than households in Kapchorwa. On the other hand more farmers in Kapchorwa consumed milk at a higher rate that those households in Manafwa. The least number of households consumed fish and eggs in both districts. Fruits were consumed by at least 50% of households in Manafwa and Kapchorwa (Table 31).

TABLE 31: HOUSEHOLD CONSUMPTION OF DIFFERENT FOOD TYPES

Household Consumption of Manafwa (%) Kapchorwa (%) Chi-square different food types n=306 n=321 test

Cereals 97.4 99.4 0.047** Sugar and sugar products 94.8 96.3 0.366 Beverages 94.1 96.9 0.094* Vegetables 93.1 88.2 0.033** Oils 91.8 94.7 0.15 Roots and tubers 90.5 58.9 0.000*** Pulses 83.7 76.9 0.035* Meat 83.7 76.9 0.335 Milk 57.8 77.3 0.000*** Fruits 52.3 53.9 0.687 Eggs 37.9 38.9 0.791 Fish 19.9 20.6 0.845

Further analysis of consumption trends between sub-counties show that some sub- counties varied slightly in consumption of different food types. In Manafwa, consumption of roots & tubers, meat, eggs, milk and vegetables did not significantly differ between the different sub-counties of interest (Table 32). Households in Mukoto consumed more cereals, sugars, oils, fruits and beverages than the other two sub-counties. While households in Namabywa consumed pulses more than the other two sub-counties and households in Butiru consumed more fish than the households in other households. In Kapchorwa, households that consumed cereals, roots & tubers, meat, fish, sugars, vegetables, fruits and beverages did not significantly differ between the three sub-counties. Tegeres sub-counties, which is situated in the upper highlands, consumed more pulses, eggs and milk than the other sub-counties. Kapchesombe sub-counties consumed more oils than the other sub-counties.

TABLE 32: FOOD TYPE CONSUMPTION ACROSS SUBCOUNTIES

Food type Manafwa (%) Chi- Kapchorwa (%) Chi- by different n=306 square n=321 square sub- Mukoto Namabya Butiru P value Kapchesombe Tegeres Kabeywa P value counties (n=70) (n=98) (n=138) (n=105) (n=126) (n=90)

Cereals 100.0 99.0 94.9 0.047** 99.0 100.0 98.9 0.517 Roots and 91.4 86.7 92.8 0.286 61.9 52.4 64.4 0.514 tubers Pulses 87.1 90.8 76.8 0.011** 76.2 85.7 65.6 0.002*** Meat 44.3 55.1 61.6 0.059 55.2 55.6 42.2 0.105 Fish 5.7 6.1 37.0 0.000*** 19.0 23.8 17.8 0.500 Eggs 44.3 39.8 33.3 0.275 42.9 49.2 20.0 0.000*** Milk 65.7 54.1 56.5 0.294 81.0 84.9 62.2 0.000*** Sugar and 98.6 96.9 91.3 0.043** 94.3 99.2 94.4 0.082 sugar products Oils 98.6 93.9 87.0 0.010** 96.2 98.4 87.8 0.002*** Vegetables 95.7 95.9 89.9 0.120 91.4 87.3 85.6 0.417 Fruits 62.9 56.1 44.2 0.026** 61.0 54.0 45.6 0.099 Beverages 100.0 94.9 90.6 0.022** 95.2 99.2 95.6 0.156

A comparison was also undertaken on how the different household types: male headed and female headed households differed in consumption of different food types. Female and male headed households did not significantly differ in consumption of almost all the different food types. Only consumption of eggs and oils differed between male and female headed households in Manafwa as higher proportion of male headed households consumed eggs. In Kapchorwa, a higher proportion of male headed households consumed cereals and oils than female headed households (Table 33).

TABLE 33: FOOD TYPES IN DIFFERENT HOUSEHOLD TYPES

Manafwa (%) Kapchorwa (%) Food types per Male headed Female Chi- Male Female Chi- household households headed square headed headed square type (n=264) households households households (n=41) (n=286) (n=35)

Cereals 97.3 97.6 0.937 99.7 97.1 0.075* Roots and 91.3 85.4 0.229 42.3 471.4 0.217 tubers Pulses 83.3 85.4 0.744 76.6 80.0 0.65 Meat 57.2 43.9 0.111 52.1 48.6 0.694 Fish 20.5 14.6 0.383 20.6 20.0 0.931 Eggs 40.9 19.5 0.009** 38.8 40.0 0.892 Milk 57.6 61.0 0.682 76.9 80.0 0.682 Sugar and 94.7 95.1 0.91 96.2 97.1 0.771 sugar products Oils 93.6 80.5 0.005** 95.8 85.7 0.012** Vegetables 93.6 90.2 0.435 88.1 88.6 0.937 Fruits 53.8 43.9 0.238 53.8 54.3 0.961 Beverages 94.3 92.7 0.679 96.9 97.1 0.926

3.5.1 COMPARISON IN CONSUMPTION OF DIFFERENT FOOD CATEGORIES In Kapchorwa and Manafwa, all households consumed at least three different food categories with a higher proportion of households consuming all five food categories within a seven day period (Table 34). The average number of food categories consumed by households in Kapchorwa and Manafwa did not significantly differ. The averages were 4.52 and 4.54 for Manafwa and Kapchorwa respectively.

TABLE 34: COUNT OF FOOD CATEGORIES CONSUMED BY HOUSEHOLDS

Number of foods categories Manafwa (%) Kapchorwa consumed by households N=306 (%) N=321

1 1.0 0.3 2 2.9 2.2 3 5.2 5.6 4 25.2 27.4 5 65.7 64.5

During the time of the study, almost all of the households could be considered diet diverse as over 50% of the households consumed all the food categories within a 7 day period before the study (Table 35). More than 75% of the households consumed proteins, pulses, fruits and vegetables, grains, sugars and oils. The consumptions of food items such as: fruits and vegetables, sugars and oils, did not differ significantly for the different districts p>0.05. Consumption of proteins was higher in Kapchorwa households than those in Manafwa p<0.05. On the other hand, pulses were consumed by households in Manafwa more than households in Kapchorwa. Grains, roots & tubers were consumed by all households interviewed.

TABLE 35: HOUSEHOLD DIETARY DIVERSITY

Manafwa (%) Kapchorwa (%) Households Dietary N=306 N=321 Chi-square Diversity (HDD)

Proteins 78.1 86.6 0.00 Pulses 83.7 76.9 0.035 Fruits and vegetables 93.1 91.3 0.386 Staples 100.0 100.0 - Sugars 96.7 98.8 0.087 Oils 91.8 94.7 0.15

An analysis of the number of times households consumed the different food varieties, showed that proteins were consumed more than any other food category. Households consumed proteins an average of 24.05 and 29.73 times in a 7 day period for Manafwa and Kapchorwa respectively (Table 36). The difference in consumption of proteins was higher in Kapchorwa than in Manafwa (p<0.000). This could be attributed to the high number of dairy cattle in Kapchorwa and as earlier indicated most of the households consumed dairy products from their farm.

TABLE 36: NUMBER OF TIMES FOOD CATEGORY WAS CONSUMED

Number of times food Manafwa Kapchorwa T test category was consumed Mean SE Mean SE p value within a 7 day period Proteins 24.05 0.91 29.73 0.88 0.000*** Staples 17.37 0.29 15.91 4.56 0.000*** Pulses 10.84 0.33 10.62 0.35 0.639 Fruits and vegetables 7.19 0.18 6.70 0.18 0.063 Sugars 5.41 0.12 5.98 0.09 0.000*** Oils 2.33 0.07 2.89 0.06 0.000***

Sugars and oils were consumed a significantly higher number of times in Kapchorwa than in Manafwa. Households in Manafwa consumed roots, tubers and grains significantly more times than households in Kapchorwa, average of 17.37 and 15.91 times respectively. There was no significant difference in consumption of pulses and fruits and vegetables between the districts.

The patterns of dietary diversity between different households did not significantly differ between male and female headed households (Table 37).

TABLE 37: PATTERNS OF DIETARY DIVERSITY

Total number of food categories Male headed Female headed consumed by households Households (%) households (%) (N=550) (N=76) 1 0.36 2 1.27 5.26 3 2.18 5.26 4 6.36 5.26 5 26.18 19.74 6 63.64 64.47 There was no significant difference in patterns of consumption between female and male headed households P>0.05.

3.5.2 DETERMINING TOTAL CONSUMPTION SCORE FOR DIFFERENT HOUSEHOLDS Food consumption scores between the two districts differed significantly as households in Kapchorwa district had higher scores than households in Manafwa (p<0.05) (Table 38)

TABLE 38: FOOD CONSUMPTION SCORE BETWEEN MANAFWA AND KAPCHORWA

Group Observations Mean SE

Manafwa 306 59.63 1.31

Kapchorwa 321 64.81 1.31

Combined 627 62.28 0.93

As earlier highlighted in the methodology section, percentiles were used to categorize households into different food consumption categories: poor, borderline and acceptable. The percentage of households in the three categories are presented in Figure 11. The proportion of households in the different food consumption categories were significantly different in both district (p<0.1). The mean scores for each of the food consumption categories were: 32.99, 62.10 and 92.49 for poor, borderline and acceptable respectively.

HH FOOD CONSUMPTION CATEGORIES

Manafwa Kapchorwa

52.02

47.39

29.41

26.48

23.2 21.5

POOR BORDERLINE ACCEPTABLE

FIGURE 11: FOOD CONSUMPTION CATEGORIES

Analysis of variance (ANOVA) was used to validate the categorization. The ANOVA results showed that there is a significant difference in total numbers of times households consumed different food categories (Fig 12). There was significant difference between consumption levels of different household types.

. anova total_dds fcs_ug;

Number of obs = 627 R-squared = 0.3838 Root MSE = .7039 Adj R-squared = 0.3818

Source Partial SS df MS F Prob>F

Model 192.53676 2 96.268379 194.30 0.0000

fcs_ug 192.53676 2 96.268379 194.30 0.0000

Residual 309.17616 624 .49547462

Total 501.71292 626 .80145834

FIGURE 12: ANOVA OF FOOD CONSUMPTION SCORE BETWEEN HOUSEHOLD TYPES

The total number of different food types taken within the 7 day period by each of the food consumption categories also differed between poor, borderline and acceptable categories

43 in both districts. In the acceptable category, households consumed all food categories in both districts while the consumption in the poor category varied (Table 39).

TABLE 39: FOOD CATEGORIES CONSUMED BY DIFFERENT FOOD CONSUMPTION CLUSTERS

Total number Manafwa Kapchorwa of food (n=306) (n=321) categories Poor Borderline Acceptable Poor Borderline Acceptable consumed

1 2.2 0.0 0.0 0 0 0 2 6.7 0.0 0.0 7.2 0.0 0.0 3 11.1 0.0 0.0 7.2 0.6 0.0 4 16.7 2.1 1.4 24.6 1.8 0.0 5 42.2 21.4 2.8 49.3 28.1 8.2 6 21.1 76.6 95.8 11.6 69.5 91.8

A comparison between the food consumption categories and proportion of different household types showed no significant difference in the households categorizations (Chi- square p=0.899) (Figure 13).

MANAFWA HH

poor borderline acceptable

47.7%

43.9%

31.7%

29.2%

24.4% 23.1%

MALE HEADED HOUSEHOLDS FEMALE HEADED HOUSEHOLDS

FIGURE 12: FOOD CONSUMPTION CLUSTERS IN MANAFWA AND KAPCHORWA

In Kapchorwa, even though a higher proportion of female headed households belonged to the borderline and acceptable categories, there was no significant difference with the proportion of male headed households in the different food consumption categories (p=0.487)(Figure 14).

KAPCHORWA HH

poor borderline acceptable

60.0%

51.0%

26.6%

25.7%

22.4% 14.3%

MALE HEADED HOUSEHOLDS FEMALE HEADED HOUSEHOLDS

FIGURE 13: FOOD CONSUMPTION CLUSTERS BETWEEN DIFFERENT HOUSEHOLD TYPES

3.6 ASSET ENDOWMENTS

We adapted the DFID sustainable livelihoods framework to identify opportunities for inclusive and sustainable value chain development to achieve balanced improvement of key livelihood assets (human, social, natural, physical and financial) as elaborated in the 5Capitals tool. This links household access to livelihood assets with greater well-being and resilience. Likewise, the economic viability and performance of smallholder enterprises is linked to their access to business assets. We used this framework to assess the extent to which existing asset endowments determine the outcomes of value chain development, relationships between asset building at enterprise and household levels, and the role of market, political and institutional factors in facilitating or hindering favourable outcomes, separating the changes caused by interactions and interventions in value chains from those induced by the overall context.

The sustainable livelihood framework comprises of five assets namely human capital, financial capital, physical capital, natural capital and social capital, then households combine these assets with activities which leads to a construction of a portfolio of activities such as agriculture, livelihood diversification to achieve their livelihood goals which can also be called a livelihood strategy which in turn generates a higher income leading to the reduction of poverty, reduced vulnerability(economic shocks, stress and seasonality) and improved food security.

People’s livelihood strategies are determined by the diversity of assets that they can access taking into account the vulnerability context as well as transforming structures and processes. In Manafwa and Kapchorwa the assets in Table 40 were owned by the interviewed households. Almost all households owned a hoe and a machete while a substantial proportion owned an axe and some a slasher. Even though a substantial percentage of farmers in both districts owned livestock, none of the households owned a chaff cutter which is used for chopping livestock feed. Similarly, few households owned an aluminium bucket for storing milk. Less than 10% had zero grazing units. This also applies to coffee, the number of coffee farmers was found to be high in both districts but only a few farmers owned coffee hullers.

A dairy cow in a zero-grazing unit

TABLE 40: FARM ASSETS OWNED BY HOUSEHOLDS

Farm assets owned Manafwa (%) Kapchorwa (%) (n=306) (n=321) Hoe 100.00 98.80 Panga/Machete 91.80 92.20 Axe 68.00 73.80 Slasher 31.70 22.10 Spade 14.10 20.90 Fork or Hoe 5.20 2.50 Plough/animal drawn plough 4.20 9.00 Pruning Knives 4.20 5.60 Watering can 3.90 10.30 Wheelbarrow 3.60 8.10 Sprayer pump 3.30 15.00 Milk Can 2.90 13.70 Zero grazing units 2.60 7.50 Aluminium bucket 1.30 1.20 Coffee Huller 0.70 0.60 Tractor 0.30 0.60 Water storage 0.30 1.90 Livestock feed mixture 0.00 3.40 Modern bee hives 0.00 0.60 Harrow/Cultivator 0.00 0.00 Smoker 0.00 0.30 Animal drawn cart 0.00 0.30 Motorized spray pump 0.00 0.30 Motorized watering pump 0.00 0.30 Chaff Cutter 0.00 0.00

Households were also asked to indicate the household assets they owned at the time of the study. From the results, a higher percentage of farmers owned furniture and furnishings, radio and mobile phones in both districts. The proportion of farmers in Manafwa that owned furniture, mobile phones and bicycles was significantly higher than farmers in

Kapchorwa (Table 41). A higher percentage of households in Kapchorwa owned radio, jewelry, household appliances and non-farm land than households in Manafwa.

TABLE 41: HOUSEHOLD ASSET OWNERSHIP

Household assets Manafwa (%) Kapchorwa % Test of N=306 N=321 significance Furniture and furnishings 76.80 51.10 *** Radio/cassette/DVD player 60.80 72.90 *** Mobile phone 51.60 44.20 ** Bicycle 24.50 2.80 *** Jewelry and watches 6.50 14.60 *** Households appliances 5.20 12.80 *** Solar panel/electrical inverters 4.20 17.10 *** Rental house 2.30 5.30 Non-farm land 2.30 21.20 *** Television 2.30 4.70 Motor cycle 2.30 3.70 *** Generators 1.00 0.30 Internet access 0.70 2.80 *** Commercial building 0.30 1.60 Computer 0.30 1.60 Other household assets 0.30 0.60 Motor vehicle 0.00 2.50 Transport equipment 0.00 0.00 Electronic equipment 0.00 1.60 *** ***p value <0.000, ** p value<0.05

Other household assets that are necessary for the households’ well-being are summarized in Table 42. From the table, most households more than one room in their main house excluding kitchen and toilets. Firewood was the main source of fuel in both districts, mud was the main floor material, a higher percentage of household got their drinking water from a protected dug well and have traditional toilet pit, the main wall materials were sticks and mud while iron sheets were mainly used as roofing materials (Table 42)

TABLE 42: OTHER HOUSEHOLD ASSETS OWNED BY HOUSEHOLDS IN MANAFWA AND KAPCHORWA

Different asset types in households Manafwa (%) Kapchorwa (%) Total (n=306) (n=321) (%) (n=627) Households with 1.0 11.8 6.5 Electricity Zero or one room 17.0 14.0 15.5 Two rooms 26.5 16.2 21.2 Number of rooms Three rooms 23.2 26.2 24.7 in the main house Four rooms 20.3 26.5 23.4 More than 4 rooms 13.1 17.1 15.2 Gas 0.0 0.6 0.3 Main source of Charcoal 1.0 1.6 1.3 fuel Firewood 98.7 100.0 99.4 Kerosene 0.3 0.0 0.2 Dirt/soil/dung 90.2 80.4 85.2 Main material Wood 2.6 8.1 5.4 used for floor Cement 6.9 11.5 9.3 Other 0.3 0.0 0.2 Piped water into home 0.3 13.4 7.0 Public tap/stand pipe 0.7 21.8 11.5 Borehole/tube well 28.1 0.9 14.2 Main source of Protected dug well 50.7 44.9 47.7 drinking water Unprotected well/spring 13.1 13.7 13.4 Water provided by car 0.0 0.3 0.2 River, pond, stream 7.2 5.0 6.1 Private flush toilet 0.0 0.6 0.3 Private improved pit 1.6 4.7 3.2 Main type of toilet Private traditional pit 72.5 84.7 78.8 facility Shared pit latrine 24.2 8.7 16.3 Bush, forest 1.0 1.2 1.1 Other 0.7 0.0 0.3 Wood 0.0 5.6 2.9 Main material Cement block 2.0 4.7 3.3 used for walls Zinc (Iron sheet) wall 2.9 2.8 1.4

Stone and mud 3.9 0.6 2.2 Dirt bricks 45.8 18.1 31.6 Sticks and mud 44.1 64.5 54.5 Other 0.0 0.3 0.2 Stone and cement 2.3 3.1 2.7 Burnt bricks 2.0 0.3 1.1 Iron sheet (zinc) 71.2 87.9 79.7 Grass/thatch/bamboo 27.8 10.9 19.1 Main Material Plastic 0.0 0.3 0.2 used for roof sheet/tarpaulin/canvas Stone and mud 0.0 0.6 0.3 Other 1.0 0.3 0.6

3.6.1 WEALTH INDEX Wealth index provides a stable and understandable yardstick for evaluating and comparing the economic situation of households, social groups and societies across all regions of the developing world. A household’s ranking on wealth index indicates to what extent the household possesses basic set of assets, valued highly by people all across the globe. Wealth index also measures a household’s level of material well-being by looking at the household’s possession of durables, access to basic services, and characteristics of the house in which it is living. Households that own more expensive durables, have a better quality house and have access to basic services are considered to have a higher level of material well-being than household with less expensive durables, worse housing and no access to services. During computation of the wealth index, assets that contribute to material well-being are important depending on the country or site of interest (Smits and Steendijk, 2014). Material well-being is associated with the satisfaction of the basic needs of food, clothing and safety/shelter, which have to be met to survive. Material well-being therefore refers to the possession of goods and access to basic services that make life easier and more comfortable. Such assets include: all kinds of relatively cheap assets but make people more comfortable (tables, chairs, carpets, beds). Household access to electricity opens up infinite new possibilities for increasing material well-being in relatively cheap ways. With electric light, the time that can be spent on useful and leisure activities

50 increases considerably. Electric tools and utensils reduce time spent on cooking and on work around the home. Access to clean water allows households workload to reduce, as this may save an often considerable amount of time spent on fetching water.

The quality of the house in which the household lives in is also an important aspect of material well-being. The kind of building and flooring material determines how much maintenance there is to the house, whether rain, wind and pests are kept outside well, and how comfortable the house is. Having more than one room, a separate kitchen and bathroom, and a decent in-house toilet facility greatly enhances quality of living. Besides technical equipment that makes life easier, material wellbeing can also be improved by means of transportation and communication equipment. With a bike, cart, boat, motorbike or car transportation of heavy loads becomes easier and travelling time is reduced. Radio and television bring the world into the home and phones, computers and the internet greatly enhance communication and access to information (Smits and Steendijk, 2014).

Assets, including farm level and household level assets, that are durable with ability to contribute to the household livelihood were included in the analysis of the wealth index (Table 43).

In Table 43, last column contains the 1st principal component index obtained by running a principal component analysis on all the identified assets. The 1st principal index is used to standardize the scores. The first principal follows a normal distribution with a mean of zero and standard deviation of . The first principal component yields a wealth index that assigns larger weight to assets that vary the most across households so that an asset found in all households is given a weight of zero.

Fuel wood, floor materials, roof, water resources had to be categorized into: high, medium and low quality assets to allow for agglomeration of different assets that are thought to have similar weights be included in the same category.

TABLE 43: SUMMARY OF ASSETS USED TO COMPUTE WEALTH INDEX

Household assets Manafwa (%) Kapchorwa % 1st Principal N=306 N=321 Component index Furniture and furnishings 76.80 51.10 -0.0454 Radio/cassette/DVD player 60.80 72.90 0.0634 Mobile phone 51.60 44.20 0.1101 Bicycle 24.50 2.80 -0.0054 Solar panel/electrical inverters 4.20 17.10 0.1429 Rental house 2.30 5.30 0.1616 Land ownership 98.4% 96.9% 0.012 Television 2.30 4.70 0.2295 Motor cycle 2.30 3.70 0.0596 Generators 1.00 0.30 -0.0033 Internet access 0.70 2.80 0.0983 Computer 0.30 1.60 0.2013 Motor vehicle 0.00 2.50 0.2732 High quality 0.0 0.6 0.2769 Fuel wood Medium quality 1.3 1.6 0.048 Low quality 98.7 97.8 -0.1624 High quality 6.9 11.5 0.2979 Floor material Medium quality 2.6 8.1 0.0278 Low quality 90.2 80.4 -0.2604 High quality 6.2 8.1 0.3009 Wall materials Medium quality 45.8 23.7 -0.0383 Low quality 44.1 67.3 -0.1156 High quality 0.0 0.6 0.2769 Toilet Medium quality 1.6 4.7 0.2076 Low quality 97.7 94.7 -0.277 High quality 71.2 87.9 0.0854 Roof materials Medium quality 0.0 0.3 0.2082 Low quality 27.8 11.5 -0.1065 High quality 0.3 13.4 0.2367 Water sources Medium quality 51.3 67.0 -0.0664 Low quality 48.4 19.6 -0.0589 Zero or one room 17.0 14.0 -0.0916 Number of two rooms 26.5 16.2 -0.0759 rooms in the three rooms 23.2 26.2 -0.0138 households four rooms and more 33.3 43.6 0.144

The categorization was as follows: Water supply:- high quality is bottled water or water piped into dwelling or premises; - medium quality is public tap, protected well, tanker truck, etc; - low quality is unprotected well, borehole, spring, surface water, etc. Toilet facility:- high quality is any kind of private flush toilet; - medium quality is public toilet, improved pit latrine, etc.; - low quality is traditional pit latrine, hanging toilet, or no toilet facility. Floor quality: - high quality is finished floor with parquet, carpet, tiles, ceramic etc.; - medium quality is cement, concrete, raw wood, etc.; - low quality is none, earth, dung etc.

3.6.1.1 WEALTH CATEGORIES Using the equation and formula described above, the following three categories of households were generated: low income, middle income and high income . Households in low income category were those that their wealth scores fell below the 25th percentile while middle income category scores fell between 25th and 75th percentiles of the wealth index score, high level income category were considered to fall above the 75th percentile score. The wealth categories had an average score of-1.3179, -0.54933 and 2.418 for low, middle and high income levels respectively. Due to the application of PCAs 1st component the wealth score can take both negative and positive values.

The proportion of households for each wealth category are presented in Figure 15

WEALTH INDEX

Manafwa Kapchorwa

53.9%

45.8%

34.0%

30.4%

20.2% 15.7%

L O W I N C O M E MIDDLE INCOME HIGH INCOME

FIGURE 14: WEALTH CATEGORIES PROPORTIONS IN MANAFWA AND KAPCHORWA

The average wealth scores for each of the districts are presented in Table 45. Kapchorwa district had a higher score than households in Manafwa, and the difference in mean score was significant (p value<0.000). Kapchorwa households can therefore be considered richer than households in Manafwa on average (Table 44).

TABLE 44: DIFFERENCE IN WEALTH SCORE BETWEEN MANAFWA AND KAPCHORWA

Group N Mean SE

Manafwa 306 -0.414 0.071 Kapchorwa 321 0.395 0.168 Combined 627 -0.000 0.094 diff -0.810 0.186

The low income category differed in scores between the two districts where the average wealth score for households in Manafwa was lower than households in Kapchorwa (Table 45). The number of farmers in this category in Manafwa was also significantly higher than households in Kapchorwa.

TABLE 45: SUMMARY STATISTICS OF WEALTH CATEGORIES

Wealth Manafwa Kapchorwa T test categories Mean SD Mean SD Low income -1.36 0.2295 -1.258 0.2062 0.0049***

Middle income -0.5505 0.2625 -0.5481 0.2642 0.9358

High income 1.8834 1.6165 2.6534 4.3616 0.2375

There were no significant differences in average score between middle and high income categories in Manafwa and Kapchorwa. The percentages between the two districts were however distinct: Kapchorwa had a higher proportion of farmers in the high income category while Manafwa had more households in the lower category.

3.6.1.2 WEALTH CATEGORIES BY GENDER There was a significant difference between the gender of the farmer in the different wealth categories. A higher proportion of female farmers were in the middle and high income category compared to their male counterparts (Figure 16).

GENDER OF FARMER

male female

53.7%

47.1%

27.7%

25.1%

24.3% 22.1%

L O W I N C O M E MIDDLE INCOME HIGH INCOME

FIGURE 15: WEALTH CATEGORIES BY GENDER

A comparison between household types shows that there was no significant difference in the wealth categories of the different household types although male headed households had a slightly higher proportion in the low income category. A higher percentage of female headed households belonged to the middle income category (Figure 17).

WEALTH CATEGORIES OF DIFFERENT HOUSEHOLD TYPES

Male headed households Female headed household

53.9%

49.3%

25.5%

25.3%

23.7% 22.4%

L O W I N C O M E MIDDLE INCOME HIGH INCOME

FIGURE 16: WEALTH CATEGORIES OF DIFFERENT HOUSEHOLD TYPES

A comparison of wealth categories between different sub counties indicated that in Manafwa district; majority of households in Mukoto and Namabya sub-counties were in the middle income category, 71.4% and 65.3% respectively while Butiru sub county had more households in the low income category (Table 46). In Kapchorwa district, a higher proportion of households belonged to the middle income category. Kapchesombe and Tegeres however had more households in the high income while Kabeywa had more farmers in the low income category (Table 46).

TABLE 46: WEALTH CATEGORIES OF HOUSEHOLDS IN DIFFERENT SUBCOUNTIES

Wealth categories Manafwa Kapchorwa of households Sub-county (%) Sub-county (%) Mukoto Namabya Butiru Total Kapchesombe Tegeres Kabeywa Total (n=70) (n=98) (n=138) (n=306) (n=105) (n=126) (n=90) (n=321) Low income 21.4 11.2 48.6 30.4 13.3 16.7 33.3 20.2 Middle income 71.4 65.3 37.0 53.9 45.7 43.7 48.9 45.8 High income 7.1 23.5 14.5 15.7 41.0 39.7 17.8 34.0

These results indicate that Namabya and Kapchesombe are high income sub counties in Manafwa and Kapchorwa respectively. On the other hand, Butiru and Kabeywa are low income sub counties in Manafwa and Kapchorwa respectively. The difference in asset ownership between the six sub counties are presented in Table 48. In all sub counties, a high percentage of farmers owned furniture with highest proportion of 90% of households owning furniture in Butiru and Tegeres sub county having a higher percentage of 59% of furniture in Kapchorwa. The average number of households owning furniture in Kapchorwa was lower than those in Manafwa. Very few households in Manafwa owned household appliances (kettle, flat iron etc) while 85% of farmers owned them in Kachesombe and 13% in Tegeres sub county. This could be attributed to availability of electricity in the area where 13% and 17% of farmers indicated to have electricity in their homes in the two sub counties in Kapchorwa. Only 1% of households had electricity in Manafwa district (Table 47).

In terms of mobility around the district, 25% percentage of households in Manafwa owned bicycles with more households in Butiru (36%) of total households owning them. In Kapchorwa, only 3% of households owned bicycles and 4 % owning motorcycles most of whom were from Kapchesombe sub county. Whereas none of the households interviewed in Manafwa owned any vehicles, 2% of farmers in Kapchorwa of whom resided in Kapchesombe and Tegeres owned vehicles. Practically all households used firewood as main source of fuel for working. This therefore means that use of firewood did not contribute much to the wealth scores. An asset owned by all farmers has a component score of zero and does not therefore contribute to overall wealth score.

TABLE 47: ASSET OWNERSHIP OF HOUSEHOLDS IN DIFFERENT SUBCOUNTIES

Manafwa Kapchorwa Sub-county (%) Sub-county (%) Mukoto Namabya Butiru Total Kapchesombe Tegeres Kabeywa Total (n=70) (n=98) (n=138) (n=306) (n=105) (n=126) (n=90) (n=321) Rental house 1 5 1 2 9 5 2 5 Commercial building 0 0 1 0 0 1 4 2 Non-farm land 1 2 3 2 17 28 17 21 Furniture 74 60 90 77 54 59 47 54 Household appliances 3 8 4 5 85 13 9 36 Television 3 3 1 2 9 5 0 5 Radio/Cassette/DV D 59 77 51 61 27 79 64 58 Generator 0 2 1 1 1 0 0 0 Panel/electric inverter 0 9 3 4 15 28 4 17

Bicycle 6 21 36 25 5 2 1 3 Motor cycle 0 5 1 2 10 1 0 4 Vehicle 0 0 0 0 3 4 0 2 Mobile phone 57 54 47 52 59 45 26 44 Computers 0 0 1 0 1 2 1 2 Internet access 0 2 0 1 0 4 4 3

Fuel for cooking 0 0 0 0 0 0 0 0 Gas 0 0 0 0 1 1 0 1 Charcoal 0 2 1 1 3 0 9 3 Firewood 100 98 99 99 96 98 100 98 Kerosene 0 0 0 0 0 0 0 0

Electricity 0 1 1 1 13 17 2 12 Type of toilet Private flush toilet 0 0 0 0 1 1 0 1 Private improved pit latrine 1 3 1 2 9 5 0 5 Private traditional pit 76 74 70 73 84 86 84 85 Shared pit latrine 20 19 30 24 5 8 14 9 Bush, forest 1 2 0 1 2 1 1 1 Floor material Dirt/soil/dung 96 87 90 90 79 76 88 80 Wood 4 2 2 3 7 8 10 8 Cement 0 10 8 7 14 16 2 12 Wall material Wood 0 0 0 0 8 6 3 6 Cement block 0 3 2 2 5 8 0 5 Zinc wall 0 0 0 0 3 2 3 3 Stone and mud 0 6 4 4 2 0 0 1 Dirt bricks 39 48 48 46 19 16 20 18 Sticks and mud 61 36 41 44 59 63 72 64

Stone and Cement 0 4 2 2 4 5 0 3 Burnt bricks 0 3 2 2 1 0 0 0 Roof material Iron sheets 83 90 52 71 90 88 86 88 Grass/thatch/bambo o 16 9 47 28 10 10 14 11 Plastic sheet/tarpaulin 0 0 0 0 0 1 0 0 Stone and mud 0 0 0 0 0 2 0 1 Water source Piped water into home 0 1 0 0 20 16 2 13 Public tap/stand pipe 0 2 0 1 20 13 37 22 Borehole/tube well 1 9 55 28 0 1 2 1 Protected dug well/spring 57 66 36 51 38 52 42 45 Unprotected well/ spring 14 20 7 13 17 10 14 14 Water provided by car 0 0 0 0 0 1 0 0 River, pond, stream 27 1 1 7 5 7 2 5

A higher proportion of households in the different sub counties in both districts had private pit latrines within their compound. Some however had shared latrines with neighbors. A comparison between asset ownership in different wealth categories shows a higher percentage of households in high income categories owned information communication technologies (ICTs) such as television and radios than those in medium and lower categories. Other characteristic include: ownership of mobile phones, bicycles, solar panels, television, have internet access, own computers, have motor vehicles and electricity in the house (Table 48). Although almost all households used firewood as main source of fuel wood, high income households still used other sources of energy such as charcoal, kerosene and gas. Medium and low income households only used firewood and crop residue as fuel for cooking.

The floor material used by low and most of the middle income households was of low quality such as dirt soil or dung. High income households had other floor materials such as wood, cement and tiles. Middle and low income used dirt/molded bricks, wood, iron sheets and sticks and mud in making walls of their homes. High income used high quality materials such as burnt bricks, stone and cement.

TABLE 48: OWNERSHIP OF HOUSEHOLD ASSETS BY DIFFERENT WEALTH CATEGORIES

WEALTH CATEGORIES (% of households) Household assets Low Middle High P value (n=158) (n=312) (n=157) Furniture and furnishings 76 63 55 *** Radio/cassette/DVD player 41 74 79 *** Mobile phone 21 48 75 *** Bicycle 13 12 17 0.401 solar panel/electrical inverters 0 6 32 *** Rental house 0 0 0 Land ownership 96 99 97 0.051 Television 0 0 14 *** Motor cycle 1 2 7 0.002** Generators 1 0 1 0.473 Internet access 0 1 6 *** Computer 0 0 4 *** Motor vehicle 0 0 5 *** Electricity 2 2 20 *** Fuel wood High quality 0 0 1 *** Medium quality 0 0 6 *** Low quality 100 100 93 *** Floor High quality 0 0 37 *** material Medium quality 0 2 17 *** Low quality 100 97 46 *** Wall High quality 0 0 29 *** materials Medium quality 25 40 33 0.007** Low quality 73 57 37 *** Toilet High quality 0 0 1 0.05** Medium quality 0 0 13 *** Low quality 100 100 85 *** Roof High quality 37 95 94 *** materials Medium quality 0 0 1 0.223 Low quality 62 5 5 *** Water High quality 0 0 28 *** sources Medium quality 59 63 52 0.075 Low quality 41 37 20 *** Number of mean (Std error) 2 (0.71) 3 (1.2) 4 (2.3) *** rooms ***p value <0.000, ** p value<0.05

Toilet facilities in low and middle income households are mainly of low quality such as use of traditional pit latrines, shared pit latrines and use of nearby bushes or forest. High income households have private flush toilets and private improved pit latrines. Shared latrines pose such risks such as cholera and other sanitation related diseases that may pose a risk to the general health of the households.

The quality of drinking water and sources of water are also important in determining the overall wellbeing of the family. Having a good source of water ensures that women and children are prevented from water related diseases. Having a high quality water source such as bottled or piped water is not only hygienic but also the water quality is assured. In both Manafwa and Kapchorwa, almost all households accessed water from medium quality sources such as protected well or spring with a few obtaining water from unprotected well and boreholes. A higher proportion of households in the high income category obtained water from medium and high quality sources while low income households mainly depended on low quality sources such as unprotected well, springs, rivers and boreholes.

3.7 INFRASTRUCTURE

3.7.1 TRANSPORT SERVICES, ROAD SYSTEMS Access to transport and good road network is important to the livelihoods and wellbeing of communities. Accessibility of usable roads ensures that farmers have access to markets, inputs and also the convenience offered when farmers are in need of other services related to wellbeing such as health and information. In Manafwa and Kapchorwa districts, there are different forms of road infrastructure in existence within and outside the village and the roads were considered somewhat usable. Feeder and community type of roads were accessible to more than 70% of farmers in both districts (Table 49). A higher percentage of farmers in Kapchorwa accessed all roads than farmers in Manafwa. This implies that Kapchorwa farmers have better access to several services such as easy mobility to and from the local government and also easy access to different types of markets.

The most common ways to reach the nearest roads are through walking and/or use of boda boda rides. Bodaboda are commercial motorcycles used for ferrying people from remote and often inaccessible areas of the villages to the main road to either catch a bus to travel to the nearby town. Community and feeder roads are mostly accessed by walking. Usability of roads in Manafwa and Kapchorwa is mainly hampered by bad weather and terrain; this is due to the fact that the two project sites are located at the slopes of Mt Elgon.

TABLE 49: ACCESS TO TRANSPORT AND ROAD SYSTEMS

Different Road Infrastructure Manafwa Kapchorwa Manafwa Kapchorwa Manafwa Kapchorwa Manafwa Kapchorwa Trunk road Murram road Feeder road Community access road Access to: (% ) No 89 86 62 40 30 21 22 21 Yes 11 14 38 60 70 79 78 79 Usability of the different road types (%) No 1 1 8 5 20 11 26 19 Yes 99 99 90 83 80 89 74 81 Commonest way of reaching nearest trunk road (%) Walking 4 50 38 89 89 91 100 94 Taxi (car) 32 1 2 0 0 0 0 0 Boda-boda 50 49 58 8 11 7 0 2 Bus/minibus 5 0 0 0 0 0 0 0 Motorcycle 7 0 1 0 0 0 0 0 Bicycle 2 0 1 0 0 0 0 0 Other (Specify) 0 0 0 2 0 0 0 0 If un usable, Why (%) Bad weather 0 0 3 3 6 7 5 10 Bad terrain 0 0 1 1 7 3 11 3 Poor drainage 0 0 0 0 2 0 2 0 Pot holes 0 0 3 0 6 0 4 0 Not applicable to the context 0 0 0 1 0 0 4 0 Bushy roads 0 0 0 0 0 0 0 6 Other (specify) 0 0 0 0 0 2 0 6

The distance and the number of minutes to reach each of the different roads was significantly different between Kapchorwa and Manafwa. Manafwa households travelled more distance and hence used more time to reach tarmac, murram, feeder and community roads (Table 50). Kapchorwa roads are more accessible and not far from the villages.

TABLE 50: DISTANCE AND TIME TO DIFFERENT ROADS IN THE COMMUNITY

District Mean SE Time taken to travel to the nearest trunk road Manafwa 99.58 12.98 (tarmac) in minutes Kapchorwa 26.11 1.57 Distance in KM from homestead to the nearest Manafwa 29.41 1.09 trunk road (tarmac) Kapchorwa 3.86 0.19 Time taken in minutes to travel to the nearest Manafwa 34.28 2.86 trunk road (murram) Kapchorwa 10.80 1.02 Distance in KM from homestead to the nearest Manafwa 42.39 34.90 trunk road (murram) Kapchorwa 0.67 0.05 Time taken in minutes to travel to the nearest Manafwa 19.50 1.56 district feeder road Kapchorwa 10.45 1.41 Distance in KM from homestead to the nearest Manafwa 2.53 0.25 district feeder road? Kapchorwa 0.65 0.08 Time taken in minutes to travel to the nearest Manafwa 8.18 0.68 community access road Kapchorwa 6.39 0.65 Distance in KM from homestead to the nearest Manafwa 0.69 0.09 community access road Kapchorwa 0.33 0.02

3.7.2 MARKET INFRASTRUCTURE AND OTHER FACILITIES Despite having slightly less access to different types of road, Manafwa has readily available market for crops and livestock and even agrovet shops. About 38%, 42% and 37% of farmers indicated to be aware of markets for crops, livestock and agrovets respectively in Manafwa compared to 16%, 13% and 21% of farmers in Kapchorwa (Table 51). This can be attributed to Manafwa district’s proximity to Mbale town which provides a market for agricultural produce and livestock for Manafwa farmers. Even though markets are not quite accessible and available in Kapchorwa, more than 80% of households considered the markets usable at any given time (Table 51). In both sites, the markets were reachable and most farmers either walked or used boda boda. Only 37% and 21% of farmers in Kapchorwa and Manafwa accessed agrovets. The high percent of farmers using boda boda to reach the nearest agrovet indicates longer distance to agrovets.

Table 51: Market and inputs infrastrucre

Manafwa Kapchorwa Manafwa Kapchorwa Manafwa Kapchorwa Availability of market Availability of market for availability of Agrovet for crops livestock No 62 84 58 87 63 79 Yes 38 16 42 13 37 21 Usability (%) No 19 7 20 6 14 6 Yes 81 93 80 94 86 94 Commonest way of reaching markets (%) Walking 33 37 35 28 18 45 Taxi (car) 0 6 0 19 6 3 Boda-boda 59 55 57 50 69 51 Bus/minibus 0 0 1 0 0 0 Motorcycle 5 1 6 0 5 1 Bicycle 2 0 2 0 1 0 If unusable, Why (%) Bad weather 5 2 5 4 4 3 Bad terrain 7 3 8 1 6 2 Potholes 2 0 2 0 1 0 Poor 5 0 6 0 3 0 drainage Bushy roads 0 1 0 0 0 0 Other 0 1 0 1 0 0 (specify)

The distance in km, covered by farmers from the homestead to the market for crops was longer in Manafwa than in Kapchorwa and therefore more time was used in reaching these markets. The time taken and distance covered to reach the crops market was significantly different between the two sites. Farmers covered an average of 8km in Manafwa while Kapchorwa farmers cover an average of 6km (Table 52). Distance and time covered by farmers to reach available livestock markets was not significantly different between the two sites, the distance to the livestock market was 8.5km on average in both sites.

As earlier indicated few farmers accessed agrovets in their communities and were more likely to use boda boda to access them. The average distance to access the nearest agrovet was 5km in Kapchorwa compared to 11km in Manafwa. The long distance covered especially in Manafwa could discourage farmers from applying herbicides and pesticides to their crops and livestock leading to low productivity.

TABLE 52: DISTANCE AND TIME TO DIFFERENT MARKETS

Distance and time to different markets District Mean SE Time in minutes taken to travel to the Manafwa 41.44 2.31 nearest market for crops Kapchorwa 35.83 4.25 Distance in km from homestead to the Manafwa 8.41 0.42 nearest market for crops Kapchorwa 6.06 0.32 Time in minutes taken to travel to the Manafwa 44.83 2.41 nearest market for livestock Kapchorwa 49.24 3.48 Distance in km from homestead to the Manafwa 8.56 0.38 nearest market for livestock Kapchorwa 8.55 0.59 Time in minutes taken to travel to the Manafwa 47.42 2.61 nearest agrovet shop using the most Kapchorwa 25.34 1.72 convenient route Distance in km from homestead to the Manafwa 11.31 0.53 nearest agrovet shop Kapchorwa 4.29 0.26

4.0 CONCLUSION AND RECOMMENDATIONS

This study sought to analyze the livelihoods of communities living in Manafwa and Kapchorwa with the aim of developing strategies that will improve household incomes and food security. Data collected was on demographic characteristics of households, education, land ownership, crop enterprises, household assets, income, institutions, agricultural and livestock production with a focus on coffee, dairy and bee keeping. The findings of the livelihood analysis of households in Manafwa and Kapchorwa disctrict has shown that households are engaged in different livelihood strategies in order to meet their basic needs. A majority of households are engaged in farming as their main livelihood strategy with a few involved in casual work and small scale business. Among the crop enterprises, maize, beans, bananas and coffee were high on the list. Among the livestock enterprises, local chicken, dairy cattle and goats were kept by many farmers. In their endeavor to meet basic needs, households face a number of challenges which if addressed will improve their livelihood prospects. This section will discuss production, institutional, marketing and infrastructural challenges faced in relation to coffee, dairy and honey value chains and

65 propose interventions/strategies that if put in place will improve the livelihoods of communities living in the two districts

Coffee value chain

Uganda accounts for 2.5% of global coffee production. It is also the country’s most important export crop. About 80% of the coffee grown is Robusta which is indigenous to Uganda while the rest in Arabica (GAIN, 2015). The crop is grown by smallholder farmers and this study has shown that the area allocated to coffee is small; 0.09 ha for Manafwa and 0.13 ha for Kapchorwa accounting for 18 % and 23% of the total land under cultivation 2015/2016 agricultural season in Manafwa and Kapchorwa respectively. Coffee yields range from 1556 Kg/ha in Kapchorwa to 1776 kg/ha in Manafwa. The data indicate that the average yields are below the potential average of 2000kg/ha for Arabica coffee under good management practices. The findings from this study have shown that farmers are facing several challenges which have led to low production. High incidence of diseases and pests and low productivity are the two most important challenges mentioned by coffee farmers. The high incidence of pests and diseases could be attributed to a number of factors such as farmers not having resources to purchase pesticides (7.4% and 16.2%) of farmers used pesticides in Manafwa and Kapchorwa respectively), not following the right agronomic practices and limited access to extension services as mentioned by a few farmers. The results also show that few farmers accessed agrovets in their communities and were more likely to use boda boda to access nearest agrovet. The average distance to access the nearest agrovet was 5km in Kapchorwa compared to 11 km in Manafwa. The long distance covered especially in Manafwa could discourage farmers from using herbicides and pesticides leading to low productivity. Low productivity on the other hand is due to a number of factors such as the coffee wilt disease, limited use of fertilizers (5.8% and 3.1 % of farmers in Manafwa and Kapchorwa respectively) and limited access to extension services. Increasing production of coffee goes beyond teaching farmers on better agronomic practices. It requires the joint effort of leading stakeholders such as BCU, UCDA, MAAF, the private sector, NGOs and farmers in the coffee industry to come together in multi-stakeholder platforms to address the challenges being faced and develop strategies for improving productivity of coffee. Some of the strategies may include (i)adopting low

66 cost extension approaches such as the use farmer-to-farmer extension to fill the gap of the dysfunctional extension system occasioned by the dissolution of NAADS, ii) Encourage farmers to grow specialty coffee by providing incentives which will motivate them to increase production

The Dairy value chain

Uganda milk production is largely dominated by small scale farmers who own 1-2 dairy cows. This study has shown that on average farmers keep at least one dairy cow in Manafwa and two in Kapchorwa, but the type of dairy cow kept varies by the various landscapes .More farmers keep improved (crossbred) cows in Kapchorwa than those in Manafwa. Livestock is kept both for subsistence and cash. Production of milk is however low. Milk yields range from 3.42 litres per day in Manafwa to 4.14 litres per day in Kapchorwa. Few farmers are however using improved feeds and feeding practices such as the use of herbaceous legumes and fodder shrubs. Reasons for not growing fodder include lack of enough land, unavailability of seeds/planting material for the preferred fodder species and limited information on fodder production due to the vacuum created in the extension system following the dissolution of the NAADs programme. There is potential to increase production through the use of improved feeds and breeding services. A strategy to address the scarcity of land is to grow fodder on contours and farm boundaries. Farmers need to be made aware of the different niches of growing fodder that do not take up a lot of land. Creating awareness can be done through low cost extension methods such as farmer- to-farmer extension. Issues of unavailability of seed can be addressed through developing mechanisms that are community based to ensure a reliable supply of fodder seeds/seedlings. A strategy to improve breeds is to encourage farmers to use the services of AI. This study has shown that farmers are making an effort to improve their breeds by paying for services rendered from shared bulls in the village.

The honey value chain

In Uganda, it is estimated that about 1.5 million households derive their income from bee keeping from which they harvest various hive products such as honey, propolis, and bee wax, among others. Findings from this study show that honey production is undertaken by

67 very few farmers in the two districts, Manafwa (4%) and Kapchorwa (13%). The findings also showed that 1% of bee keepers harvested honey in Manafwa compared to 39% in Kapchorwa. Bee hives in the two districts were mostly sited in the national park, a tedious process that requires a permit from UWA, which explains the small number of producers engaging in the enterprise. Given the subsistence nature of honey production, only 39% of the producers sold honey in Kapchorwa. The results therefore suggest that more efforts need to be put in promoting honey production in order to increase production. On average, farmers in Kapchorwa have 18 hives per year while in Manafwa they have three hives per year. Amount of honey produced per hive is relatively low. This could be attributed to the fact that most farmers still use traditional bee hives. Another challenge is the overreliance on the National park for siting bee hives. Farmers also mentioned that they do not have a reliable market. Improving bee keeping practices will involve modern training, combined with improved commercial bee stocks and a focus on increased agricultural production through pollination of food crops. But on the wider scale of things, farmers could also be encouraged to site their hives on their farmers. This requires awareness creation. The sector, which has a high employment potential for especially youth and women, could do even better with local institutional strengthening so that farmers can market their honey collectively. This study has shown that very few farmers belong to groups. Reasons given include lack of commitment, lack of trust, benefits not visible among other reasons. Institutional strengthening should therefore involve strategies that include awareness creation of the benefits of joining groups, training on group dynamics, governance and building trust among group members. Farmers need to be made aware that by working in groups, they stand a higher chance of accessing credit from financial institutions and also the fact that they will have a higher bargaining power. Training communities in modern apiary management and considering involving more youths can also improve the sector. Packaging and branding will also improve marketing of honey.

5.0 REFERENCES

DE SATGE, R., HOLLOWAY, A., MULLINS, D., NCHABALENG, L. & WARD, P. 2002. Learning about livelihoods: Insights from Southern Africa, South Africa, Oxfam GB Practical Action Publishing. DFID. 2000. Sustainable livelihoods guidance sheets [Online]. UK: Department for International Development. Available: http://www.ennonline.net/dfidsustainableliving [Accessed 17-07- 2017 2017]. DONOVAN, J. & STOIAN, D. 2012. 5Capitals: A Tool for Assessing the Poverty Impacts of Value Chain Development. In: SHECK, R. (ed.) Rural Enterprise Development Collection. Turrialba, CR, CATIE,: ICRAF. FILMER, D. & PRITCHETT, L. H. 2001. Estimating Wealth Effects Without Expenditure Data—Or Tears: An Application To Educational Enrollments In States Of India. Demography, 38, 115- 132. GAIN 2015. Assessments of commodity and trade issues. In: SERVICES, U. F. A. (ed.) Global Agricultural Information Network. Nairobi: USDA. MCKENZIE, D. J. 2005. Measuring Inequality with Asset Indicators. Journal of Population Economics, 18, 229-260. SMITS, J. & STEENDIJK, R. 2014. The international wealth Index (IWI). Social Indicators Research 122. UBOS. 2014. Uganda Bureau of statistcis (UBOS), Kapchorwa population preliminary data [Online]. Available: http://www.ubos.org/2014/11/28/national-population-and-housing-census- 2014-provisional-results/