Uganda December 2010

Food Security and Livelihoods Assessment

Lango Sub-region

Northern Uganda

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda

TABLE OF CONTENTS

Executive summary ...... 5

1. Background ...... 11

1.1. Purpose of the survey ...... 11

1.2. Methods of the survey ...... 12

2. Findings of the survey ...... 13

2.1. Demographic information ...... 13

2.2. Household Dietary diversity and food sources ...... 19

2.3. Household expenditures ...... 25

2.4. Income sources and household assets ...... 26

2.5. Crop production ...... 31

2.6. Land access and utilization ...... 36

2.7. Animal production ...... 37

2.8. Credits and savings ...... 40

2.9. Trainings ...... 42

2.10. Social networks ...... 43

2.11. Access to basic services and markets ...... 44

3. Analysis ...... 45

3.1. Vulnerability profile by geographic area ...... 45

3.2. Conclusion ...... 57

4. Recommendations ...... 59

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 1

Tables

Table 1: Overview of villages surveyed per district ...... 12

Table 2: Residential status of interviewed ...... 13

Table 3: Relationship of interviewee to head of household (HHH) ...... 13

Table 4: Age groups and gender of respondents ...... 14

Table 5: Highest education level of household heads total ...... 14

Table 6: Highest education level of household heads per districts and gender ...... 15

Table 7: Household composition for gender and contributors to HH income ...... 15

Table 8: Health status of household heads ...... 17

Table 9: Diseases of children 6‐59 months ...... 17

Table 10: Reasons for not being satisfied with security ...... 19

Table 11: Effects of perceived insecurity ...... 19

Table 12: Frequency of meals eaten ...... 20

Table 13: Household dietary diversity score HDDS ...... 21

Table 14: Overview of income earned from various activities in % ...... 28

Table 15: Assets owned ...... 31

Table 16: Seed sources ...... 33

Table 17: Second harvest and utilization in Otuke 2010 ...... 34

Table 18: Second harvest and utilization in Lira 2010 ...... 34

Table 19: Percent of households with products until Jan and Apr 2011 ...... 35

Table 20: Land ownership and land under cultivation ...... 36

Table 21: Reasons given for not opening more land ...... 36

Table 22: Ranking of challenges in crop production ...... 37

Table 23: Types of animals owned ...... 38

Table 24: Milk production and consumption per Sub‐county ...... 39

Table 25: Animals sold over the past three months ...... 39

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 2

Table 26: Access to veterinary services ...... 40

Table 27: Ranking of challenges affecting livestock production ...... 40

Table 28: Sources of credit ...... 41

Table 29: Types of credit ...... 42

Table 30: Savings ...... 42

Table 31: Trainings and training topics ...... 43

Table 32: Other training topics ...... 43

Table 33: Types of associations and membership ...... 44

Table 34: Distance and level of access to socio‐economic services ...... 44

Table 35: Income and dietary diversity per category of head of household ...... 46

Table 36: Productive assets owned by female and elderly HHH ...... 47

Table 37: Correlating income groups, livestock and income from crop production ...... 50

Table 38: Correlating income groups and dietary diversity ...... 50

Table 39: Comparing livestock owners, average income and dietary diversity...... 52

Table 40: Income of households with traction equipment in relation to total average ...... 54

Table 41: Group membership and livelihoods indicators ...... 55

Table 42: Income categories for people without livestock ...... 56

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Figures

Figure 1: Household composition per age group ...... 16

Figure 2: Food items consumed in both districts ...... 21

Figure 3: Food sources per district ...... 22

Figure 4: Sources for various food groups ...... 23

Figure 5: Lean months ...... 23

Figure 6: Ranking adaptation strategies ...... 24

Figure 7: Average expenditures over one year in % ...... 26

Figure 8: Number of households gaining income from different sources for Lira and Otuke ...... 27

Figure 9: Contribution of different activities to total income ...... 28

Figure 10: Respondents per income category in Otuke ...... 29

Figure 11: Respondents per income category in Lira ...... 30

Figure 12: Types of crops grown in both districts ...... 32

Figure 13: Acres per crop and district ...... 32

Figure 14: Livestock in Lira and Otuke ...... 38

Figure 15: Means of access to basic services and markets ...... 45

Figure 16: Correlating income groups and household expenditures ...... 51

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EXECUTIVE SUMMARY

The reported Food Security and Livelihoods Assessment was conducted by Action Against Hunger Uganda (ACF) in two districts of the Lango Sub‐region, Lira and Otuke, in early December 2010 in coordination with the district agricultural officers. The main objective was to carry out a situation analysis and create vulnerability profiles disaggregated by the geographic area to project scenarios for the coming year thereby helping to identify appropriate response interventions to potential food insecurity and livelihoods in the short, medium and long‐term.

The survey comprised a total of 402 household interviews in 5 parishes of each district. Parishes and villages were selected according to geographical criteria to represent the total of the districts population (excluding Lira town). Respondents in the villages were selected randomly. The questionnaire covered demographic data, household dietary diversity, details on income sources, agriculture and livestock production, and household expenditures, as well as access to training, credits and socio‐economic services.

Main findings

For most indicators, Otuke district had lower scores than Lira district, indicating a worse food security and livelihoods situation in Otuke.

Demography and security

 All respondents were either residents or returnees. There are no IDPs left in either of the districts.  Education for women remains a concern: Over 26% of all heads of households had never attended school. That figure was significantly higher for women (38.7%) than for men (11%).  Households are composed of slightly more women (50.8%) than men (49.2%). Youth below the age of 18 make up a total of 57.3% of the household members.  19.2% of the household heads were chronically ill. Diseases affecting children under five are respiratory diseases and fever (referred to as malaria).  Households are composed on average of 5.9 members. On average, 2.4 household members contribute actively to the household income. Women contribute slightly more to the household income than men.  Only about 6% of the population is not satisfied with the security situation. Reasons for that vary drastically between the districts: in Otuke, land wrangles and cattle rustling is seen as the main threat, in Lira it is robberies and the return of the LRA.

Household Dietary diversity and food sources

 67.7% of adults and 80.5% of children had two meals the previous day.  On average, the population consumes products from 3.9 different food groups (HDDS score), mainly roots and tubers, pulses, vegetables and cereals. Animal products (including dairy products and eggs) were included in 16% of the respondents’ diets.

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 Months with reduced food intake are experienced by 99% of households, mainly before the first harvest in June and July. Most households have more than one adaptation strategy to overcome the lean period.

Household expenditures

 Food expenditures take the biggest share of household expenditures with 53.9% of total expenses.  Health care is next with 13.1%, followed by education related expenses (7.9%).

Income sources and household assets

 The selling of crops contributes to 23.8% of household income. 45.5% of the respondents are creating an income from selling crops.  Other relevant income sources are salaries from unskilled labor (21.8% of total income) of which 59.7% of all respondents benefit.  Over 80% of respondents fall within the lowest income category (less than 1 million UGX1) per year.  These 80% generate only about 25% of the overall income generated in the districts.  Land is owned by over 95% of respondents at an average of 3.8 acres per household. However, agricultural equipment like ox‐ploughs to labor the land, are owned by 20% of the households.  Half of the respondents have a bicycle; half of the respondents have a radio, 20% have a mobile phone.

Crop production

 Crop varieties differ greatly between Otuke and Lira. Lira produces more for commercial use. Crop production is the single biggest contributor to household income.  In Otuke, most households grow sorghum, pigeon peas and sesame for home consumption. Rice is grown by 30% of Otuke respondents and is the major cash crop in the district.  In Lira, cassava is the crop cultivated by most households, followed by beans and by sesame. Over 25% of households in Lira grow soybeans, sunflower, maize and cotton as cash crops.  Seeds are mainly purchased from seed dealers in Lira town.  Expected harvest and utilization vary greatly between the districts. Lira respondents expect to earn 5 times more than those from Otuke from crop sales.  16% of the total expected harvest is for sale in Otuke (mainly rice).  7% of the total expected harvest is for sale in Lira. Main income is expected from sunflower (39%), soybeans (24%), and cotton (12%).  The majority of the harvest will have been consumed between January and April 2011.

1 1 US$ = 2,300 UGX (approximate figure 2011)

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 Otuke inhabitants own on average nearly 1 acre more than Lira inhabitants (4.3 acres compared to 3.4 acres). However, Otuke respondents cultivate on average less land (2.6 acres) than Lira respondents (3.1 acres).  Reasons for not cultivating more land are the lack of labor force or oxen and the preference to use land for grazing, especially in Otuke.  Lack of labor force is ranked the greatest challenge to crop production in both districts, followed by access to land and the access to quality seeds and fertilizer. In Lira, flooding seems a problem, having been listed by 22% of respondents.

Animal production

 19% of Otuke respondents and 10% of Lira respondents do not own any livestock. Most respondents (78.1%) owned poultry.  40.3% of respondents own neither cattle nor goats.  Goats are owned by nearly half of the respondents (Otuke ‐ 44.8%; Lira ‐ 56.3%).  33.2% of Lira respondents and 18.2% of Otuke respondents owned one or more cows.  27.6% of Lira respondents own one or more oxen, the same is the case for 14.3% of Otuke respondents.  Only 42% of the households owning cows manage to produce milk from them; only 25.5% of the cows are said to give milk.  Chicken and goats are the most commonly traded animals. Out of all households interviewed, only 1.2% had sold a cow and 1.7% had sold an oxen the year preceding the survey.  Less than half of the households owning cattle or goats (43.4%) have access to veterinary services. The majority hires private veterinarians (30.1% of all respondents).  Access to veterinary services and access to grazing land are the most stated challenges for animal production.

Credits and savings

 28% of Otuke respondents and 38% of Lira said they had accessed credit over the year 2010.  Most respondents used either a VSLA or received a credit from their neighbors. Most credits were taken for one month.  Banks and micro finance institutions were hardly used for borrowing money. However, when they were used, bigger amounts than from other credit sources were borrowed.

Trainings

 80% of the population never received any training or education on technical skills, vocational training, or health and hygiene promotion.  The majority of trainings provided by a variety of agencies were on livestock rearing and crop production.

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Social networks

 Just over 50% of Lira respondents and 38% of Otuke respondents were members of an association or group.  Of all respondents organized in associations, 55% were members of credit and savings associations (VSLA, Bolicap or Kalulu).  Labor sharing groups (22%) and IGA groups (15.3%) make up most of the remaining memberships.

Access to basic services and markets

 Respondents from Otuke spend on average more time to reach market places and basic services. In order to reach a trading centre, Otuke residents spend over 120 min to reach a market place, (80 min for Lira residents), 135 min to reach a health unit (105 min for Lira) and 60 min to reach a school (35 min for Lira).  Water access in both Lira and Otuke is limited and the water points are 30 min (Lira) or nearly 45 min (Otuke) away. Households take an average of 75l (Otuke) and 83l (Lira) daily, equaling an average of 12.9l per HH member (Otuke) and 13.9l per HH member (Lira) per day.

Scenarios for 2011

 The relatively good harvest of 2010, the return of peace in the region and the impact of government programmes as well as interventions of various organizations have had a positive impact on the livelihoods and food security situation in Lira and Otuke.  In the FEWSNET forecast, Uganda is not listed as being at high risk of “la nina” effects. They do foresee above average rains, though, which might have negative effects on particular crops.  Price increase for all goods will negatively affect the purchasing power of the local population in Otuke and Lira. As such, services and products will become less accessible. This scenario holds true for all those households who produce mainly for their household needs. However, price increase might be an incentive for farmers to cultivate more land and to dedicate some fields to commercial production.  The stock of animals is slowly increasing. The region is said to have supported larger numbers of animals before the 1980s. The accessibility of quality veterinary services continues to pose a challenge to herds. Outbreaks of dangerous diseases are usually quickly reported to the DVO and reacted upon, but on an individual level, veterinary service remains expensive or difficult to access. Households in rural Lira and Otuke still largely lack the funds to ensure the health of their animals.  The degree of mechanization of agriculture remains minimal. Oxen and ox‐ploughs, very basic means of laboring land, are available to less than one third of the population. The remaining population uses hand hoes and has in consequence very limited production capacities. This situation is likely to prevail until the stock of animals has further increased and profit made from other activities allowed more households to purchase ox‐ploughs.

Recommendations

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 Programmes targeting the most vulnerable parts of the population are still needed to stabilize their basis of production and to prevent them from slipping back into the need for food aid. These most vulnerable households should be supported with cash or in‐kind aid (seed fairs, livestock, ox‐ ploughs) and with training for better production practices and improved post harvest handling.  Programmes aiming general economic development of the region should target surplus producing farmers and assist them with market access (collection points for marketable seeds, market price information) as well as processing facilities.  Crop production needs to be further supported to facilitate the cultivation of more land, more diverse crops including food and commercial crops to secure household nutrition and household income. o In view of increasing risk of weather irregularities, adapted or more resistant plants need to be promoted. Research on appropriate varieties should be intensified. o Irrigation and drainage for specific areas could be envisioned. However, because of high costs and potential environmental effects, their feasibility and cost‐benefit need to be properly assessed. o Especially in Otuke, marketing facilities should be promoted to encourage commercial production. Production conditions vary between districts but also between Sub‐counties – the comparative advantages of each area need to be properly mapped. o Mechanization of crop production using drought animals has to be further promoted to increase productivity.  Promotion of livestock production should be another priority of interventions: animals are more mobile and thus less liable to climate irregularities and represent the main capital investment of the rural population. o Veterinary services need to be improved to become more reliable and more accessible. o Herding practices other than free and uncontrolled roaming can be improved to raise more productive animals with less destructive effects on the environment.  Training on different production techniques needs to be further pushed in order to reach out to a wider percentage of the population and to encourage multiplication effects.  Options for income generation other than in agriculture seem limited. With good targeting, some enterprises to cater for growing local demand can be supported.  Natural resources (land, firewood) are currently not sparse and trees and soil are exploited to produce charcoal and bricks. Land and wood might however become an issue. o Land rights should be regulated taking traditional rights into account o Promotion of fuel efficient stoves and of energy saving brick making techniques should be expanded to save and manage the exploitation of wood. Resource management policies or actions should not be enforced against income generating interests of the local population.  Improving access to safe water sources should continue to be priorities in the area as people still spend a lot of time fetching water.

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 Efforts to reduce structural disadvantages faced by women need to be reinforced. This includes access to basic and higher education but should also consider double burdens (as bread winners and domestic workers).  Sensitization for family planning to reduce the growing pressure on land and natural resources, to allow local government structures to provide basic services (health, school), to ensure their livelihoods and to alleviate the workload of women should be strengthened.

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1. BACKGROUND

Action Against Hunger (ACF) has been implementing Nutrition, Water, Hygiene and Sanitation as well as Food Security and Livelihood programmes in Northern Uganda since 1995. The end of the 20 year old armed conflict in Northern Uganda in 2005 and the return of displaced families to their villages of origin have both contributed to the improvement of the food security situation in the region. When travelling in the countryside today, decentralized settlement patterns, lush fields of cassava and beans for home consumption as well as rice, sesame or cotton as cash crops, cannot be avoided. Goats and cattle roam the bush and bustling local markets tell of increasing business activities and trade. After a 3 year dry spell, 2010 was marked by favorable climatic conditions with abundant rains during the first and second rainy season. In some places the water has even been too abundant: second season crops have suffered from flooding and the harvests of some households were lost. During food security cluster meetings in Lira in August and October 2010, all agencies including government structures and UN organizations, spoke of a reasonably food secure situation in the region – without having detailed figures or an in depth analysis of the situation. ACF therefore went forward to collect on the ground data and to provide an analysis to allow for better planning and action.

1.1. PURPOSE OF THE SURVEY

The food security and livelihoods (FSL) assessment was conducted in early December 2010 to assess the food security and livelihoods situation in two districts of the Lango Sub‐region ‐ Lira and Otuke, which had been assessed and classified by regional monitors such as FEWSNET as “generally food secure”. Evidence from the field however suggested that this might not be true for vulnerable segments of the population and/or that it might be liable to change if less favorable production conditions prevailed. The assumption was that there are relevant differences between districts and Sub‐counties depending on market access, infrastructure, micro‐climate and population structure and therefore different levels of food and livelihoods security, and hence different needs of support for focused programmes. The survey was coordinated with the District Agricultural Officers (DAO) to contribute to the Ministry’s food security database and to serve as a tool for improved local planning. It had two main objectives:

 To carry out a food security and livelihoods situation analysis and create vulnerability profiles disaggregated by geographic area.

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 To project possible scenarios for the year to come based on an analysis of the causes of food and livelihood insecurity and threats to livelihoods in the surveyed area.2

1.2. METHODS OF THE SURVEY

SAMPLING The survey is based on 402 household interviews, 203 in Otuke district and 199 in Lira. In both districts, five Sub‐counties and in each Sub‐county, one parish was visited. One marginal and one central village per parish were selected in order to reflect the diversity of livelihood strategies in the districts. (Table 1) In the selected villages respondents were picked randomly: the survey team visited all households along an imaginary line drawn from the centre of the village into one direction randomly selected by spinning a pencil. If a homestead was vacant, the surveyors proceeded to the next one until the quota for the village was fulfilled. The selection of parishes and villages was done with the advice from the DAO in order to best complement the data acquired by the districts.3

Table 1: Overview of villages surveyed per district

District Otuke Sub‐counties Adwari Ogor Okwang Olilim Orum Parishes Okee Oluro Olworngu Anepkide Alai Alekloatin Baropiro Okurunyang Loro Ayito Villages Obel Te‐dam Yabwangi Ojutolwio Okonga District Lira Sub‐counties Adekokwok Agali Agweng Aromo Ogur Parishes Boroboro East Abongorwot Abala Acutkumu Adwoa Ajunga Apwoyotic Agali A Acan Mak Kweri Apurimon Villages Arikino Ilong Bardago Agungu Bediamwol

METHODOLOGY The FSL programme used a prepared questionnaire with mostly closed questions as the core assessment tool. Not all questions were necessarily applied, as some would be asked following specific responses.

2 As a step to identify interventions to address food and livelihoods insecurity in the short, medium and long term 3 Districts are supposed to conduct surveys in cooperation with the National Bureau of Statistics. Due to limited resources, these surveys often have to give way to other priorities.

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Ranking was applied to find out the severity of certain problems, issues or obstacles. This questionnaire was administered at the household level in Luo language by trained field enumerators. It was developed based on a monitoring tool used by ACF Lira in its project area in 2010 and included input from other ACF bases and the DAO of Lira and Otuke. Individual interviews lasted around 45 minutes. Data collection was supervised by the ACF Monitoring and Evaluation Officer. The questionnaire can be found in the Annex.

2. FINDINGS OF THE SURVEY

2.1. DEMOGRAPHIC INFORMATION

RESIDENTIAL STATUS, AGE AND GENDER OF RESPONDENTS Otuke and Lira respondents show relevant differences regarding their residential status (Table 2).

 None of the respondents were IDPs.  Nearly 22% of the Lira respondents were constant residents, i.e. they had not been displaced during the war. In contrast, just over 4% of the Otuke respondents were constant residents.

Table 2: Residential status of interviewed

Otuke Lira Total Status # % # % # % Returnees 194 95.6% 156 78.4% 350 87.1% Constant residents 9 4.4% 43 21.6% 52 12.9% IDP 0 0.0% 0 0.0% 0 0.0% TOTAL 203 100% 199 100 % 402 100 %

Over 55% of the respondents were the heads of households in both Sub‐counties and over 30% were the spouses (Table 3).

Table 3: Relationship of interviewee to head of household (HHH)

Otuke Lira Total Relationship # % # % # % HHH 120 59.1% 114 57.3% 234 58.2% Spouse 68 33.5% 76 38.2% 144 35.8% Parent 15 7.4% 7 3.5% 22 5.5% Brother/sister 0 0.0% 2 1.0% 2 0.5%

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Total 203 100% 199 100% 402 100%

The majority of the respondents (70.1%) were between 20 ‐ 49 years old. Only 5% were under the age of 20; 13.4% were between 50 and 59 years old, 11.4% were over the age of 60 (Table 4).

With around 57% of the respondents in both districts, women were more represented than men.

Table 4: Age groups and gender of respondents

Otuke Lira Total Age Total f m Total f m Total f m < 20 10 6 4 10 7 3 20 13 7 20‐49 141 85 56 141 85 56 282 170 112 50‐59 25 10 15 29 12 17 54 22 32 > 60 27 16 11 19 9 10 46 25 21 Total 203 117 86 199 113 86 402 230 172

HIGHEST EDUCATIONAL LEVEL OF HOUSEHOLD HEAD The education level of the heads of household differs for men and women (Table 5), and between districts (Table 6). Generally speaking women are disadvantaged in terms of their education levels as compared to men. Otuke women are equally disadvantaged as compared to Lira women.

 Over 26% of all heads of households had never attended school. This figure was significantly higher for women (38.7%) than for men (11%).  More than half of the heads of households (58.2%) received primary education. Relatively less women enjoyed primary education (53%) than men (65.1%).  Nearly 15% of household heads (14.9%) have secondary or higher education.  For women, that figure is only at 8.3%.  In Otuke this pattern is exacerbated: 41.9% of female household heads have never attended school.

Table 5: Highest education level of household heads total

Total Highest education Total Women Men level # % # % # % Never attended 108 26.9% 89 38.7% 19 11.0% Primary 234 58.2% 122 53.0% 112 65.1% Secondary 54 13.4% 17 7.4% 37 21.5% Tertiary 6 1.5% 2 0.9% 4 2.3% Total 402 100% 230 100% 172 100%

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Table 6: Highest education level of household heads per districts and gender

Otuke Lira Highest Total Women Men Total Women Men education level # % # % # % # % # % # % Never attended 60 29.6% 49 41.9% 11 12.8% 48 24.1% 40 35.4% 8 9.3% Primary 111 54.7% 56 47.9% 55 64.0% 123 61.8% 66 58.4% 57 66.3% Secondary 29 14.3% 11 9.4% 18 20.9% 25 12.6% 6 5.3% 19 22.1% Tertiary 3 1.5% 1 0.9% 2 2.3% 3 1.5% 1 0.9% 2 2.3% Total 203 100% 117 100% 86 100% 199 100% 113 100% 86 100%

AGE AND GENDER OF HOUSEHOLD MEMBERS Households are composed of an average of 5.9 members with an average of 2.4 members actively contributing to the household income. In Otuke, households are on average composed of less people (5.8) with less people contributing to household income (2.2) (Table 7).

 In relation to total numbers, more women (41.5%) than men (39.5%) contribute to household income. In Lira district, the percentage of men contributing to household income is nearly equal to that of women, whereas in Otuke, relatively fewer men contribute.  Households are composed of slightly more women (50.8%) than men (49.2%). In Otuke district, men represent only 47.8% of household members.

Table 7: Household composition for gender and contributors to HH income

HH members contributing to HH Total HH income District interview Gender # average % # average % of total Otuke m 564 2.78 47.8% 211 1.04 37.4% f 615 3.03 52.2% 254 1.25 41.3% 203 Subtotal 1179 5.81 465 2.29 39.4% Lira m 604 3.04 50.6% 250 1.26 41.4% f 590 2.96 49.4% 246 1.24 41.7% 199 Subtotal 1194 6.00 496 2.49 41.5% m 1168 2.91 49.2% 461 1.15 39.5% f 1205 3.00 50.8% 500 1.24 41.5% Total 402 2373 5.90 961 2.39 40.5%

 Youth below the age of 18 make up a total of 57.3% of the respondent households (Annex).

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 In Otuke, this figure is composed by slightly more under 5 year olds (20.7% compared to 19.1% in Lira) while in Lira the 5–17 year olds take a bigger share (39.3% compared to 35.5% in Otuke).  The average percentage of people over 60 years per household is 3.7% with Otuke having a slightly higher percentage at 4.2% and Lira being at 3.2%.

Figure 1: Household composition per age group4

Household composition per age group

Total

Lira Districts

Otuke

0 500 1000 1500 2000 2500 # of HH members

Younger than 5 5‐17 years Adults (18‐ 59) Above or equal to 60

MARITAL STATUS Over 80% of the respondents were married at the time of the survey. The percentage of widows was significantly higher in Otuke (18.7%) then in Lira (11.6%) (Annex).

The percentage of monogamous marriages was similar in both districts and totaled 86%, the remaining 14% living polygamous marriages (Annex).

HEALTH In total, more than 77.9% of household heads of the interviewed households were in good health, while 19.2% were chronically ill (the type of chronic illness was not investigated but covered HIV/AIDS, TB, asthma and others) (Table 8).

 In Otuke district, 24.6% of the household heads were said to be chronically ill, a number significantly higher than in Lira with 13.6%.  The percentage of disabled heads of household is 3% in total and in both districts.

4 Details in annex

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Table 8: Health status of household heads

Otuke Lira Total Health of HH # % # % # % Chronically ill 50 24.6% 27 13.6% 77 19.2% Disabled 6 3.0% 6 3.0% 12 3.0% Good health 147 72.4% 166 83.4% 313 77.9%

Over 65% of children of the respondents between 6‐59 months had fallen sick over the past two weeks in both districts. Most commonly quoted were malaria, representing more than half of all illnesses, followed by cough and diarrhea (Table 9).5

Table 9: Diseases of children 6‐59 months

Otuke Lira Total Total under 5 years 244 228 472 # of sick children 6‐59 months 159 155 314 % of total under 5 65.2% 68.0% 66.5% % of cases per type of disease Malaria 49.7% 56.8% 53.2% Cough 18.9% 14.8% 16.9% Diarrhea 8.2% 7.1% 7.6% Skin disease 2.5% 0.6% 1.6% Flu 0.6% 1.3% 1.0% Eye disease 0.6% 0.6% 0.6% Swollen body/leg 1.3% 0.0% 0.6% Kwashiorkwo 0.6% 0.0% 0.3% Sickle cell 0.0% 0.6% 0.3% Epilepsy 0.6% 0.0% 0.3% Not specified/known 17.0% 18.1% 17.5% Total 100% 100% 100%

 Malaria is mentioned as the most common disease with 53.2% of all reported cases.  Cough/respiratory diseases are seen as the second most common disease among the children with 16.9% of reported cases of illness.

5 Diseases were asked from respondents. No medical records were checked. In Langi, the terms for malaria and fever are interchangeable.

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 The third and fourth are diarrhea & skin diseases while others like swellings (body, legs etc), flu, eye infection, kwashiorkor and sickle cell6 are minimal but cannot be ignored.

LIVING CONDITIONS OF THE HOUSEHOLDS Enumerators counted the number of dwelling units excluding latrine and/or bathrooms and assessed the quality status of the houses. Dwelling units included sleeping quarters and cooking facilities, usually round or square mud bricked huts with thatched roof, and one‐room kitchen with open fire. No difference was made here between mud‐walled grass thatched houses and brick and metal roof structures (Annex).

 Most households in Otuke (75.8%) and in Lira (61.8%) had less than three units. Significantly more households in Lira have three or four dwelling units (34.6%) than in Otuke (21.7%).  The majority of houses (nearly 80%) were evaluated as being in good conditions.7 However, 17% of households live in dilapidated houses. The ratio is approximately the same for Lira and Otuke.  Very few houses were under construction or renovation (together under 4%).  Households owned a range of 1‐7 dwelling units with the exceptional case of 10 structures in one household in Lira.  Living conditions in Otuke were slightly more congested than in Lira: in Otuke (2.9 persons) use one unit compared to 2.6 persons in Lira (Annex).

SATISFACTION WITH THE SECURITY SITUATION Respondents were asked how they felt about the security situation in the region (from satisfied to not satisfied) and what were the reasons for their dissatisfaction if that was the case (Annex).

 In total, 78.4% of respondents are very satisfied with security and 15.9% are fairly satisfied.  In Otuke the ratio is slighlty different: 70.4% are very satisfied and 22.7% are fairly satisfied.  Otuke has 6.9% of respondents not satisfied with security, while Lira has only 4.5%.  The most common reason for not being satisfied with the security situation is the threat of cattle rustling which affects Otuke district only (13.3% of Otuke respondents).  Violent land conflicts are the second most serious threat in Otuke with 9.4% of respondents mentioning them. In contrast, only 1% of Lira district respondents mentioned the same.  On the other hand, Lira respondents are more concerned by robberies (6%) and the threat of a return of the rebels (4%), than Otuke respondents (4.9% and 2% respectively) (Table 10).

6 One case of epilepsy and one of sickle cell were recorded during the interviews. For these diseases, medical records were presented

7 Surveyors would rank houses being in good condition when they have closed outer wall, a water proof roof not discarded by rains and wind. They’d rank them dilapidated when the bricks of the walls were deteriorating and/or the roof was old and no longer water resistant.

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 18

Table 10: Reasons for not being satisfied with security

Otuke Lira Total Reasons for dissatisfaction # % # % # % Too many robberies 10 4.9% 12 6.0% 22 5.5% Threat of rebel incursion 4 2.0% 8 4.0% 12 3.0% Violent land conflicts 19 9.4% 2 1.0% 21 5.2% Cattle rustling 27 13.3% 0 0.0% 27 6.7% Others: domestic violence, post election 5 2.5% 5 2.5% 10 2.5% violence, witchcraft, hatred

When looking in more detail at the situation within Otuke district, differences between the Sub‐counties are apparent (Annex): people living close to the border to Karamoja (Olilim, to a lesser degree Ogor and Orum) feel significantly more threatened by cattle rustling.8

The felt insecurity has psychological and economical impacts on the respondents. The preception of insecurity and its impacts are more pronounced in Otuke than in Lira (Table 11).

 In total, 13.4% of respondents say they are living in fear. This percentage is higher for Otuke (18.7%) than for Lira (8%).  Out of all respondents, 6.5% claim not to be as productive as they would be if they did not fear insecurity. Again, that figure is higher for Otuke (8.4%) than for Lira (4.5%).

Table 11: Effects of perceived insecurity

Otuke Lira Total Impact of feared insecurity # % # % # % Living in fear 38 18.7% 16 8.0% 54 13.4% Can't move freely 4 2.0% 2 1.0% 6 1.5% Can't be as productive 17 8.4% 9 4.5% 26 6.5% Can't resettle on original land 1 0.5% 0.0% 1 0.2% Hatred 1 0.5% 0.0% 1 0.2%

2.2. HOUSEHOLD DIETARY DIVERSITY AND FOOD SOURCES

8 Cattle rustlers are reportedly mainly Jie warriors, an age group within this Karamajong tribe living mainly in Kotido district of Karamoja. It reached a peak in the 1980ies when automatic guns spread among the warriors and resulted in a massive depletion of livestock in Otuke. Now, a disarmament programme in Karamoja is being implemented (with disputable results) and a special army unit is trying to fend off incursions of cattle rustlers.

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 19

NUMBER OF MEALS EATEN The majority of adults and children ate one or two meals the day before the survey. In Otuke, the percentage of children having only one meal a day is higher than in Lira (Table 12, Annex).

 67.7% of adults had two meals the previous day, mainly lunch and supper.  80.5% of children had two meals the day before the survey, mainly lunch and supper.  29.7% of adults had one meal the day before the survey, mainly supper.  19.5% of children had one meal the day preceding the survey, mainly supper.  In Otuke, 21.7% had only one meal the day before the survey. 68.9% of children had two meals, 9.4% had three meals.  In Lira, only 13.7% of children had one meal the day preceding the survey, while 77% had two meals and 8.7% had three meals.  58.5% of Otuke adults had two meals the day before the survey, while this figure was significantly higher in Lira (77.5%). 36.5% of Otuke adults had to settle for one meal, while this applied for 22.5% of Lira adults.

Table 12: Frequency of meals eaten

Otuke Lira Total # of adults children adults children adults children meals # % # % # % # % # % # % 1 73 36.0% 39 21.7% 42 21.1% 25 13.5% 115 28.6% 64 17.5% 2 117 57.6% 124 68.9% 145 72.9% 143 77.3% 262 65.2% 267 73.2% 3 10 4.9% 17 9.4% 12 6.0% 17 9.2% 22 5.5% 34 9.3% No meal 1 0.5% 0 0.0% 0 0.0% 0 0.0% 1 0.2% 0 0.0% Average 1.7 1.9 1.8 2.0 1.8 2

HOUSEHOLD DIETARY DIVERSITY The household dietary diversity score (HDDS) indicates the number and type of different food groups eaten by the household. It is surveyed by asking respondents for their food intake over the 24 hours preceding the survey.

 In total, the HDDS is 3.9 – the total of participants ate 3.9 different food groups the day before the survey (Table 13).  The HDDS is lower for Otuke (3.8) and higher for Lira (4).  Roots and tubers (85.6%) and pulses (80.3%) are the most consumed food groups.  Only a small percentage of respondents consumed animal products: most common sources of animal protein were milk and/or dairy products (7.5%), followed by meat (3.5%), fish (3.2%) and eggs (2.5%).

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 20

 Fresh fruit was indicated by 8.2% of respondents, whereas vegetables were eaten by 63.9%.

Table 13: Household dietary diversity score HDDS

Otuke Lira Total Food item # % # % # % Cereals 108 53.2% 81 40.7% 189 47.0% Roots & tubers 165 81.3% 179 89.9% 344 85.6% Vegetables 134 66.0% 123 61.8% 257 63.9% Fresh fruit 10 4.9% 23 11.6% 33 8.2% Meat 5 2.5% 9 4.5% 14 3.5% Pulses 157 77.3% 166 83.4% 323 80.3% Eggs 5 2.5% 5 2.5% 10 2.5% Fish 2 1.0% 11 5.5% 13 3.2% Milk and dairy products 15 7.4% 15 7.5% 30 7.5% Oil, fat, butter 142 70.0% 140 70.4% 282 70.1% Sugar, honey 35 17.2% 48 24.1% 83 20.6% HDDS Score 3.83 4.02 3.92

Generally speaking, fewer respondents from Otuke consumed any of the food groups discussed above than Lira respondents with the exception of cereals and vegetables (Error! Reference source not found.). Lira respondents consume more food items purchased on markets than Otuke respondents (Figure 3). All respondents obtained their food from two major sources: home production and market purchases. (Figure 4) The origin of food is likely to change throughout the year (e.g. in March, hunting and fishing increases, in May harvesting of wild mangoes etc).

 Pulses (beans, lentils, peas) and oil or fat are the items majorly purchased from markets in both districts. Variations in prices for these items will therefore have relevant impact on the purchasing power of the households and their food security.  Roots and tubers as well as vegetables consumed by respondents are mainly from their own harvest.  The few eggs and dairy products consumed mainly come from home production.  None of the households indicated to receive food aid, but the number of households receiving food gifts from neighbors or relatives or who receive food as a credit (mainly vegetables) are significantly higher in Otuke than in Lira.9

Figure 2: Food items consumed in both districts

9 The term employed was “borrowed”. It is usually expected to be returned in kind (without interest) but according to circumstances the credit can just turn into a gift

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 21

Food groups consumed per district 100.0% 90.0% 80.0% 70.0% 60.0% 50.0% respindents

40.0% of 30.0% % 20.0% Otuke 10.0% 0.0% Lira

Food group consumed

Figure 3: Food sources per district

Overview of food sources per district

Otuke

Districts Lira

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% %

Own production Market purchase Hunting/ Labor for food Borrowed Food gift Food aid

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 22

Figure 4: Sources for various food groups

Sources of different food groups

Eggs Fish Meat Own production Dairy products Fresh fruit Market purchase groups

Sugar, honey Borrowed Cereals Food gift Food Vegetables Labor for food Oil, fat, butter Pulses Hunting/gathering Roots & tubers Food aid

0 100 200 300 400 Respondents

LEAN MONTHS AND ADAPTATION/COPING STRATEGIES Almost all households in both districts (99%) report to experience food shortages over a period of one year.

 Food shortages are experienced mainly before the first harvest in June and July (Figure 5).  The lean months start slightly earlier in Lira (May) and end later in Otuke (August).  This assessment was conducted in December, when hardly any respondents reported food shortage.

Figure 5: Lean months

Lean months 200 180 160 140 120 HH 100 of

# 80 60 40 20 0 Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Otuke 2 4 8 7 24 156 112 30 3 0 1 1 Lira 0 2 9 8 53 174 61 10 1 0 1 0

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 23

Adaptive mechanisms are measures used to manage and minimize the risk from chronic food insecurity and recurring situations. Adaptation is a process of adjustment to a longer‐term solution, for instance pastoralists moving to areas of better rainfall and pasture growth. Coping mechanisms are temporary responses to reduce or minimize effects of a stressful event or an unfavorable situation where food access is abnormally disrupted, for instance by drought, flood, earthquake or military activity. Consumption and livelihood coping mechanisms are often distinguished. Distress mechanisms, also known as crisis or survival mechanisms in their more radical form, are measures that households will undertake in response to severe crisis that are largely irreversible, damaging to people’s livelihoods or their dignity and that may permanently undermine future food security and livelihoods. They are an extreme form of a coping mechanism. (ACF Food Security Assessment guidelines (2009), page 74)

Most respondents mentioned more than one strategy to cater for their needs during the lean months (Figure 6Error! Reference source not found.). According to the above mentioned definition, most of these strategies are rather adaptive than coping mechanisms since they are applied to respond to chronic and recurrent events. Some are livelihood strategies (e.g. purchasing from markets when own stocks are exhausted) some affect the consumption behavior (eating premature crops, reducing dietary diversity). They range from work to generate income (either in kind or in cash, e.g. paid labor, petty trade), selling of assets and/or livestock, bartering valuables for food to premature harvesting, the eating of seeds originally earmarked or planting and the gathering of edible wild plants.

Figure 6: Ranking adaptation strategies

Adaptation or coping strategies

Exchange of labor for food Premature harvest Wild gathering Purchase from market Borrowing Selling assets Exchange of labor for cash Planted fast maturity crops Nothing Begging Eating seeds Petty trade Barter (items for food) Sale of livestock Brewing Receiving gifts 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% % of total respondents

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 24

These strategies are seldom applied in advance of the expected lean periods, partly because there is no need to earn additional income, partly because there are no free capacities to work for cash since the own production is demanding all possible labor force, partly because some of the activities are seasonal (specific labor for crop production, brick making, gathering, hunting…): when the need arises people sell assets, and when opportunities appear people engage in paid labor or other activities. The pattern is similar in both districts with Lira respondents having more access to paid labor and market purchases and sales.

The strategies applied have a different impact (severity) on the long term food and livelihoods security of the households: while most of the activities mentioned are consumption strategies (shifting to other foods such as wild plants; reducing food intake (“nothing”)), reversible livelihood strategies and do not affect the capital assets of the households (labor for food or cash, spending cash savings for market purchases, borrowing until the coming harvest), others can have more severe impact:

 Eating of premature crops (done by 23% of respondents) affects the yield and reduces the benefit from labor invested.  Selling of assets (9.2%) reduces the capital reserves of households and affects their long term resilience.  Eating of seeds (2%) negatively affects the coming production cycle.  Selling of livestock (0.5%) severely reduces the capital reserves of households.

The survey suggests that the majority of strategies applied are reversible and adaptive and do not have a long term negative impact on the food security situation. Households are very aware of the value of their most important productive assets (seeds, livestock, tools) and few are the respondents forced to sell them in a comparably good year (in terms of food production).

2.3. HOUSEHOLD EXPENDITURES

Household expenditures were collected corresponding to a specified period of time: food expenditures were asked for the last 7 days, whereas school fees were asked for one term. In order to have comparable figures, the expenditures were than calculated for a period of one year and distributed on the number of respondents (Figure 7Error! Reference source not found.). Over one year, Otuke households spend on average only 85% of the average Lira household.10

 Food expenditures take the biggest share in both districts: over one year food purchases amount to 53.9% of total expenditures (Otuke 54.4%, Lira 53.5%).  Health care with a yearly average of 13.1% of the total (Otuke 13.7%, Lira 12.7%) is the next single biggest expenditure.

10 A table with absolute figures as collected during the survey is presented in the annex. However the figures should be seen as indications as few households keep exact records and amounts are likely to be inaccurate.

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 25

 School fees in Lira over a one year period are 1.6 times more expensive than in Otuke. In percent of total expenses, they represent 8.4% in Lira and 7.3% in Otuke.

Figure 7: Average expenditures over one year in %

Average expenditures over one year

Total

Lira

Otuke

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% % of total expenditures

Food Health care School fees Debt repayment Soap Paraffin Social contribution Transportation Clothing House improvement Cooking fuel Bedding Agro/veterinary inputs House rent

2.4. INCOME SOURCES AND HOUSEHOLD ASSETS

SOURCES OF INCOME The inhabitants of both districts rely heavily on unskilled labor11 and agriculture for their household income (Figure 8). In Otuke district, more respondents gain income from a greater variety of activities such as brewing, selling of fuels, receiving gifts from relatives, selling of handicraft, skilled labor etc.12

11 Unskilled labor is mainly used for casual work in other people’s gardens (digging, weeding, sowing, harvesting). It also encompasses activities like fetching water for people with an income (e.g. schoolteachers) or working on community roads can

12 HH income was asked for different time periods (the last harvest for crop sales, since January 2010 for animal and animal products sales, per month for all other categories). For the annual income, an approximation was done (2x harvest, 12 x per monthly income) despite the fact that income from 1st and 2nd harvest may be different and that certain activities are seasonal. The results represent trends more than facts.

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 26

Figure 8: Number of households gaining income from different sources for Lira and Otuke

Number of households gaining income from different sources

Lira

Otuke

0 50 100 150 200 250 300 350 400 450 # of households per income source

unskilled labour crop sales small trade animal sales brewing gift from relatives sale of fuel handicrafts skilled labour (artisans) wages, pension sale of local construction materials animal product sales begging, assistance remittance commercial trade sale of food gifts boda boda cycling fishing

Despite the greater diversity of income sources in Otuke, the average income per year and household is slightly higher in Lira (by a factor 1.4). The contribution of different income sources varies strongly between the two districts. (Figure 9, Annex).

 In total, the selling of crops represents 23.8% of household income. 45.5% of all respondents get income from crop sales, a figure higher for Lira (56.3%) than for Otuke (35%). In Lira, crop sales represent 34.8% of the total income, in Otuke it is only 9.4%  Income from unskilled labor is overall the second greatest contributor to household income account for 21.8% of total earnings (23.9% in Otuke, 20.2% in Lira). 59.7% of all respondents get at least some income from it, a percentage higher in Otuke (62.1%) and lower in Lira (57.3%).  Small trade (11.3.%), wages/pensions (9.5%), brewing (7.8%) and selling local construction material (mud bricks burned in a kilt, wood) (7.4%) are other major activities accounting for the total income. Especially the brewing is more relevant in Otuke (12.5%) than in Lira (4.3%). In Otuke it is done by 19.2% of respondents (compared to 7.5% in Lira).  In Otuke, the selling of handicrafts (woven winnowers, mats, fishing baskets; clay pots) (6.6%) and of fuel (charcoal, wood) (4.6%) are also relevant activities for generating income.

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 27

Figure 9: Contribution of different activities to total income

Contribution of different activities to total income

Lira

Otuke

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Crop sales Unskilled labour Small trade Wages, pension Brewing Sale of local construction materials Skilled labour (artisans) Handicrafts Commercial trade Gift from relatives Sale of fuel Animal sales Boda boda cycling Begging, assistance Animal product sales Remittance Fishing

Table 14: Overview of income earned from various activities in %

Main income source Otuke Lira Total Crop sales 9.4% 34.8% 23.8% Unskilled labour 23.9% 20.2% 21.8% Small trade 12.2% 10.7% 11.3% Wages, pension 10.8% 8.4% 9.5% Brewing 12.5% 4.3% 7.8% Sale of local construction materials 8.6% 6.5% 7.4% Skilled labour (artisans) 3.4% 3.0% 3.2% Handicrafts 6.6% 0.1% 2.9% Commercial trade 0.0% 4.5% 2.6% Gift from relatives 3.3% 1.9% 2.5% Sale of fuel 4.6% 0.5% 2.3% Animal sales 1.1% 2.8% 2.1% Boda boda cycling 2.4% 0.0% 1.0% Begging, assistance 0.1% 1.4% 0.8% Animal product sales 0.6% 0.1% 0.3% Remittance 0.0% 0.3% 0.2% Fishing 0.2% 0.2% 0.2% Sale of food gifts 100.0% 100.0% 100.0%

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 28

Incomes of respondents were then clustered into “income categories”. 13 While the figures themselves should not be taken as the exact figure the categories of very low income (no more than 1,000,000 UGX a year), low income (1,000,000 ‐ 2,000,000 UGX a year) medium income (2,000,000 – 4,000,000 UGX/year) and relatively high income (more than 4,000,000 UGX a year) can be taken as indications. Figure 10 and Figure 11 indicate the percentage of participants per category and the part of the total income generated in the districts they contribute to (Annex).

 The greatest part of respondents in both districts falls into the “very low income” category (Otuke 87.7%; Lira 83.9%). These more than 3/4th of the population generate less than half of the total income (47.3% Otuke, 44.4% Lira).  Within this “very low income” category, the majority earns less than half of the maximal amount of that category (less than 500,000 UGX) (72.4% Otuke, 65.8% Lira).  In Otuke, 6.9% of respondents are within the “low income” category and earn 18.9% of the total income of the district.  In Lira, this category is composed of slightly more people (10.1%) who earn 23.8% of total income.  In Otuke, medium and relatively high income is earned by 5.4% of respondents; in Lira, this ratio is slightly higher (6% of respondents). Their earnings make up for roughly 1/3rd of the total income in the districts (33.8% in Otuke, 31.7% in Lira).

Figure 10: Respondents per income category in Otuke

Income generated by Otuke respondents per income category (in %)

more than 5,000 4,001 ‐ 5,000 3,000 ‐ 4,000 UGX

2001 ‐ 3,000 1,000 1,000 ‐ 2,000 in 501 ‐ 1,000 0 ‐ 500

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0%

% of total income % of respondents

13 The categories can only be taken as indications of the real income: parts of the economy is not cash based; respondents may have vague ideas only of their real total income as book keeping is not very common; respondents might be reluctant to share exact figures with outsiders

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 29

Figure 11: Respondents per income category in Lira

Income generated by Lira respondents per income categopry (in%)

more than 5,000 4,001 ‐ 5,000 3,000 ‐ 4,000 UGX

2001 ‐ 3,000 1,000 1,000 ‐ 2,000 In 501 ‐ 1,000 0 ‐ 500

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

% of total income % of respondents

HOUSEHOLD ASSETS Examining the asset base includes household assets such as mattresses and cooking utensils, assets allowing mobility or communication, such as bicycles and mobile phones as well as productive assets such as land and agricultural14 or business tools.

In both districts, the asset base is still very low. Apart from land, Otuke has even lower figures than Lira (Table 15).

 Not all households own mattresses (average 0.9/HH). More than one blanket is owned by most households (average 1.8).  Half of the respondents have a bicycle while motorbikes are hardly represented.  Half of the respondents have a radio and only every fifth has a mobile phone.  Land is owned by over 95% of respondents at an average of 3.8 acres per household. However, advanced tools like ox‐ploughs to labor the land are sparse (only 20% of households own one) and only a part of the land is being cultivated (see below).

14 The most common agricultural tool is the hand hoe. Other relevant tools and widely owned are the “panga” (a kind of machete to clear encroached land) and other items for digging – they are all listed under “hand hoe”. Ox‐ ploughs are listed separately as they represent a modest stage of mechanization and intensification of crop production.

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 30

Table 15: Assets owned

Otuke Lira Total max. # max. # max. # # of owned by owned owned Assets HH qty ave. 1 HH # of HH qty ave. by 1 HH qty Ave. by 1 HH Household assets Mattress 89 137 0.7 5 134 213 1.1 2 350 0.9 5 Blankets 155 358 1.8 8 146 369 1.9 12 727 1.8 12 Cooking pans/pots 198 673 3.3 9 193 738 3.7 9 1411 3.5 9 Mobility/communication assets Bicycle 93 97 0.5 2 116 124 0.6 4 221 0.5 4 Motorcycle 0 0 0.0 0 4 4 0.0 1 4 0.0 1 Radio 71 74 0.4 1 120 127 0.6 2 201 0.5 2 Mobile phone 27 29 0.1 2 48 49 0.2 2 78 0.2 2 Productive assets Land (in acres) 197 863 4.3 30 195 676 3.4 12 1539 3.8 30 Hand hoe 198 465 2.3 8 196 485 2.4 7 950 2.4 8 Ox‐plough 23 23 0.1 1 36 44 0.2 6 67 0.2 6 Business tools 1 3 0.0 3 5 14 0.1 6 17 0.0 6

2.5. CROP PRODUCTION

TYPES OF CROPS GROWN Crops grown are both for home consumption and for sale. However, the types of crops grown and their use vary between the two districts, depending on market access and local soil and climatic differences (Figure 12).

 In Otuke, most households grow sorghum, pigeon peas and sesame.  In Lira, cassava is the crop cultivated by most households, followed by beans and by sesame.  Over 25% of households in Lira grow soybeans, sunflower and maize, crops nearly absent from Otuke fields. Cotton and millet are equally cultivated by some 15% of Lira households but hardly appear in Otuke.  Sweet potato is grown by nearly 30% of Otuke residents but is largely absent from Lira.  Rice is equally an important crop for Otuke with over 30% of respondents growing it but can hardly be found in Lira.

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 31

Figure 12: Types of crops grown in both districts

Numbers of households cultivating varieties of crops 160 140 120 Otuke 100 80 Lira 60 40 20 0

When looking at the acreage dedicated to specific crops, the differences between both districts are significant (Figure 13Error! Reference source not found.).

 Lira has six types of crops occupying more than 50 acres of land; Otuke has only four types of crops occupy more than 50 acres.  Sorghum, pigeon peas, sesame and rice take the major share of cultivated land in Otuke.  Beans, cassava, sesame, sunflower, groundnut and soybeans take the biggest share of cultivated land in Lira.

Figure 13: Acres per crop and district

Acres per cultivated crop

Cowpeas Soybeans Sunflower Cotton Matoke Maize Vegetables Millet Groundnut Otuke Rice Cassava Lira Sweet potatoes Beans Sesame Pigeon Peas Sorghum 0.0 50.0 100.0 150.0 200.0 250.0

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 32

The majority of seeds are purchased from seed dealers in both districts (Table 16).

 Seeds kept at home after the last harvest, are equally a major source of seeds for most crops (with the exception of beans).  People benefit of free distributions (from international organizations, NGOs, churches, government programmes/NAADS) mainly for cassava but also for sorghum and pigeon pea, and to a lesser degree, for beans and sesame.  Gifts from within the community (from relatives, friends, wealthy neighbors) or seed borrowing or purchasing from within the community make up for a small percentage of seeds.

Table 16: Seed sources

seed source Community Free Home donations distributions Seed dealers Otuke 200 54 113 318 Lira 208 40 93 301 Total 408 94 206 619

HARVEST AND UTILIZATION Since the cultivation pattern differs between the districts, both will be discussed separately concerning the utilization of the 2010 harvest (Table 17 for Otuke and Table 18 for Lira).

 Out of the 3650 basins15 harvested (mainly rice, sorghum, cassava and sweet potatoes), only 16% are for sale. The rest will be consumed by the household or set aside for the coming year.  Rice is one of the main cash crops being sold: 60% of the basins planned for sale are rice amounting to 62% of the expected overall profit from crop sales.  The few basins of sunflower and cotton expected will be sold.  Cotton and rice are the most profitable crops: average profit from selling cotton for households producing it is 65,000 UGX, average profit from rice 44,000 UGX.  Lira respondents expect to earn 5 times more profit than Otuke respondents from their crop’s sales.  The major contributors are sunflower, accounting for 39% of the expected profit, soybeans representing 24% of the expected profit and cotton with just over 12%.  However, 93% of the expected harvest will be used for home consumption. In particular beans, representing the biggest share in terms of volume, will be mainly used for home consumption.

15 Key for crop approximate weights per basin: Simsim/ Pulses, maize Sweet potato/ G‐nut sunflower (loose) Vegetables Matoke cassava Basin 1 1 1 1 1 1 kg 10.0 9.3 16.7 8.3 17.5 25.0

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 33

Table 17: Second harvest and utilization in Otuke 2010

Acres Basins harvested HH consumption For market Profit from sale (UGX) total total average total Average Total average total average Sorghum 151.0 758.5 5.2 647.3 4.4 62.5 0.4 211,700 1,440 Pigeon Peas 90.8 287.1 2.3 275.3 2.2 7.8 0.1 144,500 1,165 Sesame 77.6 266.7 2.4 228.2 2.1 38.5 0.4 651,500 5,977 Beans 47.5 153.5 1.8 155.0 1.9 4.5 0.1 80,000 964 Sweet potato 27.1 403.0 5.3 386.0 5.1 11.0 0.1 30,500 401 Cassava 40.8 642.0 9.0 621.0 8.7 17.0 0.2 183,000 2,577 Rice 66.2 871.0 13.0 481.5 7.2 357.0 5.3 2,948,400 44,006 Groundnut 13.5 142.5 7.5 87.0 4.6 47.5 2.5 343,500 18,079 Millet 4.7 40.0 5.0 40.0 5.0 0.0 0.0 0 0 Vegetables 1.0 13.0 2.2 13.0 2.2 0.0 0.0 0 0 Maize 3.8 14.0 3.5 14.0 3.5 0.0 0.0 0 0 Matoke 1.3 9.0 2.3 7.0 1.8 2.0 0.5 7,000 1,750 Cotton 1.5 16.0 8.0 0.0 0.0 16.0 8.0 130,000 65,000 Sunflower 0.8 32.0 32.0 0.0 0.0 32.0 32.0 n/a n/a Soybeans 1.0 2.0 2.0 0.0 0.0 1.0 1.0 10,000 10,000 Cowpeas 0.5 0.5 0.5 2.0 2.0 0.0 0.0 0 0 Total 3650.7 2957.2 596.8 4,740,100

Table 18: Second harvest and utilization in Lira 2010

Acres Basins harvested HH consumption For market Profit from sale (UGX) total total average total average total average total average Sorghum 52.23 483 11.21 364 8.88 103.5 6.11 623,600 34,467 Pigeon Peas 24.65 115 2.87 91 2.26 7.5 1.8 103,800 25,950 Sesame 69.12 227.3 2.44 175 1.9 27.75 2.8 682,300 75,811 Beans 90.76 376,725 3.28 354,325 3.1 36 3 236,306 23,630 Cassava 89.17 1,833.0 14.5 1,632 13.8 201.5 11.2 648,000 38,118 Rice 15.9 263.5 15.5 64 4.6 191.5 17.4 1,355,000 135,500 Groundnut 57 83 8.3 38 3.8 42 42 240,000 240,000 Millet 25.1 181.5 5.2 160 5.3 14.5 1.6 118,900 13,211 Vegetables 0.7 4.5 1.5 5 1.5 n/a n/a Maize 48.78 612 11.608 232.6 5.566 342 11.44 1,729,600 62,553 Matoke 0.02 7 0 7 14,000 Cotton 32.9 989.5 35.3 n/a 989.5 35.3 2,804,700 103,878 Sunflower 61.77 1,199.5 21.4 0 0 1199.5 21.4 8,909,200 171,350 Soybeans 53.72 500.5 10.01 18 2.5 460.5 10.7 5,534,900 131,783 Total 383,224.3 357,102.9 3,622.8 23,000,306

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 34

In both districts, the majority of the harvest will have been consumed by January 2011 or by April 2011 (Table 19). Only few households and specific crops were reported to last longer (Annex).

Nearly 50% of Otuke respondents expected to have sufficient sorghum and 40% to have enough pigeon peas until January 2011.

 In April there will still be 35.5% of Otuke households with sufficient sorghum.  These figures are drastically lower for Lira (around 14% for both crops in January and around 5% in April).  Cassava seems relatively available for Lira respondents: 52.3% have enough until January, and 45.7% have enough until April.  In Otuke cassava is less grown than in Lira. Overall availability is therefore significantly lower: while 24% have sufficient Cassava until January, only 19.2% have enough until April.  Otuke respondents have nearly no stocks of beans. In Lira, 30% of respondents said to have enough until January, only 15.6% had enough until April.  While Lira respondents have no mentionable home grown stock of rice, 23% of Otuke respondents have sufficient rice for home consumption until January and 13.3% have enough until April (note that 57% of rice produced in Otuke is for home consumption and 43% is said to be sold).

Table 19: Percent of households with products until Jan and Apr 2011

% of HH with sufficient crops until Jan‐11 Apr‐11 Type of crops Otuke Lira Otuke Lira Sorghum 48.8% 14.6% 35.5% 6.0% Pigeon Peas 40.9% 13.1% 19.2% 5.5% Sesame 31.5% 27.6% 17.2% 13.1% Beans 1.5% 30.2% 0.0% 15.6% Sweet potato 16.7% 0.0% 3.4% 0.0% Cassava 24.1% 52.3% 19.2% 45.7% Rice 23.2% 3.5% 13.3% 2.0% Groundnut 5.9% 2.5% 4.9% 1.0% Millet 3.0% 12.1% 1.5% 8.5% Vegetables 2.0% 0.0% 0.5% 0.0% Maize 0.5% 11.6% 0.0% 1.0%

 9% of respondents in both districts estimated they had sufficient cassava until September.  Until the first harvest of 2011, other products, be it roots and tubers, pulses or vegetables, will have to be purchased from the market or they will have to wait for the first harvest of 2011 if they are to be consumed after February 2011.

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 35

2.6. LAND ACCESS AND UTILIZATION

In both districts, land property does not seem to be a limiting factor for crop production (Table 20).

 Otuke inhabitants own on average roughly 1 acre more than Lira inhabitants (4.3 acres compared to 3.4 acres).  However, Otuke respondents opened on average only 2.6 acres of land, compared to Lira respondents who opened an average of 3.1 acre.

Table 20: Land ownership and land under cultivation

total acres 863

owned average per HH 4.3

Otuke total acres 529 opened average per HH 2.6 total acres 676

owned average per HH 3.4 Lira total acres 622 opened average per HH 3.1 total acres 1539 owned average per HH 3.8

Total total acres 1151 opened average per HH 2.9

Land property and land under cultivation differs between the Sub‐counties (Table 20).

 In Otuke, respondents own an average of 4.3 acres, with Olilim and Okwang respondents owning more than that (4.7 acres). On average, Otuke respondents opened 2.6 acres.  In Lira, the average respondent would own 3.4 acres of land and open 3.1 acre, thus approaching production limits.

Reasons for not opening more land are the lack of labor force or oxen and the preference to use land for grazing, especially in Otuke (Table 21).

Table 21: Reasons given for not opening more land

Reasons Otuke Lira Total Lack of oxen 41 22 63 Lack of labor force 82 59 143 Lack of seeds 33 28 61 Land used for grazing 47 18 65

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 36

Land needs rest 3 13 16 Dry spell 22 28 50 Floods 1 10 11

When asked to rank the challenges encountered in crop production, the lack of labor force was ranked the greatest challenge in both districts (Table 22).

 In Otuke, the access to land is also ranked high in terms of challenges. Further investigated respondents mentioned the bad quality of much of the soils they use for cultivation.  The access to affordable quality seeds and fertilizer is ranked more challenging than access to markets for selling their product.  In Lira, flooding seems a problem, having been listed by 22% of respondents.

Table 22: Ranking of challenges in crop production16

average score Challenges Otuke Lira Limited labor force 3.6 2.5 Access to land 3.7 4.7 Access to quality seeds 4.6 3.6 Post harvest losses 4.9 5.1 Access to inputs (fertilizer, pesticide) 5.2 5.3 Market access 5.6 5.7 Processing of products 6 5.9 Flooding (nominations by respondents) 9 44

2.7. ANIMAL PRODUCTION

ANIMALS OWNED 19 % (38 HHs) of Otuke respondents and 10% (20 HHs) of Lira respondents do not own any livestock at all. Most respondents (78.1%) owned at least poultry (Table 23).

 Goats are owned by nearly half of the respondents, with the figure being slightly lower in Otuke (44.8%) and higher in Lira (56.3%). The biggest number of goats in one single household was 13 heads (in Lira) and 9 heads in Otuke.

16 Scoring was from one to seven. 1 was most severe, 7 was least severe. “Other” problems were evoked but not ranked: flooding was the most prominent one, being mentioned by over 10% of total respondents

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 37

 33.2% of Lira respondents owned one or more cows, the biggest number owned by one household being 6 animals.  Only 18.2% of Otuke respondents owned one or more cows. However, an Otuke household had the greatest number of animals (13 compared to 6 in Lira).  Nearly twice as many respondents own oxen in Lira as compared to Otuke: 27.6% of Lira respondents own one or more oxen, compared to 14.3% in Otuke.  Pigs and sheep play a negligible role in overall livestock numbers but are slightly more frequent in Lira.

Table 23: Types of animals owned

Otuke Lira Total Max. # Max. # Max. # Types of owned owned owned animals HH % # Average by HH HH % # Average by HH HH % # Average by HH Cows 37 18.2% 79 0.4 15 66 33.2% 113 0.6 6 103 25.6% 192 0.5 15 Oxen 29 14.3% 61 0.3 8 55 27.6% 106 0.5 7 84 20.9% 167 0.4 8 Goats 91 44.8% 276 1.4 9 112 56.3% 301 1.5 13 203 50.5% 577 1.4 13 Sheep 3 1.5% 5 0.0 3 8 4.0% 21 0.1 7 11 2.7% 26 0.1 7 Pigs 2 1.0% 2 0.0 1 15 7.5% 19 0.1 3 17 4.2% 21 0.1 3 Chicken/fowl 153 75.4% 843 4.2 25 161 80.9% 1071 5.4 30 314 78.1% 1914 4.8 30 Rabbits 1 0.5% 2 0.0 2 0 0.0% 0 0.0 0 1 0.2% 2 0.0 2

Generally, Lira respondents have more animals than Otuke respondents (Figure 14Error! Reference source not found.).

Figure 14: Livestock in Lira and Otuke

350 300 250 200 Otuke 150 Lira 100 50 0 Cows Oxen Goats Sheep Pigs

INCOME FROM ANIMAL PRODUCTS Despite the number of cows, only few are producing relevant amounts of milk (Table 24).

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 38

 Only 42% of the households owning cows use their milk.  Only 25.5% of the cows give milk.  On average, the milk giving cows provide 1.1l of milk per day.  In some Sub‐counties of Lira, milk production is around 2l per day and 25 – 50% are sold (depending on the overall production of milk).

Table 24: Milk production and consumption per Sub‐county

Ave. milk Ave. milk # of production consumption milk sales HH cows (liters/day) (liters/day) (liters/day) Adwari 2 3 0.1 0.1 0.0 Otuke Okwang 6 7 0.3 0.3 0.1 Orum 2 2 0.1 0.1 0.0 Olilim 3 3 0.1 0.1 0.0 Ogor 0 0 0.0 0.0 0.0 Adekokwok 6 7 1.7 0.6 1 Agali 4 5 2.1 1.6 0.5 Lira Agweng 8 9 2.3 1.5 0.8 Aromo 1 2 3 1.5 1.5 Ogur 10 11 1.65 1.2 0.5 Total 42 49 1.1 0.8 0.5 % 40.8 25.5

Respondents were asked how many animals the households had sold in the past three months. As could have been expected, chicken and goats are the most commonly traded animals (Table 25). Out of all households interviewed, only 1.2% had sold a cow and 1.7% had sold an oxen.

Table 25: Animals sold over the past three months

Cows Oxen Goat Poultry Pigs Adwari 1 0 4 9 0 Okwang 1 2 6 21 0 Orum 0 1 1 2 0 Olilim 0 0 1 20 0 Otuke Ogor 0 0 0 8 0 Adekokwok 0 3 4 9 0 Agali 0 0 3 20 0 Agweng 2 0 1 13 1 Aromo 0 0 4 8 0 Lira Ogur 1 1 9 49 1

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 39

Total 5 7 33 159 2 Respondents were asked through which channel they accessed veterinary services. Less than half of the households owning cattle or goats (43.4%) have access at all. That figure is relevantly lower in Otuke (33.9%) than in Lira (51.3%) (Table 26)

 The majority hires private veterinarians (30.1% of respondents owning cattle or goats).  7.5% use community “para–veterinarians”17 and only 5.5% have access to government veterinarians (district or Sub‐county).

Table 26: Access to veterinary services

Otuke Lira Total access of # of HH with # of HH with vet cattle or cattle or # of HH that %of HH that services goats % goats % accessed vet accessed vet HH 56 165 33.9% 101 197 51.3% 157 43.4% Private 34 20.6% 75 38.1% 109 30.1% Community vet 12 7.3% 15 7.6% 27 7.5% District/Scty vet 10 6.1% 10 5.1% 20 5.5% NGO 0 0 1 0.5% 1 0.3%

CHALLENGES AFFECTING LIVESTOCK PRODUCTION Accesses to veterinary services and good land for grazing are the main challenges affecting livestock production (Table 27).

Table 27: Ranking of challenges affecting livestock production

Challenges for livestock Otuke Lira Access to veterinary services 2.62 2.51 Land for grazing 3.50 2.71 Access to water point 3.60 3.65 Market access 3.83 3.95 Processing of products 3.96 3.97 Insecurity 3.80 3.98

2.8. CREDITS AND SAVINGS

17 Para‐veterinarians are community members who received basic veterinary training and are able to apply certain medicines.

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 40

33.3% of respondents had accessed credits over the past year (Table 28). Generally, more credits had been taken in Lira than in Otuke.

 For Otuke, 28% of respondents said they had accessed a credit, while in Lira 38% said so.  Most respondents in Otuke than in Lira used either a VSLA or received a credit from their neighbors. Most credits were taken for one month.  Banks and micro finance institutions were hardly used for borrowing money. However, when they were used, bigger amounts than from other credit sources were borrowed.  In Otuke, 29 credits had not yet been serviced and 52 had. In Lira, the ratio of not yet serviced credits was higher (46 not serviced and 31 serviced).

Table 28: Sources of credit

Serviced Duration Average less 1 1 week ‐ 1‐6 6‐12 over 12 Credit sources HH amount No Yes week 1 month months months months Bank 1 500,000 1 0 0 0 0 1 0 MFI 0 0 0 0 0 0 0 0 0 VSLA 27 56,296 11 16 0 10 12 5 0

Money lender 4 10,625 1 3 2 1 1 0 0

Otuke Spouse/ relative 8 74,875 1 7 1 7 0 0 0 Neighbors 16 41,925 5 10 1 5 10 0 0 Others (church, traditional healer) 1 12,000 0 0 0 0 1 0 0 Bank 1 168,000 1 0 0 0 1 0 MFI 1 2,500,000 1 0 0 0 1 0 VSLA 32 68,469 19 13 1 9 12 2 0

Money lender 2 30,000 1 1 1 1 0 Lira Spouse/relative 12 59,167 5 7 1 2 5 3 1 Neighbors 28 46,250 18 10 2 10 12 4 0 Others (church, traditional healer) 1 100,000 1 0 0 0 1 0 0

Credits were taken to respond to cash needs: according to respondents, the main reasons for credits were the need to purchase food or to pay for health care or education, but also to purchase household assets or service social obligations.

Credits were not only taken in cash but could also be in the form of food or in the form of borrowing assets of somebody else (e.g. traction animal) (Table 29).

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 41

Table 29: Types of credit

% o Otuke Lira Total credit Cash credit 56 77 133 62.7 Food 13 14 27 12.7 Use of animal/mechanical traction 2 2 0.9 Agro/livestock inputs 0 0 0.0 HH commodities 2 2 0.9 Medicine 2 2 0.9 Labor 0 2 2 0.9

A majority of respondents from Otuke said they did not have any savings, while slightly more respondents from Lira said to have them. In Lira, more than 50% of respondents who have savings keep them at home, and over 35% use a VSLA (Table 30).

Table 30: Savings

Otuke Lira Total Yes 88 102 190 No 115 97 212 Where savings are kept On body 5 6 11 At home 3 55 58 VSLA 1 40 41 MFI 1 1 2 SACCO 1 0 1 Bank 2 2 4

2.9. TRAININGS

When asked which kind of trainings had reached the interview partners, over 80% responded they had never been trained. Slightly less people in Lira enjoyed training than in Otuke (Table 31).

 The majority of trainings provided by most agencies were on livestock or crop production.  Government training was focused on business skills, crop production, livestock and others (health and hygiene, peace building etc).

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 42

Table 31: Trainings and training topics

Otuke Lira Total Training topics # Trainers % # Trainers % # % None 159 78.3 168 84.4 327 81.3 Business skills 3 CRS; government 1.5 3 GAA; government 1.5 6 1.5 Crop DITREC, Government, production 7 NAADS 3.4 16 VEDCO, Government 8.0 23 5.7 FIDA, LEADS, Government, VEDCO, Livestock 3 Government, LEADS 1.5 2 medical team international 1.0 5 1.2 Other trainings 24 see table below 11.8 11 see table below 5.5 35 8.7

 The topics of the trainings differed depending on the focus of the agencies active in each district (Table 32). Otuke respondents were exposed to many trainings on water and sanitation, whereas Lira received trainings focused on HIV/AIDS, advocacy and domestic violence.

Table 32: Other training topics

Otuke Lira Topics # Trainer # Trainer Total WASH 14 ACF, CARE, CRS 0 ‐ 14 Adult literacy 4 Government 0 ‐ 4 HIV/AIDS Numat, Government, counseling 2 Samaritans' purse, UNICEF 4 Redcross 6 Health 1 Government 1 government 2 Child protection 1 CPA 0 ‐ 1 Immunization 1 government 0 ‐ 1 Leadership training 1 Samaritans' purse, UNICEF 0 ‐ 1 Advocacy 0 ‐ 1 Flat Form 1

Credit & saving 0 ‐‐ 1 CARE 1 Domestic Violence 0 ‐ 2 Government, Redcross 2 Group dynamics 0 ‐ 1 VEDCO 1 Preaching the gospel 0 ‐ 1 Church 1

2.10. SOCIAL NETWORKS

Just over 50% of Lira respondents were members of an association or group. In Otuke, only 38% were members of such a network.

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 43

Of all respondents organized in associations, 55% are members of credit and savings associations (VSLA or Bolicap and Kalulu). Labor sharing groups (22%) and IGA groups (15.3%) make up for most of the remaining memberships (Table 33).

Table 33: Types of associations and membership

Otuke Lira Total Type HH % HH % HH % Credit and saving 46 22.7% 52 26.1% 98 24.4% Marketing association 2 1.0% 1 0.5% 3 0.7% Labor sharing 14 6.9% 25 12.6% 39 9.7% IGA group 10 4.9% 17 8.5% 27 6.7% Counseling (HIV, health, hygiene) 2 1.0% 1 0.5% 3 0.7% Others(adult education) 5 2.5% 2 1.0% 7 1.7% Total 79 38.9% 98 49.2% 177 44.0%

2.11. ACCESS TO BASIC SERVICES AND MARKETS

Access to basic services as well as to markets is relatively more difficult in Otuke than in Lira (Table 34).

 Respondents from Otuke spend on average more time to reach specific socio‐economic institutions. In order to reach a trading centre, Otuke residents spend over 2 hours to reach a market place, whereas Lira residents spend some 80 minutes to reach one.  Health Units are more difficult to access and further away than the trading centre or the nearest school. On average, Otuke respondents spend 135 min in order to reach a health unit and Lira respondents spend 105 min.  Schools in Otuke are nearly one hour away for the children, in Lira they are some 35 min to walk.  Water access in both Lira and Otuke is limited, and the water points are 30 min (Lira) or nearly 45 min (Otuke) away18. Households take an average of 75l (Otuke) and 83l (Lira) daily, equaling an average of 12.9l per HH member (Otuke) and 13.9l per HH member (Lira) per day.

Table 34: Distance and level of access19 to socio‐economic services

District Nearest T/C Nearest Health Nearest School Nearest water Water Unit point consumption

18 Access time was given in time needed to go and come back from the water point

19 Respondents had 4 categories of access: 1 = “with ease”; 2 = “with some difficulties”; 3 = “with great difficulties; 4 = “no access”

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 44

Level of Time of Level of Time of Level of Time of Level of Time of Total Average access travel access travel access travel access travel per HH Otuke 2.04 2.06 2.25 2.23 1.68 0.97 1.67 0.70 15,240 75.07 Lira 1.84 1.18 2.12 1.80 1.60 0.61 1.67 0.49 16,570 83.30

The majority of people in both Lira and Otuke walk to the socio‐economic service points (Figure 15). While cycling plays a role to access health units and, to a lesser degree trading centers, schools and water points are nearly exclusively accessed by foot.

Figure 15: Means of access to basic services and markets

200 180 160 140 120 100 80 respondents 60 of

Otuke

# 40 20 Lira 0 cycling cycling cycling cycling driving driving walking walking walking walking nearest T/C nearest Health nearest nearest Unit School water point

3. ANALYSIS

3.1. VULNERABILITY PROFILE BY GEOGRAPHIC AREA

The sustainable livelihoods framework developed by DFID is one way to approach the complex issue of livelihoods and vulnerability. It looks at livelihood assets of households organized into 5 categories:

 Human capital (education, knowledge/skills, health, nutrition, capacity to work/adapt)  Natural capital (land ownership and access, water, trees/forest and other wild products, biodiversity)  Financial capital (income, savings, remittances etc)  Physical capital (infrastructure; tools and equipment (incl. productive assets and productive inputs)  Social capital (family & friends, networks, formal/informal groups, rules and sanctions, participation)

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 45

Households and communities are in risks of shocks, i.e. external factors affecting their different assets/capital bases. Their capacity to respond to those shocks is termed resilience.

HUMAN CAPITAL The status of the head of household, whether female or male, age, education level, health and marital status are significant factors for the livelihoods of households. To highlight that, the average income for various categories was correlated to the overall average income (in % of total income) and to the household dietary diversity scores (HDDS). A relevant disadvantage for female headed households in most of the mentioned categories is apparent. (Error! Reference source not found.)

Table 35: Income and dietary diversity per category of head of household

Total Female headed HH % of total % of total Category Status of HHH # income HDDS20 # income HDDS Gender Female HHH 74 65.3% 3.5 74 65.3% 3.5 60 + 38 57.7% 3.8 17 25.5% 3.6 Age 20 ‐ 60 194 101.3% 3.7 57 77.2% 3.5 Never attended school 61 49.8% 3.7 43 37.3% 3.7 Education Primary 234 101.8% 3.8 27 81.2% 3.3 level Secondary + 60 161.6% 3.5 4 259.9% 3.8 Chronically ill 51 70.4% 3.7 25 64.2% 3.6 Health status Disabled 7 63.1% 3.6 2 40.0% 4.1 Married 170 104.9% 3.8 21 67.8% 3.1 Marital Widow 50 37.2% 3.7 44 32.3% 3.7 status Divorced/never married 14 158.9% 3.6 9 220.9% 4.1

Nutrition and health

The survey took place in December during the second harvest when products are abundant in the granaries and markets and prices are still low before they start increasing for the festive season. Wild fruits (mango) and wild vegetable are not yet ripe. People’s diet rests mainly on roots and tubers (cassava) and pulses (beans) as well as vegetable.

On average, Otuke respondents consume 3.8 different food groups a day whereas Lira respondents consume 4 different food groups. This HDDS score can be categorized as “low to medium dietary score”21 with a deficiency in animal protein and possibly iron. Lira respondents eat a greater variety of

20 Note that the average HDDS for respondents who were the heads of households is at 3.7, which is lower than the overall HDDS of 3.9.

21 See: FAO (2007): Guidelines for measuring household and individual dietary diversity

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 46

food groups and have more access to animal protein. Female headed households have a lower dietary diversity than other households (HDDS 3.5)

Lean months, i.e. months in which households reduce their quantity of meals, gather wild fruit or change their diet to less favored foods can start from February and last till August. Many crops produced for home consumption will no longer be available after January 2011 for most households (see chapter 2.5, page 31ff and Table 19) in Lira and Otuke. 2010 harvest was better than the previous years, however it cannot be expected that it was sufficient to spare most households from experiencing some degree of food shortage or reduced food intake.

With 24.6% of household heads being chronically ill in Otuke (compared to 13.6% in Lira), a higher vulnerability to shocks can be assumed. The number of children under 5 falling sick is similar in both districts with respiratory diseases and fever (usually classified as “malaria”) being the most common diseases.

Education

The education level of the household heads is correlated to the household income (Error! Reference source not found.): in households where the head never attended school, the total average income represents merely half of the overall average (49.8%). If these households are female headed, the average income is only 37.3%. The higher the educational level, the more above average the income of the households are.

As discussed in chapter 2.1. (Page 13), the education level of the head of households differs for men and women and between the districts. Generally speaking women are disadvantaged compared to men and Otuke women are disadvantaged compared to Lira women.

 Of all female head of households, 38.7% never attended school. Only 8.3% of female head of households enjoyed secondary education or higher education than that.  In Otuke this pattern is exacerbated: 41.9% of female household heads have never attended school.

Despite efforts by government and various institutions, women in Lira and even more in Otuke are still disadvantaged in access to basic education and to higher education. That gender‐imparity will continue to shed its effects on nutrition, mother and child health, access to income generation activity, quality and knowledge acquired through training etc.

Gender and income generation

Table 36: Productive assets owned by female and elderly HHH

Assets Average Average Average Ox‐ Average income owned Cows # cows Bulls # bulls Goats # goats ploughs from crops (UGX) Female HHH 18.9% 0.36 10.8% 0.19 47.3% 1.27 8.1% 43,328

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 47

60+ 26.3% 0.39 5.2% 0.08 42.1% 0.95 5% 43,550 Total 25.6% 0.5 20.4% 0.4 50.5% 1.4 16.7% 70,389

Households are composed of slightly more women (50.8%) than men (49.2%). In Otuke district, men represent only 47.8% of household members. In relation to total numbers, more women (41.5%) than men (39.5%) contribute to household income. In Lira district, the percentage of men contributing to HH income is nearly equal to that of women, whereas in Otuke, fewer men are contributors.

Households headed by women are disfavored in terms of household income and ownership of productive assets: they earn only 67.3% of the total average income (Error! Reference source not found.). Female headed also earn significantly less from crop production than the general average (61.5%) and own less livestock (Error! Reference source not found.).

Besides contributing slightly more to household income than men, women also bear the bulk of domestic tasks (looking after children, fetching water and firewood, cooking etc).22 Women bear a double burden of being bread winners and domestic workers. This imparity continues to be an aspect of the society of the districts surveyed.

Age groups

Households headed by elderly (60+) earn on average lower income (Error! Reference source not found.) and own on average less livestock (Error! Reference source not found.). They earn only 57% of the total average income; their income from crop production is only 62% of the total average. Property of cows is just above the average (26.3% of elderly headed households own cows). Given their reduced capacity to do hard field work, property of bulls is relevantly lower (5.2% compared to an overall average of 20.4%) and so is ownership of ox‐ploughs.

The fact that youth under 18 makes up nearly 60% of the population suggests that efforts to accommodate adolescents in terms of education, social services and income opportunities has to be pursued.

Otuke has more people beyond productive age (elderly above 60) and before being able to contribute relevantly to household economy (below 18 according to child labor law) – this higher dependency suggests that Otuke inhabitants are on average more at risk of facing situations their households cannot cope with than in Lira.

NATURAL CAPITAL

22 Information from informal discussions with local population; confirmed in group discussion with ACF food security team in December 2010

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 48

As discussed in previous chapters, access to land is not a primary issue in Lango region.23 But land does not equal land: soil quality and fertility, water retention, accessibility are factors that have to be taken into account when assessing the availability and suitability of land for various economic enterprises. Households in Otuke own on average more acreage of land than households in Lira. Considerably more private land is being cultivated in Lira than in Otuke. If land availability is to become an issue in the future, it is more likely to first happen in Lira.

With the return process, land wrangles have become an increasing cause of conflict between regions and districts, as well between and within clans. Mechanisms to resolve these problems exist (clan meeting, clan elders meetings, jurisdiction) and function largely. However, overall population growth will further increase the pressure on land and in the future increase potential conflicts if the productivity of land and the valorization of products are not increased.

Lango Sub‐region is just on the rim of the bi‐modal crop production pattern. In Otuke district, where more arid conditions prevail when compared to Lira, the second season harvest is limited to specific crops and often prone to small yield or complete loss of harvest. Respondents mention the increasingly unpredictable weather conditions and thus the disruption of production patterns as a major challenge to their agricultural activities.

Forecasts for the first half of 2011 predict erratic and above average rainfalls in consequence of the “La Nina” phenomenon24. Floods have already been mentioned as a problem mainly by Lira farmers. While moderate above average rainfall would certainly be of benefit to the overall harvest, inundations could jeopardize the outcome of the first cultivation period.

This assessment has not looked into the use and potential overuse of natural resources such as timber and non timber forest products (NTFP). As mentioned in the previous sub‐chapter, Otuke population is more dependent on using and selling products extracted from natural resources. Burning of charcoal is one potential income generating activity, and the burning of bricks requires important quantities of firewood. In certain seasons hunting (bush rats, small antelopes – mainly in February/March) and gathering of greens (mainly in April/May) does add to household diets which is significant as that corresponds to the timing when harvested reserves run low.

FINANCIAL CAPITAL As mentioned in the chapters above, remittances and cash savings play a very minor role for the livelihoods of households in Lango Sub‐region. The major part of the capital is kept as livestock or in

23 Figures generated during the survey suggest that 60% of private land in Otuke and 90% of private land in Lira is being cultivated (added acreage per crop cultivated). While that figure appears very high and is likely to be lower (since some crops are grown on mixed fields – it is not always clear to which acreage respondents would refer), it still indicates that land resources are limited.

24 See: FEWSWG (November 2010): La Nina Alert

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 49

other household assets and will be discussed in the following chapter. Cash income is mainly earned through crop production and unskilled labor.

Income and sources of income

Over 80% of the population claims to earn less than 1,000,000 UGX (just over 400 US$) a year (87% in Otuke, 83% in Lira). On average, Otuke respondents earn about 82% of what Lira respondents earn in cash. In Lira, 34.8% of the income comes from crop sales (in Lira it is only 9.4%). In Otuke, unskilled labor accounts for 23.9% of total income, in Lira it is 20.2%. Otuke residents are more likely to exploit natural resources to make an income (charcoal, wood, bricks).

Table 37: Correlating income groups, livestock and income from crop production

Average Cows Bulls Goats % % Land Profit Access Income income in owning owning (acres) from to groups (in % of total % Aver. # % Aver. # % Aver. # bull + ox‐ crops (% private 1,000 UGX) # average owning owned owning owned owning owned cow plough of total) vet 0 – 500 278 35.9% 22.3% 0.44 16.2% 0.32 47.1% 1.23 9.7% 10.4% 3.6 58.5% 23.0% 501 ‐ 1,000 67 125.5% 28.4% 0.40 20.9% 0.40 49.3% 1.40 10.4% 14.9% 4.4 141.9% 29.9% 1,001 ‐ 2,000 34 255.2% 35.3% 0.59 47.1% 0.85 79.4% 2.65 17.6% 35.3% 3.9 183.1% 41.2% > 2,000 23 571.4% 43.5% 0.96 47.8% 1.00 56.5% 2.17 26.1% 26.1% 4.9 535.0% 34.8% Total 402 100.0% 25.6% 0.48 21.40% 0.42 50.70% 1.44 11.40% 14.20% 3.8 100% 26.40%

As expected, respondents with the lowest average income (below 500,000 UGX) own on average less livestock and ox‐ploughs and own below average acres of land. In consequence, they make less profit from selling crops and have below average access to private veterinarians (Table 37). Concurrently, their dietary diversity is below average (3.6 compared to 3.9). Female headed households are overrepresented in this lowest income category, and the lowest income category also has an above average dependency ratio (Table 38).

Table 38: Correlating income groups and dietary diversity

Income groups (in dependency 1,000 UGX) # f HHH ratio HDDS 0 ‐ 500 278 63 0.47 3.6 501 ‐ 1,000 67 6 0.41 4.3 1,001 ‐ 2,000 34 3 0.44 4.9 > 2,000 23 2 0.4 4.6 Total 402 74 0.45 3.9

Respondents with a high yearly income (income group over 2 million UGX) spent on average 1.9 times more than respondents from the lowest income group. When looking at the expenditure pattern of different income groups, the ratio of what is spent on food, health, education, clothing, productive

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 50

inputs or social contribution is surprisingly similar. Only the income group between one and two million UGX yearly spends relevantly less (proportionally) on food (35.8% compared to 45‐50% for the other groups) and more on clothing (24% compared to 11‐13% for the other income groups). The two highest income groups spend 9‐11% of their total expenditures for servicing loans and approximately double what lower income groups pay. (Figure 16)

Figure 16: Correlating income groups and household expenditures

Proportion of expenses per income group

> 2,000 UGX)

1,001 ‐ 2,000 1,000 501 ‐ 1,000 (in

< 500 group

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% % of total expenses Income

food health school fees clothing debt/loan social contributions house improvement / rent bedding agro‐vet input transport soap parrafin cooking fuel

Natural and financial capitals are only some of the factors determining the wealth of households. In the rural Lango society, property of livestock, productive assets and the capability of laboring the land set the base for feeding the households.

PHYSICAL CAPITAL Livestock

In a region where the average herds per household counted more than twenty heads25 before the armed raids in the 1980s and the war with LRA, that figure is still very low. The population has not yet recovered its herds after the war and is still struggling to return to the pre‐war status. Overall, 40.3% of the population has no cattle or goats. That ratio is higher in Otuke where nearly half the population has no such animal (49.3%). Above average female headed households owned no cattle or goats (51.4%) (Table 39).

25 BRASESCO, F.: Enlarging existing knowledge about direct cash transfer projects in support or returnee livelihoods. MSc thesis; Montpellier/Kampala 2009.

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Goats are a crucial asset to the household economy. With a fast reproduction rate, they are used as mobile capital and can be turned into cash when needed. Half of the population owns at least one goat (50.3%) where here again female headed households are disadvantaged (47%). According to research, a herd of 6‐7 goats would be a base for capital growth. 26 In Otuke and Lira, 5.2% of the all households fall under that category: 3.5% of Lira households have more than 6 goats, 6.9% of Otuke households have more than 6 goats. Only 2.7% of female headed households have 6 goats or more.

Livestock ownership is a strong factor determining average income, dietary diversity and diversity of income sources. Looking at combinations of animals owned and the corresponding average income of the household, the above average income of people with different types of livestock is apparent and can exceed the overall average yearly income by 226%. These households also have a greater dietary diversity than the average.

 Overall livestock property is more developed in Lira (apart from households owning more than 6 goats).  People without livestock own below average acreage of land (3.1 ac. compared to 3.8 average).  People with livestock are likely to have higher average income (respondents without livestock earn 75% of the average income, people with a minimum of 1 of each earn 160% of the average income or double what respondents without livestock earn).  People without livestock have below average diversity of food sources (3.7 compared to 3.9).  The probability of living in a household without any livestock (and thus lower income, lowerd dietary diversity and lower diversity of income sources) is relevantly higher when the head of household is a female.  The greater the diversity of animals owned the higher the income, the greater the land property and the greater diversity in food and income sources.

Despite the high value of livestock and the fact that households that own animals are generally better off, have a more balanced diet and have more access to training, it should be noted that the overall productivity of the animals in terms of by‐products (eggs, milk) is low.

Table 39: Comparing livestock owners, average income and dietary diversity

Otuke Lira Total Female Average income HDDS # income acres % of % of % of HHH (UGX) compared to (total sources land # of livestock owned Otuke Lira total (total total average aver. (aver. (aver. (minimum) resp. resp. resp. 74) income (%) 3.9) 1.8) 3.8) 6 goats, 1 bull, 1 cow 2% 2% 2% 0% 1,238,250 226% 4.9 2.5 6.2 1 goat, 1 bull, 1 cow 5.9% 11.6% 8.7% 6.8% 876,343 160% 4.2 2.1 4.6

26 See: Enlarging existing knowledge about direct cash transfer projects in support of returnee livelihoods: the case study of Otuke County, Northern Uganda; Montpellier, page 24

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 52

1 goat, 1 cow 14.8% 23.1% 18.9% 17.6% 757,605 138% 4.1 2.1 4.6 1 goat, 1 bull 13.3% 20.1% 16.7% 10.8% 942,194 172% 4.1 2.1 4.5 1 cow, 1 bull 6.4% 16.6% 11.4% 6.8% 843,935 154% 4.2 2.1 4.5 1 bull 15.3% 27.6% 21.4% 10.8% 937,070 171% 4.2 2 4.5 1 cow 18.2% 33.2% 25.6% 18.9% 722,379 132% 4.1 2 4.4 6 goats 6.9% 3.5% 5.2% 2.7% 951,000 174% 4.7 2.4 5.8 1 goat 45.3% 56.3% 50.7% 47.3% 620,938 113% 4.1 2 4.4 No cattle/goat 49.3% 31.2% 40.3% 51.4% 412,941 75% 3.7 1.7 3.1

Crop production

As shown in chapter 2.5 (page 31 ff), crop production in Lira district is more diverse and more oriented towards cash crops than in Otuke. Cash crop production brings income. Production of cash crops in Otuke and Lira does not lead to a large scale reduction of production for home consumption: when looking at the dietary diversity of the 19.2% households selling crops for 100,000 UGX or more during the second season harvest, the score is above average (4.5), nearly as high as the one for households with relatively large herds of animals. Again, Lira respondents seem better off: 30.2% of Lira respondents sell parts of their harvest for 100,000 UGX or more, for Otuke the ratio was only 8.4%.

The 2010 harvest was comparably good despite losses on specific crops (e.g. beans in Otuke) due to small scale climatic irregularities (localized hailstorms, dry conditions, flooding). Not all available land was cultivated. Reasons for leaving vast acres of privately owned land in fallow while suffering lean months and low harvests are the following:

 lack of good seeds  lack of mechanization  hesitations to cultivate more after recurrent weather irregularities (dry spell, floods, hailstorms)  land used for grazing

Animal traction

As discussed in the livestock section of this chapter, only 15% of Otuke households and 27% of Lira households own one bull. People owning one bull only have to match up with another owner of a bull to have a pair of traction animals. Only 8.8% of Otuke respondents have two bulls, 12.6% of Lira respondents have two bulls.

Furthermore, they need an ox‐plough to open the land. In Lira, 13.1% own an ox‐plough. 10.6% of respondents own 2 bulls and a plough and are thus in a position to independently labor their land and even rent their work force. In Otuke, 9.3% own an ox‐plough, while 5.9% of respondents own an entire set (2 bulls and one plough).

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Table 40 shows how households with traction equipment are generally earning higher income, are generating more income from crop sales and have an above average dietary diversity. The trend is confirmed when looking into more detail in two districts. Households with a minimum of one bull and an ox‐plough can earn more than 2.5 times than households without any of these assets. Their dietary diversity is likely to be substantially better. The same is true, if to a lesser degree, for ownership of one of the productive assets only.

The lack of tools and animals to labor the land is clearly an obstacle to opening more land and to bring in higher yield and thus income. Otuke inhabitants own fewer bulls and ox‐ploughs and are clearly disadvantaged when compared to Lira inhabitants.

Table 40: Income of households with traction equipment in relation to total average

% of total % of average HDDS score (total Assets of households # average income from crop sales average 3.9) No bull no plough 302 78.7% 60.3% 3.8 Ox plough only 14 122.7% 158.6% 4 Bulls only 41 146.6% 204.6% 3.9 Bulls and ox‐ploughs 45 193.4% 252.7% 4.5

Houses

Otuke inhabitants live on average in more congested situations than in Lira. In Otuke, households on average consist of less dwelling units: 75.8% of Otuke inhabitants have less than 3 dwelling units (i.e. 1 kitchen and two grass thatched houses) compared to 61.8% in Lira.

Houses usually consist of mud bricked walls, a grass thatched room and a one room interior. They are generally in good conditions. However, Otuke has more respondents who have returned to their villages over the past 2 years. The process of settling is still ongoing in certain parts of that district with all the difficulties and expenses a resettlement process entails.

SOCIAL CAPITAL Trading centers are trading places for products and information and as centers are more likely to provide access to social and political participation. They further provide basic services (health, schools, long distance transport). Generally speaking, Otuke households have to walk (or cycle) further for any kind of basic infrastructure or water. That includes access to health units, to schools and to the market place.

Groups can alter the negotiating power of individuals, they can be networks of mutual support in times of need, they can help to start businesses and they are generally speaking a materialization of individuals or households being socially embedded.

ACF USA, Food Security and Livelihoods Assessment, Dec 2010 Uganda 54

Table 41: Group membership and livelihoods indicators

Profit Training Income Average from # HDDS sources income crops Crop Livestock Savings Credit Group members 168 4.1 2 662,306 109,368 10.1% 2.4% 68.5% 48.2% Total 402 3.9 1.9 547,850 70,389 5.7% 1.2% 52.7% 33.3% Compared to total 41.8% 120.9% 155.4% 176.9% 191.4% 129.9% 144.8%

42.8% of respondents were members of groups for labor sharing, saving, marketing and IGAs (possibly less since double nominations were possible). Group members rate favorably on some indicators for livelihoods (dietary diversity, income sources, and average income) and at the level of access to training, saving and credits (Table 41). The combination of better access to training, to savings and credits as well as to labor and possibly inputs increases the profit from crop sales disproportionally. The survey does not indicate whether respondents were above average earners before they joined groups or whether the group membership had these positive effects.

 Lira inhabitants are more likely to be organized in groups. Nearly 50% of Lira respondents said to be part of some group, mainly a traditional savings and credit scheme.  In Otuke, that proportion is lower. Still, over one third (38.9%) of households are members of some group.  One can conclude that despite the conflict and displacement, the organizational capacities on the community level are high. Note that this assessment does not look into family and clan as well as neighborhood and village affiliations which might represent much stronger bonds than formal groups.

RISKS OF SHOCKS Floods and dry spells are recurrent climatic events disrupting local production. Here, crop production is most severely affected. These erratic weather conditions are one reason why animal husbandry is increasingly becoming the main or at least a complementary economic activity. However, animal husbandry is prone to cattle rustling and to animal diseases. Cattle rustling expeditions are a recurrent tragedy in three Sub‐counties of Otuke district. Prevention units have been set up, yet the fear prevails. Access to veterinary services is given, but the quality of the service and of the drugs is not always ensured. Many respondents use private veterinarians rather than government veterinary services:

It seems that a majority of the population of Otuke and, to a lesser degree of Lira, has not yet developed the resilience to some of the above mentioned shocks:

 Household food stocks are insufficient to absorb a failed harvest  Seed banks are not available to most households to absorb a loss of seeds  Livestock numbers per household are so small that any loss will be relevant

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 Cash income is so small it cannot balance potential loss of productive assets or harvest  No relevant savings (cash or livestock) are available to the majority of the population

Vulnerability is defined above as lacking resilience to shocks affecting the different capitals of households. This assessment tries to identify livelihoods characteristics of the poorest of the poor of the region.

Poorest of the poor

As discussed above, property of livestock is one good indicator to indentify people with less than average dietary diversity and with less access to land. The fewer livestock people own, the less diverse their household economy is likely to be and the less diverse their household diet. (Table 39) However, some of these people without livestock might earn considerable income from other sources and might have an above average dietary diversity. (Table 42)

 In Otuke, 37.9% of respondents own no livestock and earn less than 500,000 UGX per year (28% of the overall average income). Their dietary diversity is relevantly lower than the average HDDS and close to 3 different food groups only.  In Lira, the situation is slightly less grave. However, there are still nearly 1/4th of respondents (24.1%) without livestock and with an income below the average (30% of the overall average income). Their dietary diversity score is slightly higher than in Otuke but still below average (3.6).

Table 42: Income categories for people without livestock

Otuke Lira average average Income (in % of income % of income 1,000 UGX) # total (UGX) HDDS # total (UGX) HDDS 0 ‐ 500 77 37.9% 153,390 3.3 48 24.1% 164,112 3.6 501 ‐ 1,000 16 7.9% 669,275 4.3 9 4.5% 677,456 4.4 > 1,000 7 3.4% 2,665,143 5.3 5 2.5% 2,158,000 5.4

These households described above must be considered “the poorest of the poor”. Some characteristics are above average in this category:

 47.29% of female headed households fall into that category.  44.7% of households headed by people over 60 years of age fall into that category.  56% of households headed by widows/widowers are in this poorest category.  46.5% of households headed by persons being chronically ill or disabled.

This list of characteristics shows that the economically most deprived households are above average headed by women and by widows/widowers or by disabled or chronically ill people.

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3.2. CONCLUSION

The relatively good harvest of 2010, the return of peace in the region and the impact of government programmes as well as interventions of various organizations have had a positive impact on the livelihoods and food security situation in Lira and Otuke. Observers speak of a “moderate recovery”.

However, the general assessment of a “food secure and livelihoods situation in Lango Sub‐region” voiced by district production officers, by representatives of various NGOs working on food security and confirmed by external research by FEWSNET27 does not give the details of the total picture.

One third of the population (31.1%) in both districts lives without relevant physical capital (livestock) and earns approximately only 1/3rd of the average yearly income. This part of the population has a less nutritious diet and has no reserves to cope with any irregularity in production. Their main source of income is casual labor (mainly on other peoples fields), which also depends on the overall availability of such occupations (depending on the needs of bigger and more affluent farmers).

In the FEWSNET forecast, Uganda is not listed as being at highest risk of adverse “la nina” effects when compared to other areas of Eastern Africa. They do foresee above average rains, which might have negative effects on particular crops but which could also lead to abundance in harvest.

With world food prices on the rise28, subsistence economies are in a relatively privileged situation: they depend less on market purchases than others. The nature of the price increase however is such that in the long run, most prices (food stuffs, transport, commodities like school uniforms, medicine etc.) are likely to increase. Subsistence farmers live within the global cash economy. Price increase for goods will negatively affect their purchasing power and make services and products they do not produce themselves less available. This scenario holds true for all those households who produce mainly for their household needs.

Some farmers mainly in Lira have engaged in cash crop production. Price developments for purchasing primary goods (sunflower, soybeans, cotton) cannot be forecasted in this assessment. The current high prices might however motivate some people to cultivate more specifically for market sales. This tendency could counter the negative effects of high consumer prices for the rural population. Particularly, the Otuke district could benefit relevantly more from producing for regional markets than it is at the moment.

The stock of animals is slowly increasing in the Lango Sub‐region. This trend is positive, since animals are more flexible than crops and more resilient to weather irregularities. The region is said to have

27 FEWSNET (November 2010): Food assistance outlook brief; FEWSNET (August 2010): East Africa Regional Food Security Outlook; July‐December 2010

28 See: FEWS Update on global food prices_Dec16; Daily Monitor, 8th February, Page 22‐23

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supported much larger numbers of animals before the 1980s, when the Karamajong warriors started large scale armed raids. That leads to the assumption that the capacity of charge for livestock is not yet reached and that more animal can be bred in the Lango Sub‐region.29

The accessibility of quality veterinary services continues to pose a challenge to the thriving of herds. Outbreaks of dangerous diseases are usually quickly reported to the DVO and reacted upon, but on an individual level, veterinary service remains expensive or difficult to access. Households in rural Lira and Otuke still largely lack the funds to ensure the health of their animals.

The degree of mechanization of agriculture remains minimal. Oxen and ox‐ploughs, very basic means of laboring land, are available to less than one third of the population. The remaining population uses hand hoes and has in consequence very limited production capacities. This situation is likely to prevail until the stock of animals has further increased and until profit made from other activities has allowed more households to purchase ox‐ploughs.

29 Such a development is not without risks: already the herding techniques (free ranging) for cows and (more explicitly) goats create tensions between neighbors. Research on carrying capacity for livestock and herding techniques is recommended.

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4. RECOMMENDATIONS

The survey indicates that the situation in the region has improved. Results, however, further suggest that a continuation of efforts is needed to sustain the improvements and to prevent the region economy to slip back into conditions of livelihoods insecurity. Efforts should focus on the most vulnerable households on one hand to increase their subsistence production and improve their nutritious status and on more general economic development supporting commercial production.

Support most vulnerable households

The 30% most vulnerable households of the population need continued support to create a base of physical or financial assets to sustain their household production. Programmes to support the most vulnerable are necessary to prevent a slipping back into the need for food aid. Working with these vulnerable households will automatically mean working with above average female headed households, or with households headed by disabled or chronically ill people. Specific attention to these characteristics needs to be given.

These most vulnerable households should be supported with cash or in‐kind aid (seed fairs, livestock, ox‐ploughs) to increase their production capacities to satisfy their subsistence needs. They should further receive training for better production practices and improved post harvest handling as these are the pillars of household economies.

Support economic development

Programmes aiming general economic development of the region should target surplus producing farmers and assist them with market access (collection points for marketable seeds, market price information) as well as processing facilities. Crop production needs to be further supported to facilitate the cultivation of more land, more diverse crops including food and commercial crops to secure household nutrition and household income.

Especially in Otuke, marketing facilities should be promoted to encourage commercial production without affecting the production for home consumption. This year’s high prices should offer good incentives to extend commercial production. Production conditions vary between districts but also between Sub‐counties – the comparative advantages of each area need to be properly mapped. Mechanization of crop production using drought animals has to be further promoted to increase productivity.

In view of increasing risk of weather irregularities, adapted or more resistant plants (to both, drought and flooding) need to be promoted. Research on appropriate varieties should be intensified. Irrigation and drainage for specific areas could be envisioned to become less dependent on timely and moderate rainy seasons. However, because of high costs and potential environmental effects of such technical interventions, their feasibility and cost‐benefit need to be properly assessed.

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Promotion of livestock production should be another priority of interventions: animals are more mobile and thus less liable to climate irregularities and represent the main capital investment of the rural population. Veterinary services need to be improved to become more reliable and more accessible to prevent loss of animals and grant healthy animal products. Herding practices other than free and uncontrolled roaming can be improved to raise more productive animals with less destructive effects on the environment. The low productivity of animals should be checked to generate higher benefits from the animals owned.

Training to improve and intensify crop production (including use of basic mechanization) and for livestock management, needs to be further pushed in order to reach out to a wider percentage of the population. These trainings can be applied for all target groups but should take the low educational level of the rural population into account. Crosscutting messages such as natural resource management should be part of the trainings.

For the rural population in Lango Sub‐region options for income generation other than in agriculture seem limited. With good targeting, some enterprises to cater for growing local demand can be supported (e.g. in the evolving district capital, Orum).

Natural resources (land, firewood) are currently not sparse and trees and soil are exploited to produce charcoal and bricks. After the long fallow due to limited production during LRA war, the soils are relatively fertile. Land, soil fertility and wood might however become an issue: Land wrangles are already one of the major sources of conflict. With population growth, issues around land ownership are likely to increase, especially since land rights have been mixed up during displacement. Land rights should be regulated by the law but should take traditional organizational levels (families, communities, clans) into account.

In a development context, resource management programmes should make sure that reforestation balances the losses in vegetation. Promotion of fuel efficient stoves and of energy saving brick making techniques should be expanded. Resource management policies or actions should not be enforced against income generating interests of the local population. Sensitization for family planning to reduce the growing pressure on land and natural resources, to allow local government structures to provide basic services (health, school), to ensure their livelihoods and to alleviate the workload of women should be strengthened.

Improving access to safe water sources should continue to be a priority in the area as the area is still underserved with safe water sources at reasonable distance from the homes.

Efforts to reduce structural disadvantages faced by women need to be reinforced. That includes access to basic and higher education but should also consider double burdens (as bread winners and domestic workers) but also ensure their access to productive assets and inputs.

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