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Reint Bakema

Habtom Asmelash

Date: 3 July 2017

Submitted by:

NIRAS A/S DINU Scoping Study Final Report Date: 3 July 2017

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Final Report 3 July 2017

Scoping Study

Development Initiative for Northern Uganda (DINU)

Letter of Contract No.: 2016/380882-1

This report was produced by NIRAS A/S with funds from the European Union. The report does not necessarily reflect the opinion of the European Union or the Government of Uganda.


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Table of Contents

LIST OF ABBREVIATIONS ............................................................................................................................................. V

1. INTRODUCTION ..................................................................................................................................... 1

1.1 BACKGROUND TO THIS REPORT ............................................................................................................................ 1 1.2 METHODOLOGY ................................................................................................................................................ 1

Review programme documents and national strategic frameworks relevant to DINU ................................ 1 1.2.1 Comment and review proposed criteria for the selection of districts and value chains of focus .................. 2 1.2.2 Gather and review available data (quantitative and qualitative) to assess, against pre-established 1.2.3

criteria, the relevance of including districts and value chains in DINU ........................................................ 3 Establish a mapping of existing and upcoming interventions funded by other donors in the region in 1.2.4

sectors relevant to DINU and identify overlaps and complementarities ...................................................... 3 Establish possible scenarios of geographical coverage and selection of value chains of focus .................... 3 1.2.5 Liaise with policy makers at central and local level to ensure buy-in of proposed scenarios ....................... 4 1.2.6

1.3 STRUCTURE OF THIS REPORT ................................................................................................................................ 4 1.4 ACKNOWLEDGEMENTS ....................................................................................................................................... 4

2. DINU BACKGROUND ............................................................................................................................. 6

2.1 BACKGROUND TO NORTHERN UGANDA ................................................................................................................. 6 Northern Uganda recovery and development .............................................................................................. 6 2.1.1 Agro-ecological and Livelihoods zones in NU ................................................................................................ 8 2.1.2 Food Security and Nutrition in NU .............................................................................................................. 13 2.1.3 Key characteristics of the DINU districts ..................................................................................................... 14 2.1.4

2.2 INTRODUCTION TO DINU ................................................................................................................................. 16 DINU Legal framework, financing and timing ............................................................................................. 16 2.2.1 DINU Objectives and Intervention Logic ..................................................................................................... 16 2.2.2 DINU Budget ............................................................................................................................................... 17 2.2.3

2.3 CONCLUSIONS ................................................................................................................................................ 19

3. GEOGRAPHICAL SELECTION CRITERIA ...................................................................................................20

3.1 THE RATIONALE FOR GEOGRAPHICAL CONCENTRATION ............................................................................................ 20 3.2 PRINCIPLES AND CRITERIA FOR SELECTING DISTRICTS FOR THE FSN&HHI COMPONENT .................................................. 21

Fair distribution of interventions and resources across the five sub-regions .............................................. 22 3.2.1 Preselected districts .................................................................................................................................... 22 3.2.2 District clustering ........................................................................................................................................ 22 3.2.3 Refugee numbers and impact ..................................................................................................................... 25 3.2.4 Other projects ............................................................................................................................................. 26 3.2.5 Population and poverty scores .................................................................................................................... 27 3.2.6 District finances........................................................................................................................................... 28 3.2.7 Other criteria ............................................................................................................................................... 29 3.2.8

3.3 STATUS OF GEOGRAPHICAL SELECTION BY CORE IPS ................................................................................................ 29 3.4 ACTIVITY AND FUND DISTRIBUTION ACROSS DINU DISTRICTS .................................................................................... 31 3.5 CONCLUSIONS ................................................................................................................................................ 35

4. VALUE CHAIN SELECTION .....................................................................................................................37

4.1 BASIC PRINCIPLES FOR VC SELECTION .................................................................................................................. 37 4.2 SHORT LISTING OF PROMISING VCS ..................................................................................................................... 39 4.3 VC SELECTION, ENVIRONMENT AND CLIMATE CHANGE ............................................................................................ 40 4.4 SELECTION FOR COMMODITY OPPORTUNITIES ....................................................................................................... 41 4.5 VC SELECTION AT CLUSTER LEVEL ........................................................................................................................ 43

VC selection for the East Karamoja cluster ................................................................................................. 44 4.5.1 VC selection for the North East Acholi cluster ............................................................................................. 44 4.5.2 VC selection for the South East Lango Teso cluster ..................................................................................... 45 4.5.3 VC selection for the South West Acholi Lango cluster ................................................................................. 46 4.5.4 VC selection for the North West Nile cluster ............................................................................................... 46 4.5.5


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VC selection for the South West Nile cluster ............................................................................................... 47 4.5.64.6 CONCLUSIONS AND WAY FORWARD IN VC PRIORITISATION ...................................................................................... 48

ANNEX 1: PEOPLE CONSULTED ..................................................................................................................................49

ANNEX 2: SOURCES OF DATA FOR SECONDARY LITERATURE REVIEW ........................................................................54

ANNEX 3: DISTRICT PROFILES.....................................................................................................................................57

ANNEX 4: COMMODITY PROFILES ..............................................................................................................................58

ANNEX 5: INTEGRATION OF EUD COMMENTS IN THE FINAL REPORT .........................................................................59

List of Figures

Figure 1: Mean annual rainfall (mm) in NU ....................................................................................................... 6 Figure 2: Agro-ecological zones in Northern Uganda ........................................................................................ 9 Figure 3: Livelihoods Zones in Uganda ........................................................................................................... 10 Figure 4: Preliminary fund allocation for SO1 ................................................................................................. 18 Figure 5: Preliminary fund allocation for SO2 ................................................................................................. 18 Figure 6: Preliminary fund allocation for SO3 ................................................................................................. 18 Figure 7: Relative size and technical overlap between major DINU components .......................................... 20 Figure 8: Overview of proposed clusters and business hubs under the FSN&HHI component ...................... 24 Figure 9: District coverage of various DINU components ............................................................................... 30 Figure 10: District coverage of the DLG CB component .................................................................................. 30 Figure 11: District coverage of the Nutrition governance components as of 6 June 2017 ............................. 30 Figure 12: Overview of activities and their target districts by IP as of 6 June 2017 ........................................ 32 Figure 13: Distribution of DINU funds (€ 000,000’) across the operational districts ...................................... 34 Figure 14: Fund distribution in '000,000 € and % between clusters, business hubs and satellite districts .... 35 Figure 15: Basic elements of the value chain .................................................................................................. 37

List of Tables

Table 1: Consultative meetings with key stakeholders ..................................................................................... 4 Table 2: Priority and strategic commodities for the agro-ecological zones of NU as per ASSP 2016 ............... 9 Table 3: AEZ, LHZ districts and major commodities in NU .............................................................................. 12 Table 4: % moderately (2SD) and severely stunted children under 5 in the NU sub-regions ......................... 13 Table 5: Basic population data for the 33 operational districts of DINU ........................................................ 14 Table 6: Fund contribution to DINU by implementing partners in million € ................................................... 16 Table 7: Preliminary time table for DINU ........................................................................................................ 16 Table 8: Fund distribution by SO in million € .................................................................................................. 17 Table 9: Fund distribution by core IP in million € ............................................................................................ 17 Table 10: Preliminary indicators for district selection, data sources, purpose and status ............................. 21 Table 11: Overview of livelihoods zones in relation to DINU districts ............................................................ 23 Table 12: Clusters, livelihood zones, districts and the % and number of subsistence farming HHs ............... 25 Table 13: Registered refugees and asylum seekers in West Nile (May 2017)................................................. 25 Table 14: Number of projects, project score per district, and highest scores compared to selected core districts ............................................................................................................................................................ 26 Table 15: Standardised population and poverty criteria scores...................................................................... 27 Table 16: District and Production and Marketing budget (UGX 000’/per subsistence HH for FY 16/17) ....... 28 Table 17: Overview of the status of geographical selection of core IPs.......................................................... 29 Table 18: HH Population, absolute and relative funding, and number of DINU activities per DINU district .. 33 Table 19: Commodity groups, commodity examples and VC interventions for VCs in NU ............................. 38 Table 20: Scoring and ranking of major commodities on the basis of DQs and district interviews ................ 39 Table 21: Key data for the shortlisted commodities in NU ............................................................................. 40


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Table 22: Scoring of selected commodities on opportunities for yield and business development .............. 41 Table 23: Proposed clusters, livelihood zones, districts and cluster VCs ........................................................ 43 Table 24: Cluster VC selection for the East Karamoja cluster ......................................................................... 44 Table 25: Cluster VC selection for the North East Acholi cluster .................................................................... 44 Table 26: Cluster VC selection for the South East Lango Teso cluster ............................................................ 45 Table 27: Cluster VC selection for the South West Acholi Lango cluster ........................................................ 46 Table 28: Cluster VC selection for the North West Nile cluster ...................................................................... 47 Table 29: Cluster VC selection for the South West Nile cluster ...................................................................... 47


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List of Abbreviations

AAP Action Document for DINU AEZ Agro-ecological zone ASSP Agriculture Sector Strategic Plan BFP (District) Budget Framework Paper CfP Call for Proposals CMP Catchment Management Planning (component) DA Delegation Agreement DDP District Development Plan DFID Department for International Development DINU Development Initiative for Northern Uganda DLG District Local Government DLG CB District Local Government Capacity Building (component) DLG NG District Local Government Nutrition Governance Building (component) DQ District questionnaire EDF European Development Fund EU(D) European Union (Delegation) FA Financing Agreement FAO Food and Agriculture Organisation of the United Nations FSN&HHI Food security, nutrition and HH income component (of DINU) FWC Framework Contract FY Financial Year GADC Gulu Agricultural Development Company GIZ Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH DLG NG District Local Government Nutrition Governance (component) GoU Government of Uganda HH Household IP Implementing Partners IPC Integrated Food Phase Classification LHZ Livelihoods Zone MAAIF Ministry of Agriculture, Animal Industries and Fisheries MoFPED Ministry of Finance, Planning and Economic Development MoLG Ministry of Local Government NAO National Authorising Officer NGO Non-Governmental Organisation NSST NIRAS Scoping Study Team NU Northern Uganda NUSAF Northern Uganda Social Action Fund NU-TEC MD Northern Uganda – Transforming the Economy through Climate Smart Agri-Business

Market Development OPM Office of the Prime Minister OWC Operation Wealth Creation PMU Programme Management Unit PRDP Peace, Recovery and Development Plan RE Rural Electrification (component) SO Specific Objective TAT Technical Assistance Team UBOS Uganda Bureau of Statistics UCA Uganda Census of Agriculture UCDA Uganda Coffee Development Authority


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UBOS Uganda Bureau of Statistics UDHS Uganda Demographic and Health Survey UGX Uganda-Schilling UNCDF United Nations Capital Development Fund UNHCR United Nation High Commissioner for Refugees UNHS Uganda National Household Survey UNICEF United Nations International Children's Emergency Fund VC Value Chain VCA Value Chain Analysis VSLA Village Savings and Loans Association WFP World Food Programme of the United Nations WfP Water for Production (component)


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1. Introduction

1.1 Background to this report

In December 2016 the European Union commissioned NIRAS A/S under Framework Contract (FWC)

2016/380882-1 to carry out a scoping study for the Development Initiative for Northern Uganda (DINU).

The contract ran from 1 February 2017 – 31 May 2017 and was executed by a team of two key-experts

supported by a field assistant.

DINU is a multi-sectoral programme that intends to implement the three focal sectors of the National

Indicative Programme (2014-2020) of the European Union (EU) and the Government of Uganda (GoU),

Food Security and Agriculture, Transport Infrastructures and Good Governance, in an integrated manner in

Northern Uganda. The programme is stretched over 33 districts and a surface area of approximately 90

thousand km2. This potentially leads to dilution of financial resources, and operational and programmatic

inefficiencies.

The DINU scoping study was commissioned to establish a geographical and technical focus, so as to speed

up the roll-out of the programme once a Programme Management Unit (PMU) becomes operational later

this year. The specific objective of the scoping study is to ‘Create a body of evidence to support the

development of a well-justified geographical coverage highlighting various possible scenarios where specific

districts and value chains (VC) of focus are clearly identified.’

The approach of the NIRAS Scoping Study Team (NSST) to achieve this was to develop a decision framework

for both geographical and VC selection, based on a set of, as much as possible, objectively verifiable

criteria. In addition, the team took into consideration the opportunities and added value of integration with

other DINU components, operational efficiencies, and the opinions and preferences of core Implementing

Partners1 (IPs), national and district level administration, agribusiness, donor agencies and NGOs.

The assignment took 60 working days, whereby at regular intervals the European Union Delegation to

Uganda (EUD) and the GoU were briefed about the progress, findings and recommendations of the team.

The findings and feedback from the briefings were integrated an interim report that was submitted to the

EUD on 1 April and presented to a national consultative meeting, hosted by the Office of the Prime

Minister, on 6 April. Some additional analytical work on VC selection and the feedback from the

consultative meeting were incorporated to produce a draft final report, which was submitted to the EUD on

21 April 2017. The EUD returned the consolidated feedback to the consultant on 23 May. The feedback was

worked into the report, and the final version was submitted on 3 July 2017.

1.2 Methodology

According to the contract the consultants were to carry out 6 tasks. The methodology applied to each of

the tasks is described hereafter.

Review programme documents and national strategic frameworks relevant to DINU 1.2.1

The NSST consulted a large body of literature related to this assignment (Annex 3), categorised as follows:

DINU related documentation: National Indicative Programme 2014-2020, the DINU action document, and the draft proposals developed by the core IPs. The DINU action document, and the related spreadsheets with the distribution of funds across the strategic objectives and activities

1 Core IPs are the IPs that are listed in the AAP and the Financing Agreement (FA) to execute parts of DINU under

Delegation Agreements, Programme Estimates (PEs) or Works and Services (WS) contracts. IPs that will be contracted later through CfPs are called secondary IPs.


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formed the basis for the NSST’s analysis of the necessity and sufficiency of the integration of components, and the financial consequences of more or less integration. The findings are briefly presented in the overall description of DINU and used to calculate the financial impact of the integration on the districts in the operational area.

The proposals of the core IPs, their focal areas and selection criteria were essential in determining the options of component integration. It should be noted that the IPs were still in the process of developing their proposals during this consultancy, and that in some cases their final analysis and decision about their target districts was not fully completed during the drafting of this report. None of the IPs had made as yet a financial breakdown of their overall budget across their DINU activities. The financial calculations of the NSST are therefore based on our estimates of allocations.

GoU strategic documents: most relevant in this category were the Peace Recovery and Development Plan III (PRDP) document and the Ministry of Agriculture, Animal Industries and Fisheries (MAAIF) Agriculture Sector Strategic Plan (ASSP), National Climate Change Policy, Draft Uganda Organic Agriculture Policy. They provided a broad contextual framework on which to base the selection of districts and value chains.

Statistical and census reports, primarily from the Uganda Bureau of Statistics (UBOS): core documents were the Uganda Census of Agriculture (UCA) 2008/09, the National Population and Housing Census (UNPHC) 2014, Uganda National Household Survey (UNHS) 2012/13, and the Uganda Demographic and Health Surveys (UDHS) 2011 and 2016. These reports provided regional and district level data sets, which were used to populate some of the district and VC selection criteria. In many cases statistical information was disaggregated to regional levels, requiring the NSST to make additional calculations to provide district level analyses. Most difficult was the collection of agricultural statistics, as the UCA data are almost 10 years old, and no reliable data are available at the district level. Agricultural data presented in this report are therefore often brought together from different sources, and required extensive interpretation by the NSST.

Programme and project design and completion reports: for example the Agriculture Cluster Development Project, Northern Uganda – Transforming the Economy through Climate Smart Agri-Business Market Development (NU-TEC MD) market assessment reports, Vegetable Oil Development Project (VODP), Project for the Restoration of Livelihoods In the Northern Region (PRELINOR) and some others provided background information on agricultural developments and trends, and in some cases detailed descriptions of VCs.

Value Chain Analysis (VCA) reports: there is an extensive library of VCA reports on practically all VCs in Northern Uganda (NU). They provided important VC information, including trends, agronomy and crop statistics.

Comment and review proposed criteria for the selection of districts and value chains of focus 1.2.2

The NSST held consultations with all core IPs to understand the progress towards their component design,

their geographical selection criteria and their selected districts. GoU (district roads and connecting roads),

Department for International Development (DFID - logistical trade hub), Deutsche Gesellschaft für

Internationale Zusammenarbeit (GIZ) GmbH (Water for Production (WfP) and Catchment Management

Planning (CMP)) and GIZ (Rural Electrification (RE)) had largely or entirely completed their selection

process, often based on extensive surveys or other analytical work.

The NSST studied their proposals and took them as a starting point for developing a set of districts for the

Food security, nutrition and household (HH) income component (FSN&HHI). For the United Nations Capital

Development Fund (UNCDF) and the United Nations International Children's Emergency Fund (UNICEF) the

selection process was ongoing during this consultancy, and was to some considerable extent influenced by

the work of the NSST to achieve a better match with the other components. The selected districts of each

IP were plotted on maps, to provide an overview of the overall coverage of DINU.


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Gather and review available data (quantitative and qualitative) to assess, against pre-1.2.3established criteria, the relevance of including districts and value chains in DINU

The NSST developed a set of criteria to establish the relative importance and necessity of the districts and

value chains to be included in the DINU programme. The preliminary selection during the inception period

was based on consultations with the stakeholders, and on an initial assessment of available data sources.

Not all data sources were of sufficient quality or sufficiently disaggregated at district level to make a

meaningful or objective selection of core districts. To fill the gap of missing district data, the NSST

distributed a district questionnaire (DQ) to the Chief Administrative Officers of all 33 districts in the

operational area of DINU, and collected the District Development Plans (DDP) and the Financial Year (FY)

16/17 Budget Framework Papers (BFP). By 19 April 33 districts had returned District Development Plans

(DDP) and District Budget Framework Papers (BFP), whereas 32 had returned their questionnaires. The

documents were used to fill data gaps and triangulate other data sources.

The DQs allowed the districts to score commodities on their importance for a number of dimensions. These

scores were summarised and integrated in the overall VC assessments. The DQs, DDPs and BFPs were

summarised into district profiles that are attached in Annex 4. All DQs, DDPs and BFPs are handed over in

softcopy to the EUD and the Office of the Prime Minister (OPM) for use by the DINU Programme

Management Unit, once it is established.

The NSST collected additional qualitative and quantitative data through semi-structured interviews with

GoU Ministries and Agencies, 15 District Local Governments across the operational area, development

partners, agribusinesses, and programmes and projects. Overall, the NSST interviewed over 150 persons

(Annex 2). All data were collected in two master spreadsheets, which are submitted with this report to the

EUD and OPM for reference. The tables in this report draw their base-line data from these spreadsheets.

Establish a mapping of existing and upcoming interventions funded by other donors in the 1.2.4region in sectors relevant to DINU and identify overlaps and complementarities

The NSST made an overview of ongoing programmes and projects relevant to the FSN&HHI component, to

establish possible areas of duplication and or synergies. For Karamoja a recently completed intervention

mapping study was used, but for Northern Uganda no consolidated overview of interventions was

available. The NSST constructed such an overview on the basis of the DQs and interviews with development

partners and projects2.

Establish possible scenarios of geographical coverage and selection of value chains of focus 1.2.5

According to the FA, the geographical coverage of the integrated programme was limited in the Financing

Agreement to approximately 15 districts. Based on the collected data, the NSST developed a logical

arrangement of 15 districts in manageable clusters, with similar livelihoods strategies within the clusters.

Given additional restrictions caused by the selection process of core IPs, and some political considerations,

the room for developing possible scenarios for district selection proved to be rather limited.

For the selection of VCs the NSST developed for each cluster a decision framework, based on available

commodity data from statistical reports, VCA reports, DQs and other data sources. The NSST developed

commodity profiles (Annex 5) in which the available commodity information was summarised and scored

against a broad range of criteria. The scores were combined and weighted to arrive at a limited number of

2 In the briefing session with the EUD of 24/2/2017 it was agreed that only the major projects in NU would be

considered.


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priority VCs for each cluster, whereby the NSST ensured that the priority VCs were together contributing to

the all the objectives of the FSN&HHI component.

Liaise with policy makers at central and local level to ensure buy-in of proposed scenarios 1.2.6

The NSST held regular meetings with the IPs,

EUD and OPM (both the political leadership

and technical team) to keep them informed

and receive feedback on critical steps in the

development of the recommendations (table

1). On 6 April, OPM hosted a national

consultative meeting, where the NSST

presented its findings and recommendations

to approximately 80 participants. The findings

presented in this report are therefore largely

known by the key stakeholders and,

presumably, broadly accepted.

The NSST recommends that during the

inception period of DINU, decision makers,

and in particular the programme steering

committee, revisits the framework, adjust the

weighing factors and take a final decision on

DINU’s geographical and VC coverage.

1.3 Structure of this report

This draft final report presents a brief description of the operational area of DINU, a description of DINU,

and the justification and limitations of component integration in chapter 2. In chapter 3, the NSST works

out the decision framework and scenarios for the selection of core districts. A core district is defined as a

district benefiting from the FSN & HHI and Local governance components. The assessment framework and

recommendations for value chains is presented in chapter 4. Some conclusions and recommendations are

added at the end of each chapter.

The report has 5 Annexes, that include the intervention structure of DINU, and the persons and documents

consulted during the assignment, district profiles and VC profiles. In addition, the NSST has handed over the

Excel data bases used for districts and VC selection to the EUD and OPM for future reference and use.

1.4 Acknowledgements

The NSST received outstanding technical and logistical support from the EUD and the OPM technical team

during the execution of this assignment. We also appreciate the time and information we received from all

interviewees. In particular we want to thank the District Local Governments (DLGs) for sharing their ideas

and opinions during the DLG interviews, and by returning their District Questionnaires, DDPs and BFPs.

We want to thank Benard Onzima for producing the maps in this report.

Lastly, the NSST enlisted Beatrice Arach for providing technical and logistical support in local level data

collection. She was invaluable for managing the distribution, follow-up and collection of district

Table 1: Consultative meetings with key stakeholders

Date Type of meeting Institutions

1 February Briefing EUD

2 February Briefing OPM technical

3 February Briefing MoFPED

6 February Selection criteria IPs UNCDF

7 February Selection criteria IPs GIZ (WfP and CMP)

9 February Briefing OPM political

10 February Selection criteria IPs UNICEF

14 February Selection criteria IPs GIZ (RE)

16 February Inception briefing EUD

17 February Selection criteria IPs MoLG

24 February Selection criteria IPs EUD and GIZ

27 February – 13 March

District consultations 15 DLGs

15 March Interim briefing OPM technical

17 March Interim briefing EUD

21 March Selection criteria IPs EUD, UNCDF/MoLG

24 March Selection criteria IPs Trademark EA

31 March Interim briefing EUD and IPs

6 April National consultations All stakeholders

7 April Nutrition EUD


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documentation, making the district profiles, organising the field work and providing logistical support

during the national consultative meeting.


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2. DINU background

2.1 Background to Northern Uganda3

Northern Uganda recovery and development 2.1.1

Northern Uganda is the potential bread basket of Uganda and its neighbouring countries. It accounts for

53% of the arable land in Uganda with land holdings at 3.6 ha/household, three times larger than the

Ugandan average. The region receives in principle

sufficient rainfall for two crop production cycles per year in

the central and western parts, and sub-optimal rainfall for

a single crop cycle in the North-Eastern Karamoja sub-

region. However, dry spells do occur. They can reduce

production considerably across NU, and may cause

frequent total crop failures in Karamoja.

Trend analyses carried out by Fewsnet show that rainfall

has been about 8% lower in 2000-2009 as compared to the

period 1920-1969. The projected rainfall declines range

from -150 to -50 mm across the northern part of the

country, exposing populations to increased rainfall

deficits4. The future impacts of climate change are

uncertain, but some estimates foresee losses to the agriculture sector of between 9 and 32% by 2050. For

example, by 2050, the value of the coffee crop could fall by half due to contraction of the area that can

support its production.

However, its once thriving agricultural production, still witnessed by now derelict ginneries and railways,

collapsed largely during almost 20 years of civil strife and conflicts. After negotiations with the Lord’s

Resistance Army and the disarmament of the Karimojong in the Karamoja region in 2006 and 2008

respectively, peace was restored and a start was made with recovery and development.

Central to the recovery and development efforts was the GoU-led Peace, Recovery and Development Plan

(PRDP), which is currently in its third phase. While initially the focus was on recovery, rebuilding,

reconciliation and revitalising5, the emphasis shifted gradually to supporting livelihoods and more recently

to private sector led economic development. This is evident from the latest PRDP, in which Strategic

Objective 2, Development of the economy, has the following priority themes: Business infrastructure,

Agricultural productivity and value chains, Skills development, Access to finance and Access to land.

Ten years of concerted recovery and investments efforts in NU had positive impacts. Basic infrastructure is

largely restored, electricity is extending deeper into the rural areas, and commercial farms and ever larger

agribusinesses are being set-up or are expanding. New processing plants for oil crops, grains, cassava, milk

and fruits widen the opportunities for farmers to earn a living from a variety of commodities. Produce

buyers and processors set up private commodity based supply and extension services to tens of thousands

3 In the context of this document, Northern Uganda comprises of the sub-regions Karamoja, Acholi, Lango, Teso and

West Nile. 4 A Climate Trend Analysis of Uganda; Factsheet 2012-3062; USGS/USAID/Fewsnet; June 2012.

5 The Specific Objectives of PRDP1 and PRDP2 were: 1) Consolidation of state authority, 2) Rebuilding and empowering

communities, 3) Peace building and reconciliation and 4) Revitalising the economy.

Figure 1: Mean annual rainfall (mm) in NU


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of farmers in order to secure the raw materials for their enterprises6. Increasingly modern farmer extension

and training tools, such as videos are being used, and where this happens many farmers are responding to

the opportunities78.

Financial services are also penetrating rural areas. At village level, the last decade saw an explosion of

informal village savings and loans associations (VSLAs), often established within the framework of broader

project support to village and farmer groups. VSLAs are important for creating a savings culture and

financial literacy amongst the rural producers. VSLAs also make local money available for small investments

for farms and rural business, and have in particular a positive impact of women, who constitute the

majority of the VSLA Membership. The spread of mobile money and the recently approved system of

banking agents, helps to lubricate transactions between farmers and agribusinesses, and reduces the costs

of doing business. However, access to finance remains the most important factor limiting growth of the

business sector9.

At a small scale Information and Communication Technology (ICT) starts to have a positive impact on small

holder farmers, for example by providing commodity price information and secure brokering and payment

services for farmers and buying agents to transparently managing aggregation and value addition along the

value chain10. Potentially, in the next few years ICT may revolutionise the way in which farmers receive

information, interact with agribusinesses and manage their finances.

As a result of all these efforts, income poverty levels in NU have reduced from 61% in 2005 to 43% 20121112.

The reduction in poverty is attributed to Uganda’s general economic development, significant public

investments in physical infrastructure and targeted government investments, such as the affirmative action

for NU through the PRDP. This has resulted in lower trade costs and a better integration of VCs. This has

helped primary producers to receive a larger share of the VCs’ value, and caused the population depending

on agricultural income to experience a much larger reduction in multidimensional poverty as compared to

other earners in other sectors13.

However, that is where the positive news for NU ends. Whereas Uganda as a whole was one of the most

successful countries in Sub-Saharan Africa to reduce absolute poverty during the last two decades, progress

has been much slower in the Northern and Eastern parts of the country. Where the PRDP intends to bring

NU at par on the major development indicators with the rest of the country, this has as not been achieved.

In actual fact, against the national income poverty average of 19.7 percent, 75 percent of the people in

Karamoja are income poor followed by West-Nile (42%) and the Mid-North (36%). Due to the high

population growth in NU, in absolute figures the number of poor people in NU have increased from 2.8

million in 2009/10 to 3.1 million in 2012/13, while in Uganda as a whole the number has dropped in the

same period from 7.5 million to 6.7 million14. Also, households in Uganda’s Northern, Eastern, and Western

regions have much lower levels of human capital, fewer assets, and more limited access to services and

6 Typical examples are Gulu Agricultural Development Company for a range of crops, Mukwano Industries for

sunflower and Shares for organic sesame. 7 Gulu Agricultural Development Company (GADC): interview 3 March 2017.

8 For example Mercy Corps (More than Markets, 2016) reports that under the RAIN programme Acholi farmers

opened up 2-3 times as much land due to secure markets offered by produce buyers such as GADC and others. 9 Northern Uganda Economic recovery Analysis: Phase II Final Report; Oxford Economics, 2015.

10 Trutrade: interview 20 February 2017.

11 Poverty Status Report 2014; MoFPED.

12 Uganda National Household Survey 2012/13; UBOS.

13 Poverty Status Report 2014; MoFPED.

14 Uganda National Household Survey 2012/13; UBOS.


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infrastructure than households in the Central region. In particular women continue to face discrimination in

their access to economic opportunities and ownership of assets15. The slow progress in Northern Uganda,

and in particular West-Nile, is further exacerbated by the influx of over 900,000 refugees from South Sudan

and Eastern Congo, which are likely to increase considerably given the current political instability in the

region16.

To equalise income poverty in NU to the national average, 2 million people in NU would have to move out

of poverty, and that would require an annual growth of GDP in NU of 11% for the next 25 years17. If and

how this can be achieved is not entirely clear. Even in its most positive transformative scenario, Oxford

Economics calculates that poverty parity with the national average will not be achieved by 2040.

Nevertheless, it is clear that the agricultural sector will remain a key driver to economic growth,

employment and poverty reduction in NU, directly creating around 50% of the 2 million jobs in the next 25

years under a transformative scenario. For that to happen, the focus should move away from micro and

primarily small-holder farmer-focused interventions to inclusive commodity and VC development, through

improving access to financial services, skills development (in particular entrepreneurship, climate-smart

and 'green' skills), farmers/producers/agri-business groups development, market development, private

sector technical and institutional development and DLG capacity building, alongside continued massive

investments in infrastructure, electrification and (ICT) connectivity.

However, some caution is needed in a commercial agricultural development scenario. Despite the

significant reduction in income poverty in Uganda, the majority of the population still lives below twice the

poverty line, and therefore remains vulnerable18. This is in particular true for small holder farmers, who are

already vulnerable to weather shocks, climate change, crop and animal pest and diseases, and price

fluctuations. Commercial agriculture is inherently risky, and may increase the vulnerability of small holder

farmers even further. Higher financial exposure because of high investments in inputs combined with

boom-and-bust cycles in commodity prices, reduced crop diversity at farm level, and increasing inequity

because of the commercialisation of food crops may move households in and out of poverty, may cause

land tensions and environmental degradation19, and may have a harmful effect on nutrition. In addition to

focusing farmer training on Good Agricultural Practices (GAP), to handle the rapid commercialisation of the

agricultural sector in NU, small holder farmers require information and education on financial

management, sustainability, and the importance of a diverse cropping pattern that reduces vulnerability to

weather, crop/animal diseases and price shocks, alongside a better understanding of how markets work.

Agro-ecological and Livelihoods zones in NU 2.1.2

NU is predominantly an agriculture economy. Out of the 1.4 million households, 1.2 million are engaged in

the primary production of food/cash20 crops and livestock. Across the region the farming systems and

related livelihoods strategies differ considerably. The first defining factor that determines farmers’

economic behaviour is the agro-ecology in which he or she operates. The Ministry of Agriculture, Animal

Industries and Fisheries (MAAIF) based its Development Strategy and Investment Plan (DSIP) of 2010 and

the Agriculture Sector Strategic Plan (ASSP) on the agro-ecological zones (AEZ) (figure 2). For each of the

15

Africa Economic Outlook 2016; African Development Bank; 2016. 16

In April 2017 close to 50,000 new refugees from South Sudan were registered in Uganda: https://ugandarefugees.org. 17

Northern Uganda Economic Recovery Analysis: Phase II Final Report; Oxford Economics, 2015. 18

Uganda Poverty Status Report 2016, MoFPED. 19

More than Markets; Building Resilience in Northern Uganda; Mercy Corps. 20

The traditional distinction between food and cash crops has largely disappeared. Most traditional food crops have commercial value and are sold and bought by farmers as well.


9

AEZs priority commodities were identified as shown in figure 2 (DSIP 2010), and in table 2 (ASSP, 2016). The

map and table give an indication of the commodities that are traditionally grown in the AEZs and that are

strategically important to the GoU.

Figure 2: Agro-ecological zones in Northern Uganda

Table 2: Priority and strategic commodities for the agro-ecological zones of NU as per ASSP 2016 Commodity Agro-ecological zones

I II III IV V Beans √ Cassava √ √ √ Coffee √ √ Maize √ Oil Seeds √ √ √ √ Citrus √ Pineapples √ Vegetables √ √ Beef Cattle √ Goats √ √ Aquaculture √ Poultry √ √ √ √ Piggery* √ √ Sorghum* √ √ √ Irish Potatoes* Sweet Potatoes* √ √ √ Rice Mango Cotton *Commodities that were not chosen among the priority strategic commodities but are important for food and nutrition security


10

Figure 3: Livelihoods Zones in Uganda


11

Notably is the absence of groundnuts and rice as a strategic and priority crops21, and the rising importance

of oil seeds (sunflower, soya and sesame) across NU. The NSST also notes that sorghum is not considered a

priority crop in the ASSP, despite its importance in Karamoja, Northern Acholi and West Nile22. Also the

absence of cotton is notable, in particular in light of the new cotton ginnery that is under construction in

Pader and the revival of the ginneries in Kitgum, Gulu and Rhino Camp by GADC. Important to note is the

commercialisation of cassava for starch and ethanol production for which four factories have been built in

NU during the last few years. Lastly, coffee is being mentioned in two of the AEZs. It is traditionally grown in

the South Western highlands of West Nile, but under the new strategy of the Uganda Coffee Development

Authority (UCDA), it is being rolled out in central Acholi and Lango in a big way.

Distinct local differences in micro-climates and soils, infrastructure, the presence or absence of commodity

markets and agri-businesses, government policies and interventions, and local preferences and practices,

create localised economic behaviours that can differ considerable from one group of people to another

within the same AEZ. This was captured in the notion of livelihoods zones (LHZ), as mapped in 2010 by

Fewsnet23 (figure 3). We briefly introduce the LHZ relevant to the DINU operational area, because they are

key to our choice of geographical and VC focus as presented later in this report.

Table 3 provides the relationship between the AEZs and LHZs, and the prevailing crops in the LHZs in order

of importance according to the DQs and our district interviews. The table shows that the LHZs provide a

more varied and detailed picture of livelihoods strategies than the AEZs. In addition, whereas the

livelihoods zoning of 2010 is still relevant, the agricultural landscape is changing fast, due to changing (food)

habits, markets, technologies and information that create new local, regional and international demands

and business opportunities. In NU, additional crops have entered the farming systems, which were

originally not captured in the LHZ descriptions. In particular soya and sunflower have gained prominence in

large areas of Lango and Acholi, whereas the upland and swamp rice areal has expanded widely in Lango

and Teso. On the other hand, it is noted that fruits are only mentioned once. Vegetables do not appear at

all in the list of major crops.

As mentioned before, the traditional difference between cash crops (coffee, cotton, tea, tobacco) and food

crops has largely disappeared. Regional and international markets for food commodities such as sorghum

(South Sudan and Rwanda), maize (South Sudan, Kenya and Southern Africa), sesame (Far East, Middle East

and Europe), soybean (Kenya), cattle (South Sudan, Kenya), goats (Sudan, Saudi Arabia) are expanding, and

new processed products are developed from grains, millet and soya (packed grains and flour), soya (food

fortifiers, animal feeds), sorghum (beer), cassava (starch, beer and ethanol), sunflower, shea nut (cooking

oil, soap and cosmetics), honey (honey and wax), chilies (bird eyes) and fruits (fruit juices). This quickly

changing economic landscape offers numerous opportunities for farmers and agribusinesses, and may

become the driver of accelerated poverty reduction in NU.

In this light, coffee is a bit of a special case. Under the new Uganda Coffee Development Strategy, coffee

exports are targeted to increase 1.5 fold from 3.1 million bags to 5.6 million bags. This is partly to be

achieved by expanding the area under cultivation across NU. The Uganda Coffee Development Authority

(UCDA) intends to establish 15 million new coffee trees in NU, leading to the production of 13,600 Mts of

clean coffee. The UCDA wishes the expansion to be climate smart. To achieve this, investments are needed

21

We think that this is likely to be an error in the ASSP. 22

The sorghum consumption in West Nile is expanding because it is the main staple for South Sudanese refugees. 23

Livelihood mapping and zoning exercise: Uganda; Stephan Browne and Laura Glaeser; The Famine Early Warning System Network (FEWS NET), January 2010.


12

in (private) coffee seedling nurseries, shade tree nurseries, an input and tools distribution system and

farmer training. These activities are ongoing but could be improved upon and scaled up.

Table 3: AEZ, LHZ districts and major commodities24

in NU

AEZ LHZ Districts Major commodities

Code Name Code Name

I North Eastern Drylands

UG24 UG25

North Eastern Central and Southern Karamoja Pastoralist zones

Eastern Kaabong and Kotido, Moroto, and Northern Amudat

Cattle, shoats, sorghum, beans, maize, groundnuts, cassava

UG23 Karamoja Livestock, sorghum bulrush millet zone

Eastern Kotido, Napak. Northern Nakapiripirit

Cattle, shoats, sorghum, millet, beans, maize

II

North Eastern Savannah grassland

UG27 Eastern central lowland cassava sorghum groundnuts zone

Amuria, Katakwi, southern Alebtong

Cassava, citrus, beans, cattle, groundnuts, maize, sorghum, rice, grams

UG21 UG 20

South Kitgum Pader Abim sesame groundnuts sorghum cattle zone

Abim, Agago, Pader, Southern Kitgum Southern Lamwo

Sorghum, millet, beans, sesame, groundnuts, maize, cassava green grams

UG16 North Kitgum Gulu Amuru West Nile sesame, sorghum livestock zone

Northern Kitgum, Lamwo,

Cassava, cattle, maize, shoats, rice, beans, sesame, groundnuts, sorghum

UG16 UG19

North Kitgum Gulu Amuru West Nile sesame, sorghum livestock zone South West Gulu beans, shoats, groundnuts cassava zone

Amuru, Adjumani, Moyo, Yumbe, Eastern Arua, Nebbi

Cassava, cattle, maize, shoats, rice, beans, sesame, groundnuts, sorghum Soya, maize, beans, cassava, rice, groundnuts, sunflower, potatoes

III

North Western Savannah grassland

Omoro, Oyam, Kole, Eastern Nwoya

UG15 West Nile Koboko, Maracha, Western Arua, Western Yumbe

Cassava, maize tobacco, sorghum

UG17 Amuru Gulu rice groundnuts sorghum livestock zone

Gulu, Amuru Beans, rice, cassava, maize, groundnuts, rice, sorghum

UG18 Karuma Masindi maize cassava zone

Otuke, Alebtong, Northern Amuria, Lira, Dokolo,

Cassava, beans, maize, rice, apiary, groundnuts, sesame, soya, sunflower, sorghum

UG5 Rwenzori Mt Elgon West Nile Arabica coffee banana zone

Zombo, South Western Nebbi

Coffee, cassava, apiary, millet, bananas, beans, rice, maize, shoats, sesame

IV Paraa Savannah

UG18 Karuma Masindi maize cassava zone

Nwoya Maize, beans, sunflower, soya, cassava, peas

Apac, Amolatar Maize, rice, cassava, soya V

Kyoga Plains

24

As indicated in the District Questionnaires.


13

Food Security and Nutrition in NU 2.1.3

The diversity of crops being grown in each of the LHZs, and

the fact that, apart from the North Eastern Pastoralist

zone, NU has two cropping seasons a year, would indicate

a potentially sufficient access to food and dietary diversity

in NU. However, malnutrition is a persistent problem in NU

for children under 5 years of age. Table 4 shows that the

stunting prevalence is highest in Karamoja, closely

followed by West Nile, and is still disturbingly high in Acholi

and Lango26. The lower stunting rates in Teso may disguise inter-sub regional differences, and stunting

rates in the two DINU districts, Amuria and Katakwi, may actually be comparable to Lango or worse27.

These high stunting rates across NU warrant nutrition interventions in all the DINU sub-regions.

The two main immediate causes for malnutrition are food insecurity and a high disease burden. Indicators

for a high disease burden, such as the prevalence of fever and malaria show indeed higher levels in NU than

in most other regions in Uganda28, and this may explain to some extent the poor nutrition status in NU.

Food insecurity, on the other hand, can be broken down in three distinct but interrelated components: 1)

food availability, 2) food access and 3) food utilisation. Food availability from HH production is a persistent

problem in Karamoja, and a recurrent issue in Eastern Acholi and Lango and in West Nile29. The Integrated

Food Phase Classification (IPC) data show that recurrent food shortages are caused by poor harvests, which

in turn are caused by adverse weather conditions combined with production constraints at HH level, such

as land, labour and poor cultivation practices. Early sales due to HH cash constraints may also contribute at

HH level to food shortages before the new harvest is in. To tackle this issue in rural NU, HH food production

levels will have to rise.

Access to food is related to the capacity of HH to buy food items to enhance dietary diversity, or when they

run out of their own food supplies. The sole dependence on agriculture for food and income of many rural

HHs in NU causes seasonal fluctuations in food stocks, and makes HH vulnerable to poor harvests and low

commodity prices. A more diversified cropping pattern that includes food and cash crops, HH-level food

and cash management strategies, better integration in the VCs, and diversifying income sources from

agriculture and wage labour reduces vulnerability by spreading risks.

Lastly, poor food utilisation is caused by low dietary diversity. For all these regions the crop/livestock

diversity would in principle be sufficient for a nutritious diet. Also dietary traditions, which include pasted30

vegetables, millet porridge and milk for young children in the greater North, should not be a major obstacle

for a nutritious diet, although poor feeding habits of young children have been noted as a risk factor31. At

this level, rural HHs, and especially rural women, need a better understanding of the importance food

diversity, including a higher appreciation of fruits and vegetables as essential components of a healthy diet.

25

Uganda Demographic and Health Survey 2016; UBOS – 2/3SD = twice/three times the Standard Deviation. 26

According to WHO standards stunting rates over 40% are classified as critical, between 30-40% as serious and between 20-30% as poor. By these standards, malnutrition is serious to critical in most, if not all, sub-regions of NU. 27

Stunting rate in the Eastern region was 37% in 2005, so the current stunting rate constitutes a remarkable drop 28

Uganda Demographic and Health Survey 2016; UBOS. 29

Integrated Food Security Classification Reports for Uganda: http://www.ipcinfo.org/ipcinfo-countries/ipcinfo-eastern-middle-africa/Uganda. 30

Vegetables cooked in a sesame or groundnut paste. 31

The Analysis of the Nutrition Situation in Uganda; FANTA-2; USAID, May 2010.

Table 4: % moderately (2SD) and severely stunted children under 5 in the NU sub-regions

25

Region % stunted children

-2SD -3SD Total

Karamoja 35.2 12.1 47.3

Acholi 30.6 6.3 36.9

Lango 22.3 4.8 27.1

Teso 14.3 3.3 17.6

West Nile 33.9 12.4 46.3

http://www.ipcinfo.org/ipcinfo-countries/ipcinfo-eastern-middle-africa/Uganda

http://www.ipcinfo.org/ipcinfo-countries/ipcinfo-eastern-middle-africa/Uganda


14

Key characteristics of the DINU districts 2.1.4

The operational area of DINU comprises of 33 districts. Table 5 below gives an overview of the key

quantitative characteristics of the DINU districts. West Nile has the highest population density and the

highest number and % of subsistence farming HHs, closely followed by Lango.

Table 5: Basic population data for the 33 operational districts of DINU

Region District Total population*

Surface area km

2 Pop

density* HHs SS

Farming No of SS

farming HHs % total SSF HH

Ach

oli

Agago 234,500 3,500 67 43,418 92% 39,858 3

Amuru 195,300 3,619 55 36,694 86% 31,630 3

Gulu 355,367 1,868 195 87,120 75% 65,174 5

Kitgum 209,600 3,960 53 39,726 80% 31,673 3

Lamwo 137,000 5,588 24 27,185 89% 24,157 2

Nwoya 152,100 4,679 33 24,571 97% 23,901 2

Omoro 105,533 1,559 68 37,500 98% 36,750 3

Pader 183,500 3,326 57 34,223 86% 29,577 2

Totals Acholi 1.573m 28,100 69 330,437 88% 282,720 23%

Kar

amo

ja

Abim 120,400 2,352 52 18,083 87% 15,732 1

Amudat 113,900 1,616 75 15,494 78% 12,010 1

Kaabong 175,400 7,310 24 29,211 81% 23,654 2

Kotido 191,600 3,610 52 26,192 74% 19,473 2

Moroto 107,800 3,538 31 22,084 47% 10,315 1

Nakapiripirit 169,800 4,202 44 25,411 84% 21,273 2

Napak 146,900 4,977 30 26,764 79% 21,211 2

Totals Karamoja 1.026m 27,604 44 163,239 76% 123,668 10%

Lan

go

Alebtong 238,600 1,534 154 46,280 92% 42,716 4

Amolatar 156,500 1,163 134 27,988 85% 23,790 2

Apac 390,000 2,947 132 71,680 89% 63,669 5

Dokolo 192,500 1,004 191 34,870 85% 29,605 2

Kole 252,300 1,073 238 48,394 92% 44,382 4

Lira 428,400 1,330 324 89,170 64% 57,247 5

Otuke 112,500 1,549 98 21,957 91% 19,881 2

Oyam 403,800 2,196 52 76,615 91% 69,796 6

Totals Lango 2.175m 12,795 166 416,954 86% 351,086 29%

Teso

Amuria 287,500 2,587 111 48,316 86% 41,769 3

Katakwi 174,400 2,306 75 30,791 83% 25,626 2

Totals Teso 0.462m 4,892 93 79,107 85% 67,395 6%

We

st-N

ile

Adjumani 228,100 2,955 80 41,360 62% 25,643 2

Arua 820,500 4,346 190 146,714 78% 114,641 9

Koboko 221,100 760 294 30,284 72% 21,788 2

Maracha 192,600 440 438 36,284 91% 32,914 3

Moyo 144,600 1,909 75 25,881 86% 22,207 2

Nebbi 420,400 2,024 201 77,503 85% 65,880 5

Yumbe 534,300 2,323 231 63,786 91% 58,059 5

Zombo 252,400 939 269 50,278 87% 43,699 4

Totals West Nile 2.814m* 12,741 242 430,730 84% 359,188 32%

Total overall 8.049m 89,087 126 1,461,827 0.83 1,209,701 * includes refugees 2016 Statistical

abstract HH census

2014 HH census

2014 HH census

2014 HH census

2014 12/13 UNHS


15

In West Nile the actual population is considerably higher because of the presence of over 900,000

refugees32, who have a profound and probably long-lasting impact on the social, economic and food

security situation of the refugee hosting districts. Despite its large surface area, Karamoja only makes up

10% of the target population in the DINU districts. These differences in target populations can be used by

the DINU components to establish fund allocations to regions and districts.

All districts have functional District Local Governments (DLG), that are financed to a large extent by a

consolidated district equalisation grant and conditional grants from the central government, and to a

smaller extent by own resources and donor grants. Across all districts 90% of the FY 16/17 budget is

financed by the central government, but there are substantial differences between districts, in particular

when it comes to donor contributions to the district budget33. The average donor contribution is highest in

Karamoja with 16% of the DLG budget, whereby the largest contributor by far is UNICEF. Refugee hosting

districts in West Nile receive between 9% and 21% donor funding, primarily from the United Nations High

Commissioner for Refugees and to a lesser extent from UNICEF34. Local revenue mobilisation remains low

across NU with on average 2% of the total district budget.

All districts are understaffed. The DQs report between 20% and 70% vacancies. The Production and

Marketing departments are currently recruiting new extension staff under the single spine extension

system, and these are expected to be posted largely at the sub-county level. During the DLG interviews, it

appeared that the staff is expected to operate a ‘traditional’ extension system, by carrying our farm visits

and demonstrations, although the DLGs agreed that their effectiveness may be compromised by staff and

facilitation constraints. The risk is that the current revival of the Production Departments may be rather

ineffective, if it is not accompanied by innovative approaches to deliver services to farmers in collaboration

with, for example, the private sector extension services, and by adopting modern outreach methods.

Apart from DLG administrative and financial data that seemed to be readily available, the NSST found a

serious lack of district statistical data about agricultural production and agribusiness. None of the districts

was able to provide reliable data on planted acreages, yields, gross margins, produce trade volumes,

agribusiness outputs and turnovers for any of the major commodities35. An exception are livestock trading

data which are collected routinely at the livestock auctions and through the issuance of animal moving

permits. But even these are not systematically stored, analysed and easily accessible. The same is true for

national level agricultural statistics, of which the only reliable recent data are the formal exports36. Lack of

agricultural data, apart from making the work of the NSST rather difficult, hamper planning, focused farmer

support, responses to new opportunities and emergencies, and agribusiness development.

Despite these shortcomings, which were broadly recognised by the DLG staff, the NSST found the DLGs

responsive to the DINU programme, and eager to work closely with the IPs to build a modern and effective

small holder farmer support system. This can only be achieved if the IPs, and in particularly the secondary

IPs that will execute the FSN&HHI component, are contractually required to co-opt the DLGs in their

programmes. During the National Consultative Meeting several interventions pointed at the same, and

requested the EUD and OPM to ensure that the DLGs are fully involved in all DINU components.

32

https://ugandarefugees.org 33

Financial flows that do not pass through the districts are not captured. 34

WFP budget for 2017 of US$ 380 million for Karamoja (470,000 persons) and refugees 616,000 persons (primarily West Nile), is not reflected in the district BFPs. 35

Some districts did provide acreages and yields in the DQs but these were rough estimates. 36

The Uganda Statistical Abstract 2016 quotes the Agricultural Census 2008/09 for its food crop statistics.

https://ugandarefugees.org/


16

2.2 Introduction to DINU

DINU Legal framework, financing and timing 2.2.1

The Development Initiative for Northern Uganda (DINU) is financed by the 11th European Development

Fund (EDF). The Financing Agreement (FA) was signed in January 2017 and assigned CRIS number

FED/2016/38781. Currently, the EUD and GoU are in the process of recruiting the Technical Assistance

Team (TAT), and preparing the Delegation Agreements (DAs) for the four core IPs already identified in the

FA.

The total cost of the action is €150.6 million, of which €132.8 million is funded by the EDF. The remaining

€19.8 million are contributions of core IPs. Some additional co-funding to the tune of €3 million is expected

from secondary grant beneficiaries (table 6).

Table 6: Fund contribution to DINU by implementing partners in million €

Partner EDF GoU UNCDF UNICEF GIZ DFID Total Others

Contribution 132.80 11.95 0.35 1.06 1.80 2.67 150.63 (3.00)

Source: Action Document for the Development Initiative for Northern Uganda (AAP); EU, 2016

According to the current timetable, the first DINU activities are expected to roll out by July 2017 (table 7).

Table 7: Preliminary time table for DINU

Period Actions Status

January 2017 Signing Financing Agreement

February – May 2017 Scoping study

March – September 2017 Finalisation of proposals of the 4 core IPs and signing of DAs ±

February – October 2017 Recruitment TAT and set-up PMU ±

October 2017 – October 2023 Direct implementation, coordination and supervision of DINU by PMU -

September 2017 – June 2021 Implementation of DAs -

December 2017 – June 2018 Call for Proposals (CfPs) for Grant Agreements -

June 2018 – March 2023 Implementation of Grant Agreements -

March 2023 – December 2023 Closure DINU -

Source: EUD personal communication, 2017

The NSST notes that it may take up to the third, and more likely fourth quarter of 2017 before the TAT

team is mobilised. In the meantime, the core IPs are finalising their proposals, and will start rolling out their

programmes without OPM as the supervising authority having any formal coordination structure in place.

In addition, if the tenders and CfPs for the secondary IPs for the FSN&HHI component are to be designed

and managed by the PMU/TAT, these contracts are likely to become operational only in the second half of

2018. In that case, a quick revisit of the findings of this consultancy may be necessary to ensure that the

recommendations are still relevant.

DINU Objectives and Intervention Logic 2.2.2

The DINU programme has three Specific Objectives (SO): 1) Food security, nutrition and HH incomes; 2)

Transport infrastructure; 3) Local government capacity. The three SOs are derived from the three sectors of

the National Indicative Programme, which have been combined and territorially restricted to Northern

Uganda (NU) in a single FA. The underlying logic is the recognition of the multi-sectoral and interrelated

dimensions of poverty, whereby weaknesses in all the three sectors are major drivers of poverty and


17

malnutrition in NU. DINU intends to tackle these weaknesses through the implementation of 30 diverse

activities across the three sectors in a holistic manner.

The FSN&HHI SO uses a value chain approach that deals with production issues at HH level for strategically

selected crop and/or livestock enterprises. Support to HH production will be complemented by a set of

WfP, land rights, nutrition and agribusiness related interventions. The latter consists of direct support to

supply, trade and processing businesses and by enhancing interactions and relationships between

producers and agribusinesses.

The Transport infrastructure SO is achieved by executing three sub-components: 1) upgrading the Atiak-

Laropi road to bitumen standard, as a critical connection in the regional road network to South Sudan and

the Democratic Republic of Congo; 2) support to 4 districts (Abim, Amudat, Adjumani and Moyo) in

upgrading and maintaining their district roads network; and 3) contributing to a logistical trade hub in Gulu

town, intended to facilitate trade between NU, South Sudan and the Democratic Republic of the Congo.

Under the DLG capacity building (CB) SO selected districts will receive a broad package of support aimed at

enhancing their public finance management, local revenue mobilisation, partnership building,

accountability, nutrition governance, land governance, and road maintenance capabilities. Central to the

DINU Action Document is the notion of an integrated approach to the three SOs and their components,

under the assumption that the whole is more than the sum of the parts. Being a rural based programme

spread out over an area of 90 thousand km2 the first step towards integration is geographical concentration

of the different components in a limited number of districts. In the next chapter the NSST will argue that

that is only necessary for components that have synergetic potentials. The second prerequisite towards

successful integration of components is a strong coordination mechanism and regular information

exchange between the IPs.

DINU Budget 2.2.3

The DINU budget is broken down per SO as per table 8. The sums include the own contributions of the

partners. Transport receives over 50% of the budget, primarily because of the large sum spent on the Atiak-

Laropi road. Table 9 shows the breakdown per IP, whereby the GoU is an implementing partner by its

management of and spending through Programme Estimates, grants, works, supplies and services

contracts, including the TAT contract. The GoU is by far the largest partner in terms of fund allocation.

Table 8: Fund distribution by SO in million € Table 9: Fund distribution by core IP in million €

SO € Implementing Partner (core) €

SO 1: Food security, nutrition and HH incomes 38.36 GoU 102.3

SO 2: Transport infrastructure 78.75 UNCDF 25.9

SO 3: Local government capacity 25.38 UNICEF 6.1

Management and contingencies 8.15 GIZ 8.8

Total 150.63 DFID 7.7

Source: AAP, 2016 Total 150.63

The EUD developed a further breakdown of fund distribution by activity, whereby in some cases activity

budgets are lumped together, to be further broken up by the IPs. For the NSST to calculate the impact of

geographical clustering on the district allocations, it split the combined activity budget lines (1.1.1, 1.1.2,

1.1.3, 1.1.6, 1.2.1, 1.2.4, 1.3.1; and 1.3.2; 3.3.1, and 3.3.3; 3.3.1, 3.1.2, 3.1.3, 3.2.1, 3.2.2, and 3.2.3) to

individually costed activities. The outcome is shown per SO on in the three following graphs. Similar colours

of the bars indicate the same IP, as indicated in the legend.


18

Alongside the use by NSST for calculating district allocations under this assignment, the breakdown of the

PE budget ((1.1.1, 1.1.2, 1.1.3, 1.1.6, 1.2.1, 1.2.4, 1.3.1 and 1.3.2) could provide an indicative budget outline

for the Guidelines for Applicants of the CfPs.

Figure 4: Preliminary fund allocation for SO1



Legend for colour bars and IPs in the above three graphs CfPs (GoU) GoU PEs Go UWS contracts UNCDF GIZ RE DFID TM EA UNICEF GIZ WfP CMP

€ 1,0

€ 0,5

€ 4,5

€ 3,8

€ 4,1

€ 0,8

€ 1,0

€ 4,0

€ 0,5

€ 0,5

€ 3,0

€ 14,8

1.3.2 Family planning

1.3.1 CB Nutrition

1.2.4 Market opportunities

1.2.3 Rural electrification

1.2.2 Incubation projects

1.2.1 Market linkages

1.1.4 Vocational training

1.1.3 Savings and Credit

1.1.2 Input supply

1.1.1 Food Production

€ 0,0 € 2,0 € 4,0 € 6,0 € 8,0 € 10,0 € 12,0 € 14,0

€ 7,7

€ 9,1

€ 62,0

€ 0,0 € 10,0 € 20,0 € 30,0 € 40,0 € 50,0 € 60,0

2.2.1 Logistical hub

2.1.2 District roads

2.1.1 Connecting roads

€ 3,2

€ 3,0

€ 1,0

€ 1,0

€ 6,1

€ 1,0

€ 0,5

€ 0,5

€ 0,5

€ 5,0

€ 0,5

€ 0,7

€ 0,5

€ 0,9

€ 1,0

€ 0,0 € 1,0 € 2,0 € 3,0 € 4,0 € 5,0 € 6,0 € 7,0

3.4.6 DLG P&M CB

3.4.5 Land governance

3.4.4 Catchment Management Support

3.4.3 Road maintenance support

3.4.2 Nutrition governance

3.4.1 JLOS support in Kja

3.3.3 CSO collaboration with DLGs

3.3.2 Barazas

3.3.1 Downward accountability

3.2.3 DLG top-up grants

3.2.2 DLG APA

3.2.1 DLG Upward accountability

3.1.3 DLG Partnerships

3.1.2 DLG Local revenue

3.1.1 DLG PFM


19

2.3 Conclusions

During the last ten years NU has made considerable progress in rebuilding its infrastructure and economy.

Poverty levels came down quickly on the back of a peace dividend and high recovery investments that

picked the low hanging economic growth fruits. The last few years, the pace of income poverty reduction in

NU may have slowed down, as the actual number of poor people in NU increased by 300,000 since 2012.

For NU to get at par on the income poverty indicator with the rest of Uganda requires continued and

massive investments in infrastructure, agricultural production and productivity, and manufacturing

(including agribusiness).

The increasing number of poor people in NU as well as the persistent malnutrition across the North are

disturbing facts that require targeted interventions for poor and vulnerable HHs, and in particular women

and youth, who fail to benefit from the economic opportunities, or move in and out of poverty because of a

variety of reasons, many of which are beyond their control. In the meantime, the agriculture sector in

Uganda is commercialising. Agribusinesses are modernising and expanding rapidly, and are skimming the

rural areas for produce. An increasing number of rural producers are ceasing the opportunities and are

getting integrated in the VCs, often with support from projects and produce buyers and processors. While

this is in principle a positive development, the associated financial and food security risks for small holder

farmers require new messages and services to be delivered to rural producers, that help them to manage

the risks and deal effectively with the harsh realities of the market.

The same is true for the DLGs. They need to modernise their strategies, systems and processes, becoming

smarter in what they do and how they do it, and more client oriented towards the general population and

the businesses in their districts.

DINU covers all the aspects mentioned above (and a few more), and has the financial clout to make a

difference in NU. At the same time, the scale, complexity and implementation modalities with several

independent IPs shall make it hard to exploit the potential synergies that do exist between some of the

components and activities. The core IPs are in the process of putting their final proposals together. These

proposals need to be carefully scrutinised on their social, technical and financial quality; roll-out needs to

be coordinated in areas where different programme components need to be coordinated to maximise

synergies between them; and an M&E and reporting system needs to be put in place so as not to lose the

grip on the programme as whole. All this requires a strong coordination unit that is taking charge of the

programme and as many Implementing Partners from the start. In our view, this unit should be put

together without delay.


20

3. Geographical selection criteria

3.1 The rationale for geographical concentration

Three major considerations underpin the rationale for geographical concentration of DINU interventions:

1. The concentration of individual component investments in a limited number of districts is likely to

increase their individual impact on the ground. The argument is that to create impact, larger

focused engagements over a prolonged period of time are necessary to generate fundamental and

sustained changes, hence impact, in (farming) communities and DLGs.

2. The concentration of more components in the same location is likely to create synergies that

further increase the impact and sustainability of the individual components on the ground. This is

particularly true when two or more components overlap in content and target group.

3. The concentration of both individual and various components in the same location is likely to

create economies of scale and reduce overheads. This argument is primarily about reducing

delivery costs by reducing time and costs of reaching and engaging with the target group, or by

combining deliverables across IPs.

The first argument is relevant at component level. When a component can only be successful with a

sufficiently high investment in time, funding or both (and both are limited), concentration of activities

makes sense. The FSN&HHI component is a typical example of this: training 5,000 farmers once a season

has most likely less impact than training 1,000 farmers five times a season. The same may also be true for

DLG capacity building: a single training to 33 DLGs is probably less effective than several trainings and

handholding for a few years of 15 DLGs. For the second argument to hold, overlap in content and target

group is a requirement. Figure 7 shows the interconnections between the major components37 in their

overlap with other components, whereby the size of the shapes is a rough relative indication of the

available funding for each component.

Figure 7: Relative size and technical overlap between major DINU components

37

The Atiak-Laropi road (activity 2.1.1) is left out, because of its relative isolation from the other components.


21

The figure shows that the FSN&HHI component sits at the heart of DINU. This component has substantial

overlaps with the district capacity building component, input supply, market access and agribusiness, and,

to a slightly lesser extent, the district roads component. The overlap between the rural electrification (RE)

component38 and other components is in the view of the NSST rather limited. It may be interesting,

depending on lessons learned from this pilot, for designing future EU or with other-donors funded

programmes. The same is true for the logistical hub component in Gulu, which is geographically restricted

to one location and primarily focused on large trade businesses with South Sudan39.

This analysis points at a first important principle to be followed when trying to maximise synergies within

the DINU components: the priority of geographical integration should be between components with the

greatest potential for synergetic effects. Starting with the FSN&HHI component, integration should

therefore be first and foremost sought with WfP in Karamoja, DLG CB, Market access and agribusiness,

District roads and Nutrition governance. The other components have limited overlap in content and target

groups with the FSN&HHI component and their geographical integration will make a relatively small

additional contribution to the overall success of DINU.

3.2 Principles and criteria for selecting districts for the FSN&HHI component

Based on discussions with stakeholders and on available data the NSST developed a preliminary list of

principles and criteria for district selection as presented in table 10.

Table 10: Preliminary indicators for district selection, data sources, purpose and status

Data sets Source Purpose Comments

All districts N/A Equal distribution All sub-regions must be fairly represented

Preselected districts EUD, Ips Automatically included

Abim, Adjumani, Amudat and Moyo (roads); Pader (nutrition), Lamwo (rural electrification), Zombo and Oyam

District clustering Align interventions per cluster

Clustered districts must be bordering and in the same livelihoods zone

Refugee numbers UNHCR, OPM District needs assessment Analysed and used for West-Nile district selection

Other projects OPM, NUD District needs and overlap Analysed and used for VC selection

Population data/densities UBOS Target group estimates SS HH data analysed and used

Poverty by district UBOS District needs assessment Analysed and used

Food security status IPC District needs assessment Analysed and used

Child nutrition status UNICEF District needs assessment Child stunting data analysed and used

District finances MoFPED, MoLG, DQ

District needs and impact assessment

Analysed overall per capita funding and per capita funding for production and marketing

District staffing MoLG District capacity Analysed but not used due to lack of reliable data

District performance MoLG District needs and impact assessment

Not used because of limited data set

38

The systems will be installed in a maximum of 25 villages and 1200 HHs, and are designed to power lights, (work) shops, small agribusiness (grinding, hulling) and small irrigation pumps; the system’s capacity (between 30 and 70 KWp) is calculated on the basis of existing consumption. Oversizing the systems based on anticipated growth of (agri) businesses is discouraged because it would drive the electricity tariff up too much (Ashley Wearne pers. com 14/2/2017 and 19/04/2017); Promotion of Mini-Grids for Rural Electrification; Annex I to the Delegation Agreement CRIS No. FED/2015/38781; GIZ; April 2017. 39

Moses Sabiiti, TradeMark EA, pers. com 24/03/2017.


22

During the assignment, for each of the quantitative criteria data were collected or calculated where

possible. The resulting quantitative outcomes were scored and standardised on a scale of 1-5, by dividing

any district outcome by the highest district outcome, multiplied by 5. Standardising the score is needed to

apply a weight to the criteria in the final assessment. In the following paragraphs each of the criteria will be

analysed and commented upon.

Fair distribution of interventions and resources across the five sub-regions 3.2.1

The basic starting point for the analysis of the NSST was the explicit wish by the GoU that all sub regions

must have a fair share of core districts. On the basis of selecting 15 core districts this means that Karamoja,

Acholi, Lango and West-Nile would have between 3 and 5 core districts, and Teso at least 1.

Preselected districts 3.2.2

At the start of the assignment the EUD conditioned that the 4 districts that were selected for the District

roads component, Amudat, Abim, Adjumani and Moyo, would automatically be core districts. Given the

potential synergies between the District roads component, the FSN&HHI and the DLG CB component, the

NSST supports this position. At a later stage, two additional districts were included as mandatory core

districts: Zombo and Oyam. During the review of the final draft report in May 2017, the EUD added Lamwo

as a core district on account of the RE component of DINU.

District clustering 3.2.3

An important consideration for district selection is the usefulness to cluster the DINU FSN&HHI

interventions (activities 1.1.1, 1.1.2, 1.1.3, 1.2.1, 1.2.4) in geographically confined areas. Under this

principle, an intervention cluster is defined as a contiguous area of districts within similar livelihoods zones,

and with a concentration of VC actors for, at least, a few common promising or viable VCs.

Clustering enhances the operational efficiencies of the secondary IPs, allows for the rational selection of a

limited number of VCs relevant for the livelihoods zones in which the cluster is located, and creates viable

concentrations of producers and farm produce to spur development of input supply chains and

agribusinesses40.

Relevant in the identification of intervention clusters is the notion of livelihood zones. LHZs were

introduced in Chapter 2, and table 11 gives an overview about which livelihoods zones are common to the

DINU districts. If the clustering is to be done within similar LHZs, the districts indicated in the rows with the

same LHZ indicated in the columns, in principle qualify for clustering. In some cases, two livelihoods zones

are similar enough to allow for a cluster across the zones; a case in point is the North-East and Central

South East Karamoja Pastoral zones.

If the principles of equal distribution, preselected districts, and clustering within livelihoods zones is

followed, the six clusters as per figure 8 and table 12 would emerge. Together the six clusters would include

18 districts. The FFSN&HHI in Kaabong and Nebbi will be restricted to relatively small areas in which the

main VC, livestock and coffee respectively, are relevant.

Alongside the six FSN&HHI clusters, the NSST identified five business hubs, Kotido, Kitgum, Gulu, Lira and

Arua, which are central to regional trade and medium- and large scale processing of any of the

commodities produced in the clusters. We propose that the business hubs will become recipients of trade

and processing related activities, such as activities 1.1.4, 1.2.1, 1.2.2 and 1.2.4.

40

Clustering is also a basic underlying principle of the World Bank-funded Agricultural Cluster Development Project.


23

Kar

amo

ja

Abim

Amudat

Kaabong

Kotido

Moroto

Nakapiripirit

Napak

Lan

go

Alebtong

Amolatar

Apac

Dokolo

Kole

Lira

Otuke

Oyam

Teso

Amuria

Katakwi

Wes

t-N

ile

Adjumani

Arua

Koboko

Maracha

Moyo

Nebbi

Yumbe

Zombo

Table 11: Overview of livelihoods zones in relation to DINU districts

Region District

Livelihoods zones

UG

25

No

rth

-Eas

t K

aram

oja

UG

24

Cen

tral

an

d S

ou

ther

n

Kar

amo

ja

UG

23

Cen

tral

Kar

amo

ja

UG

16

No

rth

Kit

gum

Gu

lu,

Am

uru

, Wes

t-N

ile

UG

20

-21

So

uth

Kit

gum

Pad

er A

bim

East

ern

cen

tral

low

lan

d

Mid

No

rth

UG

17

Am

uru

Gu

lu

UG

19

So

uth

Wes

tern

Gu

lu

UG

18

Kar

um

a M

asin

di

Oya

m

UG

15

Wes

t N

ile

UG

27

Eas

tern

low

lan

d

UG

05

Rw

enzo

ri W

est

Nile

Ach

oli

Agago

Amuru

Gulu

Kitgum

Lamwo

Nwoya

Omoro

Pader


24

Figure 8: Overview of proposed clusters and business hubs under the FSN&HHI component


25

Table 5 on page 14 also shows that there are considerable differences in the number of subsistence farming

HHs across the clusters. The NSST proposes that these differences are taking into account when allocating

funds to clusters or secondary IPs.

Table 12: Clusters, livelihood zones, districts and the % and number of subsistence farming HHs

Cluster Preselected district(s)

Livelihoods zone Cluster districts % Population Total SS HH

Eastern Karamoja

Amudat Northern Central and Southern Karamoja zone

Amudat, Moroto and (Kaabong)

5 30,120

Central Acholi

Abim South Kitgum Pader Abim zone, combined with North Kitgum Amuru West Nile zone

Abim, Pader, Lamwo and Agago

19 109,324

Eastern Lango/Teso

Amuria East Central lowlands zone, combined with Mid North zone

Amuria, Otuke and Alebtong

18 104,366

South West Acholi/Lango

Oyam Karuma, Masindi Oyam zone, combined with Mid North zone

(Oyam), Omoro and Kole

27 150,928

Northern West Nile

Adjumani, Moyo

North Kitgum Amuru West Nile zone

Adjumani, Moyo Yumbe and Lamwo

19 105,909

Southern West Nile

Zombo Rwenzori West Nile zone Zombo and (Nebbi) 12 65,659

() districts in which the FSN&HHI component operates in a small geographical area 566,306

After applying the above criteria, the room for manoeuvre through applying other quantitative selection

criteria, such as poverty and food security status, becomes rather small.

Refugee numbers and impact 3.2.4

A specific criterion for West Nile is the impact of refugees on refugee hosting districts, expressed by the number of refugees in each district. The number of refugees per district are tallied by the UNHCR, and the data of May 2017 were used to establish the most affected districts. Table 13 provides the refugee numbers for the districts in West Nile and Lamwo, where a new refugee camp with a capacity of 30,000 people has just opened. A second one is supposed to open in the same district.41.

The table shows that Arua and Yumbe are most affected in terms of absolute numbers and that Adjumani

and Moyo carry the largest share in relation to the citizen population. The total refugee population has

almost doubled since January 2017. In May the daily cross-border average was around 2,000 persons,

indicating that the situation continues to be critical for the foreseeable future for the hosting districts and

the refugees.

41

A new refugee camp has just opened in Lamwo with a capacity of 30,000 people. A second one is scheduled to open in the same district. 42

As of end of May 2017; source: https://ugandarefugees.org. 43

The citizen population of Adjumani as estimated by UNHCR is considerably lower than the official census figures.

Table 13: Registered refugees and asylum seekers in West Nile (May 2017)

District Number of refugees and

asylum seekers42

Citizen population % refugees on total population

Adjumani 224,343 168,91743

57%

Arua 198,053 825,639 19%

Yumbe 272,707 555,697 33%

Moyo 171,529 144,793 54%

Lamwo 22,038 137,785 14%

Total 888,670 1,663,914 32%



26

The impact of the refugee crisis is considerable for the hosting communities, the district resources and the

district environment. In particular the environmental footprint of the refugee crisis requires immediate

action. For example, the fuelwood consumption of refugees in Bidi Bidi refugee camp is estimated at 3.5 kg

per person per day44 or over 347,000 MTs per year. Under the current scenario of cooking on open fires

and no tree planting, this would mean that all the biomass in a radius of 5km from the camp would be

depleted in 3 years. The inclusion of the four most heavily affected districts in West Nile is therefore fully

warranted. Apart from Arua, all the districts listed here also qualify as core districts under the earlier

analysed criteria.

Other projects 3.2.5

To what extent is the preliminary list of districts overlapping with FSN&HHI and DLG CB interventions by

other projects and programmes? The NSST produced an overview of major projects in NU, whereby

projects were being counted if their focal area was related to food security, nutrition, HH income or

agribusiness, had a budget of over €500,000 per year and were listed to be operational beyond July 2018.

Table 14: Number of projects, project score per district, and highest scores compared to selected core districts

Region District Total project count Standardised score Preliminary core

districts

Ach

oli

Agago 4 2.5

Amuru 5 1.7

Gulu 5 1.7

Kitgum 5 1.7

Lamwo 5 1.7

Nwoya 6 0.8

Omoro 3 3.3

Pader 3 3.3

Kar

amo

ja

Abim 4 2.5

Amudat 5 1.7

Kaabong 3 3.3

Kotido 3 3.3

Moroto 5 1.7

Nakapiripirit 5 1.7

Napak 5 1.7

Lan

go

Alebtong 4 2.5

Amolatar 3 3.3

Apac 5 1.7

Dokolo 4 2.5

Kole 5 1.7

Lira 4 2.5

Otuke 3 3.3

Oyam 7 -

Teso

Amuria 3 3.3

Katakwi 2 4.2

We

st-N

ile

Adjumani 5 1.7

Arua 4 2.5

Koboko 2 4.2

Maracha 5 1.7

Moyo 2 4.2

Nebbi 5 1.7

Yumbe 4 2.5

Zombo 4 2.5

44

Rapid Woodfuel Assessment; 2017 Baseline for Bidi Bidi Settlement, Uganda; FAO and UNHCR, May 2017.


27

The outcome is shown in table 14, whereby the 15 districts with the least projects are indicated in red and

compared with the selected districts according to the earlier discussed selection criteria. The table shows

that out of the 16 preliminary selected districts 11 would qualify as a core district under this criterion,

which is according to the NSST a strong enough correlation. Under this criterion Katakwi and Koboko would

qualify before most other districts, but were not included because of other criteria mentioned above.

An overview of the considered projects is contained in the accompanied district and crop databases.

Population and poverty scores 3.2.6

To what extent do the preliminary selected districts match with population and poverty scores? To answer

this question, the NSST defined four sub-criteria which were scored individually: number of subsistence

farming HHs, number of poor HHs, number of food insecure HHs, and number of stunted children.

Table 15: Standardised population and poverty criteria scores

Region District

SS farming score

Poverty score

Food insecurity score

Stunting Score

Sum of weighted score

Districts Weight> 20% 40% 10% 30%

Ach

oli

Agago 2.86 2.43 3.95 2.19 2.56

Amuru 2.27 2.06 3.15 1.76 2.09

Gulu 4.67 2.78 2.58 2.58 4.04

Kitgum 2.27 2.23 2.73 1.79 2.12

Lamwo 1.73 1.52 0.87 1.24 1.39

Nwoya 1.71 1.38 0.95 1.29 1.35

Omoro 2.63 2.10 1.39 0.98 1.77

Pader 2.12 1.92 2.10 1.57 1.85

Kar

amo

ja

Abim 1.13 2.13 2.83 1.88 1.89

Amudat 0.86 1.82 0.86 1.84 1.51

Kaabong 1.69 3.43 5.00 2.76 2.99

Kotido 1.39 3.08 3.94 3.07 2.78

Moroto 0.74 2.60 3.21 1.83 2.02

Nakapiripirit 1.52 2.99 2.46 2.71 2.51

Napak 1.52 3.15 3.96 2.62 2.70

Lan

go

Alebtong 3.06 2.60 1.83 1.00 2.09

Amolatar 1.70 1.57 0.38 0.63 1.17

Apac 4.56 4.02 1.65 1.55 3.09

Dokolo 2.12 1.96 0.41 0.78 1.45

Kole 3.18 2.71 1.46 1.05 2.14

Lira 4.10 5.00 2.92 1.64 3.53

Otuke 1.42 1.23 2.09 0.48 1.11

Oyam 5.00 4.30 2.07 1.72 3.38

Teso

Amuria 2.99 1.89 1.98 0.59 1.70

Katakwi 1.84 1.20 2.51 0.31 1.18

We

st-N

ile

Adjumani 1.84 2.77 2.23 2.69 2.47

Arua 8.21 9.83 36.71 8.22 11.57

Koboko 1.56 2.03 0.47 2.36 1.85

Maracha 2.36 2.43 0.85 2.10 2.12

Moyo 1.59 1.73 0.81 1.35 1.47

Nebbi 4.72 5.19 3.92 4.40 4.66

Yumbe 4.16 4.27 1.90 5.00 4.17

Zombo 3.13 3.37 1.13 2.74 2.86

Data sources 2014 UNC 12/13

UNHS

2014 UNC UDHS


28

The scores were standardised, weighted and added (table 15)45. The data for Arua are excluded from the

analysis, because they seem to include refugees, which have already been taken into account in another

criterion. The match between population and poverty scores and core districts is 40%, which is according to

the NSST a rather poor match. In this analysis, Napak and Nakapiripirit would qualify before some of the

other districts, but are not included because of criteria related to livelihoods zones and clustering.

District finances 3.2.7

The NSST developed a district finances criterion based on the overall budget and the Production and

Marketing budget per subsistence farming HH for FY 16/17. The data are based on the BFPs of the districts.

Table 16 shows the budget in thousands of Uganda Shilling (UGX), and the combined standardised score.

Table 16: District and Production and Marketing budget (UGX 000’/per subsistence HH for FY 16/17) Region District Total budget

per SSF HH P&M budget per

SSF HH Standardised budget

allocation score Scoping study

choice

Ach

oli

Agago 532 14 1.7 Amuru 564 20 1.9 Gulu 405 26 1.5 Kitgum 800 31 2.7 Lamwo 647 17 3.4 Nwoya 570 11 1.8 Omoro 488 30 1.8 Pader 756 20 2.6

Kar

amo

ja

Abim 1,072 19 3.4 Amudat 613 17 2.0 Kaabong 992 165 5.0 Kotido 758 27 2.6 Moroto 652 240 5.0 Nakapiripirit 836 36 5.0 Napak 606 35 3.6

Lan

go

Alebtong 411 31 1.6 Amolatar 561 20 1.9 Apac 566 17 1.9 Dokolo 583 15 1.9 Kole 461 26 1.8 Lira 548 29 2.0 Otuke 601 21 2.01 Oyam 489 18 1.6

Teso

Amuria 508 35 1.9

Katakwi 695 18 2.3

We

st-N

ile

Adjumani 979 38 3.3 Arua 564 9 1.8 Koboko 602 21 2.02 Maracha 637 26 2.2 Moyo 1,048 57 3.8 Nebbi 444 17 1.5 Yumbe 557 22 1.9 Zombo 422 23 1.51

The scores were calculated by awarding higher points for higher budgets, assuming that higher budgets per

subsistence HH, may indicate higher outcomes. The match with earlier selected core districts is 40%. This

seems a bad match, but two considerations make this a weak criterion: 1) the difference between the

45

The higher the sum the higher a district qualifies for DINU support, whereby the 15 highest scores are highlighted, and compared with the earlier selected core districts as highlighted in the last column.


29

districts is too small to have any measurable impact in the field, and 2) the argument might be reversed in

that districts with a lower score are more in need in support.

Other criteria 3.2.8

Two criteria were analysed but not used. Firstly, data about district staffing were received through the DQs,

but our field worked showed that districts are currently recruiting staff for the Production and Marketing

Department under the new single spine extension system. The provided data are therefore not reliable

enough for use in this analysis. Secondly, the NSST intended to use district performance data as collected

by the Ministry of Local Government, but the existing dataset only covers 8 of the 33 districts, and coverage

is therefore too small to be used as a criterion46.

3.3 Status of geographical selection by core IPs

Table 17 shows the status of the geographical selection process by the core IPs as of 6 June 2017.

Table 17: Overview of the status of geographical selection of core IPs

IP Component Sub components47

Sub-

contractor Geographical

selection Link with

scoping study

GoU

SO1 1.1.1, 1.1.2, 1.1.3, 1.1.6, 1.2.1, 1.2.4, 1.3.1, 1.3.2

TBD Scoping study N/A

SO3 3.3.1, 3.3.3 TBD

SO1 1.1.4 GoU ?? Weak

SO3 3.3.2 GoU ?? Moderate

3.4.1, GoU ?? Weak

3.4.6 GoU ?? Strong

SO2 2.1.1 TBD Completed None

UNCDF

SO3 3.1.1, 3.1.2, 3.1.3, 3.2.1, 3.2.2, 3.2.3 MoLG Completed Strong

SO3 3.4.3 TBD Completed Strong

SO3 3.4.5 FAO Completed Strong

SO2 2.1.2 MoW* Completed Strong

UNICEF SO3 3.4.2 UNICEF Completed Strong

GIZ SO3 3.4.4 GIZ Completed Strong

SO1 1.1.5 MoWE* Completed Strong

GIZ SO1 1.2.3 MoE Completed Moderate

DFID SO2 2.2.1 TradeMark EA Completed Weak

TBD = to be decided; *most likely candidates

In the case of UNICEF, GIZ, DFID and UNCDF, extensive needs assessments have been carried out to

determine the geographical areas of operation. Technical studies were done for the rural roads component,

the WfP and CMP component, the RE component and for the logistical hub in Gulu. The outcome of this

work is shown in the figures 9, 10 and 11.

GIZ intends to roll-out the WfP (activity 1.1.5) and CMP (activity 3.4.4) interventions to all seven districts of

Karamoja. The selection of water for production sites is ongoing, and would ideally fully overlap with the

FSN&HHI interventions in Karamoja. For that to happen, GIZ would need to incorporate Northern Amudat

in their WfP plans, and if funds are in short supply, drop Napak and/or Nakapiripirit from their plans.

46

The MoLG and UNCDF used a district performance score for their selection of districts. Theirs is a geometric average of two scores: the general performance score based on internal MoLG assessment and an audit score based on the OAG report for 2016. 47

All activities and activity numbers mentioned in this document are derived from the AAP.


30

For the DLG BC building component under

UNCDF, district selection was completed

(figure 10). The dark brown districts indicate

districts that are scheduled to receive a top-

up grant in addition to the CB interventions.

The geographical selection of districts for the

Land governance component, also under

UNCDF, is currently done by the Food and

Agriculture Organisation of the United

Nations (FAO), which is also likely to

implement its activities. Twelve of the 15

districts of UNCDF overlap with the FSN&HHI

component.

For UNICEF, the nutrition governance

component (activity 3.4.2) is largely a

continuation and further expansion of their

ongoing programme. Their choice is based on

a comprehensive equity analysis that includes

a broad range of survival and health,

education, and protection indicators, but has

been adjusted for DINU to have a reasonably

good match with the FSNHHI and the CB

component (figure 11). It will be executed in

2 phases.

It may be clear from the above that selecting

15 core districts on the basis of the combined

criteria of all the core IPs is not possible, and

that some activities will spill-over in none-

core districts if they were to follow their

current priorities. Figure 9 shows no

geographical overlap between the Roads,

Logistical hub and Catchment management

and water for production components.

However, figure 1 in chapter 2 shows that this

is not necessarily a problem. The potential

synergies between any of these components

with FSN&HHI are rather limited, and the

NSST sees no benefit in enforcing

geographical overlap between these

components48.

On the other hand, the NSST strongly

48

The NSST does not see strong synergies between the current RE and the FSNHHI components, but the EUD has decided to include Lamwo as a core district to explore the possibilities of such synergies for future programmes.

Figure 9: District coverage of various DINU components

Figure 10: District coverage of the DLG CB component

Figure 11: District coverage of the Nutrition governance components as of 6 June 2017


31

advocates for full overlap between the FSN&HHI component and the WfP component49, full overlap

between the DLG CB and DLG Nutrition governance components, and as close as possible overlap between

FSN&HHI and the DLG CB components. If need be, this should be enforced by the EUD and the supervising

authority during the negotiations for the Delegation Agreements.

3.4 Activity and fund distribution across DINU districts

Based on the above considerations, the proposed distribution of activities across the districts is shown in

figure 12 on the next page. Apart from the advocated overlap in the previous paragraph, the NSST proposes

to allocate specific activities related to supporting input supplies, market linkages, incubation funding for

agribusiness and vocational training within SO1 to benefit the five business hubs.

Following the activity allocation, the NSST established how core district, business hubs and satellite districts

differ in their financial benefits from the DINU programme. This was done by going through the following

steps:

1. Determine in which districts the individual activities will be rolled out, based on the

implementation plans of the IPs and the NSST recommendations.

2. Allocate financial resources to each of the 30 activities of DINU. Where the budget is not yet broken

down into individual activities, the NSST made an estimated allocation to each activity as per the

figures 4, 5 and 6 in chapter 2.

3. Allocate the FSN&HHI pro rata as per the number of subsistence farming HHs, whereby for Kaabong

and Nebbi one third of the total SS farming HH is being used in the calculations.

4. Allocate the DLG CB and DLG Nutrition Governance (DLG-NG) pro rata as per the number of total

HHs in the benefitting districts.

5. Allocate additional funding for input supplies and market opportunities to the 5 business hubs.

6. Remove the location specific allocations: Connecting roads and the Logistical hub, which would

have a disproportional impact on the calculations.

7. Reduce the district benefit to 75% of the total budget50.

8. Sum the component contributions per district to get a combined district fund allocation.

The outcome of this exercise is shown in table 18 and figure 13. Table 18 shows some key characteristics

for all the DINU districts in relation to the funding and number of activities, as compared to the relative

number of HHs per district.

The table shows that the districts for the Food security component appear at the top of the table, but that

some satellite districts may receive substantial funding from other components. The district with the

highest fund allocation is Amudat. Within the group of 15 core districts, Kole receives the lowest funding.

Nebbi and Kaabong receive even lower funding because the FSN&HI component only operates in a

relatively small area of the district, and their funding is therefore proportionally reduced. The focused

funding for the business hubs results in an allocation of € 0.7 million per hub. The remaining 11 districts will

receive a small proportion of the DINU funds, related to small agribusiness incubation projects.

49

Overlap between FSN&HHI and district roads has already been achieved and should be maintained. 50

The remaining 25% is considered to be consumed as IP overheads; out of that 7% is unaccounted overhead as contained in the delegation agreement; the remainder is local implementation overheads; the amount is based on overheads of IPs of ALREP and KALIP.


32

Activity

District

1.1

.1 F

P

1.1

.2 IS

1.1

.3 S

&C

1.1

.4 V

T

1.1

.5 W

fP

1.1

.6 C

LR

1.2

.1 M

L

1.2

.2 IP

1.2

.3 R

E

1.2

.4 M

O

1.3

.1 C

B N

1.3

.2 F

P

2.1

.1 C

R

2.1

.2 D

R

2.2

.1 L

H

3.1

.1 P

FM

3.1

.2 L

R

3.1

.3 P

3.2

.1 U

A

3.2

.2 A

PA

3.2

.3 T

UG

3.3

.1 D

A

3.3

.2 B

3.3

.3 C

SO

3.4

.1 J

LOS

3.4

.2 N

G

3.4

.3 R

M

3.4

.4 C

MP

3.4

.5 L

G5

1

3.4

.6 P

&M

Agago

Amuru

Gulu

Kitgum

Lamwo

Nwoya

Omoro

Pader

Abim

Amudat

Kaabong

Kotido

Moroto

Nakpiripirit

Napak

Alebtong

Amolatar

Apac

Dokolo

Kole

Lira

Otuke

Oyam

Amuria

Katakwi

Adjumani

Arua

Koboko

Maracha

Moyo

Nebbi

Yumbe

Zombo

Legend NGOs GoU PEs UNCDF UNICEF 1 GIZ GIZ GoU WS DFID UNICEF 2

Figure 12: Overview of activities and their target districts by IP as of 6 June 2017

51

The component of land governance is still being designed and the exact coverage will be refined based on this table


33

Table 18: HH Population, absolute and relative funding, and number of DINU activities per DINU district

District % HHs % SS HH Total funding % Funding

No of interventions

Co

re d

istr

icts

an

d c

lust

ers

Karamoja

Amudat 1% 2% 3.94 7.2% 25

Kaabong 2% 1% 1.66 3.0% 16

Moroto 2% 2% 1.91 3.5% 23

North-East Acholi

Agago 3% 7% 1.93 3.5% 20

Lamwo 2% 4% 4.04 7.4% 19

Pader 2% 5% 1.78 3.3% 21

Abim 1% 3% 3.03 5.6% 24

South-East Lango Teso

Alebtong 3% 8% 1.55 2.8% 13

Otuke 2% 4% 1.84 3.4% 22

Amuria 3% 7% 3.59 6.6% 22

West Acholi Lango

Omoro 3% 6% 2.99 5.5% 22

Kole 3% 8% 1.94 3.6% 14

North West Nile

Adjumani 3% 5% 3.43 6.3% 22

Moyo 2% 4% 3.71 6.8% 22

Yumbe 4% 10% 3.04 5.6% 20

Zombo 3% 8% 2.39 4.4% 20

Bu

sin

ess

hu

bs

Gulu 6% 0.88 1.6% 6

Kitgum 3% 0.88 1.6% 5

Kotido 2% 1.84 3.4% 8

Lira 6% 0.88 1.6% 5

Arua 10% 0.88 1.6% 5

Sate

llite

dis

tric

ts

Oyam 5% 12% 2.86 5.2% 14

Nebbi 5% 4% 1.43 2.6% 13

Napak 2% 0.74 1.4% 11

Dokolo 2% 0.43 0.8% 8

Amolatar 2% 0.35 0.6% 8

Nakapiripirit 2% 0.27 0.5% 3

Amuru 2% 0.05 0.1% 1

Nwoya 3% 0.05 0.1% 1

Apac 2% 0.05 0.1% 1

Katakwi 5% 0.05 0.1% 1

Koboko 2% 0.05 0.1% 1

Maracha 2% 0.05 0.1% 1

Figure 13 breaks down the financial benefits in activities. It shows that there is a clear distinction in

allocated funding between the 16 core districts, 2 satellite FSNHHI districts, 5 agricultural business hubs,

and the 12 satellite districts, when non-core activities are removed from the calculations.

Five satellite districts have been selected by the NSST as business hubs. The NSST proposes that they

receive focused funding related to agribusiness development, vocational training, SME incubation funding

and market linkages. Of the remaining 10 districts, Napak and Amolatar receive some additional funding

through the DLG CB governance component. The remaining 8 district may receive funding through SME

incubation funding, provided that they are successful in the business CfPs for incubation funding.

NIRAS A/S DINU Scoping Study Final Report

Date: 3 July 2017

34

Figure 13: Distribution of DINU funds (€ 000,000’) across the operational districts

- 0,500 1,000 1,500 2,000 2,500 3,000 3,500 4,000

Amuru Nwoya

Apac Katakwi Koboko

Maracha Nakapiripirit

Amolatar Dokolo Napak

Kitgum Gulu Lira

Arua Nebbi

Alebtong Kaabong

Pader Kotido Otuke

Moroto Agago

Kole Zombo

Oyam Omoro

Abim Yumbe

Adjumani Amuria

Moyo Amudat Lamwo

1.1.1 Food Production 1.1.2 Input supply 1.1.3 Savings and Credit 1.1.6 Communal land registration 1.2.1 Market linkages

1.2.4 Market opportunities 1.3.1 CB Nutrition 1.3.2 Family planning 1.1.4 Vocational training 1.1.5 Water infrastructure in Kja

1.2.2 Incubation projects 1.2.3 Rural electrification 2.1.2 District roads 3.1.1 DLG PFM 3.1.2 DLG Local revenue

3.1.3 DLG Partnerships 3.2.1 DLG Upward accountability 3.2.2 DLG APA 3.2.3 DLG top-up grants 3.3.1 Downward accountability

3.3.3 CSO collaboration with DLGs 3.3.2 Barazas 3.4.1 JLOS support in Kja 3.4.2 Nutrition governance 3.4.3 Road maintenance support

3.4.4 Catchment Management Support 3.4.5 Land governance 3.4.6 DLG P&M CB


35

Figure 14 shows the fund distribution for the clusters, business hubs and satellite districts according to the

proposed allocation of activities and funds to the districts. The funding for the Atiak-Laropi road in

Adjumani and the Gulu business hub have been left out as they are location specific and have limited

immediate impact on the DINU programme as a whole.

Figure 14: Fund distribution in '000,000 € and % between clusters, business hubs and satellite districts

Funding is highest for the Northern West Nile cluster because it includes two districts that are included in

the districts road programme. Apart from this windfall for Northern West Nile cluster, funding across the

clusters is fairly distributed. This was one of the main criteria that was adopted at the start of this

assignment, and this criterion has been met by the proposals of the NSST.

3.5 Conclusions

The selection of 15 core districts for the FSN&HHI component of DINU on the basis of a transparent set of

criteria proved to be a challenging process. The NSST notes that overriding principles for district selection,

such as preselected core districts based on criteria of other components or on other considerations such as

equal distribution across sub-regions, and district clustering according to livelihoods zones and proximity,

would already determine to a large extent which districts can be included as core districts, before

quantitative criteria are being applied. The NSST found that these three principles define to a large extent

the 15 core districts.

Other quantitative criteria can be used to check if the core districts are not too much out of line, but by

applying quantitative criteria some contradictions and comparison issues come into to play. The

contradiction lies in the fact that weaker districts (districts that may score low on some quantitative

criteria) may at the same time and for the same reason be in greater need of support. In addition, the NSST

noted that different quantitative criteria would often lead to a different set of priority of districts, whereby

it is difficult to argue which of the criteria would gain priority over the others.

Nevertheless, the findings of the NSST point to a selection of districts that can be supported by the

overriding principles and some of the quantitative criteria. The most important ones are in this respect the

refugee status for West Nile, and the presence of other projects in the districts. Other quantitative criteria

sometimes give a match with the selected districts individually, but give inconclusive results when all

lumped together.

Karamoja; € 7,5; 14%

North-East Acholi; € 10,8; 20%

South-East Lango Teso; € 7,0; 13%

West Acholi Lango; € 7,8; 14%

North West Nile; € 10,2; 18%

South West Nile; € 3,8; 7%

Business hub; € 5,4; 10%

Satellite; € 2,1; 4%


36

The NSST therefore recommends to weigh the overriding principles heavily in the selection process. This

would result in choosing the 15 districts for the FS&HI and CB components as presented, provide similar

support in only a part of Nebbi and Kaabong, and provide focused agribusiness support to the five key

business hubs in Northern Uganda: Kotido, Kitgum, Gulu, Lira and Arua.

The NSST also recommends to ensure maximum overlap between the FSN&HHI component, the WfP and

CMP component, the DLG CB component, and the Nutrition governance component. This may require

some additional negotiations with some of the IPs.

Overall the proposed core districts receive considerably higher fund allocations than satellite districts when

one-off high capital intensive projects such as the Atiak-Laropi road and the logistical hub are not

considered. The number of activities and fund allocations to the bottom 6 of the satellite districts with

respect to Incubation funding is almost negligible. The NSST is of the opinion that it would be more efficient

and effective to shift those allocations to core districts and business hubs.


37

4. Value chain selection

4.1 Basic principles for VC selection

The term value chain describes the full range of value adding activities required to bring a product or

service through the different phases of production to an end product. This embraces all activities needed to

produce and process a product, including regulatory functions, infrastructure, information/extension,

planning, input supply and finance (figure 15). Likewise the value chain actors are all persons, institutions

and business that operate in one or more steps of the value chain.

Figure 15: Basic elements of the value chain

In the previous chapter, the NSST identified six clusters comprising of 15+2 districts in which the FSN&HHI

component shall operate. The districts within the clusters fall largely into the same or comparable LHZs.

This is done to ensure that within a cluster the selected VCs are relevant in all districts. The agribusiness

interventions overlap geographically fully with the FSN&HHI component, but also spill over into the five

proposed business hubs.

In the selection of VCs for support by DINU following core principles come to the fore on the basis of the

objectives of DINU. VCs should offer the most promising scenarios for:

1. Reducing poverty; 2. Providing a diversified and nutritious food basket, specifically aimed at children and pregnant and

lactating women; 3. Increasing HH resilience; 4. Increasing HH income, with specific focus on benefits to women; 5. Increasing value addition along the VC, including employment, and in particular ‘green jobs’. 6. A positive impact on the environment.

It is quite obvious that no single VC will fulfil all these criteria, and that more than one VC per cluster will

have to be selected if the broader range of DINU objectives is to be achieved. In addition, the diverse agro-

ecological, livelihoods, and economic make up of NU and Karamoja is likely to produce location specific VCs

for each of the DINU clusters.

Based on these principles, the NSST identified three commodity groups that would need to be present in

each cluster to meet the objectives of DINU (table 19). In the selection of VCs for each cluster, the NSST

paid attention that all three commodity groups are represented.

The list of sample commodities and possible interventions per commodity group is not exhaustive. But both

show a gradual shift between group 1 to 3 from farmer oriented production and handling interventions of

traditional food crops, towards quality and commercialised production of new commodities, bulk marketing

according to product standards, and high-end storage, processing and marketing.

Business Environment (regulatory, infrastructure etc.)

Services (advisory, financial, certification, etc.)

Provision of inputs

Production Bulking

Cleaning Grading

Trade Processing Packaging Retail


38

Table 19: Commodity groups, commodity examples and VC interventions for VCs in NU

Description Sample commodities in NU Interventions

Group 1

Commodities that are widely cultivated, produce sufficient quantities in a normal season to provide a substantial portion of the daily HH food consumption, and make, in combination with other food items, an important contributing to a balanced diet. Surpluses will be sold in local markets, to cater for HH cash needs. It may have in that respect some overlap with group 2.

Sorghum, maize, millet

beans and peas

cassava

sweet potatoes

sesame

groundnuts

soya

fruits and vegetables

poultry and shoats

on farm production and productivity

farm level PHH

food preparation

local (bulk) marketing

savings and loans groups

Group 2

Commodities that farmers produce in sufficient quantities to market a surplus for income. They are in steady demand as a (staple) food for middle class consumers, for animal feeds or for raw materials in the local manufacturing of consumer products such as soap, flour or beverages. As such they generate local revenue and employment through trade and low to medium level processing. Some commodities in this group also have an international market if they meet minimum quality standards and volumes.

apiary

maize and sorghum (beer)

soy bean (food fortification and animal feed)

sunflower (cooking oil, soaps)

cassava (flour, chips, ethanol)

rice

sesame

piggery

shoats and cattle

quality vegetables and fruits

apiary

savings and loans groups, SACCOS and banking

input supply

productivity

basic quality standards

bulk marketing

cooperatives

small and medium scale processing

end-product quality and marketing

product development

Group 3

Commodities that are in high demand in neighbouring countries and international markets because of the quality and volumes that Uganda produces. Their markets are expanding, and their niche end products fetch good markets and high prices. Exports may still be in raw materials, but there are good prospects for in-country high-end processing

cotton and coffee (largely new to NU)

maize

sunflower and soy bean

cassava

sesame

chilies

apiary

shea nut

fruits

expansion in new production areas

quantity and quality production

on-farm quality standards

bulk marketing and cooperatives

strategic planning

management and financing

high-end storage

processing and marketing

product development

international market development

Ideally, all three commodity groups should be part of any farming enterprise. The mix provides sufficient

diversity in the cropping system to satisfy immediate HH food, nutrition and cash requirements, spread the

risks related to individual commodity production and price set-backs, and exposes the farmer to modern

and, proposedly, sustainable and climate smart farming techniques, quality standards, and advanced

marketing and financing services, that would over time reduce HH vulnerability, and enhances incomes and

resilience. It also produces the necessary raw materials for the development of SMEs at various levels, add

value, create employment in trading centres and business hubs, and stimulates the growth of other

supporting services.

Not all farms have the land, labour and financial means, and the technical know-how to engage in the

production of all three commodity groups. In such cases, the obvious focus of the interventions is on

commodity group 1, thereby securing HH level food security, dietary diversity and a basic income.


39

Interventions may gradually shift into group 2 and 3, whereby farmers get more integrated in the VCs, and

producers and agribusinesses gradually professionalise.

4.2 Short listing of promising VCs

Starting point for the VC selection was the establishment of a long list of crops that are grown in NU.

Approximately 30 crops were identified in the initial listing, of which approximately 17 are sufficiently large

to analyse further. Their importance for NU was established first on the basis of their ranking by the DLGs in

the DQs of the six most important commodities in their districts, combined with their ranking done during

the district interviews. The DQ and interview rank score is standardised to a maximum of 5. The % DQ and

% interviews indicates the number of DQs and interviews in which the commodity was mentioned. In the

final ranking the DQ score was given 25% and the Interview score 75%. The outcome is shown in table 20.

Table 20: Scoring and ranking of major commodities on the basis of DQs and district interviews

Commodities DQ Rank % DQs DQ Score Interview

Rank %

Interviews Interview

Score Final Rank

Apiary 2.9 7% 0.3 3.3 47% 1.9 1.5

Beans 3.2 75% 4.1 2.5 33% 1.0 1.8

Cassava 3.8 82% 5.0 4.3 93% 5.0 5.0

Cattle 2.8 18% 0.8 3.8 27 1.3 1.1

Coffee 3.3 11% 0.6 2.5 13% 0.4 0.5

Millet 3.1 22% 1.2 1.7 13% 0.3 0.5

Shoats 2.5 14% 0.6 4.2 7% 0.3 0.4

Groundnuts 2.4 66% 2.7 3.0 33% 1.3 1.6

Maize 3.5 81% 5.0 3.1 67% 2.6 3.2

Rice 2.7 32% 1.4 3.1 53% 2.1 1.9

Sesame 1.9 38% 1.3 2.6 73% 2.4 2.0

Sorghum 3.2 47% 2.6 3.8 53% 2.5 2.4

Soya bean 3.3 18% 0.9 2.9 47% 1.7 1.5

Sunflower 1.6 25% 0.7 1.9 53% 1.3 1.1

Vegetables

0% 0.0 1.7 7% 0.1 0.1

Citrus/Fruits 4.2 4% 0.2 3.3 4% 0.1 0.2

Peas 1.7 7% 0.2

0% 0.0 0.0

Green grams 0.8 7% 0.1 2.9 13% 0.5 0.4

Potatoes 0.8 4% 0.0

0% 0.0 0.0

Bananas 2.5 4% 0.1

7% 0.2 0.2

Sweet potato 1.7 18% 0.5

27% 0.0 0.1

Cassava scores highest, closely followed by maize, sorghum, beans, sesame, rice and groundnuts. Cattle,

coffee, shoats, apiary and vegetable score low because it appeared in only few questionnaires (cattle,

apiary, citrus, shoats, coffee), or because of their limited economic importance (vegetables). On the basis of

the aggregated scoring, peas, potatoes, bananas52 and green grams were removed for further analysis. The

remaining commodities were analysed on their size and income earning opportunities at HH level (table

21). The data in table 21 come from various sources53, and, where data differed between sources for the

same commodity, an average or informed estimate was taken.

52

Bananas is proposed as an alternative crop in the South West Nile cluster. 53

The data sources for all crop information in this chapter are listed in the crop profiles (Annex 5); in some cases the NSST had to make informed estimates.


40

Table 21 shows the importance of sesame, maize, beans, sorghum, groundnuts and cassava in terms of the

number of HH involved in their production, and cassava, maize, sweet potatoes, beans, sorghum and soya,

in terms of their production volumes. From a HH income perspective, cassava, coffee, soybean and apiary

are standing out as the most profitable crops by far, followed at a distance by sesame, maize, beans, rice,

and sunflower.

Table 21: Key data for the shortlisted commodities in NU

Commodity Number of HHs Volume in north

(Mt) Income for SS HH

(US$/year) Commodity Groups

Apiary 75,000 640 420 1, 2, 3

Beans 270,000 254,000 214 1, 2

Cassava 202,000 983,000 1,288 1, 2, 3

Cattle 71% 3,895,000 ? 2, 3

Coffee 150,500 106,000 624 2, 3

Groundnuts 238,000 83,000 80 1, 2

Maize 402,000 306,000 180 1, 2, 3

Millet 120,000 79,000 60 1

Rice 28,000 44,000 150 2

Sesame 591,000 95,000 282 1, 2, 3

Shoats 71% 6,975,000 ? 1, 2

Sorghum 336,000 117,000 75 1, 2

Soybean 44,000 40,000 608 1, 2, 3

Sunflower 83,000 105,000 178 2, 3

Fruits NA NA NA 1, 2

Vegetables NA NA ? 1

Sweet potatoes ? 293,000 50 1

The last column in table 21 indicates how the shortlisted commodities perform in each of the three

commodity groups54. For commodity group 1, an aggregated food security, nutrition and gender score was

created from the DQs and the DLG interviews. The analysis shows, rather unsurprisingly, that sorghum,

groundnuts, sesame, beans, sweet potatoes, and vegetables score well on these aspects, and at least one

of these commodities must be included in the VC selection for a cluster.

Rice, beans, sorghum and shoats are local cash commodities with substantial regional demand, but with

limited international prospects. Cattle has a regional and international market, but the VC is not well

developed. Sunflower and coffee are primarily cash commodities traded and processed through

sophisticated VCs in the international market. Maize, sesame and soya bean are important commodities at

all three levels.

4.3 VC selection, environment and climate change

From an environmental/climate smart perspective practically all smallholders grow these crops under a

low-input-output system, and the environmental footprint of all crops is relatively small. An exception

could be sunflower, maize and rice (and cotton, but it is not in the list), which heavy feeders and are grown

on a more industrial scale, but even the inputs used on these crops are comparatively limited. The

environmental risks are therefore not so much posed by crops and crop husbandry practices, but by the

low yields as a result of the low-input-output system. Compounded by the high population growth, it

requires farmers to open up new land, and keep ever smaller areas under fallow, at the expense of natural

54

The listings do not mean that a commodity is not featuring in another group at all, but that its importance in another group is relatively small.


41

forests, bushlands and swamps (rice!). Increasing productivity and yields, even if achieved by moderate

supplies of inputs, is therefore the most effective way of reducing poverty and the heavy agricultural

workloads of women (larger areas under cultivation require larger labour inputs in planting, weeding and

harvesting) in a climate smart manner.

Alongside increasing yields by using improved seeds and better soil fertility management, proven

environmentally sound and climate smart agricultural practices include a broad range of practices, such as

conservation tillage, composting and manuring, intercropping, agroforestry, and smart crop rotations. None

of these is without its own constraints: conservation tillage often requires the use of herbicides, and many

others demand high labour inputs, without necessarily increasing yields. The application of such practices

must therefore be carefully analysed before they are being introduced in the farming system.

4.4 Selection for commodity opportunities

After the initial selection, the NSST looked at the possibilities of DINU to make a difference at production

and agribusiness level. Production opportunities were scored on the basis of a yield gap analysis. The yield

gap was established by subtracting the average commodity yield in NU from the optimal average and

thereafter standardise the score. Table 22 shows the outcome in the column ‘YG score’, whereby the higher

score, to a maximum of 5, denotes the highest opportunity for on-farm production and productivity

improvements.

Table 22: Scoring of selected commodities on opportunities for yield and business development

Commodities Yield gap YG score AB opportunity score Overall opportunity score

Apiary 67% 3.4 5.0 4.2

Beans 65% 3.2 0.8 2.0

Cassava 75% 3.8 4.6 4.2

Cattle 2.1

Coffee 50% 2.5 5.0 3.8

Groundnuts 63% 3.1 1.2 2.2

Maize 43% 2.1 1.7 1.9

Millet 64% 3.2 1.7 2.4

Rice 48% 2.4 1.7 2.5

Sesame 47% 2.3 2.8 2.6

Shoats 2.8

Sorghum 67% 3.3 1.2 2.3

Soybean 76% 3.8 2.5 3.2

Sunflower 64% 3.2 1.7 2.4

Citrus/Fruits -

Vegetables -

Sweet potato 72% 3.6 1.7 2.6

All commodities have yield gaps, and all have market opportunities, if only on account of country-wide

population and purchasing power increase. However, the analysis shows that substantial production and

agribusiness gains can be achieved in apiary, cassava, coffee, and soybean, followed by sesame, sorghum,

millet, sweet potatoes and sunflower.

In cassava the gains are primarily driven by the large yield gap, and new markets for cassava as a raw

product for flour, starch and ethanol. Coffee scores high because of the business opportunities, driven by

the GoU strategy to boost coffee production and export 6 times in the next few years. Also soybean has

good production and market opportunities, the latter in particular because of the increasing demand for

animal feeds in East Africa.


42

What makes these commodities interesting for DINU is that there are technologies/practices available for

increasing production and market development. For cassava, new disease resistant varieties are available

and badly needed in the rural areas55, and the opportunities are in the development (with the National

Agricultural Research Organisation - NARO), multiplication and distribution of planting materials, some GAP

extension, small scale chipping or grating and drying, and expansion of processing units. For apiary, modern

hives56 easily double to triple yields, which in turn could trigger local processing businesses.

Soybean has opportunities in seed multiplication under the new Quality Declared Seed system, substantial

yield increases through the use of inoculum and GAP, and an underutilised processing capacity in Northern

Uganda. NU-TEC MD is also active in soybean and sunflower, but operates exclusively through

agribusinesses. Complementary activities to boost HH production and strengthen market linkages are

possible, and welcomed by the NU-TEC MD team57.

Coffee is a special case, in that it is still not a mainstream crop in NU, apart from South West Nile, and

therefore does not appear in the DQs and DLG interviews. However, as explained in the introduction, UCDA

tries to expand the area under coffee into NU with some reasonable success. Around 72,000 farmers have

taken up its cultivation and this is to increase to several 100,000 in the next 5 years. Therefore there is a

business case for private coffee seedling nurseries, an input supply system, and coffee processing plants.

The UCDA has indicated in its concept note on coffee expansion in Northern Uganda, that it wants to do

this in a sustainable and climate smart manner, thereby contributing to the wider sustainability and climate

smart agenda of DINU.

Yields of groundnuts, millet, sorghum and sesame can be increased considerably. Groundnuts, sorghum

and millet will primarily enter the expanding local and regional markets. Technologies for yield

improvements of sorghum are rather limited and interventions at farm level will have to focus on GAPs,

improved seeds and PHH for improving on-farm storage. Millet is extremely labour intensive, especially the

weeding, with little prospects for improvements through GAP. Sesame has a good international market, but

prices fluctuate sending farmers into boom-and-bust cycles. Some farmers mix sesame with sand to

increase weight and income during glut periods58.

Beans are grown everywhere in NU, make up 45% of the protein intake in Uganda and are therefore

important from a nutrition perspective in rural areas. Overall increase in consumption is marginal because

of reducing per capita consumption. Coupled with the fact that beans are hardly processed beyond drying,

sorting and marketing, the business opportunities beyond trading are very limited. Beans have a

considerable yield gap, but specific local demands for tastes, sizes and colours make consumers often

prefer local varieties. Improved seeds are available and grown by around 30% of the producers. Given the

business limitations, the NSST sees little additional value for DINU to engage in promoting the bean VC,

although its dietary importance should be emphasised in the community based nutrition component.

Maize and sunflower operate in high-end processing and marketing systems, where DINU would have little

added value. The yield gap is still substantial but GAP, and in particular improved varieties are widely

available, although with quality issues. From a climate smart perspective, maize and sunflower are both

heavy feeders, can only be grown in single stands, and therefore require relatively large fertilizer inputs.

55

Currently the brown streak virus is decimating the cassava crop in NU. 56

87% of the 2 million hives are log hives. Two modern types are available: the Kenya Top Bar (KTB) and the Langstroth. 57

NU-TEC MD Deputy CoP, pers. com 22/3/2017. 58

GADC, pers. com 3/3/2017.


43

The same is true for rice and cotton, which also require relatively high levels of crop protection measures

that may not always be environmentally friendly.

Apart from niche markets close to urban centres, vegetable growing is a small scale women’s affair in rural

areas. On-farm vegetable production should and can easily be stimulated with targeted support to women

to set up kitchen gardens, accompanied by seed distribution in small affordable quantities, simple watering

systems, nutrition education. This type of interventions does not substantially increase the workload of

women and has an immediate and positive impact on HH dietary diversity. The vegetable VC should

therefore be integrated in every cluster as part of the broader community nutrition and nutrition

governance interventions. Incorporated in vegetable growing and community based nutrition interventions

should be use of local fruits, such as mangos and avocados.

For apiary the yield gap and business opportunities are large and the technology (modern hives and

harvesting methods) is relatively simple, cheap and available. Honey is traditionally part of the HH diet and

a healthy supplementary food item. As with vegetables, apiary hardly increases the on-farm workload, does

not require (large) land ownership, contributes positively to crop pollination and provides additional HH

income. For these reasons the NSST proposes to include apiary in the VC mix in all clusters.

4.5 VC Selection at cluster level

Out of the district selection process, the NSST established six intervention clusters for the FSN&HHI

activities. For each of the clusters, the 17 shortlisted VCs were scored against the cluster-specific criteria

related to their suitability for the AEZ and LHZ of the respective clusters. Cluster specific scoring was done

on two aspects: technical suitability and existence of other projects59 dealing with the same commodities.

Technical suitability was established on the basis of the DQs and DLG interviews. The commodity ranking

done in the DQs was disaggregated by cluster and combined with the ranking done during the interviews.

The number and type of projects was listed and taken into consideration in the final assessment. The

outcome is listed for each of the clusters in the paragraphs below. Although maize and beans score rather

high in some of the clusters they are not being considered for reasons explained in the previous

paragraphs. Table 23 gives a summary of the VC selection for the 6 clusters.

Table 23: Proposed clusters, livelihood zones, districts and cluster VCs

Cluster Livelihoods zone Cluster districts Cluster VCs

Eastern Karamoja

Northern Central and Southern Karamoja zone

Amudat, Moroto and (Kaabong)

Livestock, sorghum, apiary and vegetables

Central Acholi

South Kitgum Pader Abim zone Abim, Pader and Agago Cassava, soya/sesame, coffee, apiary and vegetables

Eastern Lango/Teso

East Central lowlands zone, combined with Mid North zone

Amuria, Otuke and Alebtong

Rice, cassava/sweet potatoes, apiary vegetables

South West Acholi/Lango

Karuma, Masindi Oyam zone, combined with Mid North zone

Oyam, Omoro and Kole Cassava, soya (groundnuts/rice) apiary and vegetables

Northern West Nile

North Kitgum Amuru West Nile zone

Adjumani, Moyo, Yumbe and Lamwo

Cassava, sesame/soybean, apiary, vegetables

Southern West Nile

Rwenzori West Nile zone Zombo and (Nebbi) Coffee, cassava, bananas, apiary, vegetables

() districts in which the FSN&HHI components operates in a small geographical area

In the following paragraphs the selection considerations and outcomes per clusters are shown.

59

Only projects that run beyond 2017 and have an annual budget>US$500,000 were considered.


44

VC selection for the East Karamoja cluster 4.5.1

Table 24 shows the outcome of the VC selection for the East Karamoja cluster.

Table 24: Cluster VC selection for the East Karamoja cluster

Commodity % HH

DQ Crop Rank

Interview Crop Rank

Final Crop Rank

Projects

25% 75%

Apiary 4.2

Beans 38% 3.1 1.7 2.0

Cassava 15% 0.8 1.7 1.5

Cattle 50% 1.7 5.0 4.2 DRIP, RPLRP

Coffee

Millet 2.5 2.5 2.5

Shoats 80% 0.8 4.2 3.3 DRIP, RPLRP

Groundnuts 35% 1.7

Maize 45% 4.2 2.5 2.9

Rice

Sesame 1.7

Sorghum 80% 5.0 3.8 4.1

Soybean

Sunflower 50% 2.5 1.7 1.9

Vegetables ACC

Citrus/Fruits

Sweet potato - DRIP= Drylands Integrated Project; RPLRP= Regional Pastoral Livelihoods Resilience Projects; ACC=Adaptation to Climate Change

Cattle, shoats and sorghum score highest, and can be combined with apiary and vegetables to have commodity groups 1 and 2 well covered. Maize still scores quite high, but this comes primarily from Western Kaabong, which is excluded from the project operational area.

In the livestock sector, there is overlap with two other projects, but both are relatively small scale. USAID is currently considering a livestock intervention60, but if, when and how big this will be is as yet not known.

VC selection for the North East Acholi cluster 4.5.2

Table 25 shows the outcomes of the VC selection of the North East Acholi cluster. Cassava stands out as a

preferred intervention, closely followed in scoring by groundnuts and sesame. The NSST, however, prefers

soybean over groundnuts and sesame, so as to have a good representation of a group 2 commodity.

Soybean could be swapped for sesame, which is also a group 2 crop, but with an international market. The

high price fluctuations makes it however a more risky crop than soybean. The group 3 commodity for this

cluster could be coffee61.

In this cluster, three other projects are dealing with cassava. Cuttings distribution by Operation Wealth

creation has time and quality issues. Given the importance of cassava in the region, there still seems to be

space for DINU in the cassava sector. VODP works on a small scale in soybean, and NU-TEC MD only works

with agribusinesses. RALNUC III has in its choice of commodities also soybean, but it will close down in

2018. Also in these commodities the NSST doesn’t see big risks for duplication.

Table 25: Cluster VC selection for the North East Acholi cluster

Commodity62

% HH DQ Crop Interview Crop Final Crop Projects

60

A pilot is currently run by Mercy Corps in Karamoja (Jimmy Ochien, pers. com 28/02/2017). 61

In light of the newly built cotton ginnery in Pader and the revived ginneries in Kitgum and Gulu, cotton could eventually become a group 3 commodity as well. However, the data are currently not sufficient to recommend this. 62

Missing data: no reliable data source found.


45

Rank Rank Rank

25% 75%

Apiary - 2.9 2.2

Beans 55% 1.7 3.3 2.9 ACDP, PRELINOR, RALNUC III

Cassava 73% 2.8 4.4 4.0 ACDP, PRELINOR, OWC

Cattle - 3.3 2.5

Coffee - ACDP, OWC,

Millet 3.3

Shoats -

Groundnuts 80% 3.1 3.8 3.6

Maize 73% 3.1 3.1 3.1 ACDP, PRELINOR, OWC, RALNUC III

Rice 40% - 1.7 1.9 PRELINOR, RALNUCIII

Sesame 81% 2.9 3.1 3.0 RALNUCIII

Sorghum 90% 4.4 1.9 2.6

Soybean - 3.6 2.7 VODP, NU-TEC MD, RALNUC III

Sunflower 0.8 2.2 1.9 VODP, NU-TEC MD, RALNUC III

Vegetables -

Citrus/Fruits - OWC

Sweet potato - - - ACDP= Agriculture Cluster Development Project; PRELINOR=Project for the Restoration of Livelihoods in the Northern Region; VODP=Vegetable Oil Development Project (closes in 2018); NU-TEC MD=NU-TEC Market Development; OWC=Operation Wealth Creation; RALNUCIII=Restoration of Agricultural Livelihoods in Northern Uganda

63

VC selection for the South East Lango Teso cluster 4.5.3

Table 26 provides the scoring for the South East Lango and Teso cluster. Rice is the highest scoring

commodity, and should be combined with cassava to cover both food security, HH income and regional

trade aspects. Cassava could be replaced or supplemented by sweet potatoes, which is an important staple

in Eastern Uganda, and is primarily a group 1 commodity64. For a group 3 commodity, soya, sunflower or

coffee could be considered.

Table 26: Cluster VC selection for the South East Lango Teso cluster

Commodity % HH

DQ Crop Rank

Interview Crop Rank

Final Crop Rank

Projects

25% 75%

Apiary 4% 3.3 3.3 3.3

Beans 65% 2.5

Cassava 83% 5.0 4.2 4.4 OWC

Cattle 1.7

Coffee - OWC,

Millet -

Shoats -

Groundnuts 67% 1.7

Maize 64% 1.7 2.5 2.3 OWC

Rice 34% 4.2 5.0 4.8

Sesame - 1.7 1.3

Sorghum -

Soybean - 2.5 1.9 VODP, NU-TEC MD

Sunflower - 1.7 1.3 VODP, NU-TEC MD

Vegetables -

63

RALNUC III choses 3 strategic crops per district of sub-region; not all listed commodities will therefore be covered. 64

The orange fleshed varieties have been promoted widely in NU, and are from a nutrition perspective a good alternative.


46

Citrus/Fruits 65% 4.2 OWC

Sweet potato 70% - - - VODP=Vegetable Oil Development Project (closes in 2018); NU-TEC MD=NU-TEC Market Development; OWC=Operation Wealth Creation

VC selection for the South West Acholi Lango cluster 4.5.4

In the South Western Acholi and Lango cluster (table 27), the NSST proposes cassava and soybean as the

target VCs for group 1 and 2, and coffee as a group 3 commodity. Sorghum scores higher but the NSST

prefers cassava as a staple over sorghum because of the better technology and business opportunities for

cassava. Groundnuts and rice are good group 2 alternatives.

Table 27: Cluster VC selection for the South West Acholi Lango cluster

Commodity % HH

DQ Crop Rank

Interview Crop Rank

Final Crop Rank

Projects

25% 75%

Apiary - 1.7

Beans 81% 3.8 3.1 2.5 ACDP, PRELINOR

Cassava 76% 4.2 3.6 4.0 ACDP, OWC, PRELINOR

Cattle - 1.7

Coffee - ACDP, OWC,

Millet - 0.8

Shoats -

Groundnuts 54% 3.7 2.5 2.6

Maize 77% 4.3 3.6 4.2 ACDP, OWC, PRELINOR

Rice 45% 2.5 2.1 3.3 PRELINOR

Sesame 65% 3.0 2.5 1.9

Sorghum 1.0 0.8 5.0

Soybean 60% 4.7 3.9 4.2 VODP, NU-TEC MD

Sunflower 35% 2.0 1.7 1.3 VODP, NU-TEC MD

Vegetables - 1.7

Citrus/Fruits - OWC

Sweet potato 2.0 1.7 - ACDP= Agriculture Cluster Development Project; VODP=Vegetable Oil Development Project (closes in 2018); NU-TEC MD=NU-TEC Market Development; OWC=Operation Wealth Creation

VC selection for the North West Nile cluster 4.5.5

In the North West Nile cluster (table 28) cassava scores exceptionally high, followed by sorghum,

groundnuts and sesame. Sesame is in our view a more promising group 1, 2 and 3 commodity, despite the

fluctuating prices, low yields and quality issues. Although soybean scores rather low in this cluster, there

are opportunities under the National Seed Sector Project to expand the current interventions in QDS seed

multiplication in the region. VODP is active in the oil crop sector, but the programme is operating on a small

scale and closes down in 2018.

North West Nile is traditionally an apiary region, with a well-established processing plant in Arua. The

opportunities for apiary expansion are therefore good.


47

Table 28: Cluster VC selection for the North West Nile cluster

Commodity % HH

DQ Crop Rank

Interview Crop Rank

Final Crop Rank

Projects

25% 75%

Apiary - 4.2 3.1

Beans 48% 3.3 PRELINOR

Cassava 82% 5.0 5.0 5.00 OWC, PRELINOR

Cattle 4.2

Coffee - OWC,

Millet -

Shoats 2.5

Groundnuts 71% 1.7 2.5 2.3

Maize 83% 3.6 2.9 3.1 OWC, PRELINOR

Rice 45% 3.3 0.8 1.5 PRELINOR

Sesame 68% 0.8 2.9 2.4

Sorghum 39% 1.3 2.9 2.5

Soybean - 1.7 1.3 VODP, NU-TEC MD

Sunflower - VODP, NU-TEC MD

Vegetables -

Citrus/Fruits - OWC

Sweet potato - - - VODP=Vegetable Oil Development Project (closes in 2018); NU-TEC MD=NU-TEC Market Development; OWC=Operation Wealth Creation

VC selection for the South West Nile cluster 4.5.6

The highland cluster in South West Nile is different from the other clusters in terms of climate, LHZs and

resultant commodities. The group 1 commodity that stands out is cassava, and the group 3 commodity is

coffee. A group 2 commodity could be bananas, although the NSST did not collect enough data to

understand this VC sufficiently to make a firm recommendation.

Table 29: Cluster VC selection for the South West Nile cluster

Commodity % HH

DQ Crop Rank

Interview Crop Rank

Final Crop Rank

Projects

25% 75%

Apiary 3% 2.5 4.2 3.8

Beans 90% 3.3 RALNUC III

Cassava 86% 4.6 5.0 4.9

Cattle -

Coffee 52% 4.6 2.5 3.0

Millet 3.3

Shoats 40% 1.7

Groundnuts -

Maize 66% 1.7 RALNUC III

Rice 10% 2.5 RALNUC III

Sesame 18% 0.8 RALNUC III

Sorghum -

Soybean - RALNUC III

Sunflower - RALNUC III

Vegetables -

Citrus/Fruits -

Bananas 58% 2.5 2.5 2.5

Sweet potato 65% 0.8 - 0.2

RALNUC III=Restoration of Agricultural Livelihoods in Northern Uganda


48

4.6 Conclusions and way forward in VC prioritisation

The proposed selection of priority VCs was done on the basis of an extensive literature review, a DLG

questionnaire and discussions with 15 DLGs, a number of agribusiness, and development partners and

projects.

However, in the current vibrant agribusiness environment in NU, opportunities rise and die quickly. The

refugee crisis, for example, has a clear impact on the maize and sorghum commodity markets. Prices of

many commodities fluctuate on the basis of local and international demands and supplies, and farmers

often respond by jumping on commodities that fetched high prices in the last season, and are disappointed

when these prices are not sustained.

What this means is that the NSST’s proposals for VC selection are not cast in stone. Developments in

commodity markets need to be monitored closely, and if need be the proposed priority commodities

should be replaced by others, if these prove to be more promising, require immediate interventions, from a

technological or market perspective, or if other programmes have stepped in the same commodity on a

massive scale, diminishing the added value of DINU in that commodity.

The NSST has given some alternative scenarios for the choice of commodities, and has provided a decision

framework on how to systematically arrive at a commodity mix that provides food security, diversity and

income at HH level, stimulates local trade and processing and provides commodities for larger international

markets.

The FSN&HHI interventions will be implemented by Organisations that are contracted through an

international Call for Proposals. We propose that in the procurement process, the applicants take note of

the proposals of the NSST, and are required to make an updated assessment of priority commodities to be

included in their proposals.


49

ANNEX 1: People consulted

Persons consulted in Kampala

Name Institution Designation Contact

Aloys Lorkeers EUD Head of Section +256 312701000

Blaise Peccia-Galletto EUD Operations Adviser (RD) +256 312 701000

John C.T. Seryazi EUD Operation adviser Infrastructure +256 312 701000

Thomas Tiedemann EUD Head of section Governance and Human Rights +256 312 701000

Paul Otim Okello EUD Operations Officer Governance and Human

Rights

+256 312 701000

John Byabagambi OPM Minister for Karamoja Affairs

Kwiyucwiny Grace Freedom OPM Minister of State for Northern Uganda

Moses K. Kizige (MP) OPM State Minister for Karamoja Affairs +256772432658

Mrs Christine Guwatudde OPM Permanent Secretary

Joel Wanjala OPM Undersecretary Finance and Administration +256772591603

Mayanja Gonzaga OPM M&E officer LG +256772484330

Lamaro Ketty OPM Undersecretary Pacification and Development +256772619526

Muhumuza N. Juvenal MoFPED Ag. Assistant Commissioner +256414707279

Samson Muwanguzi MoFPED Senior Economist +256414707308

John Charles Ogol MoFPED Ag. Commissioner +256414707110

Emmanuel I. Nyibigira UCDA Managing director +256312263009

Omwa Samuel Samson UCDA Business Dev. Manager +256312263009

Chesang Francis Bhatia UCDA Director Dev. Services +256312263009

Andrew Kilama Lajul UCDA Director Corp. Services +256312263009

Ms. Winifred N. Mulindwa UBOS Director District Statistics +256414706026

Patrick Okello UBOS Director Agr. & Env. Statistics +256414706015

Stephen Baryahirwa UBOS Principal Statistician +256414706080

Ms. Flavia Oumo UBOS Agriculture Statistician +256772392010

Dickens Ocen UBOS Agriculture Statistician +256782145742

David Hirsch USAID Deputy Director Economic Growth

Martin Fowler USAID Agr.& Livestock Adviser +256414306001

Simon Byabagambi USAID Program management specialist +256414306001

Robert Bagyenda USAID Environment and climate change specialist +256772138453

Mark Wilson USAID Crisis Stabilization and Governance Officer +256772138396

Juma Afrida USAID Project Management Specialist

Johannes Rumohr GIZ Deputy Head of Programme +256312263069

Martin Trojanow GIZ Consultant +34952762769

Dimitry Pozhidaev UNCDF Regional Technical Adviser +256780339094

Abiud M. Omwega UNICEF Nutrition Manager +256417171405

Mrs Nelly Birungi UNICEF Nutrition Specialist

Massimo Castiello FAO Deputy head of Mission +256787140291

Abdul Saboor Jawad FAO Soil and water for production specialist

Maria Guglielma de Passano FAO Land specialist

Beatrice Okello FAO Agricultural Economist

Emmanuel Zziwa FAO

Patricia Nsiime FAO

https://en.wikipedia.org/wiki/John_Byabagambi


50

Benard Onzima FAO Programme Monitoring Officer +256414349916

David Wozemba Palladium NU-TEC MD +25675788700

Ashley Wearne GIZ RE +256790540655

Jenny Rafanomezana TruTrade Director +256754451628

Kristen Turra Palladium Deputy NU TEC CoP +256751820847

Pontian Muhwezi IFAD Programme Officer VODP +256701444770

Moses Sabiiti TM EA Country Director +256778920179

Nigel Nicholson EU Nutrition Advisor to the EU; [email protected]

Persons consulted during the field visit

Name Institution Designation Contact

Alupo Scola Katakwi DLG Principle Assistant Secretary 0772948579

Ongom B Silver Katakwi DLG District Production Officer 0772687044

Okoba Julius Farm Africa Market Engagement Officer 0785156143

Ajalo Esther C Farm Africa Agricultural Officer 0779109019

Emmanuel Moses Tuksi Farm Africa Project Manager 0704641729

Ogwang David Farm Africa Project Coordinator 0782984803

Chelimo Alex Amudat DLG Chief Administrative Officer 0772587855

Dr. Kaziro Michael Amudat DLG District Production Officer 0782529503

Jaap van Kranabry ZOA Amudat ZOA – Amudat 0777317545

Amutale Yeko Newton ZOA Amudat ZOA - Amudat 0784081991

Nanziri Dina ZOA Amudat ZOA - Amudat 0785073490

William Obonyo Otoke Mercy Corps Moroto Market & Financial Services Team Leader 0392003966

Ochien Jimmy Mercy Corps Moroto Programme Coordinator 0772883364

Ewaju Emmanuel Mercy Corps Moroto Livestock Extension Specialist 0392903734

Joshua Mabiya Kotido DLG Chief Administrative Officer 077257779

Okuda Robert Kennedy Kotido DLG District Agricultural Officer 0772356128

Dr. Mulonde Henry Kotido DLG District Veterinary Officer 0782165915

Baguma Rogers Kotido DLG Finance Officer 0772153421

Okello Oyado Sam Kotido DLG Senior Community Dev’t Officer 0775100134

Draiera Hamet Kotido DLG SLMO 0784021023

Kedi John Paul Kotido DLG Ag. P/E 0772999876

Opoli Moses Odong Kotido DLG AEO 0772698155

David Odong Kotido DLG Ag. DCO 0772786967

Locheng Julius Kotido DLG Speaker Kotido 0779044449

Irar Sabina Kotido DLG Secretary Production 0772419863

Aur Charles Kotido Business Forum Chairperson 0757946066

Komakech John Kotido Business Forum Treasurer 0772688357

Longoli Francis Kotido Business Forum Business 0779596475

Esatu Elilo World Vision Kotido DCOP 0750090514

Ongom Advemson Abim DLG Chief Administrative Officer 0772835362

Amwona Leo Abim DLG District Agricultural Officer 0775418601

Oluge Peter Abim DLG Ag. District Veterinary Officer 0772938092

Opira Boniface Omara Abim DLG District Commercial Officer 0778160666

Akileng Jimmy Abim DLG Human Resource Officer 0782747568

Okech Godffrey Abim traders Trader / Farmer 0777809003

Owilli Mathew Abim traders Trader / Farmer 0784457796

Awilli Amabile Abim traders Trader / Farmer

Obonyo Benson Ali Abim traders Trader / Farmer


51

Akullo Christine Abim traders Trader / Farmer 0784747232

Alir John Miltono Abim traders Trader / Farmer 0785614505

Aballa Richard N Abim traders Trader / Farmer 0775174450

Auma Rosemary Abim traders Trader / Farmer 0789541151

Okello Patrick Abim traders Trader / Farmer 0781810609

Akongo Irene Abim traders Trader / Farmer 0779264402

Akello Rosemary Abim traders Trader / Farmer 0779486961

Aringo Florence Abim traders Agro dealer 0775570747

Canon George Adoko Pader DLG Chief Administrative Officer 0772586244

Asekenye Catherine Pader DLG Senior Entomology Officer 0772381036

Tyan John Pader DLG Fisheries Officer 0772848105

Peter Odongkara Pader DLG District Agricultural Officer 0774106882

Dr. Okeny S. Robert Pader DLG District Production Officer 0772692238

Latigo Robert Pader DLG Ag. Community Dev’t Officer 0789154555

Amony Catherine Pader DLG Population Officer / Planner 0772671423

Omony Lakwonyero Kitgum DLG Assistant Agricultural Officer 0772619609

Omony Alfred Kitgum DLG District Fisheries Officer 0777074492

Ocan Bosco Kitgum DLG Assistant Agricultural Officer 0782595646

Dr. Otto Alfred Best Kitgum DLG District Veterinary Officer 0772969939

Oboni Godfrey Oloya Kitgum DLG Senior Entomologist 0779945733

Anywar Martin Kitgum DLG Ag.DistrictCommercialOfficer 0786016944

Wilfred Kamylegeya Gulu Agriculture Development Company

GADC 0782353640

Richard Okedi Gulu Agriculture Development Company

Agribusiness Specialist-Prelnor 0777773202

Komakech Alfred Gulu Agriculture Development Company

Agronomist 0773435858

Richard Okedi PRELINOR Ag. Project Coordinator 0777773202

Komakech Alfred PRELINOR Agronomist 0773435858

Felix Otim Equator seeds Ltd Inventory Manager

Oloya Tourist Oloya Oil Processors Managing Director 0772591126

HARREE Company Ltd Operations manager 0711742742

Okot Francis Kehong Peyero Rice Millers Field Supervisor / Warehouse Manager 0785005097

Andrew Mawejje Adjumani DLG Chief Administrative Officer

Dr. Anthony Mugenyi Adjumani DLG District Production Officer 0772493168

Dr. Okello David Adjumani DLG District Veterinary Officer 0772375183

Abau Zubeida Esia Mixed Farm Ltd Accountant 0779580360

Taban Patrick DIA Company Ltd Managing Director 0783871501

Mawa Faustus NGUVU DIARY Managing Director 0783504287

Ojock K Bran Yumbe DLG Ag.ChiefAdministrativeOfficer 0772541867

Charity Farida Yumbe DLG Ag. District Chairperson 0774289839

Albert Franco Odongo Yumbe DLG District Senior Planner 0787555106

Ali Aluma Yumbe DLG Secretary Works 0782938141

Alejo Jane Yumbe DLG Secretary Social Services 0772867793

Agotre Zubaili Yumbe DLG Ag. Head of Finance 0784179987

Dalili Sebidega Yumbe DLG District Commercial Officer 0782953709

Alamiga Haruna Yumbe DLG Secretary Production 0774980169

Drassy K Ally Yumbe DLG Chairperson Works 0782517588

Waiga Rashid Yumbe DLG Chairperson Social Services 0783977316

Acile Mathew Yumbe DLG Chairperson Production 0777450673


52

Rashid Kawacor Yumbe DLG Ag. DPMO 0772340967

Dr. Yayi Alfred Yumbe DLG Ag. District Health Officer 0772535450

Ratib Abdulahisadi Yumbe DLG Ag. Human Resource 0782807507

Luriga Rasulu Yumbe DLG Ag. District Education Officer 0772388609

Taban Hussein R AFARD Field Officer 0774810575

Asimu Swadik AFARD Field Officer 0773268736

Eyote Moses AFARD Field Officer 0775774816

Anguyo Josua AFARD Field Officer 0773657714

Bayo Richard AFARD Field Officer 0773991326

Canpara Robert Nebbi DLG District Entomology 0772855719

Dr. Okwir Anthony Nebbi DLG District Production Coordinator 0772635397

Munguacel Nelson Nebbi DLG Vermin Control Officer 0784109055

Nyakuni Levy Nebbi DLG District Fisheries Officer 0772303151

Oloya Michael Nebbi DLG Senior Fisheries Officer 0773363313

Enyanga Faustino Nebbi DLG Agricultural Officer 0772350245

Piwa Joyce Nebbi DLG Agricultural Officer 0772949799

Pimundu O. Kemiss Nebbi DLG Agricultural Officer 0712823514

Oryem Richard Nebbi DLG Senior Planner 0774248599

Musisi Joel Nebbi DLG D/Chief Administrative Officer 0772479166

Ochieng O.J William Nebbi District Bee Keepers Association

Chairperson 0772312932

Ario Mike Lira DLG Agricultural Officer / Ag. DCO 0772344771

Patrick Amora Latigo Kamtech logistics (u) ltd General Manager 0772874753

Apollo Seremba Kamtech logistics (u) ltd Production Manager 0772591814

Surjit Singh Guru Nanak Oil Mills (U) Managing Director 0772704545

Odit Amon Tabu Rice Millers Managing Director 0772595802

Ojok Isaac Newton Omoro DLG LC5 Vice Chairperson 0774558906

Odongo Damasco Omoro DLG District Speaker 0714503268

Nyakorach Caroline Omoro DLG Secretary Health 0779754631

Lakot Susan Omoro DLG Secretary Production 0781377276

Oyet Godfrey Jomo Omoro DLG Ag, District Production Officer 0777367393

Achan Stella Omoro DLG Ag. District Planner 0782569470

Komakech W. Oola Omoro DLG Ag. Chief Finance Officer 0788003281

Aciro Peter V Omoro DLG Ag. District Agricultural Officer 0772184074

Ongom Robert Omoro DLG Ag. District Health Officer 0774541543

Arach Betty Omoro DLG Senior Assistant Secretary / Clerk 0772308950

Watdok Francisco Omoro DLG Secretary Works 0777363242

OchengVincent Ocen Omoro DLG District Education Officer 0772534019

Okello Peter Douglas Omoro DLG District Chairman 0782925451

Apollo Stephen Kole DLG ICT Officer 0782724971

Okello Joseph Kole DLG District Commercial Officer

Monday Stephen Kole DLG Chief Administrative Officer 0787735549

Odur Francis Kole DLG Senior Agricultural Officer 0777111164

Adupa Nixon Kole DLG District Agricultural Officer

Apion David Dokolo DLG Agricultural Officer 0772067599

Opondo Patrick Dokolo DLG Veterinary Officer 0777333679

Otim Benard Dokolo DLG Fisheries Officer 0756123403

Alyeryo Patrick Dokolo DLG Commercial Officer 0782629126

Rebecca Monma Dokolo DLG Ag. Chief Administrative Officer 0772647617

Aula James Alebtong DLG D/Chief Administrative Officer 0782663793


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Ochan Morris Alebtong DLG District Planner 0774006073

Jennifer Oyuru Alebtong DLG District Agricultural Officer 0772873699

Ogwal Moses Alebtong DLG Senior Agricultural Officer 0757925329

Mugoya Samuel Alebtong DLG District Finance Officer 0782576557

Okullo Edward Alebtong DLG Agricultural Officer 0782972442

Ojok Tonny Otuke DLG District Agricultural Officer 0782510546

Kamala Francis Otuke DLG Ag.District Information Officer 0772654364

Alute Julius Otuke DLG Senior Planner 0783643975

Bua Benard Otuke DLG Fisheries Officer 0772872456


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ANNEX 2: Sources of data for secondary literature review

1. 11th European Development Fund (EDF) NIP – Uganda; EUD 2. 2015 Statistical Abstract; UBOS; 2016 3. A Climate Trend Analysis of Uganda; Factsheet 2012-3062; USGS/USAID/Fewsnet; June 2012 4. Action Document for the Development initiative for Northern Uganda; European Union 5. Agriculture (Beef); Uganda Investment Authority; ?? 6. Agriculture Cluster Development Project – project appraisal document; World Bank; March 2015 7. Agriculture Sector Development Strategy and Investment Plan 2010/11 – 2014/15; MAAIF 8. Agriculture Sector Strategic Plan - 2015/16-2019/20; MAAIF 9. Agriculture Value Chain Analysis in Northern Uganda: Maize, Rice, Groundnuts, Sunflower and

Sesame; ACF, March 2014 10. Agriculture Value Chain Development Programme (AVACHDEP) Aide Memoire - AfDP 11. ALREP 2010 – 2015 End of Programme Report; Office of the Prime Minister; March 2015 12. Analysis Market Prioritisation Report Version 3; NU-TEC Market Development; 2016 13. Analysis of Demand and Value Chains of Sweet Potato Sub-sector in Uganda; Kilimo Trust; June 2013 14. Annual Assessment of Minimum Conditions and Performance Measures for Local Government

FY2014/15; 2015; Ministry of Local Government; 15. A summary Report of the National Livestock Census, 2008; MAAIF; May 2009 16. Boosting Coffee Production in Northern Uganda; Concept Paper; UCDA, February 2017 17. Budget Increases to Protracted Relief and Recovery Operations, Uganda 200852; WFP, February

2017 18. Cassava Market and Value Chain Analysis; C:AVA; July 2012 19. Cereal Crops: Rice, Maize, Millet, Sorghum, Wheat Cereal Crops - ICRISAT 20. Climate Change and Adaptation strategies in the Karamoja sub-region: DanChurchAid 21. Climate Risk Screening For Food Security Karamoja Region, Uganda; USAID; ?? 22. Coffee Production in mid-Northern Uganda: Prospects and Challenges; Mbowa Swaibu, et al; EPRC

Policy brief; May 2014 23. Comprehensive Food Security and Vulnerability Analysis; World Food Programme; 2013 24. DAR RALNUC III Brochure; RALNUC III 25. FAOSTAT Fao.org 26. Farmers’ Perceptions of Finger Millet Production Constraints, Varietal Preferences and Their

Implications to Finger Millet Breeding in Uganda; Lawrence Owere et al; Jopurnal of Agriculture Science; February 2014

27. Honey market assessment report for RWANU Project In Southern Karamoja; RWANU; ?? 28. KALIP 2010 – 2015 End of Programme Report; Office of the Prime Minister; March 2015 29. Karamoja Donors Mapping Report; 2016; USAID; 2016 30. Karamoja Livestock Market Assessment Report; USAID Feed the Future; 2016 31. Karamoja NGO Mapping Report; USAID; 2016 32. Karamoja, Uganda Enhanced Market Analysis; USAID and FEWS NET; 2016 33. Livelihood mapping and zoning exercise: Uganda; Stephan Browne and Laura Glaeser; The Famine

Early Warning System Network (FEWS NET), January 2010 34. Livestock and Market Assessment Mission to the Karamoja Region, FAO/GIEWS; April 2014 35. Scope of Maize Value Chain Uganda; Multicrop Value Chain Phase II; Bill and Melinda Gates

Foundation; October 2014 36. Market assessment and baseline study of staple foods – Uganda; country report; Uganda; 2010 37. National Population and Housing Census 2014; Main Report; UBOS 38. National Social Service Delivery Equity Atlas 2013/14-2014/15; UNICEF; 2017 39. Northern Uganda – Economic Recovery Analysis; Oxford Economics; 2015 40. Northern Uganda – Transforming the Economy through Climate Smart Agribusiness, Market

Development Inception Report; NU-TEC MD; 2016


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41. Northern Uganda – Transforming the Economy through Climate Smart Agri-Business, Market Development Aggregation and Storage Market System Assessment; NU-TEC MD; 2015

42. Northern Uganda – Transforming the Economy through Climate Smart Agri-Business, Market Development Land Preparation Market System Assessment; NU-TEC MD; 2016;

43. Northern Uganda – Transforming the Economy through Climate Smart Agri-Business, Market Development Improved Seed Market System Assessment; NU-TEC MD; 2016;

44. Open Sesame: A Value Chain Analysis of Sesame Marketing in Northern Uganda; ICRISAT, Nairobi, 2013

45. Peace, Recovery and Development Plan for Northern Uganda 2007-2010 (PRDP); Government of Uganda, September 2007

46. Peace, Recovery and Development Plan for Northern Uganda July 2012- June 2015 (PRDPII); Government of Uganda; November 2011

47. Peace, Recovery and Development Plan 3 for Northern Uganda (PRDPIII); Government of Uganda; May 2015

48. Profitability of finger millet production in Eastern Uganda; Jimmy Bushoborozi; 2013 49. Promotion of Mini-Grids for Rural Electrification; Annex I to the Delegation Agreement CRIS No.

FED/2015/38781; GIZ; April 2017 50. Rapid Woodfuel Assessment; 2017 Baseline for Bidi Bidi Settlement, Uganda; FAO and UNHCR, May

2017 51. Resilience to food insecurity and malnutrition in Karamojong, Uganda, FAO, UNDP, UNICEF, WFP;

April 2015 52. Situation Analysis of Child poverty and Deprivation in Uganda; Ministry of Gender, Labour and Social

Development; UNICEF; EPRC 53. Stabilization-Driven Value Chain Analysis of Rice, Groundnuts and Maize in Northern Uganda; ? 2008 54. Statistical Abstract 2016; Uganda Bureau of Statistics; 2017 55. Strategy for Livestock Development in Karamoja Region; Karamoja Livestock Development Forum;

December 2013 56. South Sudanese refugees in Uganda latest figures: https://ugandarefugees.org 57. The Analysis of the Nutrition Situation in Uganda; FANTA-2; USAID, May 2010 58. The Feed the Future (FTF) Multi-Year Strategy Uganda FY; USAID; 2011–2015 59. The Karamoja Integrated Development Plan (KIDP I & II); Office of the Prime Minister 60. The Northern Uganda Peace Recovery and Development Plan 3 (PRDP3); Office of the Prime

Minister 61. The Uganda Oilseed Sector; KIT Case study; Mirjam Schoonhoven-Speyer and Willem Heemskerk; 62. Uganda climate change vulnerability assessment report - USAID African and Latin American

Resilience to Climate Change (ARCC); August, 2013 63. Uganda Demographic and Health Survey, 2011; UBOS 64. Uganda Demographic and Health Survey, 2016; UBOS 65. Uganda Emergency Update on the South Sudan Refugee Situation; UNHCR, January 2017 66. Uganda Flash Update on the South Sudan Emergency Response; UNHCR, January 2017 67. Uganda Multi-sector Food Security and Nutrition Project – project appraisal document; World Bank;

December 2014 68. Uganda National Census of Agriculture 2008/2009; UBOS 69. Uganda National Census Report 2014 - Northern Region; UBOS 70. Uganda National Coffee Strategy 2040: Plan for 2015/16-2019/20; Uganda Coffee Development

Authority; June 2015 71. Uganda National Household Survey 2012/2013; UBOS 72. Uganda Staple Food Market Fundamentals, January 2017; Famine Early Warning Systems Network;

USAID 73. Uganda Vision 2040; Government of Uganda



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74. Understanding the Rice Value Chain in Uganda: Opportunities and Challenges for Increased Productivity; EPRC/PASIC; July 2016

75. Value Chain Analysis of the Bean sub-sector in Uganda; UNDP, November 2012 76. Value Chain Analysis of the Cassava sub-sector in Uganda; UNDP, November 2012 77. Value Chain Analysis of the Coffee sub-sector in Uganda; UNDP, November 2012 78. Value Chain Analysis of the Honey sub-sector in Uganda; UNDP, November 2012 79. Value Chain Analysis of the Rice sub-sector in Uganda; UNDP, November 2012 80. Value chain analysis and mapping for groundnuts in Uganda; Johnny Mugisha, Stephen Lwasa and

Kai Mausch; ICRISAT; January 2014 81. Value Chains and the Cluster Approach; USAID; 2008


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Annex 3: District Profiles

District profiles are provided in Volume II of this report.


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Annex 4: Commodity Profiles

Commodity profiles are provided in Volume III of this report.


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Annex 5: Integration of EUD comments in the final report

1. The comments were received on 23 May. The integration was completed on 5 June. 2. All typos and reference errors were corrected. 3. All maps and tables were redrawn to reflect the inclusion of Lamwo as a core district, and the

updated selection of districts for the Nutrition Governance component. 4. Additional references on climate change and Climate smart agriculture have been inserted in the

report where relevant. 5. Additional references on gender and youth have been inserted in the report where relevant. 6. The poverty analysis has been updated. 7. The refugee data were updated to reflect the latest figures according to the new UNHCR refugee

web portal for Uganda: https://ugandarefugees.org. Refugee issues are discussed in detail in paragraph 3.2.1.

8. All tables and figures were updated to reflect the inclusion by the EUD of Lamwo in the UG 20/21 Livelihoods zone and the Central North Acholi cluster.

9. Page 16: the suggestion of the NSST to establish the PMU before the TAT is recruited, so as to participate in the assessment for the core IPs’ proposals, do the logistical preparations for the full PMU, organise the first DINU Steering Committee Meetings, and start drafting the Guidelines for Applicants for the CfPs, has been removed from the main text.

10. Page 17: the fund allocation graphs have been updated to reflect the latest figures. 11. Page 19: figure 7 was updated to include family planning. 12. Page 19: JLOS refers to activity 3.4.1 JLOS support in Karamoja. 13. Page 20: the text about the relative importance of the RE component was adjusted. However, the

NSST maintains its opinion that the potential synergies between the RE component and the FSNHHI component are limited.

14. Page 20: footnote 37 was maintained as this reflects accurate information received from the RE component manager.

15. Page 20: according to TradeMark EA, the logistical hub is Gulu is aimed primarily at supporting large scale trading and transport companies. TradeMark EA did not mention any links with the refugee crisis in South Sudan and Northern Uganda. Although the NSST acknowledges that the hub might be used for that purpose so long the crisis lasts, the hub is not and should not be built for the refugee crisis.

16. Page 30: 7% administrative cost in the DA is unaccountable overhead for HQs. Experience from previous programmes shows that the actual overhead (in-country investments and staff) is closer to 35%, so the 25% quoted in the report is a conservative estimate. The figure can be adjusted in the attached spreadsheets.

17. Page 30: the RE allocation has been included as Lamwo is now a core district. 18. Page 39: we added a brief reflection on the environmental / climate smart aspects of VC selection.


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