Capacity Needs Assessment for Improving Agricultural ...
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Table of Contents
Preface vi
Acknowledgement viii
Acronyms ix
Executive Summary 1
Chapter 1: Introduction 4
Agricultural Statistics and the Minimum Set of Core Statistics 4
Need for Agricultural Statistics in Uganda 6
Users of Agricultural Statistics in Uganda 7
Agricultural Statistics Support and Best Practices for Developing Countries 7
Capacity Assessments in Uganda 10
Purpose of This Report 11
Chapter 2: The Agricultural Statistics System in Uganda 12
UBOS’s Role in the NASS 12
MAAIF’s Role in the NASS 14
Other Agency Contributions to the NASS 17
Local Government Contributions to the NASS 18
Current Sources of Uganda Agricultural Statistics 18
Censuses 18
Crop and Livestock Statistics 19
Forestry Statistics 19
Fisheries and Aquaculture Statistics 19
Agricultural Markets and Price Information Systems 19
Water and Environment Statistics 19
Rural Development Statistics 19
Food Security and Nutrition 20
Chapter 3: Methodology 22
Identification of Key Stakeholders 22
Initial Desk Review and Stakeholder Consultation 22
Panel Discussions and Key Informant Interviews 22
Standard Assessment Questionnaire 23
Participatory Local Organizational Assessment Interview 24
Limitations of the Study 25
Chapter 4: Findings from the Assessment 27
Themes from Stakeholders Interviews 27
Capacity Assessment of DAES and MAAIF 29
Capacity Assessment at the District Level 31
Chapter 5: Challenges in the Uganda NASS 36
Institutional 36
Methodological 37
Scant Statistics at the District Level 37
Personnel 38
Technological 38
Financial 38
Chapter 6: Recommendations for Strengthening Agricultural Statistics in Uganda 40
Chapter 7: Global Best Practices for Agricultural Data 51
Country Example of Agricultural Data Collection and Survey Programs 51
Rwanda 51
South Africa 53
Sweden 55
Best Practices for Agricultural Data: Probability Samples and Two-stage Multiframes 57
International Initiatives That Can Be Leveraged to Build Capacity Around Agricultural Statistics 58
Collaborations between the Public and Private Sectors 60
Technology and Quality Assurance Standards 61
Bibliography 64
Appendix 1: Documents Reviewed 66
Appendix 2: Cost Assumptions. 67
List of Figures
Figure 2.1: Current Structure of the Uganda NASS 12
Figure 2.2: DAES Organizational Structure 14
Figure 2.3: Division of Statistics Organizational Structure 16
Figure 6. 1: Harmonized Uganda NASS 41
Figure 7.1: Organogram of the Ministry of Agriculture, Forestry, and Fishing, South Africa 54
Figure 7.2: Distribution of Budget Shares 59
List of Tables
Table 1.1: Minimum Set of Core Data 4
Table 2. 1: Status of the Minimum Set of Core Statistics Collected within the Uganda NASS 20
Table 3.1: Key Stakeholders Interviewed 23
Table 3.2: ASCI Classification 24
Table 3.3: Core Functions Examined in the PLOCA 25
Table 3.4: Districts Surveyed 25
Table 4.1: Standard Assessment ASCIs for DAES and MAAIF 30
Table 4.2: Districts Scores for Different Measures of Statistical Capacity 32
Table 4. 3: Correlation Between Core Functions Critical to Organizational Performance at the District Level 35
Table 6. 1: Recommendations for a Harmonized NASS 42
Table 7.1: Land use strata codes, definition, and areas 52
Table 7. 2: Projected AGRISurvey Budget 59
Table A2.1: Exchange Rate 68
Table A2.2: Workshop, Seminar, and Meeting Costs 68
Table A2.3: Consulting Costs 69
Table A2.4: Staffing Costs 69
Table A2.5: Office Costs 70
Table A2.6: Advertising Costs 70
Table A2.7: Office Equipment Costs 70
Table A2.8: Meeting and Workshop Costs 71
List of Boxes
Box 7.1: Sampling frames for agricultural statistics 58
Box 7.2: Use of technology in collecting agricultural data 62
Box 7.3: Use of technology in data dissemination: Examples of publishers that are Data Documentation Initiative compliant and of data visualization tools 63
Preface
Agriculture is the main source of livelihood for about two thirds of Africa’s population. It accounts for 70% of employment, overwhelmingly on small farms; occupies half of all land area, and provides half of all exports and one-quarter of GDP in Uganda. It is considered a leading sector for future economic growth and economic inclusion in the current National Development Plan. Thus, enhancing its performance is central to food security and sustainable poverty reduction. According to Uganda Vision 2040, agriculture contributed approximately 21 percent of the gross domestic product (GDP) and employed roughly 65 percent of the labor force in 2010 (National Planning Authority 2013).
The agricultural sector in Africa is however faced with increasing demand for agricultural data, but the Agricultural Planning Department and National Statistical Agencies have many challenges in making the required data available. There is lack of capacity to provide reliable statistical data on food and agriculture and to provide a blueprint for long-term sustainable agricultural statistical systems. A Partnership in Statistics for Development in the 21st Century (PARIS21)[1] review found that only 10% of International Development Association (IDA) countries[2] had included agriculture in the National Strategies for Development of Statistics (NSDS) process. Even so, agriculture-related NSDS quality is very low as reflected in agricultural policy and development in most IDA countries. It is imperative that these challenges of reliable and accurate statistics are addressed.
A number of development organizations are working with various developing countries to improve their agriculture statistics systems. For example, the World Bank is actively working with the Government of Uganda to improve the quality and quantity of agricultural statistics through the Living Standards Measurement Survey (LSMS) - Uganda National Panel Survey (UNPS). “UNPS is a national panel household survey that has been collecting multi-sectoral micro data with a strong focus on agriculture. FAO on the other hand supports the Government of Uganda to generate reliable and detailed information on the nature of food security and malnutrition for decision making through implementation of the Integrated Food Security Phase Classification (IPC) in Uganda. FAO facilitates the analysis of food security using the IPC Analytical Protocols resulting in the availability of up-to-date and reliable food security information, which is used for planning and early warning. AfDB has developed “Country Assessment of Agricultural Statistical Systems in Africa - Measuring the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics”.
It is crucial for developing countries to develop their agriculture statistics systems as it is a critical resource for public policy analysis and design, policy implementation and monitoring, and decision making. Further, they provide a key input into other statistics, including the national accounts. For this reason, agricultural statistics need to be comprehensive, reliable, up-to-date, consistent, and available in a form that renders them intelligible and usable (FAO. 2011)
Acknowledgement
This report summarizes the findings and recommendations of the Capacity Needs Assessment for Improving Agricultural Statistics led by Ademola Braimoh at the World Bank and conducted by Frederick Smith, Michael Jacobsen and Karis McGill of RTI International and Paul Kibwika, Joseph Mugagga Sengendo, Richard Kibombo, Florence Birungi Kyazze, and Rosemirta Birungi of Development Research and Social Policy Analysis Center (DRASPAC). The work was carried out under the overall guidance of Diarietou Gaye, Trichur Balakrishnan, Christina Malmberg Calvo and Dina Umali-Deininger.
The team gratefully acknowledges the support to this work by Patrick Okello, Flavia Oumo, Contace Nakiyemba, Emmanuel Menyha, Daphne Arinda, Israel Nsiko and Mulmina Maloru (UBOS); Richard Ndikuryayo, Medard Nabaasa, Efulansi Mutesi, Jovan Lubega, Steven Kayongo, Agnes Nagayi, Kyagaba Ssekimwany (MAAIF); Tonny Odokonyero, Mildred Barungi, Francis Mwesigye and Swaibu Mbowa (EPRC); Juma Ndhokero, Jimmy Semakula and Losira Nasirumbi Sonya (NARO); Emmanuel Iyamulemye Niyibigira, James Kizito-Mayanja and Samuel Samson Omwa (UCGA); John Diisi, Julius Ariho, Ssenyonjo Edward and Joseph Mutyaba (NFA); Caleb Gumisiriza and Mwenda Augustin (UNFFE); Robert Kalyebara and Paul Dhabunansi (aBi Trust); Yamagami Keisuke and Lubega Paul (JICA); Nangulu Moses and Nabbosa Maxensia (UNADA); Martin Fowler and Ochieng (USAID); Kamugisha Godwin (NEMA); Martin Emau, Edward Tanyima and Andrew Ateny (FAO); and Vuzzi Azza Victor and Joyce Alaro (DANIDA).
The report benefited from invaluable suggestions from peer reviewers. We would like to thank Johan Mistiaen, Forhad Shilpi; Talip Kilic; Carolina Mejia, John Ilukor; Joanne Gaskell. Special thanks are due to Gandham Ramana, Holger Kray, Kevin Crockford, Joseph Oryokot and Jane Nalunga for the support provided to this work.
We are also grateful to all the stakeholders who attended the validation workshop for their active engagement and for the valuable inputs and assistance from Damalie Nyanja and Janet Christine Atiang of the World Bank.
Acronyms
|aBi |Agricultural Business Initiative |
|AfDB |African Development Bank |
|ASCI |Agricultural Statistics Capacity Indicator |
|ASSP |Agricultural Sector Strategic Plan |
|BOU |Bank of Uganda |
|CAPI |Computer-Assisted Personal Interview |
|CDO |Cotton Development Organization |
|CIO |Chief Information Officer |
|COA |Census of Agriculture |
|DAES |Directorate of Agriculture and Environment Statistics |
|DANIDA |Danish International Development Agency |
|DDA |Dairy Development Authority |
|DHS |Demographic and Health Survey |
|DRASPAC |Development Research and Social Policy Analysis Center |
|EPRC |Economic Policy Research Centre |
|FAO |Food and Agriculture Organization of the United Nations |
|GDP |Gross Domestic Product |
|GIS |Geographic Information System |
|HR |Human Resources |
|ICT |Information and Communication Technology |
|ITC |Informational and Computational Technology |
|JICA |Japan International Cooperation Agency |
|KII |Key Informant Interview |
|LSMS |Living Standards Measurement Survey |
|M&E |Monitoring and Evaluation |
|MAAIF |Ministry of Agriculture, Animal Industries, and Fisheries |
|MAFAP |Monitoring African Food and Agricultural Policies |
|NAADS |National Agricultural Advisory Services |
|NAGRIC |National Animal Genetic Resource Centre |
|NARO |National Agricultural Research Organization |
|NASS |National Agricultural Statistics System |
|NASTC |National Agricultural Statistics Technical Committee |
|NDP2 |Second National Development Policy |
|NEMA |National Environment Management Authority |
|NFA |National Forestry Authority |
|NFASS |National Food and Agricultural Statistics System |
|NGO |Nongovernmental Organization |
|NPHC |National Population Household Census |
|NSDS |National Strategy for the Development of Statistics |
|NSS |National Statistical System |
|NSSF |National Social Security Fund |
|ODA |Official Development Assistance |
|PARIS21 |Partnership in Statistics for Development in the 21st Century |
|PLOCA |Participatory Local Organizational Assessment |
|PNSD |Plan for National Statistical Development |
|RAADRS |Routine Agricultural Administrative Data Reporting System |
|SAQ |Standard Assessment Questionnaire |
|SSPS |Sector Strategic Plan for Statistics |
|UBOS |Uganda Bureau of Statistics |
|UCDA |Uganda Coffee Development Authority |
|UCGA |Uganda Coffee Growers Association |
|UN |United Nations |
|UNADA |Uganda National Agro-inputs Dealers’ Association |
|UNFFE |Uganda National Farmers' Federation |
|UNHS |Uganda National Household Survey |
|UNPS |Uganda National Panel Survey |
|USAID |U.S. Agency for International Development |
|UTCC |Uganda Trypanosomiasis Control Council |
|WCA |World Program for the Census of Agriculture 2020 |
Executive Summary
Agriculture is a key driver of Uganda’s economy accounting for 70% of employment, providing half of all exports, and one-quarter of GDP in Uganda. Thus, enhancing its performance is central to food security and sustainable poverty reduction. Recent policies have called for analyzing and monitoring the growth of the agricultural sector. To accomplish this, policy makers have identified the need for a strong agricultural statistics system to collect and disseminate timely, accurate, and relevant statistics.
Ugandan agricultural statistics are used by numerous entities both within and outside of Uganda. Policy and decision makers within the national government use statistics to enable effective governing. Agricultural statistics are also needed for planning, administration, monitoring, and accounting at subnational level. Good statistics are required for exploring the profitability of agribusiness opportunities, planning and investment, monitoring, evaluation, and reporting of business activities. Non-Governmental Organizations use statistics to plan, implement, monitor, and evaluate their activities. They also use statistics to monitor and inform government policy, lobby politicians, hold governments accountable, and report to their key stakeholders. Lastly, development partners use a country’s agricultural statistics to determine the need for and impact of assistance or the requirements for participation in development initiatives.
The current system struggles to provide this level of data. To improve the system so that it can provide the appropriate data, a capacity needs assessment was undertaken to determine areas of improvements to the current system. The purpose of this report is to describe the assessment, its findings, and the recommendations for updating Uganda’s agricultural statistics system.
This assessment was conducted between June 2017 and March 2018. Both external and internal stakeholders were interviewed to determine the challenges and opportunities facing Uganda’s agricultural statistics system. Officials from 16 districts were surveyed regarding their district’s ability to collect and produce agricultural statistics. Representatives from the Uganda Bureau of Statistics (UBOS) and the Ministry of Agriculture, Animal Industries, and Fisheries (MAAIF) answered the Standard Assessment Questionnaire (SAQ), and a snapshot of the current capacity of these institutions to produce agricultural statistics was obtained. A draft report was prepared in November and a stakeholder workshop was held on March 1, 2018, to get feedback and information from both users and developers of agricultural statistics in Uganda.
Common themes arose from the stakeholder interviews. These themes, listed below, describe a disharmonized system that fails to produce the necessary statistics.
• Different agencies create different systems to produce agricultural statistics due to lack of clarity regarding institutional mandates.
• There is little faith in the reliability of the current agricultural statistics system.
• Administrative data is collected and compiled without employing standard statistical procedures. There is also an issue of untimely and incomplete flow of data from the lower to the higher reporting levels
• Methodologies for collecting commodity-specific statistics are not adequate.
• There is a lack of investment and prioritization of agricultural statistics.
The districts expressed varying levels of capacity for collecting agricultural data from their farmers. Districts such as Mayugi and Kaabong reported having moderate capacity, while other districts, such as Masaka and Mbarara, reported only a basic capacity for collecting agricultural data. The districts tended to report moderate capacity in the following categories: the mandate, governance structure, management, and personnel. However, they did not feel they had sufficient financial resources or the capacity for public dissemination and publicity.
The above findings point to three major challenges within Uganda’s agricultural statistics system:
1. There are multiple agencies that collect and disseminate agricultural statistics and there are challenges to build coordination and cooperation between them due to lack of clarity on institutional mandates.
2. Human capacity constraints hinder the collection of credible statistics at local and national levels
3. The types of statistics that are considered to be official are neither clearly defined nor the methodology required to collect them standardized
Therefore, it is recommended that the current structure of Uganda’s agricultural statistics system should become more harmonized and that an office be created to collect and produce subnational agricultural statistics. The recommendations for accomplishing these goals are listed below:
Institutional:
• Establish the Global Strategy core minimum set of statistics as the set of official agricultural statistics.
• Delineate the responsibilities between agencies for collecting the core minimum set of statistics.
• Develop a coordination committee for agencies that produce agricultural statistics.
• Establish working committees that codify methodologies for collecting the core minimum set of statistics.
• Provide training to personnel on emerging methodologies to estimate statistics and data collection.
Methodological:
• Develop commodity-specific methodologies for the collection of agricultural statistics.
• Implement methodologies for improving agricultural statistics from administrative data.
District:
• Strengthen the capacity for collection and dissemination of district-level agricultural statistics by building the required human capacity.
• Monitor district mandate, prioritization, and funding for collection of district-level statistics.
• Promote the utility and benefit of agricultural statistics to farmers.
Personnel:
• Hire qualified statisticians in UBOS DAES to effectively manage the production of agricultural statistics. Both UBOS and MAIFF require a cadre of agricultural statisticians that are highly qualified in data production and properly trained in the latest survey methods for core data needs, analysis and reporting.
• Promote agricultural statistics to statistics students.
Technological:
• Develop the information communication technology strategy for the collection, analysis, and dissemination of agricultural statistics. It is however important to properly test the technologies for their suitability and reliability before they are fully rolled out.
• Create a database of agricultural statistics for data users.
• Update the current computer and network systems within agencies.
• Update the software for data collection and analysis.
Financial:
• The Ugandan government must establish and maintain funding for agricultural statistics and data collection.
There are two windows for World Bank support for improving agricultural statistics in Uganda. The first window is through agriculture projects under the MAAIF, with the Agricultural Cluster Development Project being restructured to include a provision for strengthening the Statistics Unit. The second window is through Statistics Payment for Results (PforR) Program for generating better and more accessible data to inform policy-makers and contributing to strengthening statistical capacity. Funding through these windows can be used to support four key interventions: (i) developing the legislative framework for agricultural statistics; (ii) developing the legislative framework for data sharing between county governments and MoALF; (iii) establishing structures where users and producers of agricultural statistics interact; and (iv) developing a Seasonal Agricultural Survey (SAS).
Chapter 1: Introduction
The Government of Uganda has identified agriculture as a key driver of economic growth and stability for Uganda. The Uganda Vision 2040 indicates that agriculture contributed approximately 21 percent of the gross domestic product (GDP) and employed roughly 65 percent of the labor force in 2010 (National Planning Authority 2013). Because agriculture is such an important part of the GDP, the Government of Uganda is prioritizing goals that will transform its agriculture from subsistence to a commercial system. However, progress on reaching these goals must be measured and evaluated using agricultural statistics.
Agricultural Statistics and the Minimum Set of Core Statistics
Agricultural statistics measure the agriculture industry and farm and rural households. Data users rely on agricultural statistics to answer different questions and inform decisions and actions at the political, academic, institutional, and individual levels. Because agriculture is affected by economic, environmental, and social factors, agricultural statistics must measure the impact of agriculture on issues within and across these factors. Additionally, agriculture includes other activities such as agroforestry, land usage, and aquaculture. Therefore, the set of statistics considered agricultural statistics are broad and multifaceted.
Agricultural statistics can encompass a variety of estimates, each created for different purposes, such as regulation, enforcement, enterprise management, environment, social, and economic factors. In this report, agricultural statistics are defined as those outlined in the Minimum Set of Core Data framework created by the Global Strategy for Improving Agriculture and Rural Statistics of the Food and Agriculture Organization of the United Nations (FAO) (Table 1.1). This set of statistics covers many estimates that can be used for other purposes.
Table 1.1: Minimum Set of Core Data
|Group of Key Variables |Key Variables |Core Data Items |
|Economic |
|Output |Production |Core crops (for example, wheat and rice) |
| | |Core livestock (for example, cattle, sheep, and pigs) |
| | |Core forestry products |
| | |Core fishery and aquaculture products |
| |Area harvested and planted |Core crops (for example, wheat and rice) |
| |Yield/births/productivity |Core crops, core livestock, core forestry, core fishery |
|Trade |Exports in quantity and value |Core crops, core livestock, core forestry, core fishery |
| |Imports in quantity and value |Core crops, core livestock, core forestry, core fishery |
|Stocks |Quantities in storage at beginning of harvest |Core crops |
|Stock of resources |Land cover and use |Land area |
| |Economically active population |Number of people in working age by sex |
| |Livestock |Number of live animals |
| |Machinery |Number of tractors, harvesters, seeders, and other equipment|
|Inputs |Water |Quantity of water withdrawn for agricultural irrigation |
| |Fertilizers in quantity and value |Core fertilizers by core crops |
| |Pesticides in quantity and value |Core pesticides (for example, fungicides, herbicides, |
| | |insecticides, and disinfectants) by core crops |
| |Seeds in quantity and value |By core crops |
| |Feed in quantity and value |By core crops |
|Agro processing |Volume of core crops/livestock/fisheries used in |By industry |
| |processing food | |
| |Value of output of processed food |By industry |
| |Other uses (for example, biofuels) | |
|Prices |Producer prices |Core crops, core livestock, core forestry, core fishery |
| |Consumer prices |Core crops, core livestock, core forestry, core fishery |
|Final expenditure |Government expenditure on agriculture and rural |Public investments, subsidies, and other expenditure |
| |development | |
| |Private investments |Investment in machinery, research and development, and |
| | |infrastructure |
| |Household consumption |Consumption of core crops/livestock/and so on in quantity |
| | |and value |
|Rural infrastructure |Irrigation/roads/railways/communications |Area equipped for irrigation/roads in km/railways in |
|(capital stock) | |km/communications |
|International transfer |Official development assistance (ODA) for | |
| |agriculture and rural development | |
|Social |
|Demographics of urban and |Sex | |
|rural population | | |
| |Age in completed years |By sex |
| |Country of birth |By sex |
| |Highest level of education completed |One-digit International Standard Classification of Education|
| | |by sex |
| |Labor status |Employed, unemployed, and inactive by sex |
| |Status in employment |Self-employment and employee by sex |
| |Economic sector in employment |International standard industrial classification by sex |
| |Occupation in employment |International standard classification of occupations by sex |
| |Total income of the household | |
| |Household composition |By sex |
| |Number of family/hired workers on the holding |By sex |
| |Housing conditions |Type of building, building character, main material, and |
| | |other information |
|Environmental |
|Land |Soil degradation |Variables will be based on above core items on land cover |
| | |and use, water use, and other inputs to production |
|Water |Pollution due to agriculture | |
|Air |Emissions due to agriculture | |
|Geographic location |
|Geographic information |Location of the statistical unit |Parcel, province, region, country |
|system (GIS) coordinates | | |
|Degree of urbanization |Urban/rural area | |
Source: World Bank 2010.
Need for Agricultural Statistics in Uganda
Agricultural statistics are recognized among policy makers, governmental officials, researchers, farmers organizations, agribusinesses, and private donors in Uganda as critical to agriculture-driven economic stability and improvement.
The Uganda Second National Development Policy (NDP2) has identified several weaknesses within the Ugandan agricultural value chain: low production, little technological innovation and adoption, a weak agricultural extension service, an inability to match producers with their final markets, and limited market and production information. One of the goals put forth in the NDP2 was to measure and address the gaps along the value chain (Government of Uganda 2015).
In their ‘Review of Food and Agricultural Policies in Uganda 2005–2011’ report, the FAO identified the lack of reliable statistics as one of the weaknesses of the current system (MAFAP 2013).
The Uganda National Agriculture Policy calls for investment in agricultural statistics. It defines the need for a ‘functional system’ that includes all ministries collecting agricultural statistics and the district governments. Furthermore, it directs the Ministry of Agriculture, Animal Industries, and Fisheries (MAAIF) to build an agricultural statistics and management system for use in monitoring and evaluation (M&E) (MAAIF 2011). MAAIF has established a Division of Statistics to develop and harmonize a system for administrative data[3] collection, storage, analysis, and dissemination to stakeholders.
The Uganda Bureau of Statistics (UBOS) has also outlined the creation of the Directorate of Agriculture and Environment Statistics (DAES) within its 2013/14–2017/18 Sector Strategic Plan for Statistics (SSPS). The DAES was created in 2011, and it is responsible for agricultural and environmental data collection, management, and dissemination (UBOS 2014d).
Users of Agricultural Statistics in Uganda
As outlined in the Plan for National Statistical Development (PNSD) 2013/14–2017/18, Ugandan agricultural statistics are used by numerous entities both within and outside of Uganda. Each group in the list below requires different sets of statistics to fulfill the users’ various needs (UBOS 2014d).
• National and local government: Policy and decision makers within the national government use statistics to enable effective governing. Agricultural statistics are also needed for planning, administration, monitoring, and accounting.
• Agribusinesses and other economic parties: Good statistics are required for exploring the profitability of future business opportunities, planning and investment, monitoring, evaluation, and reporting of business activities.
• Nongovernmental organizations (NGOs): NGOs use statistics to plan, implement, monitor, and evaluate their activities. They also use statistics to monitor and inform government policy, lobby politicians, hold governments accountable, and report to their key stakeholders.
• Media: The media present statistics within their reports and articles to inform the public about agriculture and report on various developments within the field.
• Research institutions: Researchers rely on good statistics to plan experiments, conduct research, and present findings.
• Regional organizations: Organizations that foster regional integration and development use statistics for this cause.
• International organizations: International groups use a country’s agricultural statistics to determine the need for and impact of assistance or the requirements for participation in development initiatives.
• General public: The general public requires high-quality statistics to educate themselves and make decisions that will provide a meaningful impact on their lives.
Agricultural Statistics Support and Best Practices for Developing Countries
In recent years, many international aid organizations have highlighted the growing need for agricultural statistics in the developing country context, which has led to the institution of global plans for improving agricultural statistics, such as the Partnership in Statistics for Development in the 21st Century (PARIS21), the Global Strategy for Improving Rural and Agricultural Statistics, and the FAO World Program for the Census of Agriculture 2020 (WCA). The goal of all these programs is to assist developing countries in building a National Agricultural Statistics System (NASS) that provides useful, accurate, and timely agricultural statistics to national and international data users.
The mandate of PARIS21 Initiative is “to reduce poverty and improve governance in developing countries by promoting the integration of statistics and reliable data in the decision-making process” (PARIS21 2016a). This is accomplished through promoting coordination between data providers, data producers, and data users; improving the use of timely and useful statistics; assisting with the creation of a National Strategy for the Development of Statistics (NSDS) document for each participating country; and providing documentation and data archiving services. All these services are available for each subsector within the National Statistical System (NSS), including agricultural statistics.
The United Nations (UN) Statistical Commission, FAO, World Bank, and various governmental agencies that produce and utilize agricultural statistics created the Global Strategy for Improving Rural and Agricultural Statistics within the FAO to assist developing nations in creating and disseminating agricultural statistics. The purpose of the Global Strategy is to provide “a framework and methodology that will lead to an improvement in terms of the quantity and quality of national and international food and agricultural statistics to guide policy analysis and decision-making in the 21st strategy” (World Bank 2010). The Global Strategy spells out three main tasks for countries:
1. Produce a minimum set of core data;
2. Better integrate agriculture into the NSS; and
3. Improve governance and statistical capacity building
The Global Strategy is closely aligned with the creation of the NSDS in that it provides an assessment of the country’s NASS and where improvements can be made. The findings from the assessment are used to build the country’s Strategic Plan for Agriculture and Rural Statistics, a critical piece of the NSDS.
The FAO has been providing assistance through the WCA for countries to develop and conduct a census of agriculture (COA) since the 1930s. The goal of the WCA is to assist countries in developing and conducting a COA using standardized methodologies that are internationally accepted. Every 10 years, the world program is updated to include the latest methodologies and concepts that all countries can implement. The FAO has recognized the COA as one of the key components for the Global Strategy.[4]
The World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) initiative has been providing financial and technical support to UBOS since 2009 towards the design, implementation, analysis and dissemination of the Uganda National Panel Survey (UNPS). UNPS is a national longitudinal survey that has been collecting multi-sectoral microdata that has a strong focus on agriculture, and that has been at the heart of rigorous research that has reported state of the agricultural sector and its linkages to a broad range of development outcomes. Thus far, the UNPS 2009/10, 2010/11, 2011/12, 2013/14, 2015/16 and 2017/18 waves have been implemented (with the anonymized unit-record microdata being made available within 12 months of completion of fieldwork); the UNPS 2018/19 is underway at the time of the writing of this report; and the UNPS 2019/20 is in the pipeline.
Similar to the approach pursued in other African countries that have been supported by the LSMS-ISA initiative, the World Bank’s investments into UBOS capability to produce and analyze high-quality microdata have been leveraged to initiate a parallel program of methodological experiments in the areas of land area measurement, soil fertility assessment, crop production and yield measurement, crop variety identification, and remote sensing, and ownership and control of physical and financial assets, with the idea of developing improved survey methods with downstream linkages to the future UNPS rounds. These experiments yield peer-reviewed methodological research outputs that are distilled into guidelines for implementing best practices in data collection, which are in turn used as reference documents by survey practitioners, including UBOS and the national statistical offices supported by the LSMS-ISA (Kilic 2017).
Best practices and guidelines have been developed by each of the above-mentioned groups to assist developing countries in collecting agricultural data and disseminating agricultural statistics. Guidebooks and technical papers on various statistical and methodological aspects are published on each group’s website for free use. Each group has given advice on different aspects of agricultural statistics. The Global Strategy has produced best practices for
• Developing a master sample frame, with examples from different developing and developed countries;
• Designing fishery survey modules in household surveys;
• Enumerating nomadic and seminomadic livestock counts;
• Costing production surveys and grain stocks surveys;
• Improving crop production forecast surveys
• Remote sensing;
• Creating food balance sheets;
• Calculating gender-based estimates;
• Making international classifications; and
• Providing data users access to agriculture microdata.
• The World Bank LSMS produces guidelines on household and farm survey data collection on a range of agricultural and non-agricultural topics, anchored primarily in randomized survey experiments that inform peer-reviewed academic research, which in turn feed into these guidelines. Relevant to agricultural statistics, the guidelines are currently available for survey data collection on:
• Land areas
• Soil fertility
• Livestock
• Forestry
• Fisheries
• Asset ownership
At the time of the writing of the report, the LSMS, in collaboration with its national and international partners, was also working on guidelines for survey data collection on agricultural labor, annual crop production, extended-harvest crop production, crop variety identification, and remote sensing for measuring crop yields. The LSMS is also part of the World Bank Development Data Group – Survey Unit, which develops the free, computer-assisted personal interviewing (CAPI) software known as Survey Solutions, which is now the UBOS software of choice for surveys collecting data based on CAPI, beyond the UNPS.
The FAO WCA has produced manuals providing best practices on agricultural statistics. Such manuals are available on several topics including:
• Linking population and housing censuses with agricultural censuses;
• Employment data collection in agricultural censuses; and
• Preparing internationally comparable agricultural statistics.
Some country examples on implementation of best practices are provided in Chapter 7.
Capacity Assessments in Uganda
The first step in the operationalization of these global plans for agricultural statistics is to determine the status of the NASS in target countries by undertaking capacity assessments. Capacity assessments provide a snapshot of the NASS across legal, institutional, financial, methodological, personnel, operational, and technological frameworks. They also provide the basis for recommendations for improvement.
Uganda has been very involved in capacity assessments for building its NASS. In 2014, the African Development Bank (AfDB) conducted a capacity assessment of the NASSs of 52 African countries, including Uganda. The assessment reported on the overall status of each participating county’s NASS, scoring the countries by their institutional infrastructure, resources, statistical methods and practices, and the availability of statistical information (AfDB 2014). MAAIF conducted a stakeholder analysis to determine their capacity for building an NASS (MAAIF 2014). In 2015, the World Bank commissioned a capacity assessment of the Uganda NASS within UBOS and MAAIF. The current report builds on these assessments to identify the pertinent areas for improving the production quality and dissemination of agricultural statistics at national and subnational levels. Subnational statistics are required to support public policy, manage food security and disaster-risks, and for day-to-day planning, monitoring and decision making.
In Uganda, agriculture is a key driver of the national economy, and the Government of Uganda is emphasizing the necessity of a functional and comprehensive NASS, including in the 2011 National Agriculture Policy. To assist in that process, UBOS and MAAIF requested that the World Bank conduct a capacity assessment of the Uganda NASS. The World Bank solicited applications for the assignment, and RTI International was awarded the contract, with activities conducted in May–December 2017.
Purpose of This Report
This report presents the results of the capacity assessment with a focus on the ability of the Uganda NASS to collect and produce agricultural statistics to inform policy and decision-making for agricultural transformation.
This capacity assessment differs from those previously done in three ways. First, this capacity assessment includes a comprehensive stakeholder analysis that includes input from data users, data providers, and donor agencies, in addition to the agencies involved in producing statistics. MAAIF utilized a stakeholder analysis conducted in 2011 to build the Agricultural Sector Strategic Plan (ASSP), but it did not conduct a new stakeholder analysis with Ugandan stakeholders. Second, this report includes a capacity analysis of selected districts’ capacity for collecting agricultural statistics. None of the previously conducted assessments included a district-level analysis that can capture the needs of decision-makers at the grassroots. Third, the study evaluates the role of the two main agencies responsible for producing agricultural statistics and identifies areas for improving their capacities for producing credible statistics. The AfDB capacity assessment on the other hand was an overall capacity assessment of the Uganda NASS. The 2015 assessment only assessed the capacity within MAAIF.[5]
The remainder of the report is organized as follows. Chapter 2 gives an overview of the agricultural statistics systems in Uganda and the institutions responsible for collecting and disseminating statistics on agriculture. Chapter 3 further describes the current sources of Uganda agricultural statistics and highlights the status of the core statistics collected within the Uganda NASS. Chapter 4 provides the methodologies employed in conducting the capacity assessments at national and local government levels, and the limitations of each approach. Chapter 5 discusses the findings from capacity assessments, while Chapter 6 provides recommendations for improving statistical capacity for agriculture Uganda. Chapter 7 concludes with examples of global best practices for agricultural data.
Chapter 2: The Agricultural Statistics System in Uganda
Uganda’s current NASS was established through the Ugandan Bureau of Statistics Act of 1998 providing the mandate for multiple institutions to collect agricultural data. The NASS is a decentralized system with multiple agencies charged with collecting and disseminating statistics on agriculture, as shown in Figure 2.1. UBOS and the Division of Statistics in MAAIF are the two main groups that capture agricultural statistics. Other agencies within the national government and local governments also collect agricultural data for their own uses. Finally, NGOs collect agricultural statistics for M&E of various programs.
Figure 2.1: Current Structure of the Uganda NASS
[pic]
UBOS’s Role in the NASS
The primary agency for statistics dissemination is UBOS, as stated in the Uganda Bureau of Statistics Act of 1998 (Government of Uganda 1998). This act gives power to UBOS as the prime agency in the Government of Uganda for the national statistics system. This act also gives UBOS the authority to determine what statistics are collected and how. Furthermore, UBOS can work with and assign duties to other agencies to collect and disseminate statistics.
The primary unit within UBOS for collecting agricultural statistics is the DAES. It was founded in 2011, and its structure is shown in Figure 2.2. The mandate of the DAES is to be the official source of agricultural statistics for Uganda. Currently, it produces statistics on crops, livestock, and the environment from the surveys and censuses. The DAES plans to expand its responsibilities to include aquaculture and fisheries statistics and will add a senior statistician of Fisheries to the organizational structure.
The goal of the DAES is to collect data and disseminate the official set of core agricultural statistics. As a department within UBOS, and under the UBOS Act of 1998, the DAES also has the following responsibilities within the agricultural statistics field according to the Government of Uganda (1998):
• “Provide high quality central statistics information services.
• Promote standardization in the collection, analysis and publication of statistics to ensure uniformity in quality, adequacy of coverage and reliability of statistics information.
• Provide guidance, training and other assistance as may be required to other users and providers of statistics.
• Promote cooperation, coordination and rationalization among users and providers of statistics at national and local levels to avoid duplication of effort and ensure optimal utilization of scarce resources.
• Promote and being the focal point of cooperation with statistics users and providers at regional and international levels.”
The DAES primarily collects data using censuses and surveys. It either designs the data collection tools or works with outside groups seeking statistics on various projects to create the surveys. It trains the enumerators on statistically sound methods of collecting data. The DAES tabulates and disseminates the final statistics to the government, the public, or the organizations partnering with the DAES to collect statistics on their projects. The DAES can also use secondary statistics from other data producers to calculate and disseminate statistics.
The DAES possesses computers and statistical software to process and produce estimates (Nalunga 2015). However, it does not possess GIS equipment.
Figure 2.2: DAES Organizational Structure
[pic]
MAAIF’s Role in the NASS
MAAIF and its various agencies collect vast amounts of agricultural data in the form of administrative records. These data are secondary products of the normal operations or part of M&E of the different policies enforced by MAAIF. Administrative data are primarily collected by agricultural extension agents as a part of their activities.
In 2014, MAAIF created the Division of Statistics within the ministry to build the capacity of MAAIF to collect and disseminate agricultural statistics as a part of its constitutional mandate “to promote and support sustainable and market oriented agricultural production, food security and household incomes.” The Division of Statistics is part of the Agriculture Planning Department.
This goal of this division is to support the NASS for Uganda, collecting and providing agricultural statistics at the national and district levels using censuses, surveys, and administrative data. The current organizational structure is shown in Figure 2.3. As of the writing of this report, the Division of Statistics has not yet produced statistical reports because it is new and still being formalized. However, it is undertaking a pilot study with USAID to examine the use of sentinel farms for the routine collection of agricultural data.
Administrative data refers to non-statistical sources of information obtained through, for example, government programs or agricultural extension, and can benefit the final statistical product in ways ranging from reduced costs to improved small area estimates.[6] Examples include data collected through soil information, farm assistance programs (e.g. subsidies and insurance), land registration and cadastral records, grain associations, and monitoring programs (e.g. livestock tracing systems). Administrative data has a variety of uses such as improving statistical sampling frame construction and sample design; filling data gaps from surveys and censuses; forecasting; planning; and provision of small area estimates and administrative uses, thereby leading to improved policy and decision-making.
Despite its importance, much of the administrative data is collected and compiled without employing standard statistical procedures or researchers trained in statistical methods. Research has shown that a large proportion of administrative data consists of guess-estimates and is believed to be of questionable quality. There is also an issue of untimely and incomplete flow of data from the lower to the higher reporting levels. This may lead to delays in the ability of governments to make policy decisions or a general lack of understanding and hence proper utilization of own country’s data (UBOS, 2007).[7]
Uganda face enormous challenges in the compilation of agricultural statistics from administrative records. First, farmers do not keep records on area planted, animals kept and production levels. Second, the quality and timeliness of the data is generally poor. Third, Local-level financial and human resources to support administrative data generation are limited. For instance, the number of local governments compiling administrative data has been on a decline, although the MAAIF has been engaging in efforts to develop the capacity of the local government staff involved in generating agricultural statistics[8]
One of the key challenges facing the National Statistical System is the generation and utilization of administrative data. A large volume of administrative data is produced; however, it is of inadequate quality due to the following reasons (GSARS, 2017):
• Poor data flow, due to unclear reporting mechanisms;
• Submission of incomplete returns or reports;
• Failure of some units to submit returns;
• Data may be collected but not used for planning purposes;
• Poor documentation of the data production processes;
• The reporting mechanisms of different sectors or institutions vary considerably, which delays the data collection process;
• The skills of the staff involved in data management are limited; and
• High turnover of the professional staff
MAAIF has been strengthening its capacity to produce, store, and analyze statistics and administrative data through the National Food and Agricultural Statistics System (NFASS) Project within the Agriculture Planning Department/Division of Statistics. The NFASS Project has three integrated components:
• Development of a data center for all agricultural statistics;
• An institutional data module; and
• Routine Agricultural Administrative Data Reporting System (RAADRS)
Figure 2.3: Division of Statistics Organizational Structure
Source: Nalunga 2015.
Note: ICT = information and communication technology.
Other Agency Contributions to the NASS
In addition to UBOS and MAAIF, other agencies within the Government of Uganda collect data and disseminate agricultural statistics. There are seven semiautonomous agencies within MAAIF that produce agricultural statistics according to their mandates, needs, and routine activities. These statistics are used by UBOS, MAAIF, and organizations such as the World Bank, UN, International Monetary Fund, Bank of Uganda (BOU), and Ministry of Finance Planning and Economic Development. The seven agencies are as follows:
• National Agricultural Research Organization (NARO): This is the agricultural research organization within MAAIF. It collects data and produces statistics related to the experiments conducted within the organization and its partners.
• Uganda Coffee Development Authority (UCDA): This is the regulatory agency for coffee production within Uganda. UCDA produces statistics on coffee production and coffee farm numbers.
• Cotton Development Organization (CDO): It is the regulatory agency for cotton production within Uganda. The CDO produces statistics on all aspects of the cotton industry within Uganda.
• Dairy Development Authority (DDA): It is the regulatory agency for dairy production within Uganda. The DDA has produced statistics on milk production and milk prices.
• National Animal Genetic Resource Centre (NAGRIC) Data Bank: The NAGRIC oversees the national animal breeding program in Uganda. The NAGRIC Data Bank contains genetic data on both commercial and indigenous livestock breeds.
• National Agricultural Advisory Services (NAADS): It exists to improve Ugandan agriculture as an advisory service to farmers and agribusinesses. The NAADS collects data from farmers though participatory M&E activities in programs that improve farm household welfare through modernized farm practices. The NAADS also produces statistics on the quantity sold and value obtained of various agricultural products.
• Uganda Trypanosomiasis Control Council (UTCC): It seeks to eradicate trypanosomiasis and tsetse in Uganda. UTCC produces statistics on tsetse and trypanosomiasis control and eradication projects.
In addition, the following directorates within UBOS produce statistics that pertain to agriculture: The NFA; Directorate of Statistical Capacity Services; Directorate of District Statistics and Capacity Development; Directorate of Business and Industry Statistics; Directorate of Population and Household Statistics; and Directorate of Socioeconomic Statistics.
To support coordination, technical issues, and dissemination of agricultural statistics, there are three additional committees and activities:
• The National Agricultural Statistics Technical Committee (NASTC) is formed by the primary stakeholders in agricultural statistics and provides a forum for discussion on concepts, methods, and technical issues. The committee is chaired by MAAIF, cochaired by the School of Statistics and Planning Department at Makerere University while UBOS serves as the secretariat. The NASTC meets quarterly.
• The PNSD is developed through sector-specific plans as its building blocks. The framework serves as the coordinating mechanism for agencies that produce agricultural statistics.
• The Country STAT Technical Working Group is made up of major producers and users of agricultural statistics and reviews and discusses statistics before dissemination through the UBOS statistics abstract each year.
Local Government Contributions to the NASS
Finally, local and district governments collect their own sets of agricultural statistics. The District Planning Unit within each district collects various agricultural data for purposes of planning and monitoring.[9] Data are primarily collected through district officers as a part of their normal activities. Statistics are calculated from these administrative data for the purposes of monitoring and policy development, enactment, and enforcement.
Current Sources of Uganda Agricultural Statistics
Censuses
UBOS conducts the National Population Household Census (NPHC) roughly every 10 years. The NPHC captures demographic information on the population of Uganda. The goal of the NPHC is “… to ensure availability of bench-mark demographic and socio-economic data for use in planning, policy formulation and program evaluation” (UBOS 2014b). The most current census was performed in 2014. This census contains an agriculture module that asks the respondent the type of animal or crop farming the household engaged in. The module also asks if land was owned by the head of the household and whether the household used irrigation.
UBOS has conducted the COA three times: once in 1967, once in 1990/91, and, most recently, in 2007/08 (UBOS 2016). The COA provides a comprehensive snapshot of Ugandan agriculture with statistics on crops, livestock, economics, socio-demographics, agro-forestry, and irrigation. To conduct the COA, a 1 percent sample of farms is drawn from the respondents to the agriculture module in the most recent NPHC, and enumerators are sent back to those households to collect the additional agricultural data. UBOS plans to conduct a new Census of Agriculture and Aquaculture in 2019/20 in cooperation with the WCA.
In 2008, MAAIF and UBOS conducted a census of livestock to provide data on livestock agriculture for the National Livestock Productivity Improvement Project (UBOS 2009). This census provided estimates on livestock production in household farms and institutional farms within a sample of enumeration areas in 80 districts. Estimates were produced on livestock and poultry, heads of households, economic inputs and costs, labor use and costs, and some livestock prices.
Crop and Livestock Statistics
The Uganda National Panel Survey (UNPS) includes a strong focus on agriculture since 2009 towards the design, implementation, analysis and dissemination. It is funded by World Bank’s LSMS-ISA and conducted by the Government of Uganda.
Forestry Statistics
The GIS and Mapping Unit in the NFA maps the availability of forest wood within the central forest reserves. This information is included in UBOS’s statistical releases.
Fisheries and Aquaculture Statistics
There are no current statistics on fisheries and aquaculture. MAAIF is currently working on reestablishing the data collection tools that use the Beachhead Management Unit as the sampled observation (MAAIF 2014).
Agricultural Markets and Price Information Systems
The BOU publishes monthly, quarterly, and annual price data on agricultural exports. Price data are available for coffee, cotton, tea, fish, maize, simsim (sesame), tobacco, beans, sugar, and other agricultural products. The BOU obtains price data from UBOS, MAAIF, and other agencies that track specific commodities. The price data are disseminated on the BOU’s website.
Water and Environment Statistics
The NPHC counts the number of households that used irrigation in agricultural production.
The National Service Delivery Survey is a multistage survey that reviews the trends in service delivery. The most recent iteration was conducted in 2015 and provided statistics on agricultural inputs and costs, extension activities, and the environment (UBOS 2015).
Rural Development Statistics
The 2012/13 UNHS published statistics on rural poverty (UBOS 2014a). As a part of monitoring NAADS program implementation among farmers, the Agriculture Technology and Agribusiness Advisory Services Project conducted a survey of 15,010 farmers over 111 districts that monitored the use of improved crop and livestock technologies during 2010 and 2011 (NAADS 2013).
Food Security and Nutrition
The 2007/08 COA published statistics on food security. The 2012/13 UNHS published statistics on food poverty (UBOS 2014a). The 2006/07 wave was the reference wave and looked at rates of growth. This survey includes crop and livestock modules (UBOS 2014b).
Table 2.1 shows the list of statistics currently collected in the Uganda NASS and their source and year of availability. The source and year of release were reported by either MAAIF or UBOS in the SAQ. The releases were then confirmed through Internet searches.
Table 2.1: Status of the Minimum Set of Core Statistics Collected within the Uganda NASS
|Statistic |Agency with Most Recent Data |Year of Most |
| | |Recent Release |
|Crops |
|Crop production: quantity |UBOS |2016 |
|Crop production: value |None |- |
|Crop yield per area |UBOS |2015 |
|Area planted |UBOS |2013/14 |
|Area harvested |UBOS |2013/14 |
|Livestock |
|Livestock production: quantity |UBOS |2013/14 |
|Livestock production: value |UBOS |2013/14 |
|Fisheries and aquaculture |
|Fishery and aquaculture production: quantity |MAAIF |2017 |
|Forestry and wood products |
|Forest production of wood16: quantity |None |- |
|Forest production of wood: value |UBOS |2016 |
|Forest production of non wood17: quantity |None |- |
|Forest production of non-wood: value |UBOS |2016 |
|External trade |
|Export: quantity |None |- |
|Export: value |BOU |2017 |
|Import: quantity |None |- |
|Import: value |BOU |2017 |
|Stock of capital and resources |
|Livestock inventories |MAAIF |2016 |
|Agricultural machinery |MAAIF |2016 |
|Stocks of main crops: quantity |None |0 |
|Land and use |None |0 |
|Water related |
|Irrigated areas |UBOS |2013/14 |
|Types of irrigation |None |0 |
|Irrigated crops |None |0 |
|• Quantity of water used |None |0 |
|• Water quality |None |0 |
|Inputs |
|Fertilizer quantity |UBOS |2013/14 |
|Fertilizer value |UBOS |2013/14 |
|Pesticide quantity |UBOS |2013/14 |
|Pesticide value |UBOS |2013/14 |
|Seeds quantity |UBOS |2013/14 |
|Seeds value |UBOS |2013/14 |
|Animal feed quantity |MAAIF |2016 |
|Animal feed value |MAAIF |2016 |
|Forage quantity |None |0 |
|Forage value |None |0 |
|Animal vaccines and drugs quantity |MAAIF |2016 |
|Animal vaccines and drugs value |MAAIF |2016 |
|Aquatic seed quantity |None |0 |
|Aquatic seed value |None |0 |
|Agro-processing |
|Main crops |None |0 |
|Post-harvest losses |None |0 |
|Main livestock |None |0 |
|Fish: quantity |MAAIF |2016 |
|Fish: value |MAAIF |2016 |
|Prices |
|Producer prices |MAAIF |2017 |
|Wholesale prices |None |0 |
|Consumer prices |MAAIF |2017 |
|Agricultural input prices |MAAIF |2017 |
|Agricultural export prices |MAAIF |2017 |
|Agricultural import prices |MAAIF |2017 |
|Investment subsidies or taxes |
|Public investment in agriculture |None |0 |
|Agricultural subsidies |None |0 |
|Fishery access fees |None |0 |
|Public expenditure for fishery management |None |0 |
|Fishery subsidies |None |0 |
|Water pricing |None |0 |
|Rural infrastructure and services |
|Area equipped for irrigation |None |0 |
|Crop markets |None |0 |
|Livestock markets |None |0 |
|Rural roads (km) |Uganda National Roads Authority |2016 |
|Railways (km) |None |0 |
|Communication |Uganda Communication Commission |2017 |
|Banking and insurance |BOU |2017 |
|Social |
|Population dependent on agriculture |UBOS |2015 |
|Agricultural workforce (by gender) |UBOS |2008/9 |
|Fishery workforce (by gender) |MAAIF |2016 |
|Aquaculture workforce (by gender) |MAAIF |2016 |
|Household income |UBOS |2015 |
|Environmental |
|Soil degradation |None |0 |
|Water pollution due to agriculture |None |0 |
|Emissions due to agriculture |None |0 |
|Water pollution due to aquaculture |None |0 |
|Emissions due to aquaculture |None |0 |
|Geographic location |
|Geo-coordination of the statistical unit (parcel, province, region, |UBOS |2013/14 |
|country) | | |
Chapter 3: Methodology
This study employed mixed methodologies to conduct the capacity assessment. This chapter describes those methodologies and the goals and limitations of each approach.
RTI conducted the study with the assistance of local subcontractor Development Research and Social Policy Analysis Center (DRASPAC) between May 2017 and October 2017. Data collection began in June 27, 2017, with kickoff meetings and key interview meetings with UBOS, MAAIF, the World Bank, and other stakeholders. Data collection occurred from June 27, 2017, to August 19, 2017. Tabulation and data analysis were performed between August 20, 2017, and September 21, 2017. The draft report was written between September 21, 2017, and September 29, 2017.
Identification of Key Stakeholders
In a series of meetings and discussions between the World Bank and RTI, a list of all key stakeholders in the Ugandan agricultural statistics system was identified. Key stakeholders included the following:
• The directors of the agencies and ministries responsible for producing agricultural statistics
• Key agricultural data users from the agribusiness, government, and academic fields
• Key members of farmers’ groups and farmers in strategic agricultural fields that provide the data
• Key members of groups that fund agricultural statistics production
These members formed the group that RTI and DRASPAC met and conducted interviews with during the study. The stakeholders included public and private organizations and development partners (Table 3.1).
Initial Desk Review and Stakeholder Consultation
RTI conducted a desk review of relevant documents, including national agricultural and statistical policies to collect secondary data to supplement stakeholder interviews. The full list of secondary documents consulted can be found in Appendix 1. The goal of this desk review was to assist in examining the current structure of the Uganda NASS and preparing for the stakeholder discussions.
Panel Discussions and Key Informant Interviews
An in-person interview stage followed the desk review. Panel discussions were conducted at the national level, bringing together multiple stakeholders to identify comprehensive ideas. Panel discussion attendees included relevant officials at UBOS and the Ministry of Agriculture Planning Department and representatives from the private sector, civil society, farmer and agribusiness associations, NGOs, and donors.
These panel discussions were carried out by DRASPAC based on panel questions and facilitation guidance from RTI. In addition to panel discussions, RTI conducted a series of key informant interviews (KIIs) to ascertain the views of individuals involved in each stage of data collection, analysis, dissemination, and use. Table 3.1 shows the stakeholders that were interviewed, the dates they were interviewed, and their role within the Uganda NASS.
Table 3.1: Key Stakeholders Interviewed
|Date |Organization |Role in NASS |
|June 27, 2017 |UBOS |Producer |
| |MAAIF |Producer |
| |World Bank |User/funder |
|June 28, 2017 |Economic Policy Research Centre (EPRC) |User |
| |Makerere University |User |
|June 29, 2017 |National Agricultural Research Organization (NARO) |Producer/user |
| |UBOS |Producer |
|June 30, 2017 |Uganda Coffee Development Authority (UCDA) |Producer |
|July 5, 2017 |National Forestry Authority (NFA) |Producer |
|July 14, 2017 |Uganda National Farmers’ Federation (UNFFE) |Provider |
|July 17, 2017 |Agricultural Business Initiative (aBi) Trust |User |
|August 1, 2017 |Japan International Cooperation Agency (JICA) |Funder |
|August 2, 2017 |Danish International Development Agency (DANIDA) |Funder |
|August 4, 2017 |U.S. Agency for International Development (USAID) |Funder |
|August 8, 2017 |National Environment Management Authority (NEMA) |User |
|August 9, 2017 |FAO |Funder/user |
Standard Assessment Questionnaire
The Standard Assessment Questionnaire (SAQ) was designed by the AfDB to assess national capacity for collecting and producing agricultural statistics. RTI employed the SAQ as one of the tools for this assessment to examine the current capacity of the Uganda NASS for agricultural statistics. The SAQ was administered to officials within the DAES in UBOS and the Division of Statistics in MAAIF to assess the capacity of these organizations to collect and generate agricultural statistics.
The SAQ was administered in parts to the appropriate personnel who could provide the most accurate responses.[10] The SAQ was sent to UBOS and MAAIF to assess the capacity within these agencies.[11] The SAQ was not administered to the seven semiautonomous agencies that also produce agricultural statistics because of time and budget constraints. Each part was mailed to the DAES director and statisticians and to the assistant commissioner and statisticians in the Division of Statistics in MAAIF. Responses were returned by mail after follow-up contacts. Missing data were left as missing because of budgetary constraints. These missing values were not imputed using other data sources. The data were then converted into agricultural statistics capacity indicators (ASCIs) using the methodology created by the AfDB and grouped into the same dimensions of institutional infrastructure, resources, statistical methods and practices, and availability of statistical information and their elements (AfDB 2014). Each ASCI was categorized based on the level of capacity as shown in 3.2.
Table 3.2: ASCI Classification
|ASCI |Capacity Classification |
|0 ≤ ASCI < 20 |Very weak |
|20 ≤ ASCI < 40 |Weak |
|40 ≤ ASCI < 60 |Moderate |
|60 ≤ ASCI < 80 |Strong |
|ASCI ≥ 80 |Very strong |
Participatory Local Organizational Assessment Interview
The Participatory Local Organizational Assessment (PLOCA) tool, developed by RTI and adapted for this survey, is a comprehensive capacity assessment tool that seeks to capture the capacity of local organizations and institutions in management and practices, policies, personnel, and materials. This provides a holistic view of the capacity of organizations, including in this instance, the capacity to collect agricultural statistics. The PLOCA examines 10 core functions (Table 3.3) considered critical to organizational performance (RTI 2014).
Table 3.3: Core Functions Examined in the PLOCA
|Core Organizational Functions |
|1. |Mission, vision, values |
|2. |Governance |
|Management and Implementation |
|3. |Strategy |
|4. |Leadership and internal collaboration |
|5. |Learning and innovation |
|6. |Project implementation and service delivery |
|7. |Human resources (HR) |
|8. |Financial and administrative management |
|9. |Collaboration and networking |
|10. |Fundraising and sustainability |
To examine the capacity of the government to obtain agricultural statistics at the district level, district officials were given the PLOCA questionnaire during the district meetings conducted by DRASPAC on July 12, 2017, in Masaka District and July 18, 2017, in Mbale District. The goal of the questionnaire was to determine the ability and capacity of officials in the districts to collect agricultural statistics.[12] The districts were selected across agroecological zones to capture diversity in Uganda’s agricultural system. The selection was based on inputs from both the World Bank and UoG teams. While the study of these sixteen districts can only provide case study insights, the common issues which have emerged imply that the analysis and recommendations are useful beyond the districts visited. The districts surveyed and the dates they were surveyed are listed in Table 3.4.
Table 3.4: Districts Surveyed
|Date |Region |District |
|July 12, 2017 |Northern |Adjumani, Apac, Gulu, Kaabong |
| |Eastern |Kumi, Mayuge, Mbale, Tororo |
|July 18, 2017 |Western |Bushenyi, Hoima, Kisoro, Mbarara |
| |Central |Kayunga, Kiboga, Luwero, Masaka |
Limitations of the Study
The current study has the following limitations because of time and budgetary constraints that prevented analyses beyond those presented in this report:
• The capacity was analyzed at the national and district levels. No attempts were made to analyze capacity at geographic levels below the district as time and resources did not permit this level of detail.
• The completed SAQs from both UBOS and MAAIF contained uncompleted sections.
o UBOS did not respond to questions on informational and computational technology (ITC), financial, personnel, and district capacity. However, the AfDB capacity analysis from 2014 looked at the capacity of UBOS in these areas and has been used for this analysis.
o MAAIF did not respond to questions on the overall structure of its Division of Statistics and ITC, financial, personnel, and district capacity. However, it outlined the needs in these areas in their 2012 ASSP.
While this study cannot provide a complete capacity analysis of UBOS and MAAIF where data are missing, the capacity in these areas has previously been assessed. Thus, by pairing existing and new information, the study team was still able to obtain a comprehensive view of the Uganda NASS and structure recommendations accordingly.
Chapter 4: Findings from the Assessment
Themes from Stakeholders Interviews
Several common themes arose from the KIIs and panel interviews. These themes are outlined in this chapter, along with statements from the interviews.
National-level Capacity for Agricultural Statistics Exists
Despite questions about the reliability of the data being reported, stakeholders did identify some capacity for agricultural statistics embedded within the current NASS.
• UBOS: It is an established organization with the mandate for and the structure to get data.
• MAAIF: It has hired 20 statisticians and obtained GIS equipment.
• aBi Trust: The macro-level statistics such as the Demographic and Health Survey (DHS) and census are good and available with UBOS.
• USAID: It also generates some good statistics for the agricultural sector.
District-level Statistics Are Greatly Desired, But the Capacity for Them Is Lacking
Although there is capacity at the national level, as stated above, at the district level, Ugandan institutions lack the human capital and economic resources needed for thorough data collection.
• NFA: More detailed statistics need to be gathered from the grassroots.
• UBOS: District officers cannot collect survey data.
• MAAIF: It feels that statisticians are needed at the district level for this to happen.
• EPRC: One of the biggest problems with data availability is the lack of access to data from the ground.
Different Agencies Create Different Systems
As described in Chapters 3 and 4, the NASS currently comprises many organizations and many independent surveys and censuses. This reflects the persistent lack of clarity in institutional mandates concerning collection and dissemination of agricultural statistics. It is known that UBOS has the overall mandate of production and dissemination of official statistics, production of statistics is a combined effort of various stakeholders including Ministries, Departments and Agencies (MDAs). Specifically, the Directorate of Agriculture and Environmental Statistics under UBOS holds the primary responsibility for production and management of agricultural statistics. However, the actual collection, analysis and dissemination involves more stakeholders than those directly under that directorate. For instance, the Division of Agricultural Statistics under MAAIF is also directly mandated by the constitution to take lead and establish a system and institutional framework for agricultural data collection, analysis, storage and dissemination to stakeholders, including UBOS.
Thus, multiple agencies reported that their mandate included the collection and dissemination of data as outline below.
• MAAIF: It stated they are creating the NASS.
• UBOS: It stated they are the clearinghouse of official agricultural statistics.
• JICA: It supported a pilot to generate statistics on rice from 44 districts at the regional level in collaboration with the NAADS and NARO.
• UCDA: It has a very good export database but not a good database on farmers.
• DANIDA: Each project/organization tries to make its own baseline survey.
There Is Little Faith in the Reliability of the Current Agricultural Statistics System
Although numerous organizations are involved in the collection and dissemination of agricultural statistics, the data are not necessarily dependable. Actors throughout the sector highlighted weaknesses in the current data being collected and reported on.
• EPRC: Data quality depends on the size of the project; larger projects tend to have fewer issues.
• aBi Trust: UBOS data are generated after long intervals. MAAIF data are quite unreliable and are scanty.
• USAID: The statistics generated by MAAIF are based on estimates and not real hard data.
• NEMA: The geomapping done by the NFA cannot be trusted because it is using out-of-date methodologies and equipment.
There Is Little Attention Paid to Agricultural Statistics
Although it faces challenges and capacity/resource constraints, the current NASS in Uganda does produce a wide array of statistics. However, stakeholders found that those statistics are not being effectively used to inform the government and private sector planning.
• JICA: Utilization of agricultural statistics in planning and decision making is low at all levels (district and MAAIF levels).
• aBi Trust: The extent and rigor in use of statistics in the country is generally low.
• Makerere University: Graduates from the Department of Statistics at Makerere University do not see a future in agricultural statistics.
Methodologies for Collecting Commodity-specific Statistics Are Not Adequate
Commodity-specific statistics, where they are being collected, are not as comprehensive as they should be and are generally specific to price data.
• JICA: The UBOS statistics are not comprehensive on individual commodities such as rice.
• Uganda Coffee Growers Association (UCGA): Coffee production statistics are problematic. Production should be calculated as the number of trees times yield.
• UBOS: Data on animal permits are only captured when animals are moved.
• UNFFE: UBOS uses approaches and methodologies that farmers do not understand and so it never gets the correct information from the farmers.
New Technologies Can Be Utilized for Digital Data Collection
Stakeholder agreed that as UBOS, MAAIF, and other organizations aim to improve agricultural statistics, the use of new technologies should be incorporated.
• NFA: Using digital devices, for example, mobile phones in the generation and transmission of data. These can easily be integrated into geospatial mapping to get more detailed and accurate data.
• EPRC: It would be ideal if we have the farmers report their data on mobile phones.
• Makerere University: UBOS has improved its data collection capabilities. It is using CAPI for data collection and it has a pilot study going using Open Data Kit.
Capacity Assessment of DAES and MAAIF
The SAQ results collected from the DAES and MAAIF are shown in Table 4.1. Overall, UBOS and MAIFF reported an average capacity for agricultural statistics, but each agency had strengths in different dimensions.
The DAES indicated that it had higher capacity within the institutional infrastructure dimension with an average ASCI of 69.4 versus 18.2 for MAAIF. Consistent with the national mandate of UBOS, its parent organization, the DAES felt that it had strong coordination within the NSS and a strategic vision and planning for agricultural statistics. However, MAAIF only reported weak capacity in the integration of agriculture within the national statistics system and no capacity in any other element.
No agency reported any capacity for the resources dimension. This same pattern of nonresponse was discovered in the AfDB capacity assessment (AfDB 2014). However, it used other sources to fill in the missing data to calculate its ASCIs. As for the resources dimension, no agency reported any capacity for statistical software, data collection technology, or information technology infrastructure. This was also reported in the AfDB report.
MAAIF tended to have stronger capacity in the statistical methods and practices dimension, excluding technology. It felt it had strong capacity in the adoption of international standards and producing agricultural markets and price information. The DAES only reported moderate capacity in adopting international standards and weak to very weak capacity in the remaining nontechnological elements.
Each agency reported moderate capacity in the availability of statistical information dimension. MAAIF reported moderate capacity in the availability of the minimum set of core statistics. Both agencies felt they had moderate capacity in overall data quality perception and data accessibility.
Despite not reporting information on personnel and technological capacity, UBOS and MAAIF provided some information during KIIs. As shown in Figure 2.2, the DAES employs a director, an assistant director, three senior statisticians, and three supporting statisticians. In addition, the DAES can rely on other directorates within UBOS to assist with producing agricultural statistics, such as the Directorate of District Statistics. Additionally, the DAES has agreements with outside agencies such as the NFA to produce statistics. Finally, the DAES utilizes the administrative, information technology, and HR directorates within UBOS to handle tasks outside of its mandate.
The Division of Statistics within MAAIF is a part of the Agricultural Planning Department. The division can rely on the Administration Planning Department for administrative and personnel tasks. However, ICT is handled separately within each agency. In the kickoff meeting, the Division of Statistics reported having a GIS unit that contains a map printer and several computers obtained using USAID funding. It has also set up a data processing unit that contains four servers for the transmission of data. In terms of personnel, more than 20 statisticians work within the division.
These semiautonomous agencies may have varying levels of personnel and ITC capacity for producing agricultural statistics. Although all agencies were not interviewed for this assessment, the interview with the UCGA provided some insight into its personnel and ITC capacities. UCGA has two statisticians who produce agricultural statistics. They do not see this as enough statisticians, and they would ideally like to have more. For ITC capacity, they reported that they would need software for statistical analysis.
Table 4.1: Standard Assessment ASCIs for DAES and MAAIF
|Dimensions |Elements |DAES |MAAIF |
|Institutional |Legal framework |60.0 |— |
|infrastructure | | | |
| |Coordination in the NSS |100.0 |— |
| |Strategic vision and planning for agricultural statistics |100.0 |— |
| |Integration of agriculture in the NSS |45.5 |18.2 |
| |Relevance of data |41.7 |— |
| |Average ASCI |69.4 |18.2 |
|Resources |Financial resources |— |— |
| |HR: staffing |— |— |
| |HR: training |— |— |
| |Physical infrastructure |— |— |
| |Average ASCI |0.0 |0.0 |
|Statistical methods and |Statistical software capability |— |— |
|practices | | | |
| |Data collection technology |— |— |
| |Information technology infrastructure |— |— |
| |Adoption of international standards |46.9 |84.4 |
| |General statistical activities |28.6 |14.3 |
| |Agricultural markets and price information |10.0 |100.0 |
| |Agricultural surveys |21.1 |15.8 |
| |Analysis and use of data |11.1 |11.1 |
| |Quality consciousness |25.0 |25.0 |
| |Average ASCI |23.8 |41.8 |
|Availability of statistical|Core data availability |14.9 |62.2 |
|information | | | |
| |Timeliness |66.7 |66.7 |
| |Overall data quality perception |60.0 |40.0 |
| |Data accessibility |— |— |
| |Average ASCI |47.2 |56.3 |
|Overall ASCI |45.1 |43.8 |
Capacity Assessment at the District Level
The results of the PLOCA administered to the 16 districts are shown in 4.2. The overall score for each function was the average of all reported scores (excluding missing and ’not applicable’ answers) from the districts. The total score for each district was a weighted average of all reported scores for each district with an equal weight applied to each section. The weighted average was used to prevent the responses from any one section from skewing the capacity score.
Table 4.2: Districts Scores for Different Measures of Statistical Capacity
|Assessment |Apac |Mbale |Kumi |Tororo |Mayugi |Adjumani |Gulu |
|Indicators | | | | | | | |
|Function I: Mission,|1.00 | | | | | | |
|Vision, Values | | | | | | | |
|Function II: |0.33 |1.00 | | | | | |
|Governance | | | | | | | |
|Function III: |0.45 |0.41 |1.00 | | | | |
|Management and | | | | | | | |
|Implementation | | | | | | | |
|Function IV: HR |0.01 |0.29 |0.47 |1.00 | | | |
|Function V: |-0.55* |-0.08 |0.06 |0.74* |1.00 | | |
|Financial and | | | | | | | |
|Administrative | | | | | | | |
|Management | | | | | | | |
|Function VI: |-0.00 |0.61* |0.41 |0.15 |0.09 |1.00 | |
|Collaboration and | | | | | | | |
|Networking | | | | | | | |
|Function VII: |0.06 |0.42 |0.39 |0.64* |0.30 |0.34 |1.00 |
|Fundraising and | | | | | | | |
|Sustainability | | | | | | | |
Correlation coefficients with * are significant at 5 percent probability level.
Chapter 5: Challenges in the Uganda NASS
Based on the interviews and capacity assessments the following challenges were identified within the Uganda NASS. These issues were also confirmed during the validation workshop.
Institutional
One of the most serious challenges facing the NASS is the lack of coordination and communication between the different agencies collecting agricultural data. Because the NASS is a decentralized system, agencies can and have produced differing values for the same estimate. MAAIF is taking steps to set up the NFASS to collect administrative data from districts in coordination with UBOS’ system for collection of official statistics. There are coordination and reporting systems in place, but the data collection, storage, and analysis systems are still emerging. The structure proposed in the ASSP and the Plan for National Statistical Development will allow for the efficient collection of agricultural data. UBOS is also building its capacity to generate the official set of agricultural statistics. The plans are in place and work toward an efficient system is ongoing, but the institutional structures are not fully operational yet. It is a critical challenge in preparation for the 2020 COA that these systems should rapidly be brought to full operational status. Oversight of overall development of the NASS is needed to ensure that these institutional challenges are resolved.
Statistical methodologies present a second institutional challenge. Developing capacity to collect accurate data using standardized methods is important to avoid inefficiencies and inaccuracies in the system. A lack of transparency in presenting statistical methodologies or the use of outdated methods can lead to inefficiencies and a general sense of mistrust of the data among data users. There is a clear challenge to the NASS to set appropriate standards for measurement, making data available for analysis and consistent operational mandates for each agency collecting data.
Data releases are inconsistent across all agencies, coming in different years and at different times. Data users have identified this issue as an institutional challenge. Finally, while there is a clear institutional mandate for agricultural statistics, some of our respondents felt the lack of available resources or prioritization among many other needs has constrained the improvement of the NASS. In summary,
• There is coordination and cooperation between UBOS and MAAIF but resource challenges slow improvements to the NASS;
• There is confusion between the agencies within the NASS as to who produces and collects each type of agricultural statistic;
• There is no agreement or standardization of statistical methodologies between agencies;
• Data releases are inconsistent; and
• There is a lack of prioritization in funding decisions that limits improvement in the collection and analysis of agricultural statistics.
Methodological
The methodologies for agricultural statistics production are either not transparent or have not been updated to reflect changes within Ugandan agriculture. Data users have criticized UBOS for not providing enough details on how statistics are produced and MAAIF for not producing any methodologies on statistics. Additionally, changes to programs or practices within Ugandan agriculture have not been implemented within Ugandan agricultural statistics. MAAIF possess administrative data that can be used to augment UBOS censuses and surveys (for example, livestock data). Additionally, agencies within the NASS are not effectively collaborating and using their institutional strengths to fulfill their mandates. For example, the NFA can produce geospatial maps for UBOS, but the spatial statistics are not being released.
Additionally, several types of statistics are not actively being collected within the NASS. These statistics are identified in the Global Strategy as a basis for the work of national and international data users. Therefore, the lack of these statistics prevents analysis and decision making relating to them. In summary,
• Methodologies for producing agricultural statistics are not adequate;
• Statistical methodologies for the minimum core set are not made available;
• Administrative statistics are not properly collected, stored, and analyzed effectively, limiting their use within the agricultural statistics system; and
• The competencies of individual agencies within the NASS are not effectively utilized by other agencies.
Scant Statistics at the District Level
One of the major challenges of the current system is the lack of statistics at the district level. Every data user group stated that their analyses were hampered by the lack of statistics at the district level. The Directorate of District Statistics within UBOS oversees the production of official statistics at the district level; however, district-level agricultural statistics are scant. The Division of Statistics within MAAIF is working on producing statistics from administrative data collected by agricultural extension workers. MAAIF currently produces statistical abstracts, but data quality is hampered by poor farm record keeping, inadequate estimation procedures, and logistical limitations affecting travel by extension workers. The new systems coordinating and upgrading data collection by the NFASS have not yet become fully operational. Other agencies within the NASS produced agricultural statistics on an as-needed basis or for the specific commodity within their mandate. In summary,
• District-level statistics are often unavailable or of questionable accuracy;
• Resource constraints limit capacity for collecting accurate, timely data at the district level; and
• Districts have varying levels of capacity or statistical personnel for collecting data and producing agricultural statistics.
Personnel
Personnel needs vary among the agencies within the NASS. The DAES has two statisticians (one senior and one junior) on secondment from MAAIF who handle different types of commodities. The Division of Statistics in MAAIF has hired 20 statisticians to produce agricultural statistics. However, other agencies outside of UBOS and MAAIF have reported not having the number of people needed to produce agricultural statistics. Additionally, statistics students do not see a future in agricultural statistics. Consequently, training new statisticians in agricultural statistical methods has been challenging. In summary,
• Not enough statisticians are present in agencies outside of UBOS and MAAIF’s Statistics Department that produce agricultural statistics and
• There are not enough new statisticians being trained in agricultural statistical methods and apparently no specialization of agricultural statistics.
Technological
ICT equipment needs vary within agencies. The DAES reported having enough computers to perform their duties. The Division of Statistics within MAAIF has received ICT equipment from external stakeholders. However, other agencies expressed the need for new or updated equipment to perform their duties.
However, two deeper ICT capacity needs among all agencies were uncovered by our conversations with key stakeholders. First, all parties said that emerging data collection technologies have not been implemented. As an example, they noted the increasing use of mobile phones and the number of data collection software packages that could be used for data collection. Second, all agencies stated that no centralized internal databases are used to store agricultural data and statistics. They see these databases as becoming necessary as the volume of data grows. Furthermore, these databases could reduce the lack-of-access problem among data users by providing an electronic portal to the needed statistics. In summary,
• Emerging data collection technologies are not adequately used;
• As data volume increases, the lack of a centralized database and the opportunity for improved analytics is a challenge; and
• Mobile phones and data collection software are not adequately used.
Financial
Above all, the main threat outlined within this report and in previous capacity assessments is the lack of a dedicated and renewable source of funding for agricultural statistics. Currently, the Government of Uganda does not create set budgets for all agencies participating in the national agriculture statistics system. Some programs that are of interest to the government received funds, which were used to establish a budget to collect statistics. However, not all agencies have this type of dedicated funding. This hampers the ability of the Uganda NASS to produce agricultural statistics in many ways: hiring data collectors and statisticians is difficult, ICT upgrades and replacements are not occurring, data collection efforts are reduced or canceled, statistics releases are delayed or eliminated, research into new statistical methodologies is lacking, and the administration of the statistical agencies is constrained.
In summary, there is no established funding source to produce, improve, and maintain the system for agricultural statistics. As the world COA approaches, there is an urgent need for funding the systems and personnel required for Uganda to participate effectively in this international effort.
Chapter 6: Recommendations for Strengthening Agricultural Statistics in Uganda
Based on the abovementioned findings, the current structure has many useful aspects that need to be harmonized to strengthen the system.
Figure 6.1 shows a structure that could potentially help improve coordination of Uganda NASS. UBOS shall remain the agency in charge of coordinating and collecting the official agricultural statistics that continue to be informed by the administrative data collected by MAAIF. UBOS would act as the agency in charge of coordinating and collecting the official set of agricultural statistics. It would work with both MAAIF and the other agencies that collect administrative data and other agricultural statistics to coordinate the work and assess statistical methodologies. Furthermore, national- and district-level statistics would be created based on the work performed by MAAIF district-level personnel for administrative data and district statisticians at district offices. Finally, NGOs would work with UBOS, MAAIF, or the other agencies collecting data to provide the necessary statistics for their projects.
The NASS will be successfully improved if data collected at the district level can be modernized with methodologies consistent with international guidelines, more efficient means of collection, trained staff at the district level, and the resources to properly collect the data. The NFASS Project is developing a data center that can collect, assess data quality and analyze administrative data coming from the field. UBOS will require district level statisticians who can administer official data collection processes. There are multiple plans in place for many of these improvements to be made. Oversight of improvements to the overall system will be required to ensure that each agency is effectively meeting their commitments to the system’s improvement.
The assessment team has also reviewed a system for regional statisticians that could provide a more uniform approach among districts. While this may be a technically valid intervention, it is outside the norms of Ugandan administrative structures and was reconsidered. However, oversight of the development of district-level statistical capacity will remain a challenge for donors, UBOS and MAAIF.
Figure 6. 1: Harmonized Uganda NASS
[pic]
This new structure is similar to the Health Management Information System for collecting health statistics and the Education Information System for collecting education statistics. In these systems, the data providers (health centers and schools, respectively) submit their data to a central database from which the relevant statistics are derived. However, agricultural data collection differs in that farms are not required to provide their data to UBOS. Furthermore, farmers do not have the same level of ITC capacity to transmit their data to a central data repository. Instead, administrative data is collected via extension agents and MAAIF offices and transmitted to the MAAIF data center and made available to stakeholders. This harmonized system builds upon development of UBOS systems for collecting official statistics and the NFASS Project’s establishment of a data center, analytical capacity, and administrative data collection processes.
UBOS will act as the clearinghouse for methodologies for generating the official set of statistics and support the creation of methodologies for other agencies. UBOS will work with Makerere University to test and implement new methodologies for data collection and estimation. Both agencies will make their methodologies transparent to data users. This is important because the current statistical methodologies need to be made relevant and transparent before data users will accept them.
This system relies on the strength of its personnel. People with training and experience in data collection and statistical estimation are needed at the national and district levels to produce accurate and relevant statistics. Enumerators will be hired from and collect data within their home districts, and training will be provided by their associated regional offices. This strategy will have the dual benefits of using people with knowledge of the district and improving employment within the district.
The evolving system will need increased ITC capacity. Data collection on mobile devices will allow for the district enumerators to quickly collect data. Additional ITC infrastructure will be needed to support the movement of data between the national and district offices and UBOS for official data and MAAIF for administrative data. It will also allow for the most efficient implementation of best practices for agricultural statistics. UBOS will be responsible for collaboration with stakeholders to choose the most appropriate guidelines to use since it is responsible for disseminating the core set of statistics. These guidelines can be distributed to its partners and established within the surveys that collect the data for the official core set of statistics.
Financing of the system will come from both internal and external institutions. The Government of Uganda must recognize and support the NASS and provide it with its own stable line of funding. External stakeholders seeking data for their projects, such as NGOs, will work with and provide the funding to UBOS, which will collect and supply the necessary statistics.
Based on the abovementioned results, the following recommendations are proposed to harmonize the NASS, improve coordination between the existing structures, and develop a system for obtaining subnational-level statistics (Table 6.1). The costs are based on current knowledge of Ugandan finances and were calculated using the assumptions outlined in Appendix 2. In many instances, the cost estimates include processes e.g. workshops and consultations that leads to the production of the required outputs.
Table 6. 1: Recommendations for a Harmonized NASS
|Area of Recommendation |Activity |Level |Responsible entity|Timeframe |Average Yearly |Remarks |
| | | | | |Costs (US$) | |
|Institutional | |
|Establish the Global |Update the UBOS Act of |National |UBOS |Short term |5,000 |One off |
|Strategy core minimum set |1996 to include the set | | | | | |
|of statistics as the set |of statistics as part of | | | | | |
|of official agricultural |the mandate for UBOS | | | | | |
|statistics | | | | | | |
| |Communicate the set to | |UBOS |Short term |10,000 |One off |
| |agencies who collect | | | | | |
| |agricultural statistics | | | | | |
| |Meet with external | |UBOS |Short term |2,000 |One off |
| |stakeholders to determine| | | | | |
| |their data needs | | | | | |
|Clearly delineate the |Establish an agricultural|National |UBOS, MAAIF and |Short term |3,000 |One off |
|responsibilities between |statistics sector | |agencies producing| | | |
|agencies for collecting |committee | |agricultural | | | |
|the core minimum set of | | |statistics | | | |
|statistics | | | | | | |
| |Draft a charter and rules| |UBOS, MAAIF and |Short term |10,000 |One off. Cost |
| | | |agencies producing| | |includes processes|
| | | |agricultural | | |e.g. workshops and|
| | | |statistics | | |consultations that|
| | | | | | |leads to the |
| | | | | | |output |
| |Set the UBOS DAES | |UBOS and MAAIF |Short term |6,000 | |
| |director as chair and | | | | |One off |
| |MAAIF statistics director| | | | | |
| |as cochair | | | | | |
| |Identify the supporting | |UBOS, MAAIF and |Short term |1,000 |One off |
| |members | |agencies producing| | | |
| | | |agricultural | | | |
| | | |statistics | | | |
| |Produce clear and defined| |UBOS, MAAIF and |Short term |13,000 |One off |
| |roles and | |agencies producing| | | |
| |responsibilities for each| |agricultural | | | |
| |type of agricultural | |statistics at | | | |
| |statistic among the | |national and | | | |
| |members | |district level. | | | |
|Engage a coordination |Establish a formal |National |UBOS, MAAIF and |Short term |3,000 |Yearly |
|committee for agencies |agricultural statistics | |agencies producing| | | |
|that produce agricultural |coordination committee | |agricultural | | | |
|statistics | | |statistics | | | |
| |Draft a charter and rules| |UBOS, MAAIF and |Short term |10,000 |One off |
| | | |agencies producing| | | |
| | | |agricultural | | | |
| | | |statistics | | | |
| |Establish the governing | |UBOS, MAAIF and |Short term |1,000 |One off |
| |body | |agencies producing| | | |
| | | |agricultural | | | |
| | | |statistics | | | |
| |Identify the supporting | |UBOS, MAAIF and |Short term |1,000 |One off |
| |members | |agencies producing| | | |
| | | |agricultural | | | |
| | | |statistics | | | |
| |Schedule regular biannual| |UBOS, MAAIF and |Short to medium |15,000 |Yearly |
| |meetings | |agencies producing|term | | |
| | | |agricultural | | | |
| | | |statistics | | | |
| |Serve as the bridge | |Coordination |Medium term |20,000 |Yearly |
| |between agencies in | |committee | | | |
| |coordinating agricultural| | | | | |
| |data collection and | | | | | |
| |addressing cross-agency | | | | | |
| |issues | | | | | |
|Establish working |Establish a formal |National |UBOS, MAAIF and |Short term |3,000 |Yearly |
|committees that codify |agricultural statistics | |agencies producing| | | |
|methodologies for |technical committee | |agricultural | | | |
|collecting the core | | |statistics | | | |
|minimum set of statistics | | | | | | |
| |Draft a charter and rules| |UBOS, MAAIF and |Short term |10,000 |One off |
| | | |agencies producing| | | |
| | | |agricultural | | | |
| | | |statistics | | | |
| |Establish the governing | |UBOS, MAAIF and |Short term |3,000 |One off |
| |body between UBOS, MAAIF,| |Makerere | | | |
| |and Makerere University | |University | | | |
| |Identify the supporting | |UBOS, MAAIF and |Short term |3,000 |Yearly |
| |members | |Makerere | | | |
| | | |University | | | |
| |Schedule annual meetings | |UBOS, MAAIF and |Short to medium |13,000 |Yearly |
| | | |Makerere |term | | |
| | | |University | | | |
| |Produce statistically | |UBOS, MAAIF and |Medium to long |749,000 |One off cost |
| |sound data collection and| |agencies producing|term | | |
| |estimation methodologies | |agricultural | | | |
| |for the core minimum set | |statistics | | | |
| |of statistics that are | | | | | |
| |achievable for each | | | | | |
| |agency | | | | | |
| |Work with supporting | |UBOS, MAAIF and |Medium term |24,000 |One off |
| |agencies to establish | |agencies producing| | | |
| |those methodologies | |agricultural | | | |
| | | |statistics | | | |
|Develop a calendar of |Map out the production |National | |Medium term |76,000 |Yearly |
|statistical releases via |cycles of the production | | | | | |
|statistical abstracts and |commodities | |MAAIF | | | |
|other methods for | | | | | | |
|dissemination | | | | | | |
| |Work with data users to | |MAAIF and UBOS |Medium term |33,000 |Yearly |
| |determine the times each | | | | | |
| |statistical release will | | | | | |
| |have the greatest impact | | | | | |
| |and relevance | | | | | |
| |Work through the | |UBOS and MAAIF |Medium term |58,000 |One off |
| |coordination committee to| | | | | |
| |organize the schedules of| | | | | |
| |each agency's activities | | | | | |
| |Create a yearly calendar | |UBOS |Medium term |65,000 |Yearly |
| |of statistical releases | | | | | |
| |for the core set of | | | | | |
| |statistics | | | | | |
| |Update the calendar | |UBOS and MAAIF |Short to medium |46,000 |Yearly |
| |within the coordination | | |term | | |
| |committee | | | | | |
|Promote the benefit and |Prepare an advocacy plan |National |UBOS and MAAIF |Short to medium |20,000 |Yearly |
|utility of statistics |for promoting | | |term | | |
|outside of the NASS |agricultural statistics | | | | | |
| |to the public | | | | | |
| |Meet regularly with | |UBOS, MAAIF and |Short to medium |41,000 |Yearly |
| |governmental officials | |agencies producing|term | | |
| |concerning the needs and | |agricultural | | | |
| |achievements surrounding | |statistics | | | |
| |agricultural statistics | | | | | |
| |Promote statistical | |UBOS |Short to medium |225,000 |Yearly |
| |releases using | | |term | | |
| |traditional and only | | | | | |
| |media platforms | | | | | |
| |Organize a yearly | |Coordination |Medium term |750,000 |Yearly |
| |agriculture statistics | |Committee | | | |
| |forum with governmental | | | | | |
| |officials and external | | | | | |
| |stakeholders | | | | | |
| | |Methodological | |
|Develop commodity-specific|Work with subject matter |National |UBOS and MAAIF and|Short to medium |9,000 |Yearly |
|methodologies for the |experts to identify the | |Academic and |term | | |
|collection of agricultural|subject-specific needs | |research | | | |
|statistics |for data collection | |Institutions | | | |
| |Collaborate with academic| |UBOS and MAAIF and|Short to medium |13,000 |One off |
| |institutions to research | |Academic and |term | | |
| |statistically sound and | |Research | | | |
| |current methodologies | |Institutions | | | |
| |Conduct pilot studies to | |UBOS |Medium term |62,000 |One off |
| |test the new | | | | | |
| |methodologies | | | | | |
| |Implement the new | |UBOS and MAAIF |Medium term |42,000 |One off |
| |methodologies within the | | | | | |
| |existing agricultural | | | | | |
| |statistics program | | | | | |
|Develop methodologies for |Catalog current |National |UBOS and MAAIF |Short term |9,000 |One off |
|creating agricultural |administrative data | | | | | |
|statistics from |sources | | | | | |
|administrative data | | | | | | |
| |Assess the adequacy of | |UBOS |Short term |7,000 |One off |
| |use of each source within| | | | | |
| |the NASS | | | | | |
| |Research methodologies to| |UBOS |Short term |9,000 |One off |
| |incorporate appropriate | | | | | |
| |administrative data | | | | | |
| |sources in data | | | | | |
| |collection and estimation| | | | | |
| |Prepare a plan within | |UBOS, MAAIF and |Short term |7,000 |One off |
| |each agency stating how | |agencies producing| | | |
| |administrative data will | |agricultural | | | |
| |be used for agricultural | |statistics | | | |
| |statistics | | | | | |
| |Coordinate between | |UBOS |Medium term |7,000 |Yearly |
| |agencies for the desired | | | | | |
| |administrative data | | | | | |
|District | |
|Establish statistical |Identify the physical, |District |UBOS |Medium term |5,000 |One off |
|personnel in district |technological, and | | | | | |
|offices whose sole purpose|personnel needs for each | | | | | |
|is to collect agricultural|office | | | | | |
|data and disseminate | | | | | | |
|district-level | | | | | | |
|agricultural statistics. | | | | | | |
|They will be responsible | | | | | | |
|for coordination with | | | | | | |
|MAAIF and other agencies | | | | | | |
|collecting statistics in | | | | | | |
|their districts | | | | | | |
| |Promote statistics | |UBOS, MAAIF and |Medium term |70,000 |Yearly |
| |functions within the | |agencies producing| | | |
| |district offices | |agricultural | | | |
| | | |statistics | | | |
| |Establish a sustainable | |UBOS and MAAIF |Medium term |Per regional office|Yearly |
| |line of funding for the | | | |needs | |
| |district statistics | | | | | |
| |personnel, activities, | | | | | |
| |and logistics | | | | | |
|Promote the utility and |Meet with farmers’ groups|District |UBOS and MAAIF |Short to medium |1,310,000 |Yearly |
|benefit of agricultural |on a regular basis to | | |term | | |
|statistics to farmers |discuss agricultural | | | | | |
| |statistics needs and | | | | | |
| |activities | | | | | |
| |Promote data collection | |UBOS and MAAIF |Short to medium |85,000 |Yearly |
| |efforts through | | |term | | |
| |traditional and online | | | | | |
| |media platforms | | | | | |
| |Offer a platform for | |UBOS and MAAIF |Medium term |73,000 |Yearly |
| |farmers to provide input | | | | | |
| |on agricultural | | | | | |
| |statistics | | | | | |
|Personnel | |
|Hire qualified |Conduct a personnel needs|National |UBOS, MAAIF and |Medium term |8,000 |One off |
|statisticians/develop |assessment in each agency| |agencies producing| | | |
|skills in agencies that | | |agricultural | | | |
|produce agricultural | | |statistics | | | |
|statistics | | | | | | |
|Promote agricultural | |National |UBOS |Medium term |47,000 |Yearly |
|statistics to statistics | | | | | | |
|students | | | | | | |
|Strengthening computing |Conduct a personnel needs|National |UBOS, MAAIF and |Medium term |8,000 |One off |
|skills in agencies that |assessment in each agency| |agencies producing| | | |
|produce agricultural | | |agricultural | | | |
|statistics | | |statistics | | | |
|Technological | |
|Utilize innovative data |Research available |National |UBOS, MAAIF and |Short to medium |14,000 |One off |
|collection software for |software for data | |agencies producing|term | | |
|mobile devices |collection on mobile | |agricultural | | | |
| |devices | |statistics | | | |
| |Perform pilot studies on | |UBOS |Medium term |59,000 |One off |
| |the effectiveness of | | | | | |
| |collecting and | | | | | |
| |transmitting data | | | | | |
| |Create the infrastructure| |UBOS |Medium term |1,000,000 |One off |
| |for transmitting and | | | | | |
| |storing the collected | | | | | |
| |data | | | | | |
| |Train enumerators on the | |UBOS and MAAIF |Short to medium |1,285,000 |One off |
| |use of the data | | |term | | |
| |collection software | | | | | |
|Create a database of |Draft a plan for the |National |UBOS and MAAIF |Short to medium |13,000 |One off |
|agricultural statistics |aggregation and storage | | |term | | |
|for data users |of the core minimum set | | | | | |
| |of statistics and other | | | | | |
| |agricultural statistics | | | | | |
| |Obtain the ICT equipment | |UBOS and MAAIF |Medium term |Per needs |One off |
| |for the database | | | |assessment | |
| |Reinstate the use of | |UBOS |Medium term |27,000 |One off |
| |Country Stat to | | | | | |
| |disseminate agricultural | | | | | |
| |statistics to an | | | | | |
| |international office | | | | | |
| |Prepare and update a | |UBOS |Medium term |50,000 |One off |
| |metadata dictionary | | | | | |
| |Hire database specialists| |UBOS |Medium term |706,000 |One off |
| |to maintain the database | | | | | |
| |Provide a portal for data| |UBOS |Medium term |74,000 |One off |
| |users to access the data | | | | | |
|Update the current |Conduct a technology |National |UBOS, MAAIF and |Short to medium |35,000 |One off |
|computer and network |needs assessment for | |agencies producing|term | | |
|systems within agencies |agricultural statistics | |agricultural | | | |
| |within each agency | |statistics | | | |
| |Purchase computer | |UBOS |Medium term |Per needs | |
| |equipment specifically | | | |assessment | |
| |for agricultural | | | | | |
| |statistics | | | | | |
|Update software for data |Determine the statistical|National |UBOS, MAAIF and |Short to medium |13,000 |One off |
|collection and analysis |estimation needs for the | |agencies producing|term | | |
|within agencies |type of statistics | |agricultural | | | |
| |produced | |statistics | | | |
| |Review the current | |UBOS |Short term |8,000 |One off |
| |software used for | | | | | |
| |statistical estimation | | | | | |
| |Purchase the necessary | |UBOS |Short term |Per needs |One off |
| |software | | | |assessment | |
|Develop the ICT strategy |Identify the |National |UBOS, MAAIF and |Short term |72,000 |One off |
|for agricultural |technological needs and | |agencies producing| | | |
|statistics collection, |capacity for the core | |agricultural | | | |
|analysis, and |minimum set of statistics| |statistics | | | |
|dissemination |within the agencies | | | | | |
| |Draft an overall ICT plan| |UBOS and MAAIF |Short term |3,000 |One off |
| |for agricultural | | | | | |
| |statistics at the | | | | | |
| |national and district | | | | | |
| |level | | | | | |
| |Work with the chief | |UBOS, MAAIF and |Short term |22,000 |One off |
| |information officer (CIO)| |agencies producing| | | |
| |within each agency to | |agricultural | | | |
| |create an agency ICT plan| |statistics | | | |
| |for agricultural | | | | | |
| |statistics | | | | | |
|Financial | |
|The Ugandan government |Identify ‘champions’ of |National |UBOS and MAAIF |Short term |12,000 |One off |
|must establish and |agricultural statistics | | | | | |
|maintain funding for |within the government | | | | | |
|agricultural statistics | | | | | | |
|and data collection | | | | | | |
| |Lobby for continued | |UBOS and MAAIF |Short to medium |32,000 |One off |
| |funding to be put into | | |term | | |
| |the national budget | | | | | |
|Training | |
|Train district- and |As methods and data |National and|UBOS, MAAIF and |Short to medium |To be decided |Yearly |
|national-level staff in |collection evolves, |District |agencies producing|term | | |
|emerging methodologies and|conduct a training needs | |agricultural | | | |
|data collection processes |analysis to develop | |statistics | | | |
| |training plans | | | | | |
Note: Short term:1-3 years; medium term: 3-5 years; long term: 5-10 years
Please see Appendix 2 for more details, including cost assumptions
World Bank Support for Improving Agricultural Statistics
There are two windows for World Bank support for improving agricultural statistics in Uganda. The first window is through agriculture projects under the MAAIF, with the Agricultural Cluster Development Project being restructured to include a provision for strengthening the Statistics Unit. The second window is through Statistics Payment for Results (PforR) Program for generating better and more accessible data to inform policy-makers and contributing to strengthening statistical capacity. Funding through these windows can be used to support four key interventions: (i) developing the legislative framework for agricultural statistics; (ii) developing the legislative framework for data sharing between county governments and MoALF; (iii) establishing structures where users and producers of agricultural statistics interact; and (iv) developing a Seasonal Agricultural Survey (SAS).
Chapter 7: Global Best Practices for Agricultural Data
Country Example of Agricultural Data Collection and Survey Programs
The World Bank highlighted the role of South-South Learning in building capacity around agricultural statistics in Africa. Two countries: Rwanda, which is part of the East African community, and South Africa, can provide opportunities for learning and country case studies. Rwanda has a very good agricultural survey program while the South Africa administrative data collection experience provides some pointers for improving data collection. In addition, as part of the action plan, the team recommends undertaking country study tours and/or desk-based research to gathering learnings relevant to Uganda in terms of agricultural survey programs but also a devolved structure where statutory powers are delegated from the central government to the subnational level.
Rwanda
The National Institute of Statistics of Rwanda conducts two survey programs around agricultural statistics.
National Agricultural Survey
The National Agricultural Survey (NAS), last conducted between September 2007 and August 2008, collected information on the two agricultural seasons and covered a sample of 10,080 agricultural households over 30 districts.
The survey collects data on
• Demographic and social characteristics of agricultural farmers;
• Farms characteristics;
• Agricultural practices and crop production;
• Livestock practices and production;
• Fishery, aquaculture, and beekeeping practices;
• Forestry practices and income; and
• Food stocks and nutrition.
SAS
The SAS aims to cover all three agricultural seasons in Rwanda: Season A, which starts in September and ends in February of the following year; Season B, which commences in March and ends with June of the same year; and Season C, which starts in July and ends in September of the same year. The National Institute of Statistics of Rwanda (NISR) conducted the first SAS in 2013 and the last survey was conducted between September 2016 and February 2017. The respondents of the survey are categorized into two groups, namely, agricultural operators (small-scale farmers) and large-scale farmers (LSFs). The NISR classifies LSFs according to specified criteria, namely, farmers growing crops on 10 ha or more of land or any farmer raising 70 or more cattle, 350 goats and sheep, 140 pigs, or 1,500 chicken or managing 50 bee hives.
The survey collects information on the characteristics of the agricultural operators, the farm characteristics including the area yield and production, agricultural practices, inputs, equipment, and use of crop production (NISR 2016). The survey uses multiple-frame sampling techniques based on probability sampling and estimation techniques combining an area and list frame. Imagery with a very high resolution of 25 cm is used to divide the county into strata (12 strata in total). The survey interviewed a sample of 195 LSFs (out of 774) and 5,089 of a total of 25,346 agricultural operators. Data collection is undertaken through paper-based questionnaires but data entry was completed through the CSPro data entry software, while summary tables were created through SPSS and Excel.
A total number of 540 segments were spread throughout the country as coverage of the survey, with 25,346 and 23,286 agricultural operators in Season A and Season B, respectively. From these numbers of agricultural operators, subsamples were selected during the second phases of Seasons A and B. Furthermore, the total number of enumerated LSFs was 774 in Season A and 622 in Season B. Season C considered 152 segments counting 8,987 agricultural operators from which 963 agricultural operators were selected for survey interviews.
Table 7.1 shows the five strata that were selected for sampling based on cultivated land and other land use characteristics.
Table 7.1: Land use strata codes, definition, and areas
|Stratum |Description |Total (ha) |Percent |
|1.1 |Intensive agricultural land (Season A and B) |1,479,081 |81.9 |
|1.2 |Intensive agricultural land (Season A and B with potential for C) |48,388 |2.7 |
|2.1 |Other marshlands |95,821 |5.3 |
|2.2 |Marshlands potential for rice |20,201 |1.1 |
|3.0 |Rangeland |133,849 |7.4 |
|10.0 |Tea plantations |28,763 |1.6 |
|Total agricultural land | |1,806,103 | |
Source: SAS, NISR 2016.
The results of the SAS are presented based on the five strata defined. Other sources of agricultural data in Rwanda include:
• Comprehensive Food Security and Vulnerability Analysis and Nutrition Survey (CFSVA) (2012);
• Census of Population and Housing (most recent in 2012); and
• Integrated Household Living Conditions Survey (most recent in 2015).
South Africa
Department of Agriculture, Forestry, and Fishing[13] (Administrative Data)
The following institutions exist under the ambit of the department:
• Meat Inspection Scheme. Setting out of the legislative mandate, authority for inspection services, procedures, and standards. Inspection services also distinguish between low-frequency slaughter houses and high-frequency slaughter houses and collect data in these.
• Crop Estimating Committee.[14] Comprises officials from the following institutions: Department of Agriculture, Forestry and Fisheries; Provincial Departments of Agriculture; various Agricultural Research Council (ARC) -Institutes (Soil, Climate and Water; Small Grains Institute; and Grain Crops Institute); Bureau for Food and Agricultural Policy (BFAP) and Statistics South Africa (SA).
• Abstract of Agricultural Statistics. South African Grain Information Services (SAGIS) is the main source of information on crop production, boards such as Sugar Cane Board, Customs and Excise Data (tax authority and South African Revenue Service (SARS)), Red Meat Abattoir Association, Cape Wool SA, and Milk SA.
Figure 7.1 below indicates the Organogram of the Ministry of Agriculture, Forestry, and Fishing, South Africa
Figure 7.1: Organogram of the Ministry of Agriculture, Forestry, and Fishing, South Africa
[pic]
Source: MoALF South Africa Strategic Plan 2015/2016–2019/2020.
Statistics South Africa (Survey and Census)
Statistics South Africa (Stats SA) based on the Population Census of 2011 published an ‘agricultural households’ report. This report covers three types of agriculture, namely, subsistence, smallholder, and commercial. The census provided some information on subsistence and smallholder agriculture but excluded important data on land farmed and yields.
The Census 2011 questionnaire included questions on the following agricultural activities:
1. What kind of agricultural activity is the household involved in?
2. How many of the following (livestock) does the household own?
3. Where does this household operate its agricultural activities?
In addition, a regular survey program also collects information related to agriculture through two surveys:
1. The Quarterly Labour Force Survey (QLFS) collects detailed information on employment in the agricultural sector on a quarterly basis. It is a panel survey in that 25 percent of the sample is rotated out every quarter. Employment in the sector can be disaggregated by sex, age, and province as well as remuneration levels. The sample is representative at a provincial level and within provinces at the metro/non-metro level.
2. The Annual General Household Survey (GHS) collects information on food security and agricultural activity based on a sample of 21,228 households. Characteristics of households involved in agriculture, main reason for involvement in agricultural activity, and type of agricultural production activity are collected (livestock, poultry, grain and food crops, industrial crops, fruit and vegetables crops, fodder grazing, pasture grass for grazing). The sample is representative at a provincial level and within provinces at the metro/non-metro level.
Sweden
The System of Official Statistics in Sweden
Statistics Sweden is a central government authority for official statistics and other government statistics. In 1994 a statistical reform was implemented of Sweden’s official statistics, implying a decentralised system for official statistics and 25 government authorities were given responsibility for official statistics in defined sectoral areas instead of a centralised system and one governmental authority responsible. One of the main purposes of the 1994 statistical reform was to give the users more influence over the statistics, for flexibility and that the efficiency of statistics production would improve.
The System for Official Statistics includes the statistics, statistical products, metadata, the production systems, final observation registers, publications, separate tables and databases. Databases can be interactive or include fixed tables that the user cannot change. The system also includes laws, ordinances, regulations, general recommendations, guidelines, tools (that are developed for the system such as methods, classifications, etc.), the statistical authorities, the Council for the Official Statistics, and Statistics Sweden as the coordinating authority.
According to the decision by Parliament, the Government determines the subject areas and statistical areas for which official statistics are to be produced, and which authorities are to be given the responsibility. For the moment there are 22 different subject areas. The statistical authorities decide on the content and scope of statistics within the statistics area(s) for which unless otherwise specified by the government. The statistical authorities also decide, in consultation with important users of the statistics and taking into account the demands made by the European Union, which objects and variables are to be studied, which statistical measurements and study domains are to be used, the periodicity of the surveys etc. Except for Statistics Sweden there is normally no special appropriation for statistics; funding for statistics is included in the authorities’ appropriation framework for their main task. The System for Official Statistics includes the statistics, statistical products, metadata, the production systems, final observation registers, publications, separate tables and databases. Databases can be interactive or include fixed tables that the user cannot change. The system also includes laws, ordinances, regulations, general recommendations, guidelines, tools (that are developed for the system such as methods, classifications, etc.), the statistical authorities, the Council for the Official Statistics, and Statistics Sweden as the coordinating authority.
A Council for Official Statistics was established in 2002 with the purpose to improve coordination and overall view of the system for official statistics. The Council, which is an advisory body, deals with matters of principle concerning the availability, quality and usefulness of the official statistics, as well as issues on facilitating the response process for data providers. The Council works to improve cooperation between the statistical authorities, and to develop and manage a statistics network. It consists of one chair and six other representatives who are managers at the statistical authorities. The Council is supported by a secretariat and different workgroups. All authorities responsible for official statistics are invited to participate in the different workgroups. Due to the users of official statistics the system and the cooperation is judged to function rather well. The duties of the Council are set out in Statistics Sweden's Directives. The authorities to be represented in the Council are appointed by Statistics Sweden after consultations with all the statistical authorities. Members serve on the Council for a period of not more than three years. Statistics Sweden’s Director General is Chair of the Council, and the Council appoints its own Deputy-Chair.
To provide a picture of this, the statistical authorities annually complete questionnaires on the provision of data and on costs and staff who work with the official statistics. The authorities also submit a list of their active products. As a complement to this information, special measurements have been made on punctuality and production time, documentation, the use of the Official Statistics of Sweden (SOS) logotype and reporting by sex in the statistics.
The cooperation within and improvement of the system Statistics Sweden, in its role as coordinator, has the mandate to issue regulations to statistical authorities regarding documentation, quality declarations and publication. The main coordination tool since the Council was established has been coordination by cooperation (soft coordination) and the development of a well-functioning infrastructure. Participation in the workgroups has been on a voluntarily basis and great interest in participating has been observed.
Common guidelines for deciding what Official Statistics are and a definition of what a statistical product is, for sufficient quality, for preliminary statistics, for the websites at different authorities have been developed. There are specified routines for deciding on which statistics are to be official. There is a database of all Official Statistics and all changes in the statistical system are continuously registered in the database. It is therefore possible to follow a statistical product from cradle to grave. The users have now one main single point of contact with the Official Statistics via Statistics Sweden’s website, though there is a decentralised system. There are slightly more than 300 statistical products within the Official Statistics and they are described in a consistent manner on the website. There is a common publishing plan that is continuously updated and there are links to the different authorities' websites where Official Statistics are published.
To date, the cooperation has led to a common view of Official Statistics, an increase in competence, more systematic assessments related to user needs of what should be included in the Official Statistics as well as a much better overview of the content of the Official Statistics. The authorities responsible for official statistics have generally organised contact nets with their users. The availability of statistics for users who have an interest in statistics covering different areas has improved. The work is still in an initial phase. Today we deal with aspects of statistics such as quality, documentation, response burden, use of administrative data and security of information. Other aspects will emerge in the future. The value of systematic cooperation has the potential to increase as there are mutual benefits which can be derived from the joint development of statistics and common statistical systems rather than the development of separate solutions for each authority
Best Practices for Agricultural Data: Probability Samples and Two-stage Multiframes
Evidence-based decision making relies on information that is based on timely, consistent, and statistically sound information, from either probability sample surveys, censuses, or administrative data. The widely used unscientific practice of ‘eye observations’ by agricultural officers, farmer groups, village elders, and other local officials who provide an opinion on the total areas planted and harvested is no longer an acceptable practice, especially in the context of climate change and the importance of monitoring impact on food security.
In the absence of highly developed administrative data systems, the use of probability sampling surveys is regarded as the most appropriate approach for obtaining robust estimates with acceptable periodicity of data collection. A sample is the collection of data from a sample of units, unlike a census that would contact all units in the population. With good fieldwork planning and management, a well-designed sample survey can be completed relatively quickly and is representative of the population with known probabilities and measures of sampling variability. In addition, a well-designed sample for producing national estimates also require a surprisingly small number of agricultural holdings.
Two-stage multiple-frame surveys use two or more sampling frames. One frame is an area frame used to collect data from small farms and the other is a list frame to collect data from large farms. List frames normally provide good coverage of the large commercial farms.
The use of multiple frames brings a great degree of flexibility to the statistician because the sampling methods can be unique to each frame. The only requirement is the need to identify any overlap between the two frames to avoid the possibility of any double counting. In addition, the classification of farms as small, medium, and commercial is required.
Two-stage sampling is a means of surveying large populations using relatively small samples and ensuring that all statistical units have an equal chance (probability) of being included in the sample to be interviewed. The course of action is to divide the area to be surveyed into small geographical units called ‘census enumeration areas’).
|Box 7.1: Sampling frames for agricultural statistics |
|A Master Sampling Frame (MSF) forms the basis for the selection of probability-based samples of farms and households. The first step in the |
|development of the MSF is to identify the data items to be measured, for example, the total production of maize, the number of beef cattle, or|
|the changes in land cover. The MSF should link the farm or agricultural holding, the household, and the land. The possible sampling frames are|
|the listing of maize fields, animals, people by gender, or land parcels. The MSF comprises a listing of the sampling units that would provide |
|a complete coverage of the population of interest. The listing of the sampling units can comprise the names of farm operators (from an |
|Agricultural Census), the names of households (from a Population Census), a list of commercial agricultural enterprises not linked to |
|households, or a list of area units defined geographically. The MSF is the joint use of two or more of these listings of sampling units. |
|Source: GSARS 2015a. |
International Initiatives That Can Be Leveraged to Build Capacity Around Agricultural Statistics
Internationally there are a number of initiatives underway in Uganda to strengthen the NASS, including the introduction of FAO’s Agricultural Integrated Surveys[15] (AGRISurveys) program. USAID are working with the FAO team and with UBOS and MAAIF on plans to take forward the AGRISurvey assessment that was undertaken in Uganda in January 2018. This includes an identification of the statistical indicators that the LSMS-ISA[16] (from the Uganda National Panel Survey) caters for and the gaps that the AGRISurvey would fill from the lists of core SDG indicators and CAADP monitoring of agricultural statistics. AGRISurveys collects economic data on farms and agriculture sector every year, while alternating modules. It is based on standard methodology and tailored to country needs. The current funding includes USAID grant to collect in 4 countries and BMGF grant to support initial TA in up to 15 countries. Table 7.2 summarizes the anticipated budget costs for AGRISurvey to be carried out on an annual basis and Figure 7.2 summarizes the funding gap.
Table 7. 2: Projected AGRISurvey Budget
[pic]Source: Emily Hogue, FAO Statistics Division
Figure 7.2: Distribution of Budget Shares
[pic]
Source: Emily Hogue, FAO Statistics Division.
AGRISurveys requires partners to: i) support and advocate, particularly through data demand; ii) support the consolidation of agriculture data collection through the Government of Uganda; iii) support the institutional framework; and give financial support for the funding gap.
Other are the GODAN initiative, the Advanced Data Planning Tool (ADAPT) developed by Partnership in Statistics for Development in the 21st Century (PARIS21), the Global Strategy for Improving Rural and Agricultural Statistics, the FAO World Program for the Census of Agriculture 2020 (WCA) as well as various data quality (Eurostat 2007) assurance frameworks.
PARIS21 ADAPT Tool
The tool has been designed to bring together stakeholders to develop the indicators framework related to monitoring development outcomes. The frameworks can be to measure national development plans or the Sustainable Development Goals (SDGs). The tool can also be used to identify reporting, financial, data, or geographic gaps related to the data for measuring indictors (World Bank, 2004)
One of the important elements of the ADAPT tool is its flexibility to map national priorities to global requirements. The Costing Module supports stakeholders in estimating the cost related to data collection for long-term planning and program-specific budgeting, once unit cost information for specific data collections has been entered into the tool. Another important element of the tool is to produce a gap analysis, for data (absolute data gaps, frequency, or disaggregation gaps), methodology, capacity, and funding gaps. The gap identification, before starting the process, requires stakeholders to undertake the costing of activities including identification of activities where there is insufficient funding, while also identifying which SDG indicators are not collected or where the data collection does not align with what is demanded. The resulting plans can then be integrated into the country NSDS.
GODAN
The GODAN initiative “seeks to support global efforts to make agricultural and nutritionally relevant data available, accessible, and usable for unrestricted use worldwide. The initiative focuses on building high-level policy and public and private institutional support for open data.” It is a voluntary association launched in October 2013, currently comprising over 600 partners from national governments, non-governmental, international and private sector organizations.
The aims of GODAN are to
• Advocate for new and existing open data initiatives to set a core focus on agriculture and nutrition data;
• Encourage the agreement on and release of a common set of agricultural and nutrition data;
• Increasing widespread awareness of ongoing activities, innovations, and good practices;
• Advocate for collaborative efforts on future agriculture and nutrition open data endeavors; and
• Advocate programs, good practices, and lessons learned that enable the use of open data particularly by and for the rural and urban poor.
This initiative can be used to support the initiatives to improve agricultural data collection activities. It promotes collaboration to harness the growing volume of data generated by new technologies to solve long-standing problems and to benefit farmers and the health of consumers.
Collaborations between the Public and Private Sectors
Collaborations between the public and private sectors around data collection and funding can present opportunities for improving the quality of agricultural data through sharing of information and freeing up of financial resources. There are a number of models for this interaction.
PPP is one avenue for this collaboration, where the private sector can invest in technology creation, adaption, and transfer through the investment in research and skills development and the dissemination of knowledge, data, and scientific knowledge. FAO (2013a) identifies that the contributions of the private sector can be financial and nonfinancial and engagements are based on the principles of mutual collaboration and sponsorships. The six areas identified for collaboration are
1. Knowledge management and dissemination;
2. Norms and standards setting;
3. Mobilization of resources;
4. Development and technical programs;
5. Policy dialogue; and
6. Advocacy and communication.
Data collaborative is a new form of partnership through which a number of stakeholders from the public and private sectors and research institutions can share and use data to help solve public problems. For this type of collaborations to be applied, there is a need to train data producers and users, matching the public demand for data and the private supply of data in a secure and confidential way, documenting activities and finally using experimentation and focusing on scaling initiatives with potential.
In the sharing of data between the public and private sectors, it is important to set the frameworks through which data sharing will occur, including establishing a code of practice, fairness and transparency, security, governance, individual rights to access information and data, and freedom of information (ICO 2011).
Technology and Quality Assurance Standards
Technology presents various opportunities to improve data quality and timeliness with which data can be disseminated. However, technology is only one aspect of a successful survey design and can only build on the existing good practices for data collection and the skills set of data collectors. To ensure that quality data are collected, a Survey Quality Assessment Framework (SQAF)[17] checklist can be utilized. This framework asks questions around the survey process and emphasizes checking, documentation, and the implementation of the systems to minimize errors and ensure the completeness of information.
|Box 7.2: Use of technology in collecting agricultural data |
|GPS |
|An important element of agricultural data is reliable information related to land, either cultivated land, grazing or fertilized land, or |
|wood land. However, farmers often are not able to provide their land size in a standard format. In addition, the traditional measure using|
|a rope in compass leads to sampling errors and is a very time-consuming activity. The advances in geo-positioning and GPS provide the |
|cropped area directly without the need for distance and angle measurements. |
|Remote sensing |
|Remote sensing can be used to identify and monitor crops; this type of information combined with GIS can serve as a useful tool regarding |
|crops and assist in decision making around agricultural strategies. Remote sensing can be used to identify crop status including stressed |
|plants, crop yield estimation, and identification |
|Crop identification |
|By observing the various kinds of crops, it is possible to map the boundaries of the fields. Mapping of the boundaries of land parcels |
|provides information for the creation of cadastral maps. Cadastral maps are usually in a vector format and in this form can be used in a |
|GIS, along with other types of data (ownership, crop types cultivated, and so on). |
|CAPI |
|CAPI is increasingly being used in the collection of data. It involves an interviewer collecting information from a respondent via a |
|questionnaire residing on a laptop, smartphone, or tablet. |
|CATI |
|Computer-assisted telephone interviewing (CATI) and self-administered web completion of questionnaires are additional ways in which the |
|high cost of personal interviewing can be reduced. |
|Software (examples) |
|Survey solutions is a tool for creating surveys using the World Bank CAPI platform and is provided free of cost. The goal of the tool is |
|to assist developing countries’ National Statistical Offices and other data producers with a sustainable method for conducting complex and|
|large-scale surveys. The tool provides functionality for data capturing, survey, and data management. |
|CSPro refers to the Census and Survey Processing System and was developed by the U.S. Census Bureau. The bureau maintains the system and |
|makes it available at no cost. The system can be used for entering, editing, tabulating, mapping, and disseminating census and survey data|
|and is in use in a number of developing countries. |
Technology should also be used in the dissemination of data. The OECD defines data dissemination as “consisting of distributing or transmitting statistical data to users.” There are various release media that can be used for dissemination purposes including the Internet; CD-ROM; paper publications; files available to authorized users or for public use; fax response to a special request; public speeches; and press releases. Dissemination formats according to the Special Data Dissemination Standards (SDDS) include hardcopy and electronic formats that detail the reference documents through which users can access the data described in the metadata or any additional data not routinely provided.
|Box 7.3: Use of technology in data dissemination: Examples of publishers that are Data Documentation Initiative compliant and of data |
|visualization tools |
| |
|Nesstar Publisher |
|This is an editor for the preparation of metadata and data for publishing in an online catalogue. It is provided free of charge and allows for|
|the editing, creation, and exporting of data and is aligned to the Data Documentation Initiative (DDI). The publisher includes tools to |
|validate metadata and variables, compute/recode/label new or existing variables to be added to a dataset before publishing and is multilingual|
|covering a number of languages including English, French, and Arabic (). |
|Microdata Cataloguing Tool National Data Archive (NADA) |
|NADA is a web-based cataloguing system that serves as a portal for researchers to browse, search, compare, apply for access, and download |
|relevant census or survey information. It was originally developed to support the establishment of national survey data archives but is |
|increasingly being used across a number of organization across the world. |
|Microsoft Power BI |
|It is a cloud-based service that allows for the creation of visualizations, reports, and dashboard by the users. It is based on Excel and |
|related PowerPivots. |
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AfDB (Agricultural Business Initiative). 2014. Country Assessment of Agricultural Statistical Systems in Africa. Abidjan, Côte d'Ivoire: AfDB.
African Development Bank, Organisation for Economic Co-operation and Development, United Nations Development Programme and Economic Commission for Africa (2013). African Economic Outlook 2013, Structural Transformation and Natural Resources . African Development Bank.
AfDB. 2014. Country Assessment of Agricultural Statistical Systems in Africa
Global Strategy to Improve Agricultural and Rural Statistics: Report of the Friends of the Chair on Agricultural Statistics. United Nations Economic and Social Council Statistical Commission Forty ‐ first session. 2010
FAO. 2011. Improving Statistics for Food Security, Sustainable Agriculture, and Rural Development: An Action Plan for Africa 2011-2015
FAO. 2018. Uganda at a glance. of Uganda. 1998. Uganda Bureau of Statistics Act. Entebbe, Uganda: Government of Uganda.
Government of Uganda. 2015. Second National Development Plan (NDPII) 2015/16–2019/20. Kampala, Uganda: Government of Uganda.
Gourlay S., Kilic T., and Lobell D. 2017. “Could the Debate Be Over? Errors in Farmer-Reported Production and Their Implications for the Inverse Scale-Productivity Relationship in Uganda” Policy Research Working Paper 8192.
MAFAP (Monitoring African Food and Agricultural Policies). 2013. “Review of Food and Agricultural Policies in Uganda.” MAFAP Country Report Series. Rome, Italy: FAO.
MAAIF (Ministry of Agriculture, Animal Industries, and Fisheries). 2011. National Agriculture Policy. Entebbe, Uganda: Government of Uganda.
———. 2014. Agriculture Sector Strategic Plan for Statistics 2013/14–2017/18. Entebbe, Uganda: MAAIF.
Nalunga, J. 2015. Agricultural Statistics Capacity Needs Assessment Report. Kampala, Uganda.
NAADS (National Agricultural Advisory Services). 2013. Cumulative Progress Report: FY 2011/2012–2012/2013. (3).pdf.
National Planning Authority. 2013. Uganda Vision 2040. Kampala, Uganda.
PARIS21 (Partnership in Statistics for Development in the 21st Century). 2016a. Our Mandate. .
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World Bank. 2010. Global Strategy to Improve Agricultureal and Rural Statistics. Washington, DC: World Bank.
Appendix 1: Documents Reviewed
• Uganda Vision 2040
• Uganda National Agricultural Policy 2011
• Uganda Census of Agriculture 2008/2009
• Uganda Bureau of Statistics Strategic Sector Plan for Statistics 2013/14–2017/18
• UBOS Act of 1998
• UBOS Statistics Abstract 2016
• FAO CountryStat Panorama Report: Uganda 2008
• Ministry of Agriculture, Animal Industries and Fisheries Sector Strategic Plan for Statistics 2007–2011
• MAAIF National Coffee Policy
• The Republic of Uganda Plan for National Statistical Development 2007–2011
Appendix 2: Cost Assumptions.
The cost estimates are hinged upon recommendations made by the researchers for the different interventions that can help improve the collection, reporting, and dissemination of the core set of agricultural statistics. This report describes the Capacity Needs Assessment for Improving Statistics for Sustainable Agriculture in Uganda.
The interventions are grouped into six categories: institutional, methodological, district, personnel, technological, and financial. To execute each of the proposed activities, assumptions were made for every activity in these categories. Although some general assumptions cut across all the categories, other assumptions are specific for different numbers/units embedded to fulfill the activity.
The Proposed Framework
The proposed recommended structural organization in the study should be noted.
Figure 8.1 presents the conceptual structure of the harmonized Uganda NASS and demonstrates how agricultural statistics should be collected, processed, and disseminated. The structure establishes UBOS as the body responsible for official agriculture statistics in Uganda. Two different national-level committees are proposed: the technical committee (five members) and the coordination committee, which will be composed of one representative from UBOS and each of the seven sectors of MAIIF (eight members). Most of the costs to drive the agendas of these committees lie in holding meetings, workshops, and coordination.
District statistics officers are proposed for each of Uganda’s 121 districts (Ministry of Local Government 2017). At the district level, four people will be assigned to work on agricultural statistics. The major cost drivers here are the training of personnel and providing them with transport in the form of a motorcycle. The motorcycle shall be fueled and maintained by the project.
Data collectors shall be hired at the subcounty level. These will be responsible for collecting agricultural statistic for all agricultural needs and forwarding the data to the district, who will, in turn, forward them to the zone for further analysis, interpretation, and dissemination. The costs at this administrative level include transport in the form of a motorcycle and training and retraining the data collectors. The breakdown of individual costs as summarized in the working document—cost sheet—is presented in this appendix.
Exchange Rate (Table A2.1). The UGX/US$ exchange rate has been ranging between UGX 2,600 (July 2014) to UGX 3,600 (June 2017). An exchange rate of UGX 3,300 per US$ has been applied to all activities for different years.
Workshops/seminars and meetings (Table A2.2, Table A2.3, and Table A2.8). Most of the interventions involve training and sharing knowledge and experiences through workshops, seminars, and meetings. The assumption is that participants in these activities shall be fully facilitated with transport, daily subsistence allowances, stationery, a workshop venue, and (in some cases) a consultant/trainer. The allowances for each have been benchmarked from the prevailing rates (2017) and the Government of Uganda Revised Rates of Duty Facilitating Allowances adjusted for by inflation.
Consulting and professional costs. We have costed for consultant fees per hour (Table A2.3). For transparency, and according to the Public Procurement guidelines, the selection criteria for such professionals require a transparent and fair process. This has also been factored in the costs.
Staffing costs. Some offices require permanent employees. We have budgeted for the selection of these employees plus their remuneration (salary and benefits). The salary is estimated to be payable in arrears based on hourly rates (Table A2.4). Different rates apply to senior staff and junior staff. A 22-day month has been proposed.
Office costs. Table A2.5 summarizes office operational costs. Daily costs were accumulated into monthly totals. They are multiplied by 12 months to derive annual totals.
Advertising. Often, some activities require publication in media (Table A2.6). The newspaper advertisement prices were derived from the prevailing prices for full-color (or black-and-white), full, half, or quarter pages in Uganda’s dailies (The New Vision and Daily Monitor). The prices for radio advertisements, announcements, and other forms were benchmarked from a select section of Uganda’s radio stations with commendable listenership (CBS, Capital FM, Sanyu Fm, and Radio 1/Kaboozi).
Capital items. Different long-term assets shall be needed to facilitate work at different levels and stations. The list in Table A2.7 shows unit costs for each of the assets. Some of the capital items are office tools and equipment. These include items to be purchased for distribution to districts and subcounties and those that shall be used in offices.
Table A2.1: Exchange Rate
| |UGX | Units | US$ |
|Exchange rate |3,300 |— |1 |
Table A2.2: Workshop, Seminar, and Meeting Costs
|Workshop Costs |UGX |Unit |US$ |
|Transport refund |200,000 |To and fro |60 |
|Subsistence (residential) |
|Kampala |330,000 |Per night |100 |
|Other places |— |Per night |— |
|Teas |— |Per day |— |
|Lunch | | | |
|Kampala |— |Per meal |— |
|Other places |— |Per meal |— |
|Dinner | | | |
|Kampala |— |Per meal |— |
|Other places |— |Per meal |— |
|Water |— |Per day |— |
|Stationery and printing |20,000 |Per person |6 |
|Coordination and mobilization |500,000 |Per day |150 |
|Subsistence (nonresidential) |
|Senior officers |150,000 |Per day |50 |
|Junior officers |— |Per day |— |
|Hire of venue (100+) |
|Kampala venues |2,500,000 |Per day |760 |
|Other venues |1,000,000 |Per day |300 |
|Hire of venue (small numbers) |
|Kampala venues |500,000 |Per day | |
|Other venues |200,000 |Per day | |
|Rapporteur |200,000 |Per day |60 |
|Meeting costs |
|Transport refund |200,000 |Per day |60 |
|Teas |15,000 |Per day |5 |
|Water |10,000 |Per day |3 |
|Stationery and printing |10,000 |Per person |3 |
|Coordination and mobilization |200,000 |Per day |60 |
|Hire of venue |
|Kampala |— |Per day |— |
|Other places |— |Per day |— |
|Rapporteur |200,000 |Per day |60 |
Table A2.3: Consulting Costs
|Consulting Costs |UGX |Unit |US$ |
|Expert selection process (designing the EOI, placing advertisements, screening, selection, |5,000,000 |One-off |1,500 |
|and contract award) | | | |
|Professional fee |1,650,000 |Per day |500 |
|Reimbursables (including stationery and reporting) |3,000,000 |Lump sum |900 |
Table A2.4: Staffing Costs
|Staffing Costs | UGX |Unit |US$ |
|Recruitment costs (including selection and meetings) |5,000,000 |Lumpsum |1,500 |
|Advertising (print media) |5,000,000 |Per advertisement |1,500 |
|Induction |1,000,000 |Lumpsum |300 |
|Salaries |
|Senior positions (50,000/hour; 22 days) |8,800,000 |Per month |2,600 |
|Junior positions (30,000/hour; 22 days) |5,280,000 |Per month |1,600 |
|Contracted staff (10,000/hour; 22 days) |1,760,000 |Per month |500 |
|Driver, security, administration assistant, and so on |880,000 |Per month |260 |
|NSSF (10%) |
|Senior positions |880,000 |Per month |260 |
|Junior positions |528,000 |Per month |160 |
|Contracted staff |176,000 |Per month |50 |
|Driver, security, administration assistant, and so on |88,000 |Per month |26 |
|Staff feeding |
|Kampala |20,000 |Per day |6 |
|Other places |15,000 |Per day |5 |
|Medical insurance per year |2,000,000 |Per person |600 |
|Workers' compensation per year |70,000 |Per person |20 |
|Unemployed data collectors' wages/month |500,000 |Per person |150 |
Note: NSSF = National Social Security Fund.
Table A2.5: Office Costs
|Office costs |UGX |Unit |US$ |
|Office equipment (see Table 3.8) | | |- |
|Rent – Kampala |1,500,000 |Per month |450 |
|Rent - other places |1,000,000 |Per month |300 |
|Stationery | | | |
|Internet |2,000,000 |Per month |600 |
|Communication |1,000,000 |Per month |300 |
|Website/technology |1,500,000 |Per month |450 |
|Transport per person (10,000/day) |220,000 |Per month |70 |
|Fuel cost per liter |4,000 |Per liter |1 |
|Fuel (20 L/day/vehicle) |1,760,000 |Per month |500 |
|Fuel (5 L/day/motorcycle) |440,000 |Per month |130 |
|Servicing and vehicle repairs |900,000 |Per month |270 |
|Other repairs and maintenance |1,000,000 |Per month |300 |
|Cleaning |300,000 |Per month |90 |
Table A2.6: Advertising Costs
|Advertising |UGX |Unit |US$ |
|Print media |
|Full page black and white |12,000,000 |Per unit |3,600 |
|Half page black and white |6,000,000 |Per unit |1,800 |
|Quarter page black and white |3,000,000 |Per unit |900 |
|Television per advertisement |1,000,000 |Per unit |300 |
|Press conference |5,000,000 |Per unit |1,500 |
|Radio per advertisement |50,000 |Per unit |15 |
|DJ mentions |80,000 |Per piece |24 |
|Spot advertisement |80,000 |Per piece |24 |
|Talkshow |
|30 minutes |2,000,000 |Per piece |600 |
|1 hour |3,500,000 |Per piece |1,000 |
|Online advertisement |
|Digital |60,000 |per day |18 |
|Uganda Business Directory per year |2,500,000 |Per annum |760 |
|Websites for other entities (50,000/day) |50,000 |Per month |15 |
|Social media advertisements (50,000/day) |70,000 |Per month |21 |
Table A2.7: Office Equipment Costs
|Capital Items |UGX |Unit |US$ |
|Motor vehicle - double cabin (Hilux 2017) |163,879,332 |Per unit |49,660 |
|Motorcycles (Suzuki 2017) |26,400,000 |Per unit |8,000 |
|Motorcycles (Bajaj 2017) |4,000,000 |Per unit |1,200 |
|Laptop (Dell) |3,000,000 |Per unit |900 |
|Desktop (Dell) |2,000,000 |Per unit |600 |
|Printer (heavy duty) |2,000,000 |Per unit |600 |
|Office chairs |550,000 |Per unit |170 |
|Waiting chairs |850,000 |Per unit |260 |
|Office table |1,500,000 |Per unit |455 |
|Boardroom table |5,000,000 |Per unit |1,500 |
|Boardroom chairs |3,000,000 |Per unit |900 |
|File cabinets |1,000,000 |Per unit |300 |
|Website/portal design |2,000,000 |Per unit |600 |
Table A2.8: Meeting and Workshop Costs
|Trainings |UGX |Unit |US$ |
|Facilitator |500,000 |Per day |150 |
|Transport refund: trainees |200,000 |Per person |60 |
|Transport refund: visiting officers |300,000 |Per person |90 |
|Transport refund: facilitator |300,000 |Per person |90 |
|Per diem |350,000 |Per person/day |100 |
|Stationery |20,000 |Per person |6 |
|Training material |20,000 |Per person |6 |
|Certificates |10,000 |Per person |3 |
|Venue |1,000,000 |Per day |300 |
|Mobilization and coordination |500,000 |Per day |150 |
|Rapporteur |200,000 |Per day |60 |
|Technology |2,500,000 |Lump sum |760 |
|Other logistics |2,000,000 |Lump sum |600 |
-----------------------
[1] A Partnership in Statistics for Development in the 21st Century (PARIS21) is a global partnership of national, regional, and international statisticians, analysts, policy-makers, development professionals, and other users of statistics. The PARIS21 Consortium was established as a global forum and network to promote, influence, and facilitates statistical capacity development and the better use of statistics.
[2] The International Development Association (IDA) is the part of the World Bank that helps the world’s poorest countries. Overseen by 173 shareholder nations, IDA aims to reduce poverty by providing loans (called “credits”) and grants for programs that boost economic growth, reduce inequalities, and improve people’s living conditions.
[3] Administrative data refers to non-statistical sources of information obtained through, for example, government programs or agricultural extension, and can benefit the final statistical product in terms of reduced costs or improved small area estimates. An area that could facilitate better linkages between UBOS and MAAIF is the integration of administrative data with household and farm survey data – but an impediment to this is the lack of publicly available, unit-record administrative data. These problems are better articulated under the section MAAIF’s role in the NASS
[4]
[5] The term ‘data’ means values provided by the selected farmers or observations that are used to calculate statistics. The term ‘statistics’ means estimates calculated from the data with an associated measure of uncertainty calculated from the data.
[6] Global Strategy to improve Agricultural and Rural Statistics (GSARS). 2017. Improving the methodology for using administrative data in an agricultural statistics system. Final Report. Technical Report n.24. Global Strategy Technical Report: Rome
[7] Uganda Bureau of Statistics (UBOS). 2007. The Development of the Agricultural Sector Strategic Plan for Statistics: A Data Collection Plan for Agricultural Statistics in Uganda. Final Report to the Uganda Bureau of Statistics by the National Consultant: February 2007. UBOS Publication: Kampala.
[8] Global Strategy to improve Agricultural and Rural Statistics (GSARS). 2017. Improving the methodology for using administrative data in an agricultural statistics system. Final Report. Technical Report n.24. Global Strategy Technical Report: Rome
[9] [pic]The data includes production of primary food crops (crop production, area harvested and yields), use of land, farm machinery, fertilizers and pesticides, fisheries, food availability for consumption, population, and labor force at the district level
[10] The SAQ tool can be found at .
[11] The findings of the self-assessment were validated in a workshop in March 2018 with key development partners and other stakeholders in attendance. The identified constraints were further discussed at the validation workshop together with a synthesis of recommendations to address the constraints. These are discussed further in Chapter 5.
[12] The meetings were titled ‘Consultation on the Development of Sentinel Farmers Sampling Methodology and Data Collection Tools Under the NFASS’.
[13] .
[14] South African Grain Information Services: .
[15] A farm-based modular survey that builds on an agricultural census and operates over a 10-year cycle, providing the critical data a country needs to understand its agricultural sector.
[16] A household survey project that conducts multiple rounds of a nationally representative panel survey with a multi-topic approach. In eight countries to date:
[17] A generic format for surveys is provided by the following resource prepared in collaboration with PARIS21: Statistical Services Centre of University of Reading. 2009. “International Household Survey Network Survey Quality Assessment Framework (SQAF).” .
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Capacity Needs Assessment for Improving Agricultural Statistics in Uganda
[pic]
Division
Agricultural Statistics
Assistant Commissioner
Principal Statistician
Fisheries
Crops
Livestock
Senior Statistician
Senior Statistician
Senior Statistician
ICT
3 Statisticians
3 Statisticians
3 Statisticians
4 Staff
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