Food Security and Livelihoods Assessment Lango Sub-region ... · The reported Food Security and...

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

Food Security and Livelihoods Assessment

Lango Sub-region

Northern Uganda

Uganda

December 2010

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

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


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


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


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


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.


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)


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.


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


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.


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.


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.


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

Villages Alekloatin Baropiro Okurunyang Loro Ayito

Obel Te‐dam Yabwangi Ojutolwio Okonga

District Lira

Sub‐counties Adekokwok Agali Agweng Aromo Ogur

Parishes Boroboro East Abongorwot Abala Acutkumu Adwoa

Villages Ajunga Apwoyotic Agali A Acan Mak Kweri Apurimon

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.


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%


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

Highest education level

Total

Total Women Men

# % # % # %

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%


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

Highest education level

Otuke Lira

Total Women Men Total Women Men

# % # % # % # % # % # %

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

District

HH

interview Gender

Total

HH members contributing to

HH income

# average % # average % of total

Otuke

203

m 564 2.78 47.8% 211 1.04 37.4%

f 615 3.03 52.2% 254 1.25 41.3%

Subtotal 1179 5.81 465 2.29 39.4%

Lira

199

m 604 3.04 50.6% 250 1.26 41.4%

f 590 2.96 49.4% 246 1.24 41.7%

Subtotal 1194 6.00 496 2.49 41.5%

Total 402

m 1168 2.91 49.2% 461 1.15 39.5%

f 1205 3.00 50.8% 500 1.24 41.5%

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).


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

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

0 500 1000 1500 2000 2500

Otuke

Lira

Total

# of HH members

Districts

Household composition per age group

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


Table 8: Health status of household heads

Health of HH

Otuke Lira Total

# % # % # %

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.


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.


Table 10: Reasons for not being satisfied with security

Reasons for dissatisfaction

Otuke Lira Total

# % # % # %

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 violence, witchcraft, hatred

5

2.5%

5

2.5%

10

2.5%

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

Impact of feared insecurity

Otuke Lira Total

# % # % # %

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.


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

# of meals

Otuke Lira Total

adults children adults children adults children

# % # % # % # % # % # %

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%).


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

Table 13: Household dietary diversity score HDDS

Food item

Otuke Lira Total

# % # % # %

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


Figure 3: Food sources per district

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

% of respindents

Food group consumed

Food groups consumed per district

Otuke

Lira

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

Lira

Otuke

%

Districts

Overview of food sources per district

Own production Market purchase Hunting/ Labor for food

Borrowed Food gift Food aid


Figure 4: Sources for various food groups

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

0 100 200 300 400

Roots & tubers

Pulses

Oil, fat, butter

Vegetables

Cereals

Sugar, honey

Fresh fruit

Dairy products

Meat

Fish

Eggs

Respondents

Food groups

Sources of different food groups

Own production

Market purchase

Borrowed

Food gift

Labor for food

Hunting/gathering

Food aid

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

020406080100120140160180200

# of HH

Lean months


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

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%

Receiving gifts

Brewing

Sale of livestock

Barter (items for food)

Petty trade

Eating seeds

Begging

Nothing

Planted fast maturity crops

Exchange of labor for cash

Selling assets

Borrowing

Purchase from market

Wild gathering

Premature harvest

Exchange of labor for food

% of total respondents

Adaptation or coping strategies


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.


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 %

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.

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

Otuke

Lira

Total

% of total expenditures

Average expenditures over one year

Food Health care School fees Debt repayment

Soap Paraffin Social contribution Transportation

Clothing House improvement Cooking fuel Bedding

Agro/veterinary inputs House rent


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

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.

0 50 100 150 200 250 300 350 400 450

Otuke

Lira

# of households per income source

Number of households gaining income from different sources

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


Figure 9: Contribution of different activities to total income

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%

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

Otuke

Lira

Contribution of different activities to total income

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


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

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

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

0 ‐ 500

501 ‐ 1,000

1,000 ‐ 2,000

2001 ‐ 3,000

3,000 ‐ 4,000

4,001 ‐ 5,000

more than 5,000

in 1,000 UGX

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

% of total income % of respondents


Figure 11: Respondents per income category in Lira

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.

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

0 ‐ 500

501 ‐ 1,000

1,000 ‐ 2,000

2001 ‐ 3,000

3,000 ‐ 4,000

4,001 ‐ 5,000

more than 5,000

In 1,000 UGX

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

% of total income % of respondents


Table 15: Assets owned

Assets

Otuke Lira Total

# of HH qty ave.

max. # owned by 1 HH # of HH qty ave.

max. # owned by 1 HH qty Ave.

max. # owned 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.


Figure 12: Types of crops grown in both districts

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

020406080

100120140160

Numbers of households cultivating varieties of crops

Otuke

Lira

0.0 50.0 100.0 150.0 200.0 250.0

Sorghum

Pigeon Peas

Sesame

Beans

Sweet potatoes

Cassava

Rice

Groundnut

Millet

Vegetables

Maize

Matoke

Cotton

Sunflower

Soybeans

Cowpeas

Acres per cultivated crop

Otuke

Lira


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

Home Communitydonations

Free 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:

G‐nut Simsim/ sunflower

Pulses, maize (loose) Vegetables Matoke

Sweet potato/ cassava

Basin 1 1 1 1 1 1

kg 10.0 9.3 16.7 8.3 17.5 25.0


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


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

Type of crops

% of HH with sufficient crops until

Jan‐11 Apr‐11

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.


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 Otuke

owned

total acres 863

average per HH 4.3

opened

total acres 529

average per HH 2.6

Lira owned

total acres 676

average per HH 3.4

opened

total acres 622

average per HH 3.1

Total owned

total acres 1539

average per HH 3.8

opened

total acres 1151

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


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

Challenges

average score

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


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

Types of animals

Otuke Lira Total

HH % # Average

Max. # owned by HH HH % # Average

Max. # owned by HH HH % # Average

Max. # owned 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

INCOME FROM ANIMAL PRODUCTS

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

0

50

100

150

200

250

300

350

Cows Oxen Goats Sheep Pigs

Otuke

Lira


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

Otuke

HH # of cows

Ave. milk production (liters/day)

Ave. milk consumption (liters/day)

milk sales (liters/day)

Adwari 2 3 0.1 0.1 0.0

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

Lira

Adekokwok 6 7 1.7 0.6 1

Agali 4 5 2.1 1.6 0.5

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

Otuke

Adwari 1 0 4 9 0

Okwang 1 2 6 21 0

Orum 0 1 1 2 0

Olilim 0 0 1 20 0

Ogor 0 0 0 8 0

Lira

Adekokwok 0 3 4 9 0

Agali 0 0 3 20 0

Agweng 2 0 1 13 1

Aromo 0 0 4 8 0

Ogur 1 1 9 49 1


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

HH

Otuke Lira Total

access of vet services

# of HH with cattle or goats %

# of HH with cattle or goats %

# of HH that accessed vet

%of HH that accessed vet

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.


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

Credit sources HH Average amount

Serviced Duration

No Yes less 1 week

1 week ‐ 1 month

1‐6 months

6‐12 months

over 12 months

Otuke

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

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

Lira

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

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).


Table 29: Types of credit

Otuke Lira Total % o 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).


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 production 7

DITREC, Government, NAADS 3.4 16 VEDCO, Government 8.0 23 5.7

Livestock 3 Government, LEADS 1.5 2 FIDA, LEADS, Government, VEDCO, 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

Total Topics # Trainer # Trainer

WASH 14 ACF, CARE, CRS 0 ‐ 14

Adult literacy 4 Government 0 ‐ 4

HIV/AIDS counseling 2 Samaritans' purse, UNICEF 4

Numat, Government, 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.


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 Unit

Nearest School Nearest water point

Water 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”


Level of access

Time of travel

Level of access

Time of travel

Level of access

Time of travel

Level of access

Time of travel

Total Average 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

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)

020406080100120140160180200

walking

cycling

driving

walking

cycling

driving

walking

cycling

walking

cycling

nearest T/C nearest Health Unit

nearest School

nearest water point

# of respondents

Otuke

Lira


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

Category Status of HHH

Total Female headed HH

# % of total income HDDS20 #

% of total income HDDS

Gender Female HHH 74 65.3% 3.5 74 65.3% 3.5

Age

60 + 38 57.7% 3.8 17 25.5% 3.6

20 ‐ 60 194 101.3% 3.7 57 77.2% 3.5

Education level

Never attended school 61 49.8% 3.7 43 37.3% 3.7

Primary 234 101.8% 3.8 27 81.2% 3.3

Secondary + 60 161.6% 3.5 4 259.9% 3.8

Health status

Chronically ill 51 70.4% 3.7 25 64.2% 3.6

Disabled 7 63.1% 3.6 2 40.0% 4.1

Marital status

Married 170 104.9% 3.8 21 67.8% 3.1

Widow 50 37.2% 3.7 44 32.3% 3.7

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


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 owned Cows

Average # cows Bulls

Average # bulls Goats

Average # goats

Ox‐ploughs

Average income from crops (UGX)

Female HHH 18.9% 0.36 10.8% 0.19 47.3% 1.27 8.1% 43,328


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


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


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

Income groups (in 1,000 UGX)

Average income in % of total average

Cows Bulls Goats % owning bull + cow

% owning ox‐

plough

Land (acres)

Profit from

crops (% of total)

Access to

private vet #

% owning

Aver. # owned

% owning

Aver. # owned

% owning

Aver. # owned

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 1,000 UGX) # f HHH

dependency 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


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

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.

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

< 500

501 ‐ 1,000

1,001 ‐ 2,000

> 2,000

% of total expensesIncome group (in 1,000 UGX)

Proportion of expenses per income group

food health school fees clothing

debt/loan social contributions house improvement / rent bedding

agro‐vet input transport soap parrafin

cooking fuel


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

# of livestock owned (minimum)

% of Otuke resp.

% of Lira resp.

% of total resp.

Female HHH (total 74)

Average income (UGX) compared to total average income (%)

HDDS (total aver. 3.9)

# income sources (aver. 1.8)

acres land (aver. 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


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).


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

Assets of households # % of total average income

% of average from crop sales

HDDS score (total 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.


Table 41: Group membership and livelihoods indicators

# HDDS Income sources

Average income

Profit from crops

Training

Savings Credit Crop Livestock

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


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

Income (in 1,000 UGX)

Otuke Lira

# % of total

average income (UGX) HDDS #

% of total

average income (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.


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


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.


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.


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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