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Biofuels bonanza? Sugarcaneproduction and poverty in villagessurrounding Budongo Forest, UgandaZinta A. Zommers a , Paul J. Johnson a & David W. Macdonald aa Wildlife Conservation Research Unit, Department of Zoology,University of Oxford, The Recanati-Kaplan Centre, Tubney House,Tubney, OX13 5QL, UK

Available online: 10 May 2012

To cite this article: Zinta A. Zommers, Paul J. Johnson & David W. Macdonald (2012): Biofuelsbonanza? Sugarcane production and poverty in villages surrounding Budongo Forest, Uganda,Journal of Eastern African Studies, 6:2, 177-195

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Biofuels bonanza? Sugarcane production and poverty in villagessurrounding Budongo Forest, Uganda

Zinta A. Zommers*, Paul J. Johnson and David W. Macdonald

Wildlife Conservation Research Unit, Department of Zoology, University of Oxford,The Recanati-Kaplan Centre, Tubney House, Tubney OX13 5QL, UK

(Received 15 April 2011; final version received 2 February 2012)

In 2006 the Ugandan government attempted to give a large portion of MabiraForest Reserve to the Sugar Corporation of Uganda Limited. The governmentargued the rainforest ‘‘give away’’ would contribute to economic developmentand create the opportunity for biofuel production. The proposal faced massiveopposition from Ugandan civil society, which countered that deforestation wouldincrease hunger and poverty. In order to help resolve the debate, this studyexamines the livelihoods of villagers living near a sugar factory and forest reservein rural Uganda. We interviewed 821 households seeking to explore possible linksbetween poverty and participation in sugarcane production at both the villageand household level. Villages closest to the factory appeared wealthier thanvillages closer to the reserve. However, households pursued different livelihoodstrategies and invested in different components of wealth. It is therefore unclearwhether the benefits of sugar or biofuel production outweigh the environmentalcosts of forest loss.

Keywords: Africa; Uganda; livelihoods; sugarcane; biofules; poverty

Introduction

During the past decade biofuels have been described as a ‘‘miracle in waiting’’ � a

means to reduce dependence on fossil fuels, limit carbon emissions and a mechanism

to spur economic growth and poverty reduction.1 However, sceptics have argued that

biofuel production contributes to an array of environmental and social problems,

including water shortages, deforestation and biodiversity loss, as well as food

insecurity.2 Biofuel production has generated heated debate in Uganda in particular.

In 2006 the Ugandan government attempted to give 71 km2 of Mabira Forest

Reserve to the Sugar Corporation of Uganda Limited (SCOUL). The government

argued the rainforest ‘‘give away’’ would contribute to economic development and

create the opportunity for biofuel production. Uganda’s president Yoweri Museveni

described the proposal as an economic ‘‘goldmine’’, stating that, ‘‘a sugar cane

plantation is also an oilfield’’.3 ‘‘The problem of Africa is not lack of forests but lack

of factories,’’ he claimed.4 Nevertheless, the government proposal faced massive

*Corresponding author. Email: zinta.zommers@lmh.ox.ac.uk

Journal of Eastern African Studies

Vol. 6, No. 2, May 2012, 177�195

ISSN 1753-1055 print/ISSN 1753-1063 online

# 2012 Taylor & Francis

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opposition from Ugandan civil society, which countered that deforestation would

increase hunger and poverty.

This study attempts to help resolve the debate by evaluating livelihoods near a

similar forest reserve, Budongo Forest, which lies adjacent to a sugar factory,

Kinyara Sugar Works Limited (KSWL). We seek to identify levels of poverty in

villages near the forest reserve and sugar factory, and to determine if households with

income from the sugar factory are wealthier than households without. We

hypothesize that if the presence of KSWL has a local development benefit, villages

closest to the factory should be wealthier than villages further from the factory.5

Additionally, within villages, individuals employed by KSWL should be wealthier

than individuals without such employment. By investigating local livelihoods we

hope to provide an empirical basis with which to develop future policies regarding

sugarcane and biofuels production.

Debating biofuels

Biofuels are defined by the Food and Agriculture Organization (2000) as, ‘‘organic

primary and/or secondary fuels derived from biomass which can be used for the

generation of thermal energy by combustion or by other technology. They comprise

both purpose-grown energy crops, as well as multipurpose plantations and by-

products (residues and wastes)’’.6 This article focuses on ethanol, an alcohol derived

by fermentation of crops such as sugar beets and sugarcane, and a biofuel of growing

importance. The International Energy Agency predicts that ethanol alone may make

up 10% of world gasoline use by 2025 and 30% by 2050.7 Proponents of ethanol

production cite a variety of positive impacts such as the diversification of rural

economies, large-scale job creation, poverty reduction, energy security and potential

for green house gas savings.8 Opponents of ethanol production raise concerns over

insufficient land availability, destruction of rainforests and wetlands, biodiversity

loss, and other environmental costs such as increased soil erosion and increased

water use.9

Arguments for and against ethanol production were raised in Uganda during

debates over the fate of Mabira Forest Reserve. Mabira Forest is the largest

remaining stand of indigenous forest in central Uganda. The reserve covers over

300km2.10 In 2006 the Government of Uganda proposed giving 71km2 of Mabira

Forest Reserve to SCOUL to grow sugarcane.11 The Minister of Water and

Environment claimed, ‘‘This will increase the sugar production in addition to

benefiting the communities with social services like schools, clinics, staff houses and

access roads.’’12 The government further argued that the proposal would contribute

to the development of the biofuels industry and reduce dependence on imported

oil.13 ‘‘It is in our interest to diversify the energy mix in order to reduce dependence,’’

argued Daudi Migereko, the Ugandan Energy Minister.14 The Ugandan Minister for

Water and Environment explained the benefits of the proposal to Parliament as

follows:

By providing additional land to SCOUL, increased sugar production will save foreignexchange of US $20�25 million per annum. In addition, SCOUL will produceadditional 10�12MW of electricity, which can be supplied to . . . the national Grid.Furthermore, [an] additional 3,500 jobs will be created with an annual earning of 3billion Ugandan shillings and infrastructure investment (schools, houses, dispensaries)

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worth 3.5 billion Ugandan shillings . . .. Besides financial and infrastructure benefits,SCOUL is planning to produce power alcohol (ethanol) which is a biofuel which can bemixed with petrol to the extent of 10�15%. This initiative will enable us to diversifyenergy production.15

With promises of jobs, wealth creation, schools and infrastructure, the conversion

of Mabira Forest was depicted as a development bonanza. In a letter to UgandanMembers of Parliament, President Museveni explained his reasons for supporting

the proposal, ‘‘Employment creation is one of my main interests when I support

manufacturers and hotels in acquiring land . . . How long shall we go on with this

problem of educated people without jobs?’’16 The president labelled opponents of the

proposal as ‘‘criminals’’ and ‘‘charlatans’’. In a statement to his caucus, Museveni

wrote:

Those opposing industrialization, apart from being enemies of NRM, are, in particular,enemies of the youth because they are the ones who need these jobs. I can no longertolerate this. I will mobilize the youth to smash politically all these cliques obstructingthe future of the youth and the country.17

Government statements implied that the environment had to be sacrificed for the

benefit of development. The Minister of State for the Lwero Triangle claimed, ‘‘Even

if Mehta or Madhvani ‘destroyed’ part of the natural vegetation, they would

contribute more to the national coffers and offer employment to thousands of

Ugandans.’’18

Nevertheless, the proposal faced massive opposition from civil society, high-

lighting the tension between economic and environmental needs. Opponents argued

that the ‘‘forest give away’’ would hurt the tourism industry, reduce biodiversity anddestroy a source of food and income for the people living around the forest,

increasing hunger and poverty.19 An anti-government demonstration on 12 April

2007 resulted in the death of several people. It was the largest street protest ever held

in Uganda.20

While the Mabira Forest proposal was eventually abandoned due to mounting

pressure, continued interest exists in both sugar plantation expansion and in biofuels.

In November 2007, the Ugandan Ministry of Energy published a Renewable Energy

Policy, which set a goal of blending 20% biofuel into all gasoline fuel.21 Kakira SugarWorks planned to start ethanol production in late 2009.22 Concerns have also been

raised that the Ugandan cabinet is reassessing the Mabira proposal.23 As these

debates continue, it is valuable to examine the livelihoods of households living near

sugar plantations and forest reserves.

Budongo Forest and KSWL

Budongo Forest offers an illuminating case study in which to examine these issues

further. Like Mabira, Budongo is a protected forest reserve near a large sugar

factory, KSWL. Budongo is located in Masindi District, Western Uganda (between18 37’ N to 28 03’ N and 318 22’ E to 318 46’ E) on an escarpment of the Western Rift

Valley, approximately 39 km west of the town of Masindi. It covers an area of 435

km2 and consists of medium-altitude moist semi-deciduous tropical forest.24

KSWL lies directly south of Budongo Forest. In 1975, it was the largest sugar

factory in Uganda with 260 km2 of sugar cane.25 Production ceased during Uganda’s

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civil conflicts, but the factory was formally rehabilitated in 1995. Between 1988 and

2002 the area under sugarcane cultivation by both KSWL and its outgrowers

increased over 18 fold, from 6.9 km2 to 127.29 km2, with a corresponding loss of 46.8

km2 of forest. 26 In 2002 Kinyara produced 50,000 of the 120,000 tonnes of sugarproduced in Uganda every year.27 Plantation size and sugar production have

continued to increase since then. In 2007 the cane fields covered approximately 140

km2.28 While KSWL is not currently engaged in ethanol production, it does burn

excess cane waste, baggase, to produce energy, feeding 5 MW into the Ugandan grid.

KSWL plans to upgrade its capacity to 10 MW.29

In this study we interviewed over 800 households in villages near KSWL and

Budongo Forest Reserve in order to explore sources of livelihood in each household

and poverty levels across the area. Poverty levels in the Western region of Uganda,which includes the Masindi District, are considerably lower than in Northern and

Eastern regions of Uganda.30 In 2002, the Western region had a poverty incidence of

34.4% while the Northern region has a poverty incidence of 66%.31 However, in the

Budongo Forest area, many households rely on subsistence agriculture. In the 1960s

four sawmills were in operation in Budongo Forest.32 When logging ceased, many

households turned to farming.33 The presence of KSWL has resulted in new

employment opportunities. Without historical or longitudinal data it is impossible to

fully evaluate the impact of KSWL on job creation and poverty reduction. However,better understanding of current differences in wealth between villages, employees of

sugar factories and other households, will allow policy makers to make informed

decisions about sugar expansion, forest reserve loss or biofuels production in future.

Methods

We conducted interviews in villages near Budongo Forest between October 2007 and

May 2008. A total of 821 interviews were conducted in 16 villages (Figure 1). Villageswere grouped based on their distance from Kinyara Sugar Works and location around

Budongo Forest Reserve. Four villages (Zebra, Kibwona, Nyabisense, Kijweka) within

6 km of KSWLwere classified as Group A. These villages are closest to the sugar factory

and furthest from Budongo Forest itself. Eight other villages are located to the west of

KSWL. Nyabyeya, Nyakafungo, Maramu and Karongo were classified as Group B.

They lie approximately 7 to 12 km from KSWL, near an old sawmill. Kanyege, Akim B,

Busingiro and Wudukuro were classified as Group C villages. They are approximately

13 to 21 km from KSWL, towards the western edge of the forest reserve. Finally fourvillages (Kigaragara, Kasenyi Bokwe, Kituka 2 and Kituka Central) lie 23 to 29 km

from KSWL, to the north of the forest by Murchison Falls Park in a part of Budongo

Forest classified as Kaniyo-Pabidi. They were classified as Group D villages.

The research methodology was reviewed and approved by the University of

Oxford Research Ethics Committee and approved by the Ugandan National Council

for Science and Technology. In each village, the chairman of the local council, the

highest elected member of village government, was approached for permission to

conduct interviews. After obtaining permission, villagers were invited to participatein the study. Subjects were informed of the purpose of the study and had to give their

verbal consent before continuing. An individual in roughly every third household

was approached for an interview, which was conducted by a local research assistant.

Interviews consisted of multiple-choice questionnaires structured according to

the livelihoods approach.34 The livelihoods approach can be used to understand the

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total portfolio of activities and assets that contribute to a household’s survival.

Livelihoods are assessed by examining: 1) assets; 2) how access to assets is modified

by social relations, institutions, and organizations; and 3) how trends and shocks

influence livelihood strategies.35 Questionnaires therefore focused on these three

areas, as well as on forest use. Questions referred to the activities of an individual’s

household. All forms of assistance, including remittances, were recorded.

We adopted a multidimensional approach to poverty, conceiving poverty as a

composite of economic wellbeing, capability, and social inclusion.36 Economic

wellbeing was determined by assessing assets, such as land and livestock ownership.

It was not possible to obtain exact income figures from respondents. Instead, a

variety of locally relevant indicators were chosen as proxies for wealth. Where

possible, indicators were based on results of previous studies conducted in Uganda.

For example, Pouw concludes that food items can also be used as a proxy for wealth

in Ugandan villages; wealthier individuals regularly add foods such as fish, meat,

milk and eggs to their diet.37 Ellis and Bahiigwa identified three different wealth

classes in Ugandan villages, based on assets:

(1) The well-off own above 2 ha or 5 acres of land, four or more cattle, five or

more goats, a bicycle or motorcycle, employ non farm labour, send their

children to primary and secondary school and may have a salaried job;

(2) Middle wealth categories have fewer assets, sell labour rather than buy it, or

engage in smaller scale non-farm activities such as trading and bicycle taxis;

Figure 1. Location of different villages in which interviews were conducted. Budongo Forest

Reserve is shaded in grey. Kinyara Sugar Works Limited (KSWL) is indicated by the star.

Circles represent Group A villages (ZB, Zebra; KB, Kibwona; NS, Nyabisense; KJ, Kijweka).

Squares represent Group B villages (NY, Nyabyeya; NK, Nyakafungo; MR, Maramu; KR,

Karongo). Triangles represent Group C villages (KN, Kanyege; AB, Akim B; BS, Busingiro;

WD, Wudukuro). Diamonds represent Group D villages (KA, Kasenyi Bokwe; KG,

Kigaragara; KT, Kituka 2; KC, Kituka).

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(3) The poorest own little or no land, no cattle or bicycles, sell their labour to

others, have few non-farm employment options, aside from beer brewing, and

are unable to pay school fees.38

We used these criteria to evaluate economic wellbeing in this study.

Capabilities and social inclusion were probed by examining household school

enrolment and participation in village activities. Respondents were asked whether or

not they, or other household members, participated in drinking societies, burial

societies, the local council, sports teams, village church, saving societies or other

community organizations. Answers where tallied to provide a score of civic

participation, which could range from 0 to 14. Zero indicates no member of a

household is involved in any organization. Fourteen indicates that at least twomembers from the household are each involved in seven different organizations.

Data were analysed using the computer programme SPSS 17. Differences

between villages and households were compared using Analysis of Variance tests

(ANOVAs). Chi-square and Fisher’s Exact Test (Monte Carlo 2 sided significance

based on 10,000 sampled tables) were used to explore categoric data. In an effort to

summarize components of wealth, a principal component analysis (PCA) was

conducted on seven assets.39 The resulting factor scores of households with different

forms of employment were compared using a Generalized Linear Model (GLM) toidentify associations between employment type and components of wealth.

Statistical interactions between village and KSWL employment were examined

using a logistic regression where employment by KSWL was treated as a binary

response.

Results

Summary statistics

On average, 51 people (SD�6.28) were interviewed per village. Household size

ranged between one and 13 individuals, with a median size of four individuals (Table

1). Household size differed significantly across villages (Welch F (15, 297.595)�3.994,p�0.000). A Games Howell Post-hoc test revealed that households in Zebra, the

village closest to the factory, were significantly smaller than other villages.

Across all villages, few households (7.5%) grew cash crops and only 1.9% of

households grew sugar cane. Near KSWL a slightly higher percentage of households

grew sugarcane (Table 2). Only one-fifth of households (20%) gained income from

wage labour, even fewer (7%) from work at KSWL. Villages differed significantly in

this respect (Fisher’s Exact Test�186.504, p�0.00). Nearly three times as many

households in Group A (43.2%) earned a wage as in Group D (15.3%) and over seventimes as many as in Group C (6.3%). Employment by KSWL also differed between

villages (x2(15)�204.51, p�0.000). Nearly all interview subjects employed by

KSWL resided in Group A villages ( 98.3%, n�58).

Differences in poverty across villages

Assets and nutritional intake

Across all the villages land ownership was limited. Of all households surveyed, 24%

owned no land, and an additional 53% owned less than three acres of land. Zebra

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Table 1. Characteristics of interview subjects and households by village. While individual subjects were interviewed, questions referred to the whole

household.

Village

group Village name

Ranked

proximity to

KSWL

Approximate distance of

households in village to

KSWL (km)

# People

interviewed

%

Male

%

Female

Average age of

interview

subjects

Min

age Max age

Mean

household

size

A Zebra 1 2.54�3.30 47 55.3 44.7 29.62 14 50 2.9892.027

Kibwona 2 3.89�6.13 66 50 50 37.77 16 70 5.3592.787

Nyabisense 3 4.32�5.44 50 46 54 33.00 19 65 4.2091.895

Kijweka 4 4.68�5.50 50 58 42 33.98 16 76 3.8892.096

B Nyabyeya 5 7.44�8.02 50 48 52 36.94 16 92 4.5692.643

Nyakafungo 6 9.77�10.25 42 50 50 41.85 19 75 4.2192.976

Maramu 7 10.66�11.11 56 42.9 57.1 38.98 14 84 3.6892.081

Karongo 8 11.60�12.24 49 53.2 46.8 36.17 18 70 4.6692.315

C Kanyege 9 12.91�14.42 50 56 44 34.58 16 64 4.1492.241

Akin B 10 18.19�19.57 60 57.6 42.4 35.5 15 70 4.9792.846

Busingiro 11 19.2�20.53 51 56.9 43.1 37.92 19 72 4.3792.607

Wudukuro 12 20.2�21.38 60 50 50 36.85 18 87 5.2592.33

D Kasenyi

Bokwe

13 23.13�24.20 49 61.2 38.8 31.9 17 56 5.0292.65

Kigaragara 14 24.48�25.25 51 56.9 43.1 36.35 17 77 4.4192.515

Kituka 2 15 25.02�26.87 45 80 20 32.84 19 63 3.3891.957

Kituka

Central

16 27.08�28.87 45 60 40 32.91 16 71 5.3393.104

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(Group A) had the lowest percentage of land ownership, fewer than 10% of

households (Table 3). Households in Group B reported the largest land holding. For

example, nearly 97% of households in Wudukuro owned land. There was a

significant difference in land ownership between village groups (x2(3)�15.74,

p�0.001).

Across all villages, only 1.8% of households owned cattle and only 17.5% of

households owned pigs. Goats and chickens were more common: 35.3% of

households owned at least one goat and 60.7% of households owned at least one

chicken. There was a significant difference in percentage of oxen ownership between

villages (Fisher’s Exact Test�8.733, p�0.018). A greater percentage of households

in Group A villages owned oxen than in all other village groups combined. Similarly,

the percentage of pig owning households in Group A was five times greater than the

percentage of pig owning households in Group C. The association between livestock

and poultry ownership and village group was statistically significant (goats:

x2(3)�26.824, p�0.000, pigs: x2

(3)�47.849, p�0.000, chicken: x2(3)�10.710,

p�0.013).

Radio and bicycle ownership was common across villages � 67. 3% owned at least

one radio and 52.5% of households owned one or more bicycles. However, only

23.7% of households owned mobile phones. Mobile phone ownership was most

prevalent in Group A villages (Figure 2), and the difference was significant between

village groups (x2(3)�44.464, p�0.000).

Across all villages, the majority (75.0%) of households lived in houses of mud,

wattle and thatch. A lower percentage of households in villages in Group A lived in

mud houses, and a greater percentage lived in brick houses (Figure 3).

Table 2. Percentage of households growing cash crops, sugarcane or earning wage labour.

Village

group Village name

% Households

growing cash

crops

% Households

growing

sugarcane

% Households

earning a wage

labour

% Households

with a member

working for

KSWL

A Zebra 4.3 2.1 89.4 68.1

Kibwona 4.5 3.0 28.8 7.6

Nyabisense 16.0 6.0 40.0 24

Kijweka 8.0 6.0 22.0 18

B Nyabyeya 0 0 8.0 2

Nyakafungo 0 0 23.8 0

Maramu 3.6 0 7.1 0

Karongo 0 0 12.2 0

C Kanyege 2.0 0 10.0 0

Akin B 10.0 0 1.7 0

Busingiro 21.6 5.9 5.9 0

Wudukuro 16.7 5.0 8.3 0

D Kasenyi

Bokwe

6.1 2.0 14.3 0

Kigaragara 11.8 0 17.6 0

Kituka 2 6.7 0 13.3 0

Kituka

Central

6.7 0 15.6 0

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The association between house type and village group was statistically significant

x2(3)�34.512, p�0.000.

Meat consumption was limited across all villages � 46.8% of interview subjectsreported eating meat only once a month or a ‘‘few’’ times a month. Only 17.5% of

interview subjects had eaten a preferred food in the last 24 hours, including rice, fish,

eggs, groundnuts, or yams. A higher percentage of respondents in Group C villages

reported eating one of these food products. The association between preferred food

consumption and village group was statistically significant (x2(3)�79.398,

p�0.000). Specifically, a greater number of individuals in Group C villages reported

Table 3. Land ownership across different villages.

Village

group Village name

% own

no land

% ownB1

acre

% own 1�2.9 acres

% own 3�5.9 acres

% own 6�10 acres

% own

over 10

acres

A Zebra 91.5 6.4 0 2.1 0 0

Kibwona 15.2 13.6 37.9 21.2 10.6 1.5

Nyabisense 14.3 22.4 46.9 12.2 4.1 0

Kijweka 22.4 16.3 42.9 14.3 2 2

B Nyabyeya 18 22 24 22 14 0

Nyakafungo 9.5 42.9 35.7 4.8 7.1 0

Maramu 10.7 19.6 32.1 26.8 8.9 1.8

Karongo 32.6 17.4 30.4 13 4.3 2.2

C Kanyege 30 22 32 14 2 0

Akin B 12.5 32.1 30.4 19.6 3.6 1.8

Busingiro 21.6 7.8 37.3 25.5 7.8 0

Wudukuro 3.4 33.9 44.1 16.9 1.7 0

D Kasenyi

Bokwe

18.8 18.8 37.5 25 0 0

Kigaragara 16 16 44 18 4 2

Kituka 2 35.6 13.3 40 8.9 2.2 0

Kituka

Central

31.8 11.4 29.5 13.6 9.1 4.5

Figure 2. Percentage of households owning a mobile phone in different village groups (n:

Group A �213, Group B �197, Group C �221, Group D �190).

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consuming fish: 12.3% of respondents in Group C reported eating fish in the last 24

hours food vs. 3.4% of respondents in Group A, 4.5% in Group B, and 2.6% in

Group D.

Education and civic participation

Across all villages, households participated in approximately 4.3792.07 social

activities. Mean participation was highest in Kibwona (Group A), and lowest in the

village of Busingiro (Group C). Participation levels differed significantly between

villages (Welch’s F(15, 300)�2.406, p�0.003). However participation did not differ

significantly between village groups (Group A�4.4391.50, Group B�4.5191.44,

Group C�4.2091.31, Group D�4.3591.51) (Welch’s F(3, 448)�1.188, p�0.132).Of the 584 households that gave information about school enrolment, 125

(21.4%) enrolled all their children in school. In 28.8% of households children did not

attend school at all. A greater percentage of children attended school in villages in

Group A and Group D (Table 4). However, this is mainly a reflection of high

attendance in two villages, Zebra and Kituka Central.

Differences in poverty between households according to employment status

The assets of the households with income from KSWL were compared to households

without anyone working for KSWL. No statistically significant difference was found

in the number of oxen, pigs or goats or in preferred food consumption. However a

significant difference was found in the frequency of meat consumption (Welch’s

F (1, 103)�30.871, p�0.000), the number of radios (Welch’s F (1, 69)�16.080,

p�0.000), bicycles (Welch’s F (1, 67)�30.148, p�0.000), and mobile phones

(Welch’s F (1, 62)�40.322, p�0.000). Individuals working for KSWL ate meat

more often and owned a greater number of radios, bicycles and mobile phones. The

Figure 3. Distribution of housing types in different village groups. While the majority of

houses are made of mud, Group A has a greater proportion of brick houses. Other includes a

variety of rare housing types including houses of cement and iron sheets and tents or plastic.

186 Z.A. Zommers et al.

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number of school-aged children attending school was not significantly different from

non-KSWL households (F (1, 582)�3.554, p�0.060).

Individuals earning a wage labour but not working for KSWL (n�100), hadsignificantly greater number of mobile phones and radios (Welch’s F (1, 195)�34.323,

p�0.000 and Welch’s F (1, 241)�18.241, p�0.000), ate a greater number of preferred

foods (Welch’s F (1, 211)�4.937, p�0.028) and had a higher percentage of children in

school (F (1, 582)�4.063, p�0.044) than those not earning a wage.

The assets of households growing sugarcane (n�16) were compared to other

farmers. Households growing sugarcane had significantly more chickens

(F (1, 697)�5.618, p�0.018), bicycles (Welch’s F (1, 15.642)�7.182, p�0.017), and

a greater proportion of children in school (Welch’s F (1, 15.046)�6.956, p�0.019).However, no difference was found between households growing sugarcane (n�14)

and households growing other cash crops (n�47).

A PCA indicated that several components of wealth clustered together, and GLMs

indicated that component scores were associated with different forms of employment.

Bicycle, chicken, goat and radio ownership clustered with the size of land owned and

farmed (Component 1). Sugarcane growing had a significant main effect on this

component of wealth (F (1, 716)�14.036, p�0.000). Employment by KSWL, or other

forms of wage labour, had no significant effect on Component 1 (F (1, 718)�1.487,p�0.223; F (1, 683)�0.861, p�0.354). Mobile phone ownership was negatively

associated with the size of land owned and farmed (Component 2). However, this

component of wealth was associated with KSWL employment (F(1, 718)�6.727,

p�0.01) and with other forms of wage labour (F (1, 683)�31.29, p�0.000).

Do differences in poverty reflect differences between villages or differences inemployment status?

Employment status did not predict whether or not a household had goats, pigs,

chicken, radios, or ate preferred foods (logistic regression, Table 5). However,

Table 4. Percentage of school-aged children attending school.

Village group Village name % of school-aged children in school

A Zebra 63.2

Kibwona 38.98

Nyabisense 40.66

Kijweka 46.16

B Nyabyeya 38.44

Nyakafungo 49.24

Maramu 38.15

Karongo 43.78

C Kanyege 41.26

Akin B 37.88

Busingiro 39.1

Wudukuro 42.64

D Kasenyi Bokwe 31.98

Kigaragara 39.77

Kituka 2 44.79

Kituka Central 73.09

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employment by KSWL did predict mobile phone ownership and meat consumption

in addition to the village effect. Other forms of wage labour significantly predicted

meat consumption, but did not significantly influence mobile phone ownership.

Individuals who worked as outgrowers were more likely to eat preferred foods.

Discussion

Poverty levels were high across the study area. Few households surveyed qualify as

‘‘well-off ’’ according to Ellis and Bahiigwa’s criteria: 24.1% of households owned no

Table 5. Results of logistic regression conducted to assess the impact of village or

employment status on different measures of wealth. Significant findings are highlighted in

bold. Results provide an indication of the importance of each factor in predicting whether or

not a household will have the given asset. Village significantly predicted whether or not

households had goats, pigs, chickens, radios, mobile phones, consumed meat or ate preferred

foods. Yet, mobile phone ownership, meat and preferred food consumption were also

influenced by type of employment.

Asset Factor B S.E. Wald df Sig.

Goats Village 43.148 15 0

Employment by KSWL �0.266 0.697 0.146 1 0.703

Interaction 3.489 4 0.479

Pigs Village 78.751 15 0

Employment by KSWL �1.368 1.108 1.526 1 0.217

Interaction 2.497 4 0.645

Chicken Village 24.48 15 0.057

Employment by KSWL 20.884 11602.718 0 1 0.999

Interaction 1.026 4 0.906

Radio Village 28.697 15 0.018

Employment by KSWL 0.121 0.88 0.019 1 0.89

Interaction 0.602 4 0.963

Mobile phone Village 29.966 15 0.012

Employment by KSWL 2.128 0.762 7.807 1 0.005

Interaction 6.984 4 0.137

Meat consumption Village 47.186 15 0

Employment by KSWL 1.292 0.694 3.468 1 0.063

Interaction 16.047 4 0.003

Preferred foods Village 72.904 15 0

Employment by KSWL 0.065 0.893 0.005 1 0.942

Interaction 5.89 4 0.207

Meat Village 34.031 15 0.003

Other wage labour 1.828 0.886 4.254 1 0.039

Interaction 6.885 15 0.961

Mobile phone Village 24.268 15 0.061

Other wage labour 0.758 0.948 0.639 1 0.424

Interaction 14.755 15 0.469

Preferred foods Village 70.106 15 0

Other wage labour 2.603 1.054 6.097 1 0.014

Interaction 8.401 15 0.907

Preferred foods Village 46.34 15 0

Sugarcane outgrower �1.012 1.254 0.651 1 0.42

Interaction 0.906 6 0.989

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land, 98.2% of households did not own oxen, 64.7% did not own goats, 47.5% of

households did not own bicycles, and 97.2% of households did not own motorcycles.

In fact, only five households meet all of Ellis and Bahiigwa’s criteria and can be

classified as ‘‘well-off ’’. Households varied along different dimensions of poverty.For example, a relatively large percentage of the landless sent all their children to

school (24.5%) or owned mobile phones (30%). While these households appeared to

have fewer assets they had greater capabilities. This unexpected result appears to

indicate that landless households are not entirely destitute and may choose to invest

in different components of wealth.

Two livelihood strategies could be identified. Households that pursue agriculture

tend to invest in assets related to farming. For example, sugarcane outgrowers scored

highly on the component of wealth related to land ownership and livestockaccumulation (see PCA results). Secondly, households with individuals working for

KSWL appear to invest in assets such as mobile phones, better housing, and

education. How or why households choose different livelihood strategies is unclear.

Perhaps households in which individuals work for KSWL do not have available

labour for agriculture or do not have access to land. Alternatively, cultural or

migration patterns may explain livelihood choices. A greater percentage of forced

migrants, including refugees and internally displaced people, work for KSWL than

do socioeconomic migrants or non-migrants.40 However, few forced or socio-economic migrants cite employment by KSWL as a primary reason for settling in

Budongo Forest area.41

Does KSWL contribute to poverty reduction?

Employment by KSWL, or work as an outgrower, appears to provide a variety of

benefits. For example, households that grew sugarcane consumed more preferred

foods. They had a greater number of chickens, bicycles and sent more children toschool than other farmers. However, village level differences, rather than employ-

ment status or outgrowing, also explains variation in wealth (Table 5). Controlling

for village, employment by KSWL significantly predicted only mobile phone

ownership and meat consumption. Other forms of wage labour also predicted

meat consumption. Thus, the clearest benefit of work at KSWL, beyond benefits

bestowed by wage labour in general, appeared to be mobile phone ownership.

Does mobile phone ownership represent a significant step towards development

and poverty reduction? Opinions vary. Mobile phones can be used to providefinancial services to those without access to traditional banks.42 Phones can provide

information about market prices for agricultural crops. Non-economic benefits

include the empowerment of women, improved law enforcement and more rapid and

effective communications during disasters.43 However, other studies conclude that

mobile phones contribute only to additional expenditure. While they are a

dependable way to contact family members, and as such surely a good thing, they

do not contribute to improved livelihoods.44 It is unclear whether or not individuals

near Budongo Forest use phones for personal communication, to promote farming,or for business. Nearly a third (30.7%) of mobile phone owners do not even own

land. This fact suggests that they are unlikely to use mobile phones to promote

farming. In short, individuals who can afford mobile phones as a result of

employment by KSWL may not be able to capitalize on phones because they do

not pursue livelihood strategies based on agriculture.

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The evaluation of poverty levels in this study is limited by the fact that income or

longitudinal data were not available. In future, it may also be useful to focus

interviews on sugarcane outgrowers or KSWL staff, particularly those living on the

commercial estate. They may earn higher salaries, or benefit from housing providedby KSWL, and may therefore contribute disproportionately to the economy of the

area. While such data would be important, it would not affect the purpose of this

study, which was to assess livelihoods in villages around Budongo Forest and

evaluate the broader impact of KSWL on the surrounding area. Any remittances

sent by KSWL employees living on the estate to family or friends in surrounding

villages were recorded in our questionnaires.

Does sugarcane production constitute a development bonanza?

Villages close to the sugar factory were wealthier by some measures than villages

further away from the factory. For example, a greater percentage of households in

Group A villages had assets such as mobile phones and oxen, and lived in superior

housing (Table 6). One village in Group A, Zebra, stands out as particularly unusual.

Nearly 70% of households in this village were employed by KSWL. While very few

households there owned land or livestock, a greater percentage of households had

superior quality housing and owned mobile phones. Meat and preferred foods wereconsumed more often and a high percentage of children attended school.

While poverty may be reduced in the vicinity of KSWL, the presence of a sugar

factory does not appear to have led to a ‘‘development bonanza’’ in the broader

Budongo area. Employment by KSWL was limited. Only 7% of interview subjects

had a household member employed by KSWL, and less than 2% of households grew

sugarcane. Most households continue to rely on subsistence agriculture.

Conclusion

In conclusion, it appears that employment in the sugar industry can bring benefits to

some households around Budongo Forest. However it is questionable if such

employment results in meaningful improvements in quality of life across a large

number of villages. It is clearly simplistic to portray the expansion of the sugar

industry and development of biofuels production as an economic goldmine. It is

unclear whether the benefits of expanded sugar production, and possible biofuels

production, outweigh the costs of destroying forests. The world’s poorest peopledepend primarily on environmental goods and services for their livelihoods.45 As a

result of this dependence on ecosystem services, poor households are particularly

sensitive and vulnerable to environmental changes. Unless biofuel production can

greatly increase opportunities for wage labour or outgrower farming, the destruction

of forest reserves could undermine access to natural resources and benefit only a

limited minority of wage labourers living closest to the sugar estate.

If the presence of a sugar factory does not generate widespread development and

poverty reduction, is it prudent for the Ugandan government to push for theexpansion of the biofuels industry at the expense of forests? Ethanol production may

have positive spillovers into the greater economy, boosting state and local tax

receipts.46 However, serious ethical questions are raised if the benefits of biofuel

production accrue to consumers, not to local producers, who may further be harmed

by losing access to forest resources.47 During debates about Mabira Forest, the

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Table 6. Results summary. Shaded boxes indicate the number of households with a given asset or quality greater than the average of all villages. For

example, a greater than average number of households in Zebra engage in wage labour, work for KSWL, grow sugar cane, do not own land, have

superior quality housing, own phones, eat meat or preferred foods frequently, or have children in school. Shading indicates that there is no clear

geographic pattern to wealth. Villages closest to KSWL do not have greater than average levels of wealth across all components.

Village

group

Village

name

Wage

labour KSWL

Cash

crop

Sugar

cane

No

land

4�6

acres

land

Over

10

acres

land Better housing

#

Ox.

#

Pigs

#

Goats

#

Chicken

#

Phones Meat

Pref.

foods

%

Kids

in

school

A Zebra

Kibwona

Nyabisense

Kijweka

B Nyabyeya

Nyakafungo

Maramu

Karongo

C Kanyege

Akin B

Busingiro

Wudukuro

D Kasenyi

Bokwe

Kigaragara

Kituka 2

Kituka

Central

Jou

rna

lo

fE

astern

Africa

nS

tud

ies1

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National Forest Authority cautioned the Ugandan government against the proposal

and several senior managers resigned over the issue.48 They warned that Mabira’s

contribution to rain formation, soil conservation, and other environmental services

could be lost as a result of the conversion. As Pender et al. write, ‘‘Substituting

other forms of capital for natural capital may be the most effective way to pursue

development in the near term . . . but the costs of depleting natural capital may not be

fully accounted for.’’49 Before expanding sugarcane production into forest reserves,

the Ugandan government must carefully evaluate the importance of ecosystem

services to local livelihoods. Households in villages closest to Budongo Forest

benefited least from the presence of the factory and may rely most on natural capital.

If forests such as Mabira and Budongo support the subsistence of rural communities,

biofuel expansion will result in social costs, and further endanger species such as

chimpanzees.50

Ultimately, if sugarcane farming and ethanol production is to assist poverty

reduction, a larger share of benefits must be passed to all the communities

surrounding sugar estates. A variety of steps may be taken to maximize economic

and environmental benefits. Greater development of credit institutions could help

small farmers meet the set-up costs of entering the sugarcane market. Education

projects could be developed to teach farmers how to grow both sugarcane and food

crops. Smallholder outgrower based sugarcane bioenergy could minimize forest loss,

contribute to broader poverty reduction and provide energy benefits.51

Acknowledgements

We thank A. Hannington Fani, A. Geoffrey, and O. Katwesige for their help conductinginterviews, and are grateful to K. Grounds and U. Zommers for entering data. We alsoappreciate the suggestions provided by two anonymous readers. Research was supported bythe Commonwealth Scholarship Commission, the Cleveland Metroparks Zoo, and theCanadian Centennial Scholarship Fund.

Notes

1. Abbott, Sugar: A Bittersweet History.2. De Keyser and Hongo, ‘‘Farming for Energy for Better Livelihoods in Southern Africa’’;

Service, ‘‘Another Biofuels Drawback.’’3. Child, ‘‘Civil Society in Uganda.’’, 248�249.4. C. Obore, ‘‘Why I Decided to Give Mabira Away,’’ Daily Monitor, April 16, 2007, http://

www.lexisnexis.com/5. The majority of KSWL employees live on the KSWL estate, or in villages within walking

or cycling distance from KSWL. Thus we expect differences in wealth between villagesbased on distance from KSWL.

6. FAO, ‘‘The Energy and Agriculture Nexus.’’, 767. Amigun, Sigamoney, and Blottnitz, ‘‘Commercialisation of Biofuel Industry.’’8. Ibid.; Blessing Mangoyana, ‘‘Bioenergy for Sustainable Development’’; Bringezu et al.,

‘‘Towards Sustainable Production and Use of Resources’’; FAO, Food Energy and Climate;UN Energy, Sustainable Bioenergy; Wang, Me, and Huo, ‘‘Life-cycle Energy andGreenhouse Gas Emission.’’

9. Mullera et al. ‘‘Some Insights in the Effect of Growing Bio-energy’’; Robertson et al.‘‘Sustainable Biofuels Redux’’; WWF, Sugar and the Environment.

10. Briggs, Uganda: The Bradt Travel Guide.11. M. Mutagamba, ‘‘Cabinet Paper on Giving Mabira Forest to SCOUL.’’ New Vision, April

2, 2007, http://allafrica.com/stories/200704030561.html12. Ibid.

192 Z.A. Zommers et al.

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13. ‘‘Deaths in Uganda Forest Protest.’’ BBC News, April 12, 2007, http://news.bbc.co.uk/1/hi/world/africa/6548107.stm

14. L. Namubiru, ‘‘Uganda: Is Biofuel the Solution to Our Energy Problem.’’ New Vision,February 8, 2008, http://allafrica.com/stories/200802110019.html

15. M. Mutagamba, ‘‘Cabinet Paper on Giving Mabira Forest to SCOUL.’’ New Vision, April2, 2007, http://allafrica.com/stories/200704030561.html

16. ‘‘Why I Support Mabira give-away�Museveni.’’ New Vision, April 18, 2007, http://www.newvision.co.ug/D/8/12/560612

17. ‘‘Ugandan President Says Controversial Forest Conversion not Resolved.’’ Daily Monitor,December 21, 2007.

18. ‘‘Poverty, not Investors, is Mabira’s Biggest Enemy.’’ New Vision, May 9, 2007, http://allafrica.com/stories/200705090984.html. The Mehta Group is the company that ownsSCOUL.

19. D. Howden, ‘‘African Forest under Threat from Sugar Cane Plantation,’’ The Independent,July 10, 2007, http://www.independent.co.uk/environment/african-forest-under-threat-from-sugar-cane-plantation-456601.html

20. H. Bogere and M. Nalugo, ‘‘Police File Murder Charges over Mabira ForestDemonstrations,’’ The Daily Monitor, April 16, 2007, http://www.lexisnexis.com/

21. F. Nakhooda, ‘‘Sugar Companies Have Answer to Fuel Shortages,’’ New Vision, June 12,2008, http://www.newvision.co.ug/D/8/459/633240

22. Ibid.23. ‘‘Cabinet Plots Fresh Mabira Giveaway,’’ Daily Monitor, June 26, 2008, http://www.

lexisnexis.com/24. Reynolds, The Chimpanzees of Budongo Forest, 9.25. Ogendo and Obiero, ‘‘East African Sugar Industry.’’26. Mwavu and Witkowski, ‘‘Land-Use and Cover Changes (1988�2002) around Budongo

Forest Reserve.’’ The figures quoted include data about sugarcane cultivated by KSWLand by outgrowers; private farmers that grow and sell sugarcane to the factory at a rate of40,000 Ugandan shillings per tonne of sugarcane.

27. ‘‘Uganda to Sell off Sugar Works.’’ BBC News, March 11, 2002, http://news.bbc.co.uk/1/hi/business/1867484.stm

28. Gerard Harel, a manager at KSWL, pers. comm.29. ‘‘Museveni Outlines Priorities in State-of-the-Nation Address,’’ New Vision, June 4, 2009.

http://www.lexisnexis.com/30. Uganda Bureau of Statistics, Spatial Trends of Poverty and Inequality.31. Uganda Bureau of Statistics and the International Livestock Research Institute, Nature,

Distribution and Evolution of Poverty.32. Reynolds, The Chimpanzees of Budongo Forest, 11.33. Lauridsen, ‘‘Workers in a Forest.’’34. Ellis, Rural Livelihoods and Diversity.35. Ibid.36. Wagle, Multidimensional Poverty Measurement.37. Pouw, ‘‘Food Priorities and Poverty.’’38. Ellis and Bahiigwa, ‘‘Livelihoods and Rural Poverty Reduction in Uganda.’’, 100139. The Kaiser-Meyer-Olkin measure verified the sampling adequacy for the analysis, KMO

� 0.715, and KMO values for individual items were � 0.611, above the acceptable limitof 0.5 (Field, Discovering Statistics Using SPSS). Bartlett’s test of sphericity x2 (759) �1119.680, p � 0.000, indicated that correlations between items were sufficiently large forPCA. An initial analysis was run to obtain eigenvalues for each component in the data.Two components had eigenvalues over Kaiser’s criterion of 1, and in combinationexplained 57.07% of the variance.

40. Zommers and Macdonald, ‘‘Protected Areas as Frontiers for Migration.’’41. Ibid.42. Donner and Telles, ‘‘Mobile Banking and Economic Development’’; Mendoza and

Thelen, ‘‘Innovations to Make Markets More Inclusive.’’43. Bayes, ‘‘Infrastructure and Rural Development.’’44. Ureta, ‘‘Mobilising Poverty?’’45. WRI, The Wealth of the Poor.

Journal of Eastern African Studies 193

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46. Evans, ‘‘Economic Impact of Demand for Ethanol.’’47. Martino et al., ‘‘Environment for Development.’’48. ‘‘More NFA Managers Resign Over Mabira,’’ The Monitor, March 25, 2007. http://www.

lexisnexis.com/49. Pender et al., ‘‘Development Pathways and Land Management.’’, 78650. Rist et al., ‘‘Hunter Reporting of Catch.’’51. Ejigu, ‘‘Toward Energy and Livelihoods Security.’’

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