Food availability and livelihood strategies among rural households across Uganda
Food availability and livelihood strategies among rural households across Uganda
Jannike Wichern 0 1 2 4 5
Mark T. van Wijk 0 1 2 4 5
Katrien Descheemaeker 0 1 2 4 5
Romain Frelat 0 1 2 4 5
Piet J. A. van Asten 0 1 2 4 5
Ken E. Giller 0 1 2 4 5
0 International Maize and Wheat Improvement Center, Sustainable Intensification and Socioeconomics programs , 06600 Mexico City , Mexico
1 International Livestock Research Institute, Livestock Systems and the Environment , Nairobi 00100 , Kenya
2 Plant Production Systems, Wageningen University & Research , 6700, AK Wageningen , The Netherlands
3 Jannike Wichern
4 International Institute of Tropical Agriculture, IITA-Uganda , Kampala , Uganda
5 Institute for Hydrobiology and Fisheries Science, University of Hamburg , Grosse Elbstrasse 133, 22767 Hamburg , Germany
Despite continuing economic growth, Uganda faces persistent challenges to achieve food security. The effectiveness of policy and development strategies to help rural households achieve food security must improve. We present a novel approach to relate spatial patterns of food security to livelihood strategies, including the contribution of on- and offfarm activities to household food availability. Data from 1927 households from the World Bank Living Standards Measurement Study were used to estimate the calorific contribution of livelihood activities to food availability. Consumption of crops produced on-farm contributed most to food availability for households with limited food availability, yet the majority of these households were not food self-sufficient. Off-farm and market-oriented on-farm activities were more important for households with greater food availability. Overall, off-farm income was important in the north, while market-oriented on-farm activities were important in western and central Uganda. Food availability patterns largely matched patterns of agroecological conditions and market access, with households doing worst in Uganda's drier and remote northeast. Less food-secure households depended more on short-cycle food crops as compared with better-off households, who focused more on plantation (cash) crops, although this varied among regions. Targeting interventions to improve food security should consider such differences in enterprise choice and include options to improve household market access and off-farm income opportunities.
Smallholder farms; Household level; Food security; East Africa; District; Crop choice
The majority of rural households in East Africa derive much of
their livelihood from agriculture. They face challenges related
to declining soil fertility and stagnating crop yields, declining
farm size as a result of population growth, poor market access,
insecure land rights and climate change
(Kristjanson et al.
2012; Jayne et al. 2006; Rufino et al. 2013)
. Household food
security has decreased in East Africa
(Kristjanson et al. 2012)
with a steady decline in calorie availability per capita over the
past 50 years
(Leliveld et al. 2013)
. By contrast the poverty rate
is reducing, particularly in Uganda, as a consequence of
national economic growth
.1 Yet, researchers disagree
whether economic growth has contributed to poverty decline
1 The Uganda Bureau of Statistics (UBOS) measures poverty as ‘the cost of
meeting caloric needs, given the food basket of the poorest half of the
population and some allowance for non-food needs’
, which allows
us to compare trends in poverty and food security.
across the whole population
(Daniels and Minot 2015)
Daniels and Minot (2015)
observed that the poverty
decline from 1995 to 2010 was much greater in the eastern and
western parts of the country, while
northern Uganda as the most food insecure region. What
remains clear is that both poverty and food insecurity are
challenges in East Africa now and for the future. East Africa’s rural
households play an important role in agricultural production
and make a major contribution to national food security and
the economy. Poor agricultural performance has been related to
a lack of supporting policies
(e.g. Cooper and Coe 2011)
assist farmers to access knowledge, credit and functioning
input-output markets. To identify suitable and effective policy
interventions, the determinants of household food security need
to be better understood.
Several studies have analysed the relationships between
household food security and underlying (household level)
drivers: larger cultivated land per capita, better education of
the household head, a wider variety of crops, and access to
market information are all positively related to food security
(Fisher and Lewin 2013; Mango et al. 2014; Silvia et al.
. Yet our understanding of what affects household food
security in East Africa remains rudimentary (Silvia et al. 2015)
and strategies to achieve household food security vary widely
across regions and among households. One challenge lies in
the complexity of the food security concept itself, which
consists of four pillars: Availability, access, utilisation, and
. No single indicator can capture all four
dimensions of food security
(Carletto et al. 2013)
Frelat et al.
developed a simple food availability indicator using
information on household on- and off-farm activities of
smallholders. This food availability indicator closely correlates to
well-established indicators such as the Household Dietary
Diversity Score (HDDS) and the Household Food Insecurity
Access Scale (HFIAS)
(Hammond et al. 2016)
Frelat et al.
observed that household food availability improved
with increasing dependency on off-farm activities, suggesting
diverse strategies among rural households.
Frelat et al. (2016)
analysed cross-sectional household data from more than
13,000 households across 97 locations in 17 countries across
sub-Saharan Africa (SSA), yet their spatial coverage across
the continent in general and Uganda in particular was poor;
e.g. no data from northern and eastern Uganda were included.
National policy makers need disaggregated regional analyses
at more local levels, such as the district to target interventions
on food security.
We aim to understand how spatial patterns in food
availability and the related livelihood strategies vary within a single
country. We chose Uganda because of its variety in
agroecological conditions and farming systems ranging from
perennial banana-coffee systems in the humid highlands and
around Lake Victoria to dryland pastoral savanna systems in
(Pender et al. 2004; Wortmann and Eledu 1999)
Using household survey data, our analysis quantifies the
contribution of diverse livelihood strategies to household food
availability and reveals how these strategies differ in their
importance across the country.
Country-wide assessments of food security for Uganda and
other countries in SSA have been conducted before; for
example the Comprehensive Food Security and Vulnerability
Analyses of the World Food Programme
the Famine Early Warning Systems Network (FEWS NET)
(www.fews.net). FEWS NET uses livelihood zones as an
aggregation level to project food insecurity across a country.
Its main purpose is to provide early warning of acute risks of
food insecurity and famine. FEWS NET stratifies countries
into zones of similar livelihood activities and uses household
information to identify wealth groups and related key sources
of food and income per zone
(Grillo and Holt 2009)
study adds to the existing approaches by quantitatively linking
food availability and contributing livelihood activities, using
household level data thereby identifying the diversity and
patterns of income and food sources using a ‘bottom-up’
approach. We investigated differences among regions and
districts, aiming to make the targeting of interventions more
Our key objectives were: (i) to quantify and understand
how on- and off-farm activities of Uganda’s rural households
contribute to their food availability, contrasting more food
secure with food insecure households, and (ii) to assess how
food availability and its relationship with different household
activities vary across Uganda.
The following questions and related hypotheses were
1. What proportion of Ugandan households has insufficient
food available and how does food availability differ
across the country?
Hypothesis 1: The Northern region is characterised by less
food availability compared to the Central,
Western and Eastern region.
2. What livelihood strategies and household activities
contribute to household food availability and how much, and how
do these differ with food availability across the country?
Hypothesis 2: For the more food secure households,
marketoriented on-farm activities and off-farm
income, and not on-farm food production, are
the major contributors to household food
availability and this is similar across the
3. How do cropping patterns relate to household food
availability and how do they differ across the country?
Hypothesis 3: Staple crops (particularly banana, cassava,
maize and sorghum) are more important for
food insecure households.
We used cross-sectional household survey data from the
World Bank Living Standard Measurement Study –
Integrated Surveys on Agriculture (LSMS-ISA) for the period
(Kilic et al. 2015)
. The LSMS-ISA data of Uganda
have been used in a wide range of livelihood studies both on
food security, for example on effects of physical activities on
food consumption and on the use of complementary indices
for food security
(Mathiassen and Hollema 2014; Hjelm et al.
and on agriculture
(e.g. Sheahan and Barrett 2017;
Gilbert et al. 2017; Palacios-Lopez et al. 2017)
comprehensive cross-country coverage and the detailed agricultural
survey of the LSMS provided an adequate dataset for the
purpose of our study.
2 Material and methods
2.1 Background of Uganda
Uganda is one of the fastest growing economies in
(Kuteesa et al. 2010)
with an annual GDP
growth rate of 5%
(World Bank 2016b)
value added ranges from 23 to 25% of the GDP and
major agricultural commodities for export are coffee,
cotton, sugar and tea
. More than 80% of
Ugandans live in rural areas
involved in agriculture. Uganda’s poverty rates reduced
from 56% in the 1990s to 24% in 2010, but the
standard of living did not improve uniformly across the
(Daniels and Minot 2015; Kakande 2010)
Population densities are highest in the western, central
and eastern parts of Uganda and most sparse in the
northeast (WorldPop 2016). Similarly, the most dense
road networks are found in the central and southwest
of the country where the major towns and cities are
. The poorer infrastructure in the
north of Uganda is partly related to the conflict that
started in 1987 and lasted for more than 20 years. The
conflict displaced millions of people and caused
agricultural production and market structures to collapse
(Tusiime et al. 2013)
. Today, roughly five years after
the end of the conflict, the region is still recovering.
Temperatures in Uganda are in the range of 15 to 30 °C
depending on elevation rather than on latitude with maximum
temperatures in the range of 25 to 31 °C for most areas.
Annual rainfall varies from 750 mm year−1 in the northeast
to >1750 mm year−1 in the areas of high rainfall. The majority
of the country receives annual rainfall between 1000 and
1750 mm year−1 (70% of the land area). Rainfall distribution
is bimodal in the southern part of the country, while, towards
the north (particularly the northeast), patterns gradually
change to unimodal with an extended dry season
. The diverse climatic patterns together with topographic
and soil characteristics result in a large diversity of farming
systems across Uganda.
We used cross-sectional household survey data sampled
across Uganda in the period of 2010 to 2011 covering
2716 households (Fig. 1). The surveys were conducted
as part of the World Bank Living Standard Measurement
Study – Integrated Surveys on Agriculture (LSMS-ISA)
(Kilic et al. 2015)
. Households were visited twice over a
12-month period to capture the two cropping seasons. The
households were sampled from a former survey conducted
in 2005 in which a stratification on urban/rural and
regional levels was used
. Details about the
sampling method can be found in World Bank (2016c).
Fig. 1 Locations of the households that were included in the analysis
(n = 1927) and administrative regions in Uganda (Sources: Thompson
2016, clipped from
). Each + represents a single household
We used survey data on household characteristics, farm
size, crop and livestock production and off-farm income.
2.3 Household level food availability
For each household a simple food availability (FA) indicator
was calculated following
Frelat et al. (2016)
. The FA indicator
(FA) estimates the average amount of potential food energy
that is available to each male adult household member
equivalent per day [kcal cap−1 day−1]:
ðEconsumed þ EincomeÞ
where Econsumed is the annual direct consumption of potential
food energy from on-farm products [kcal year−1], Eincome is the
annual indirect consumption of potential food energy from
onand off-farm income [kcal year−1], and nhh-mae is the
household size in male adult equivalents. For the estimation we used
annual data on agricultural and off-farm income generating
activities and on the household composition. The contribution
to FA was calculated for the following activities: Consumption
of on-farm food crops and livestock products, sales of on-farm
food crops and cash crops, sales of on-farm livestock products
and off-farm income. The crop and livestock related activities
were further differentiated into key crops and livestock groups
(contributing to the crop part and livestock part of the food
availability, respectively). A threshold of 2500 kcal
cap−1 day−1, representing the daily energy need of a male adult
, was chosen to distinguish households with
sufficient and insufficient food available.
Kilo-caloric energy values for crops and livestock products
were retrieved from the standard product list of the US
Department of Agriculture (source: ndb.nal.usda.gov/ndb/
search/list, accessed 02/07/16) and from the Food and
Agricultural Organization of the United Nations (source:
accessed 02/07/16). We converted prices from local currency
to US dollar (USD) using the currency-conversion rate of the
first of January 2011.
We assumed that all money earned in a household
was used to purchase a staple crop (in this case maize)
for food consumption. With this assumption we
overestimate the actual supply of energy to the household
because no account is made of other household
expenses (e.g. clothing, school fees, transport). The
indicator thus shows the potential to obtain sufficient
energy for the household, and not whether this actually
(Frelat et al. 2016)
. We also assumed that the
amount of crops consumed by the household was the
difference between the reported quantities harvested
and sold. Hence, post-harvest losses, gifts, in-kind
trading or saving of crop seeds were not considered. Cash
crops were defined as crops of which more than 90% of
the annual produce was sold
(Frelat et al. 2016)
for crops and livestock products reported in the dataset
varied substantially among the households. To reduce
the possible effect of erroneous price reporting, we used
the median of the reported prices per region per year to
calculate income from sold crops and livestock
products. We excluded all households from the analysis that
reported both zero agricultural production and zero
offfarm income in the year of sampling. We further
excluded households that reported no area for cultivation.
The final household sample for the analysis resulted in
1927 households out of a total of 2716 households.
2.4 Food availability classes and additional indicators
We aggregated the individual households into three food
availability classes to understand how on− and off-farm
activities differ according to the degree of food
Hammond et al. (2016)
correlated the food
availability indicator with other food security indicators,
including the Household Dietary Diversity Score
(HDDS) and the Household Food Insecurity Access
Scale (HFIAS). These food security indicators improved
up to a food availability indicator value of 5000 kcal
cap−1 day−1, but not beyond
(Hammond et al. 2016)
Hence, we split our dataset based on the following
thresholds: Class 1 included households with food
availability below 2500 kcal cap−1 day−1 (deficient food
availability); Class 2 comprised households with food
availability between 2500 and 5000 kcal cap−1 day−1
(adequate food availability); and Class 3 included
households with food availability above 5000 kcal
cap−1 day−1 (surplus food availability). Henceforth, we
call Class 1 ‘food deficient households’, Class 2 ‘food
adequate households’ and Class 3 ‘food surplus
Besides the food availability indicator, we calculated
five production and income-related indicators to provide
information about households’ performance and
livelihood orientation. We calculated a food self-sufficiency
indicator (FSS) to assess the importance of on-farm
production for household food consumption:
FSS ¼ Econsumed
Eneeded is the annual energy required for the household
[kcal year−1], calculated from 365 [days year−1] × 2500 [kcal
day−1] × household size in male adult equivalents (nhh-mae).
Gross daily income per capita (Igross) [USD cap−1 day−1]
quantified the total income that a household generated per
I gross ¼ 365
Itot is the total annual household income generated from
sold on-farm products and off-farm activities [USD year−1],
nhh is the household size.
Gross on-farm income per capita (Igross, on-farm) [USD
cap−1 day−1] identified the income that a household
generated per household member from the sold on-farm
I gross;on−farm ¼
I crops þ I livestock
Icrops [USD year−1] is the annual income from sold cash and
food crops, and Ilivestock [USD year−1] is the annual income
from sold livestock products.
While income indicators related to the real income
generated from sold products and off-farm activities, cash value
indicators related to the potential income that could be
generated from produced goods. The cash value of production
(CVproduction) [USD cap−1 day−1] identified the potential
income that could have been generated if all on-farm products
had been sold:
CVcrops þ CVlivestock
CVcrops is the cash value of crops [USD year−1], CVlivestock
is the cash value of livestock [USD year−1].
Market orientation (MO) [%] identified the share of
agricultural products that were sold relative to the cash value of
crops and livestock:
I crops þ I livestock
MO ¼ CV crops þ CV livestock
2.5 Spatial aggregation levels
weighted) to identify relative differences in contributing
activities and crops per class. At district level, we used the simple
(unweighted) median values because of the small sample sizes
per district and the large skewness in the data.
LSMS data for Uganda are representative at the
national and regional levels
(World Bank 2016a)
. However, we
use a more fine-grained aggregation of data to allow
analysis at the district level, so as to visualise spatial trends in
household characteristics as a function of strong
socioeconomic and environmental gradients within regions,
agricultural production zones (MAAIF 2010) and livelihood
(FEWS NET 2013)
. In addition, if insights from
this analysis are to provide support to sub-national policy
processes, then districts are the highest aggregation level
at which policy decisions are taken, as neither policy
engagement nor policy decisions take place at a zonal/
(an exception are the zonal agricultural
research and development institutes that manage and apply
agricultural research for specific agro-ecological zones,
The National Agricultural Research Act, 2005)
Consequently, several ongoing agricultural research
programmes for policy advice engage with policy
stakeholders at the district and not zonal/ regional level (e.g.
www.ccafs.cgiar.org/policy-actionclimate-change-adaptation-east-africa, PASIC – www.pasic.
However, our approach of aggregating the data to
district levels may introduce statistical bias (i.e.
Modifiable Areal Unit Problem, MAUP)
and Taylor 1979)
. In addition, sample numbers of
households per district were small (n = 8 to 64
households for the districts included in the aggregation) as
compared with the district populations (ranging from
50,000 to 2 million inhabitants) to generate accurate
and representative data at the district level
. To improve our understanding of, and confidence
in (i.e. subject to spatial bias), spatial patterns across
districts, we compared results of district level to
livelihood zone level aggregation
(FEWS NET 2013)
patterns observed were similar at both aggregation levels,
then we considered results to be robust. All analyses
were performed in R, version 3.2.3
Core R Development Core Team 2008)
and maps were
created in ArcMap, version 10.2.1
2.6 Scope of the study
The food availability indicator addresses part of what
defined as ‘food security’. Nutritional food
security, for example, is not included. Yet, Hammond
Results of the analyses are presented at regional level (four
regions, Fig. 1), for the three food availability groups, as well
as at district level for those districts for which the dataset
included at least 8 households (87 districts). At regional level,
we used simple mean values (i.e. the means were not
et al. (2016) observed that the food availability indicator
correlates with indicators of dietary diversity. Because
the majority of Uganda’s households depend on own
, food availability plays an
important role for their food security. For that reason,
the food availability indicator was considered suitable to
answer our research questions. Still, areas that we
identify as having large food availability can be areas
having small dietary diversity. The food availability and
food self-sufficiency indicators were sensitive to the
threshold of minimum energy requirements, which we
set at 2500 kcal cap−1 day−1. In our food availability
analysis we compared only the proportional contribution
of on- and off-farm activities, potentially obscuring
differences in absolute energy values among the regions.
However, as an analysis of the absolute values of the
on- and off-farm activities did not provide additional
insight, they were not included in the further analysis.
When interpreting the results of the aggregated
household data, we need to consider the MAUP and the loss
of information on variability among households within
an aggregation unit. In the interpretation of district level
data, focus should be given to trends across districts
rather than outcomes for individual districts. Household
locations (in latitude/ longitude) were randomly off-set
by the publisher
. When interpreting the
aggregated household data to small districts or
livelihood zones, the risk of allocation of households to
wrong livelihood zones must be considered. However,
large-scale patterns are not affected.
3.1 Household food availability patterns across Uganda
Food availability varied strongly among the rural
households with values ranging from well below 2500 kcal
cap−1 day−1 to values beyond 40,000 kcal cap−1 day−1
(Fig. 2a). Households with insufficient food availability
constituted 23% of the overall dataset. Also, at the
regional level, food availability varied strongly: Mean
food availability ranged from 10,000 kcal cap−1 day−1
for eastern Uganda to 49,000 kcal cap−1 day−1 for
central Uganda (Table 1), but the variability of households
within the regions was large (standard deviations from
2 0 , 0 0 0 k c a l c a p − 1 d a y − 1 i n e a s t e r n U g a n d a t o
491,000 kcal cap−1 day−1 in central Uganda). Because
of the large variability we used the percentage of
households per class to determine regional differences in food
availability. Food surplus households (Class 3, >
5000 kcal cap−1 day−1 available) constituted the
majority of households (66 and 72%) in the Central and
Fig. 2 a Household level food availability for 1927 households across
Uganda. Households are ordered by increasing food availability (FA)
along the x-axis where each bar represents one household. The red dashed
line represents a food availability value of 2500 kcal cap−1 day−1, the
daily energy need of a male adult
(Holden et al. 2001)
and the blue dashed
line represents 5000 kcal cap−1 day−1, the lower boundary of ‘food
surplus households’. A moving average was applied with a window length
of 10 households. The large values of consumed crop on the right side are
a result of a few households that reported high amounts of consumed
crops, which are expected to result from a bias in the survey rather than
to reflect real consumption figures. b Contribution of on- and off-farm
activities to FA per class (Class 1, deficient FA <2500 kcal cap−1 day−1;
Class 2, adequate FA between 2500 and 5000 kcal cap−1 day−1; Class 3,
surplus FA >5000 kcal cap−1 day−1) for n = 1927 households. The
thickness of bars represents the number of households per class. Livestock is
divided into poultry, shoats (sheep and goats), and cattle
Western regions, respectively, while food deficient
households (Class 1, < 2500 kcal cap−1 day−1 available)
constituted the minority of households (15 and 10%)
respectively. In contrast, in the Eastern and Northern
regions, only 48 and 40% were food surplus
households, respectively, while 27 and 34% were food
deficient households, respectively. The patterns observed at
district level resembled the observations at the regional
level. Median food availability was largest in the
western and central districts and smallest in the northeastern
districts (part of the Northern region), where food
insufficiency (< 2500 kcal cap−1 day−1) prevailed (Fig. 3a)
Median, mean and standard deviation of farm and food availability characteristics per region and for the overall dataset
*7 households located in Kampala region were not included in regional analysis, but in national and district level analysis
and the proportion of food deficient households was
Similarly, at the livelihood zone level median food
availability was smallest in the Northeast and North/
Northwest and largest in the southwest and central areas
(Fig. 3b). Yet, differences between the two aggregation
levels were also apparent revealing aggregation bias.
For example, two districts in the northwest and two
districts in the Eastern region had small median food
availability, while these trends were not observed at
the level of the livelihood zone level. In contrast, at
livelihood zone level the area in the north indicating
smallest median food availability was not identified at
the district level. Overall, patterns at the district level
were more variable than at the livelihood zone level.
3.2 Household activities and strategies
3.2.1 Household activities contributing to food availability
Contributions of activities differed strongly across
households with similar food availability (Online
Resource A1) demonstrating a large diversity in how
households across Uganda acquired food. Further, the
role of contributing activities changed along the FA
gradient as revealed by the moving average (Fig. 2a).
Consumption of food crops produced on-farm
contributed to a basic level of food availability for almost all
households. However, beyond this basic level, the
contribution of the other activities to food availability
increased. Cash income from sale of on-farm products
(food crops, cash crops and livestock products) first
became more important with increasing food availability
(moving to the right along the x-axis in Fig. 2a),
followed by an increase in off-farm income. Similarly,
in food deficient households (Class 1, < 2500 kcal
cap−1 day−1 available) off-farm income contributed least
to food availability (14%), while in food surplus
households (Class 3, > 5000 kcal cap−1 day−1 available) this
contribution was largest (34%) (Fig. 2b). In contrast,
food deficient households had the largest contribution
to food availability of consumed crops produced
onfarm (66%) and food surplus households had the
smallest (33%). While the contribution to food
availability of small livestock including poultry, goats and sheep
(‘shoats’) did not show clear differences among classes,
the contribution of cattle increased from deficient to
food surplus households. However, the variability
around the mean per contributing activity was large
(Table 2), pointing again to diverse strategies of rural
households within the classes.
Between regions, the contribution of on- and off-farm
activities to food availability was similar with only a
smaller mean contribution of consumed crops produced
on-farm and an equivalent larger mean contribution of
off-farm income in the Northern region (Fig. 4). At
district level, the median contribution of sold food crops
to food availability amounted to less than 10% for the
majority of the districts (Fig. 5a). For some 13 districts
scattered across all regions median contributions
Fig. 3 a Median food availability
at district level. All districts with
at least eight households taken
into account. The numbers in the
figure represent the number of
observations (households) per
district. b Median food
availability at livelihood zone level. All
zones with at least eight
households taken into account. The
maps reveal patterns, while
individual district/ livelihood zone
values must be interpreted with
care as LSMS data is not
representative at the district/ livelihood
zone level and their location (in
latitude/longitude) was randomly
off-set adding uncertainty to the
2012; WRI 2009; FEWS NET
2013; Thompson 2016)
amounted to 10–20% or above. The median contribution
of cash crops was zero for many districts in the
Northern and the Eastern regions (Fig. 5b). Median
off-farm income contribution was large (> 20%) in four
large districts in the northeast, in five districts in the
northwest (Fig. 5c), around Kampala and in a few
districts in the Eastern, Central and Western regions.
Observations of the contribution of these livelihood
activities to food availability were similar at livelihood
zone levels. For example, the large median off-farm
Contribution of on- and off-farm activities to food availability per class and region [%]
Class 1: Food deficient households, Class 2: Food adequate households, Class 3: Food surplus households
income contribution in the north was confirmed at
livelihood zone level. Yet, particularly in areas where
districts were small, district level maps showed variation at
short distance while livelihood zone maps indicated
larger patterns (Online Resources A2-4).
3.2.2 Household production, income and food self-suff iciency
Food self-sufficiency is the ratio of the household’s annual direct
consumption of potential food energy from on-farm products
(Econsumed) to the annual food energy required for the household
Fig. 4 Contribution of on- and
off-farm activities to rural
household food availability (FA) per
class (Class 1, deficient food
availability <2500 kcal
cap−1 day−1; Class 2, adequate
food availability between 2500
and 5000 kcal cap−1 day−1; Class
3, surplus food availability
>5000 kcal cap−1 day−1) and per
region. Number of households:
nWestern = 458, nNorthern = 554,
nCentral = 397, nEastern = 511 (7
households from Kampala not
included in analysis). The
thickness of bars represents the number
of households within the class.
Livestock is divided into poultry,
shoats (sheep and goats), and
Fig. 5 a Median contribution of sold food crops to household food
availability per district. b Median contribution of cash crops to
household food availability per district. c) Median contribution of
offfarm income to household food availability per district. All districts with
at least eight households were taken into account. The maps reveal
patterns, while individual district values must be interpreted with care as
LSMS data is not representative at the district level and their location
(in latitude/longitude) was randomly off-set adding uncertainty to the
(Sources: UBOS 2012; WRI 2009; Thompson 2016)
(Eneed). Eneed was calculated from 365 [days year−1] × 2500
[kcal day−1] × household size in male adult equivalents.
Overall, 38% of the rural households were food
self-sufficient, but patterns differed among the regions. In the Central
and Western regions, 52 and 66%, respectively, of the
households were food self-sufficient as compared to 29 and 14%,
respectively, in the Eastern and Northern regions (Table 1).
Per class, the proportions of food self-sufficient households
were largest in the Western region with 83% of the food
surplus households and 35% of the food adequate households.
By contrast, in the Northern region, only 27% of the food
surplus households and 15% of the food adequate households
were food self-sufficient (Online Resource A5). Household
income, production and production resources differed among
the regions. Mean gross daily income was smallest in northern
and eastern Uganda (0.3 USD cap−1 day−1) and largest in
central Uganda (1.2 USD cap−1 day−1) (Table 1). However,
mean gross daily income in central Uganda also had the
largest variability around the mean. The mean cash value of
production was smallest in the Northern region (0.1 USD
cap−1 day−1) and largest in the Western and Central regions
(0.3 USD cap−1 day−1), but also here variability around the
mean was large. Mean farm size was similar (1.4 ha) for the
Western and Eastern regions, but 20% larger for the Central
region and more than 40% larger in the Northern region
compared with the Western and Eastern regions. For all regions
except the Central region, mean farm size increased from food
deficient households to food surplus households (Online
Also, at the district level, the median cash value of
production was larger in the west than in the northeast and the
northwest (Fig. 6a). Median farm size was not correlated with
m e d i a n c a s h v a l u e s of pr o d u c t i o n (l i n e a r m o d e l ,
R2 = 1 × 10−5, p = 0.98). For example, the median cash value
of production was small in the northeast and northwest,
despite the larger median farm sizes (Fig. 6b). Some districts in
the west had smaller median farm sizes, yet their median cash
value of production was larger than in areas with larger farm
sizes. Median food availability and median farm size were
not correlated at the district level (linear model, R2 = 0.006,
p = 0.44).
Similar to the district level, the median cash value of
production at the livelihood zone level was largest in the
southwest and smallest in the north and east. Median
farm size aggregated per livelihood zone revealed more
distinct patterns than at the district level: At livelihood
zone level, an area of median farm sizes >1 ha stretched
from the northwest (West Nile) to the Eastern region,
while median farm sizes <1 ha covered most of the
Central and Western regions, parts of the Eastern region
and Karamoja (northeast). Median farm sizes <0.5 ha
were apparent in the mountain areas (Online Resources
3.3 Cropping patterns related to food availability
The contribution of individual crops to the household food
availability differed per food availability class (Fig. 7). The
mean contribution of banana, one of the important food crops
in Uganda, was largest for food surplus households (33%) and
smallest for food deficient households (14%). In contrast, the
mean contribution of the other important food crops (maize,
cassava and sorghum) was least for food surplus households
(14%, 9% and 2%, respectively) and most for food deficient
households (19%, 15% and 10%, respectively). The mean
contribution of coffee was relatively similar (3–5%) for all
classes, though the lowest contribution was consistently
observed for the food deficient households across all regions.
At the regional level, we observed differences in the
importance and type of crops contributing to food
availability in terms of consumption and cash generation.
While banana was the most important food crop in the
Western and Central regions, cassava, maize and sorghum
were the most important in the Northern and Eastern
regions (Fig. 8). Coffee was an important cash crop in the
Central region (and to a lesser extent in the Eastern
region), while there was no single dominant cash crop in
the other regions. In the Western region, banana was most
important for food surplus households showing that
banana was an important crop for income generation as well
as food. Most of the food deficient households consumed
all their produced bananas (almost 90% of the food
deficient households), while 66% of the food surplus
households (comprising more than 70% of all households in the
Western region) sold on average 19% of their banana
production. In the Northern region, ‘other crops’, including
rice and tobacco, contributed most for the food surplus
Fig. 7 Contribution of crops to the crop part of household food
availability per class (Class 1, deficient food availability <2500 kcal
cap−1 day−1; Class 2, adequate food availability between 2500 and
5000 kcal cap−1 day−1; Class 3, surplus food availability >5000 kcal
cap−1 day−1). The crop part includes energy from food crops consumed
and energy equivalent from income from food and cash crops sold (N.B.
coffee). Number of households: n = 1927. The thickness of bars
represents the number of households within the class
Fig. 8 Contribution of crops to
the crop part of household FA per
class (Class 1, deficient food
availability <2500 kcal
cap−1 day−1; Class 2, adequate
food availability between 2500
and 5000 kcal cap−1 day−1; Class
3, surplus food availability
>5000 kcal cap−1 day−1) and
region. The crop part includes
energy from food crops
consumed and energy equivalent
from income from food and cash
crops sold (N.B. coffee). Number
of households: nWestern = 458,
nNorthern = 554, nCentral = 397,
nEastern = 511 (7 households from
Kampala not included in
analysis). The thickness of bars
represents the amount of
households within the class
households. Legumes contributed similarly to the crop part
of food availability across all regions and classes with a
mean of 14 to 20%.
We structure the discussion around our research questions and
hypotheses on how food availability differed across Uganda,
how activities contributing to food availability and cropping
patterns differed with food availability and how our findings could
guide intervention strategies of policy and development actors.
4.1 What proportion of Ugandan households is food
deficient and how does food availability differ
across the country?
Overall 23% of the households across Uganda were food
deficient. Similarly, the national poverty rate was observed to be
around 24% in 2010
(Daniels and Minot 2015)
and the FAO
Hunger map identified 24.8% of the total population to be
unable to meet their minimum dietary energy requirements
over one year in the period from 2010 to 2012
Because our food availability indicator did not consider
nonfood expenses, our figures may underestimate the country’s
food insecurity status.
In the northern region more households are food deficient
Household food availability varied greatly across Uganda
with generally smaller food availability in the north, which
corresponds to less optimal agroecological conditions (e.g.
rainfall quantity and distribution) and poorer market access
due to weaker road infrastructure and absence of large urban
markets. The low food availability in the north matched with a
smaller mean cash value of production and a smaller mean
Agroecological conditions are known to affect food
(Hyman et al. 2005)
. For example, in Malawi
observed that relatively high annual rainfall
corresponds to a greater likelihood of households being food
secure. Rural households in the Northeast experience low
annual rainfall and a prolonged dry season
can affect agricultural production and subsequently household
food availability. However, while parts in the Southwest
receive similar amounts of annual rainfall as in the Northeast,
food availability in the Southwest was generally greater,
indicating that regional differences in food security are subject to
multiple factors that go beyond rainfall distribution.
Differences in infrastructure and market access may be one
reason for the regional differences in food availability.
et al. (2016)
observed a positive relation between market
access and food availability. The long-lasting conflicts in
northern Uganda prior to 2009/2010
(Tusiime et al. 2013)
insecurity and destroyed infrastructure, including food
production and distribution systems in the north
2010; Martiniello 2013)
. In addition, most larger urban centres
in Uganda with a high demand for food are located in the
Central and Western regions. Mean values of farm production
resources (farm size and tropical livestock units) could not
explain the lesser food availability in the Northern region
compared to the other regions.
Households in the Eastern region were more food deficient
than in central and western Uganda Regional mean
household food availability was less in the east than in
the Central and Western regions, although aggregations
at district level showed a more diverse picture. Also
regional mean gross daily income and mean cash value of
production were lower in the Eastern region. Similar to
the north, farming systems in the east (except for the
Mount Elgon region) are more based on annual crops
and less on major cash crops such as coffee.
4.2 How do contributing activities differ with food
availability and across Uganda?
The activities contributing to food availability varied strongly
between households and regions and with household food
availability. Consumption of crops produced on-farm was
the major contributor to food availability across all
households, matching observations by
Frelat et al. (2016)
However, while consumption of crops produced on-farm
was particularly important for households with low food
availability, off-farm income and market-oriented on-farm
activities increased in their importance with greater household food
availability, thereby partly supporting Hypothesis 2. The
changes in the activities along a food availability gradient
suggest that rural households follow different livelihood
strategies, related to their food availability. Three major strategies
for food availability are discussed: Food self-sufficiency (1),
cash crop production (2), and off-farm income generation (3).
Food self-sufficiency as a strategy for rural households
Although consumption of crops produced on-farm was
generally important for food availability, the majority of
the households were not food self-sufficient. Instead, the
sale of food crops played an important role also for the
food deficient households. Two factors may be at play:
First, many households choose to diversify their
livelihoods towards income-generating activities before they
are food self-sufficient
(Frelat et al. 2016; Ritzema et al.
. Second, particularly the food deficient
households are often unable to achieve food self-sufficiency,
because they need to sell some of their food crop
harvest to pay for non-food expenses (Leonardo et al.
2015). In the Northern region, food self-sufficiency
was less than in the other regions both overall and per
class. Food self-sufficiency was thus not a strategy
towards food security for households in the north despite
poor market access and infrastructure. This is perhaps
partly due to low productivity resulting from the low
and variable rainfall.
Harris and Orr (2014)
three potential pathways out of poverty: Extensification
by increasing land area, diversification and commercialisation
of crop production, and diversification of income. The smaller
proportion of food self-sufficient households on relatively
large farms in the north suggests that extensification seemed
not to be a strategy towards food security and poverty
reduction in that area. Instead, households focus on
incomegenerating activities, particularly off-farm income.
Cash crops as a strategy for rural households Crop
commercialisation can lift households out of poverty
and Orr 2014)
and has positive effects on food security
(Kristjanson et al. 2010)
. In addition, cash crops have
beneficial effects on the overall farm, as they generate money for
households to reinvest in their food crops. This reinvestment
can increase the productivity of the food crops and thereby
benefit household food security
(Govereh and Jayne 2003)
Indeed, the contribution of cash crops (e.g. coffee) increased
from deficient to food surplus households. Yet, the
contribution strongly depended on the region. At district and
livelihood zone levels, the median cash crop contribution was zero
for most of the north and the northeast of Uganda and between
zero and 10% in most districts in the west. These patterns
match the differences in infrastructure across Uganda and
highlight that market access is paramount for venturing into
Off-farm income as a strategy for rural households
Overall, the contribution of off-farm income to food
availability increased with the household food
availability status. This observation contrasts with other studies
that identified off-farm income to be particularly
important for the poorest
(e.g. Jayne et al. 2014)
Haggblade et al. (2010)
observed that poorer households
have no access to high quality sources of off-farm
income. Instead, they remain in low-pay market segments
(e.g. unskilled casual labour) with few opportunities to
step out of poverty. For these poorer households,
offfarm income is rather a means of survival. Yet, off-farm
income activities can serve as an important safety net
for food insecure or poor households during periods of
stress. For example, during drought years off-farm
income activities stabilise household income
et al. 2010)
. The north of Uganda is particularly
vulnerable to droughts and weather-related impacts because of
its unimodal rainfall and variability of rainfall
. Indeed, at all spatial aggregation levels, off-farm
income contribution was largest in these low-potential
agricultural areas in the north. This is in line with
Matsumoto et al. (2006)
who observe that the likelihood
of households participating in off-farm activities is
greater in low-potential areas.
4.3 How do cropping patterns differ with food availability
and across Uganda?
The contribution of major crops (banana, maize, cassava and
sorghum) to the crop part of food availability varied along a
food availability gradient and across regions. While the
contribution of sorghum and maize as major staple crops
decreased from deficient to food surplus households, the
contribution of banana increased, thereby only partly supporting
Hypothesis 3 that staple crops are more important for food
insecure households. Banana was a predominant crop in
western and central Uganda with increasing importance from
deficient to food surplus households, while cropping systems in
northern and eastern Uganda had greater crop diversity. Such
regional diversity patterns are expected to reflect the crop
diversity at farm level. What remains hypothetical is to what
extent such differences in crop diversity on the farm level
result in differences in household dietary diversity
Carletto et al. 2015; Dillon et al. 2015)
. In fact, the Western
region of Uganda has poor nutritional diversity
2010; UBOS 2013)
, which matches our observations of low
crop diversity in western Uganda.
East African highland banana is an important food and
cash crop East African highland banana is one of the most
important food crops in Uganda
and contributes considerably to household
food availability. At national level, the contribution of
highland banana was largest for food surplus households. Banana
is both an important food crop and cash crop in Uganda,
particularly in western Uganda
AhmadiEsfahani 2011; Jassogne et al. 2013)
. The larger banana
contribution for food surplus households can be explained in two
ways. On the one hand, most food surplus households are
located in western and central Uganda, two regions that are
important for banana production as a cash crop. However, this
coincidence results in a large banana contribution to the crop
part of food availability for the overall dataset, but does not
reflect causal relations between banana contribution and food
availability. On the other hand, the larger banana contribution
to the food availability of food surplus households may also be
related to the properties of banana as a perennial crop.
Perennial crops show potential benefits for food security over
annual crops (e.g. maize, sorghum). These benefits include
reduced expenditures on seeds, fertilizers and other inputs,
reduced labour for planting and weeding (thereby saving
labour to invest in off-farm activities), and extended growing
seasons that enable farmers to harvest over longer periods of
time (Batello et al. 2013). The second reason can also explain
the greater food self-sufficiency in western and central as
compared with northern and eastern Uganda. Finally, as one of the
most important staple crops in central and western Uganda,
demand for highland banana is high in the urban areas
generating an attractive market.
Crop contributions to FA were quantified assuming that all
harvest that was not reported as sold, was consumed. In
reality, farmers use part of the harvest as seed for the following
season and part is lost during handling or storage. Because of
this simplification, the FA approach quantifies the potential
FA rather than the actual FA. Differences in on-farm
postharvest losses (PHL) between crops could affect the
contribution of crops to household FA. For example, while maize
grains can be easily stored, harvested banana must be sold
and consumed soon after harvest. This difference in storage
characteristics of crops can result in differences in on-farm
PHL between crops.
At the national level in Uganda PHL for banana are larger
than for maize (approximately 12% for banana as compared to
6% for maize), while on-farm PHL are similar (less than 3%
for banana as compared to about 4% for maize)
Kikulwe, personal communication, 31 March 2017;
Kaminski and Christiaensen 2014)
. On-farm, banana is
harvested for each meal thereby reducing food wastage
(Enoch Kikulwe, personal communication, 31 Mar 2017).
By contrast, crops like maize are stored for longer, increasing
the risk of damage
(Affognon et al. 2015)
. Estimating the
effect of PHL on FA remains challenging, because data are
(Affognon et al. 2015)
, and PHL vary depending on
crops, post-harvest management and location
(Affognon et al.
2015; Kaminski and Christiaensen 2014)
. Yet, the figures
mentioned above suggest that differences in on-farm PHL
between major crops are small.
4.4 Targeting interventions: Intensifying food production,
increasing market access or generating off-farm income
sources – What makes households food secure?
Our results show that agricultural interventions alone will not
achieve household food security in Uganda’s north, where
market access is poor and agroecological conditions are
unfavourable. Instead, holistic livelihood interventions are
needed that promote opportunities for off-farm income
generation, such as improved access to education and urban
employment (Haggblade et al. 2010). Yet, interventions must
also support agriculture (particularly through food markets
and security of land tenure). Given that northern Uganda is
still recovering after a period of insecurity this may explain the
smaller role of agricultural activities for household food
availability as compared with the other regions
Cash crops were important for food availability while the
contribution of food crops remained limited. Improving access
to (cash crop) markets and to urban centres will probably
contribute more to improving household food security than
focusing solely on closing the yield gap of food crops. This
observation is confirmed by the small proportion of food
selfsufficient households and the fact that sale of food crops,
particularly short-cycle crops such as maize, was not related
to increasing food availability.
smallholder participation in food crop markets only benefits
the households with sufficient available assets (land,
livestock, capital and technology similar to the sustainable
livelihood framework capital assets). The poor households, in
contrast, do not manage to produce marketable surpluses from
which to derive income that could be used to borrow or buy
assets and thus to step out of poverty
. Also the
often low prices for food crops
(Harris and Orr 2014)
large investment barriers for closing the yield gap (e.g.
Ti t t o n e l l a n d G i l l e r 2 0 1 3 ) m a y b e r e a s o n s w h y
commercialisation of most food crops was not observed as a
key strategy for food availability in our data. For that reason,
current programmes that focus on the promotion of maize for
poverty reduction (e.g. the USAID Feed the Future
programme in Uganda) need to be evaluated on their success in
increasing household food security.
East African highland banana is an exception to our
observations on the role of food crops for household food
availability in western and central Uganda, as banana is an important
cash crop but also important in supporting household food
security. Potential interventions include breaking down the
barriers for access to cash crop markets along with facilitating
the uptake of yield-improving technologies and establishing
access to productive assets
. However, simply
establishing cash crop markets is not enough to improve
household food security. While the cultivation of cash crops
increases the frequency and amount of household income,
they also increase the dependency on local markets and on
highly unstable food prices
(Anderman et al. 2014)
Therefore, for a positive effect of cash crop cultivation on food
security, interventions (e.g. cross-border trade, grain storage,
no export bans in drought period) also need to promote access
and price stability of food crop markets
(Anderman et al.
4.5 Zooming in and zooming out – Contributions of the FA
District aggregation is strictly administrative and can be
questionable when contrasting livelihood systems are found within
districts. Averaging indicators across these systems introduces
bias. Therefore, FEWS NET developed livelihood zones to
represent core livelihood activities
some areas in Uganda these livelihood zones are defined at a
coarser level than the districts, for others in finer detail. Our
approach, based on individual household data, showed that
aggregation to livelihood zones did not capture small-scale
variation within zones, suggesting bias also at livelihood zone
level. This said, aggregation at both district and livelihood
zone levels must be interpreted with caution. For any policy
decision, further zooming in using representative datasets is
A major contribution of our FA approach is the use of
household data to provide quantitative information on livelihood
strategies in relation to food availability. The approach enables
us to describe countrywide patterns while preserving the
original household level variability, thus capturing a large diversity
of household strategies at the smallest scale. In this way it is a
useful addition to frameworks on vulnerability and risk
assessments (such as the sustainable livelihood framework linking
assets, vulnerability, livelihood strategies and outcomes). The
FA approach can further be used to validate aggregation zones,
for example by comparing livelihood zone descriptions on
major crops with the household information from the FA approach
(Browne and Glaeser 2010)
. Such a comparison on crop level
among the livelihood zone descriptions and our LSMS
household information revealed that in some zones where cassava
was described as an important crop, it contributed little to
households’ food availability (e.g. in the northwest and in
central and southern parts of Uganda). In contrast, in livelihood
zones where banana was not described as an important crop,
the household data revealed that banana was important for
household food availability (in central and southern areas)
(Browne and Glaeser 2010)
Uganda’s rural households follow diverse livelihood
strategies, which differ across the regions and with their degree of
food availability. Households with greater food availability
tend to diversify their on-farm and off-farm activities in order
to spread the risk. Those households with surplus food
availability have more income from on-farm and off-farm activities
as compared to households with insufficient food availability.
In areas with good market access and infrastructure, cash
crops can be an important strategy contributing to household
food availability, while in areas with poor infrastructure and
less favourable agroecological conditions, off-farm income,
probably of low quality, plays a more important role. Most
staple crops are more important for the households with
insufficient food availability, while East African highland banana
was identified to be one of the key crops for income
generation in western and central Uganda and most important for
households with surplus food availability.
The diversity of livelihood strategies must be considered
when targeting interventions. Holistic livelihood
interventions, including access to off-farm activities, are needed to
improve household food availability in Uganda’s north.
Instead of focusing on food self-sufficiency, households with
low food availability already diversify towards
incomegenerating activities. Interventions need to facilitate these
diversification strategies by improving access to food and cash
crop markets and to off-farm activities. Current programmes
often focus on promoting maize as a cash crop for food
security, but our results show that maize is important for
households with insufficient food availability, but not as a cash crop
for the households with a food surplus. In contrast, we show
East African highland banana to be both an important food
and cash crop. However, this crop has so far received scant
attention in investment programmes.
Our analytical framework provides a basis to account for
diverse household strategies in decision-making on
interventions. The food availability analysis quantifies where and
which activities are important for which group of farmers
and can help to identify suitable interventions for rural
households. We identify differences in livelihood strategies across a
food availability gradient and across the country. Both
dimensions are necessary for targeting interventions.
Acknowledgements This work was financed by the CGIAR Research
Program on Climate Change, Agriculture and Food Security, with
additional support from the CGIAR Research Program on Humid Tropics and
Wageningen University & Research.
Compliance with ethical standards
Conflict of interest
We declare no conflict of interest.
Open Access This article is distributed under the terms of the Creative
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Jannike Wichern is a PhD
candidate at Wageningen University
& Research in the group of Plant
Production Systems. Her research
focusses on food security and
smallholder farming systems in
East Africa. With a background
in environmental sciences, she
uses geostatistical approaches
and empirical survey data to
identify larger-scale patterns of food
security and related livelihood
Mark van Wijk is a Senior
Scientist at the International
Livestock Research Institute,
based in Turrialba, Costa Rica.
His research focuses on analysing
farming systems in developing
countries, trying to harvest the
added value of combining
modeling, experimental, participatory
and statistical approaches. New
work concentrates on developing
tools to better target intervention
options to improve income and
diets of smallholder farmers, and
quantify their adoption potential
and observed effects. Previously he worked for almost 10 years as
Assistant Professor at Wageningen University in the Plant Production
Katrien Descheemaeker is
assistant professor in the Plant
Production Systems group of
Wa g e n i n g e n U n i v e r s i t y &
Research (Netherlands). Her
research focuses on farming
systems analysis, resource use
efficiency, natural resources
interactions, and environmental
sustainability, with a special interest in
the functioning and dynamics of
mixed crop-livestock systems.
Current research combines
experimental trial work with simulation
modelling to identify and assess interventions for improved resource use
efficiency and farm profitability, and reduced risks associated with
climate and market variability. Across various projects on smallholder
farming systems in sub-Saharan Africa, Dr. Descheemaeker and colleagues
develop effective methods for participatory research with farmers and
other stakeholders to increase the potential adoption and impact of
technology and management options and to increase farm productivity, food
security and natural resource integrity.
Romain Frelat is a PhD
candid a t e a t t h e U n i v e r s i t y o f
Hamburg, Germany. During the
period this study was conducted,
he worked as a research associate
for the Centro Internacional de
Mejoramiento de Maiz y Trigo
( C I M M Y T ) a n d f o r t h e
International Livestock Research
Institute (ILRI). He uses data
mining approaches on empirical
survey data to extract information
about household and farming
systems in developing countries.
Previously, he worked and
obtained field experience in Mexico, Ethiopia and India.
Piet van Asten s a senior scientist
at the International Institute of
Tropical Agriculture based in
Uganda and working across
Africa. He earned both PhD and
MSc degrees from Wageningen
University in Agricultural and
Environmental Sciences. His
main interests are the
development of more productive,
profitable, and resilient crop and soil
management systems for African
smallholder farmers through
sustainable intensification in order to
improve livelihoods and conserve
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