Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics

Transcription

1 Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years Co-funded by the European Union Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 1

2 This report was prepared by the Statistical Capacity Building Division of the Statistics Department at the African Development Bank. The findings reflect the opinions of the authors and not necessarily those of the African Development Bank or its Board of Directors. Every effort has been made to present reliable information as provided by the countries that participated in the assessment of agriculture statistical capacity in Africa during the periods of 2014 and 2016 respectively. Statistics Department Economic Governance & Knowledge Management Complex African Development Bank Avenue Joseph Anoma 01 BP 1387 Abidjan 01 Abidjan, Côte d Ivoire Tel.: (225) Statistics@afdb.org Website: Copyright 2017 African Development Bank Design/layout by Phoenix Design Aid A/S. Denmark. ISO14001/ISO 9001 Certified and approved CO2 neutral company. Printing by Scanprint using environmentally friendly recycled paper with vegetable inks. ISBN:

3 Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years May 2017 Action Plan to Improve Statistics for Food Security, Sustainable Agriculture, and Rural Development in Africa Co-funded by the European Union

4 TABLE OF CONTENTS LIST OF BOXES... iv LIST OF FIGURES... v LIST OF TABLES... vi FOREWORD...viii ACKNOWLEDGMENTS... x ABBREVIATIONS... xi EXECUTIVE SUMMARY... xii 1. BACKGROUND Introduction Objectives of the 2015 Country Assessments in Africa Background and scope of the Country Assessments DESIGN AND METHODOLOGY Preparation of Country Assessment instruments Data collection Evaluation and analysis of the status and trend of data reporting Data verification and validation, plus endorsement of preliminary results How to measure country capacity to produce timely, reliable, and sustainable agricultural statistics Data tabulation and analysis Dissemination strategy of the LCA results EXPERIENCES, LESSONS LEARNT, AND CONSTRAINTS Experiences and lessons learnt Developing the LCA Excel template The usefulness of the regional workshop in launching and conducting the LCA Validation and country ownership of the LCA process and the ASCI results Documentation of country best practices Constraints...12 ii

5 4. AGRICULTURAL STATISTICS CAPACITY INDICATORS (ASCIs), 2013 and Composite Indicator of all four dimensions Ranking countries under the Composite ASCI Country groupings under the Composite ASCI Prerequisites Dimension Level of institutional infrastructure in Africa Ranking countries using the Institutional Infrastructure Dimension Grouping countries under the Institutional Infrastructure Dimension Input Dimension Resources availability in Africa Ranking countries under the Input (Resources) Dimension Grouping countries under the Input (Resources) Dimension Throughput Dimension Availability of Statistical Methods and Practices in Africa Ranking countries under the Throughput Dimension Grouping countries under the Throughput Dimension Output Dimension Availability of Statistical Information in Africa Ranking countries under the Output Dimension Grouping countries under the Output Dimension CONCLUSIONS Improved results and enabling factors The way forward ANNEXES...53 Annex 1: ASCI 2013 and 2015 Dimension and composite indicators...54 Annex 2: ASCI 2013 Element indicators...58 Annex 3: ASCI 2015 Element indicators...66 Annex 4: Word template of the LCA questionnaire...74 Annex 5: Computation/Scoring procedure of the ASCI Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years iii

6 LIST OF BOXES Explanatory factors contributing to national ASCI levels for the following countries: 1 Ethiopia Kenya Ghana Côte d Ivoire Mozambique Democratic Republic of Congo Cabo Verde Botswana Tanzania Burkina Faso Madagascar Morocco Comoros South Africa Zambia Burundi Togo Malawi...45 iv

7 LIST OF FIGURES 1 Trend of country responses to survey by year ASCIs in Africa by dimension, 2013 and Change in Agricultural Statistics Capacities in Africa between 2013 and Composite ASCIs by country, 2013 and Proportion of countries per group by Composite ASCI, 2013 and Level of Institutional infrastructure by element in Africa, 2013 and Institutional Infrastructure by country, 2013 and Proportion of countries grouped by institutional infrastructure scores, 2013 and Level of resources by element in Africa, 2013 and Capacity level of resources by country, 2013 and Proportion of countries grouped by level of resources, 2013 and Level of statistical methods and practices by element in Africa, 2013 and Level of statistical methods and practices by country, 2013 and Proportion of countries grouped by level of statistical methods and practices in Africa, 2013 and Level of availability of statistical Information by element in Africa, 2013 and Availability of statistical information by country, 2013 and Proportion of countries grouped by level of availability of statistical information in Africa, 2013 and Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years v

8 LIST OF TABLES 1 Elements of ASCI by dimension Scale of dimension score of ASCI Scale of score of GDP per capita and agriculture VA (% of GDP) Country groupings by agricultural statistical capacity, GDP per capita, and agriculture VA (as % of GDP), Country groupings by institutional infrastructure, GDP per capita, and agriculture VA (as % of GDP), Country groupings by resources, GDP per capita, and agriculture VA (as % of GDP), Country groupings by statistical methods and practices, GDP per capita, and agriculture VA (as % of GDP), Country groupings by availability of statistical information, GDP per capita, and agriculture VA (as % of GDP), vi

9 Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years vii

10 FOREWORD The Statistics Department of the African Development Bank is pleased to present this report titled Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics: Agricultural Statistics Capacity Indicators for the 2013 and 2015 reference years. Agriculture forms a significant role in the economies of all African countries and the majority of the African populations rely on it for their livelihoods. However, agricultural data are not readily produced by National Statistical Systems in Africa and there exists a paucity of appropriate agricultural indicators to facilitate policy formulation, decision-making, and monitoring and evaluation. In this regard, a Global Strategy to Improve Agricultural and Rural Statistics was developed by the international statistical community and endorsed in February 2010 by the UN Statistical Commission. Following the endorsement of the Global Strategy, the Africa Region was the first to develop an Action Plan titled Improving Statistics for Food Security, Sustainable Agriculture, and Rural Development, , 1 to guide the implementation of the Global Strategy. This Action Plan for Africa focuses on the three pillars, which are: (i) the establishment of a Minimum Set of Core Data (MSCD) which countries need to produce to meet the current and emerging demands; (ii) the integration of agriculture into National Statistical Systems to link statistical information across the economic, social, and environmental domains, and to meet the requirements of policymakers and other data users; and (iii) building the foundation for the sustainability of the National Agricultural Statistical System (NASS) through good governance and statistical capacity building. The Action Plan is being implemented by the African Development Bank (AfDB), the United Nations Economic Commission for Africa (UNECA) and the Food and Agriculture Organization (FAO) through three main components, namely Technical Assistance, Training, and Research. To date, the strengthening of governance structures in African countries, the improvement of statistical capacity, and the use of newly introduced, cost-effective methods are ongoing through the implementation of Strategic Plans for Agricultural and Rural Statistics (SPARS), and other technical assistance activities, as well as the training of agricultural statistics experts in African countries. In addition, a monitoring and evaluation (M&E) system was established to monitor activities at regional and country levels. The M&E systems also track the impact of the implementation of the Action Plan for Africa on the production of the Minimum Set of Core Data (MSCD), which represents the first pillar of the GS. The Country Assessments (CAs) of NASS serve as the data source informing the monitoring and evaluation system. This is then used to assess the impact of the implementation of the Action Plan, as well as the capacity of individual African countries to produce the required agricultural statistics for both national and international users. When the Action Plan for Africa was formulated, the Country Assessments (CAs) were scheduled to be carried out in three cycles. The first cycle, which was carried out for the 2013 reference year, served as the baseline for the implementation of the Action Plan. The second cycle for the 2015 reference year, which is encapsulated in this report, measures countries progress in terms of their performance. The third cycle is scheduled to cover the 2017 reference year; this will provide information on the final impact of the Action Plan in terms of NASS capacity to produce agricultural statistics. This report focuses on Agricultural Statistics Capacity Indicators (ASCIs) as one of the major outputs of the CAs for the 2015 reference year. It also covers updated results of the CAs for the 2013 reference year. These ASCI results have been endorsed by the 5th meeting of the Regional Steering Committee of the Action Plan for Africa, which was held in January 2017 in Dakar, Senegal. 1 The implementation period of the Action Plan has been extended from 2015 to year-end viii

11 It is important to note that this report is based on the information provided by 52 and 51 countries that participated in the CAs of the 2013 and 2015 reference years, respectively. The success of these CAs and the subsequent results are therefore entirely due to the commitment of country teams from the National Statistical Offices and Ministries of Agriculture, as well as a broad cross-section of stakeholders. On behalf of the AfDB, I would like to express my profound gratitude to all those involved for the continuous commitment they have shown in contributing to the implementation of the Action Plan in general, and for their active participation in the CAs in particular, which has helped to make this exercise a massive success. My appreciation goes also to the Agricultural Statistics Team of the Bank s Statistics Department, who implemented the whole CA process. Finally, a debt of gratitude is also due to the UK Department for International Development (DfID), the Bill and Melinda Gates Foundation (BMGF), and the European Union for their financial contributions toward the implementation of the activities of the Action Plan. Charles Leyeka Lufumpa Director, Statistics Department African Development Bank Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years ix

12 ACKNOWLEDGMENTS This report was prepared under the supervision of Fessou Lawson, Officer-In-Charge of the Statistical Capacity Building Division of the AfDB, and the overall guidance of Charles Leyeka Lufumpa, Director of the Statistics Department. The core team was composed of the following members of the Agricultural Statistics Team of the Statistics Department: Mr. Vincent Ngendakumana (Consultant) and Ms. Estella Addiko (Consultant) who prepared the report, and Adam Abdoulaye (Consultant) and Stephen Bahemuka (Senior Statistician) who reviewed and provided contributions to the report. Editorial services were provided by Sandra Jones, an AfDB consultant. A total 51 countries (excluding Libya, Eritrea, and the Central African Republic) participated in the collection and validation of the country data for the 2015 reference year under the close supervision of Estella Addiko and Vincent Ngendakumana. The success of the Country Assessment process and the production of the Agricultural Statistics Capacity Indicators (ASCIs) were mainly due to the work of the agricultural statisticians from the Ministries of Agriculture and National Statistical Offices of 51 participating African countries. The following 18 countries provided valuable additional information to explain the enabling factors that contributed to the level of their respective ASCIs: Botswana, Burkina Faso, Burundi, Cabo Verde, Comoros, Côte d Ivoire, Democratic Republic of Congo, Ethiopia, Ghana, Kenya, Madagascar, Malawi, Morocco, Mozambique, South Africa, Tanzania, Togo, and Zambia. The AfDB coordinating team acknowledges the support of all participating countries in collecting, editing, and reviewing data inputs, which greatly assisted the CA process. The team also benefited from the practical experiences and best practices shared during the workshops and one-on-one consultations during and after the assessment exercise. x

13 ABBREVIATIONS AFCAS AfDB Agric. VA AGRIS ASCIs ASLMs AUC BMGF CA CAPI CSA DRC EU FAO GDP GS LCA M&E MoA MSCD NASS NSDS NSO NSS RSTC SPARS TA UNECA VA African Commission on Agricultural Statistics African Development Bank Agriculture Value Added Agricultural Integrated Survey Agricultural Statistics Capacity Indicators Agricultural Sector Lead Ministries African Union Commission Bill and Melinda Gates Foundation Country Assessment Computer-Assisted Personal Interviewing Central Statistical Agency of Ethiopia Democratic Republic of Congo European Union Food and Agricultural Organization of the United Nations Gross Domestic Product Global Strategy to Improve Agricultural and Rural Statistics Light Country Assessment Monitoring and Evaluation Ministry of Agriculture Minimum Set of Core Data National Agricultural Statistics System National Strategy for the Development of Statistics National Statistical Office National Statistical System Regional Steering Committee Strategic Plan for Agriculture and Rural Statistics Technical Assistance United Nations Economic Commission for Africa Value Added Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years xi

14 EXECUTIVE SUMMARY Introduction The implementation of the Action Plan for Africa for Improving Statistics for Food Security, Sustainable Agricultural and Rural Development ( ) (hereafter referred to as the Action Plan for Africa) is ongoing and the participating countries have begun to show signs of improvement in their capacity to produce relevant agricultural statistical data, in terms of quality, quantity, and timeliness. Agricultural Statistics Capacity Indicators (ASCIs) The first cycle of the Country Assessments (CAs) for the 2013 reference year has been successfully carried out, with the results published in This produced the baseline Agricultural Statistics Capacity Indicators (ASCIs) across four dimensions that measure countries capacity to produce requisite agricultural statistics. These dimensions relate to their status of: (i) Institutional Infrastructure (Prerequisite Dimension), (ii) Resources (Input Dimension), (iii) Statistical Methods & Practices (Throughput Dimension), and (iv) Availability of Statistical Information (Output Dimension). The CAs are the major source of data to inform indicators for the monitoring and evaluation (M&E) system. This system is vital to ensure effective measuring and tracking of the implementation performance of the Action Plan for Africa. This report highlights those countries that have shown signs of improvement in their capacity to produce relevant agricultural statistics, as well as those exhibiting some weakness. These findings can help to focus attention on the underperformance of individual countries, so that these countries may be targeted for additional assistance where it is most badly needed whether this be on the technical side or financially. It also helps to identify the high-performers so that their best practices may be emulated by those countries showing slower progress. Overall status of Agricultural Statistics Capacity Indicators for the region By comparing the ASCIs of countries for the years 2013 and 2015, it becomes clear that there has been a general improvement in the agricultural statistical systems in Africa in the quest to improve the quality and quantity of agricultural data. This is borne out by the 6.4% increase (from 46.5% in 2013 to 52.9% in 2015) in the Composite Indicator 3 for Africa as a whole. Ethiopia emerges in both surveys as the country with the highest level of the NASS development (66.5% in 2013 and 78.8% in 2015); and is therefore ranked as the best performer in running an effective and efficient agricultural statistics system to produce timely, reliable, and sustainable statistics. Other strong performers for the year 2015 include South Africa (73.5%), Mali (68.8%), Rwanda (68.8%), Kenya (68.3%), and Morocco (68.1%). Equatorial Guinea, despite having the capacity to fund its own statistical activities, is the country with the lowest capacity (below 20%) to effectively undertake agricultural statistics activities. This underscores the need for south south cooperation in the subregion, so that low-performing countries such as Equatorial Guinea can learn from the experiences and best practices of higher-level performers to improve their operational standards and meet data user requirements.. (i) Prerequisites Dimension: Institutional Infrastructure There was an increase in the number of countries (from 7 to 16) scoring above 80% for the Prerequisite Dimension in 2015 compared to In 2015, Botswana, Cabo Verde, Cameroon, Ethiopia, Liberia, Mali, Mauritius, Namibia, Niger, Nigeria, Rwanda, Senegal, South Africa, Tunisia, Uganda, and the United Republic of Tanzania all scored above 80% under the Institutional 2 This can be viewed online at: Web_11_2014.pdf 3 The Composite Indicator measures the development of NASS for the whole region. xii Executive Summary

15 Infrastructure Dimension. This indicates that these countries have almost achieved the required standards to effectively establish an institutional framework for their National Agricultural Statistical Systems (NASS) in a sustainable manner. On the other hand, Madagascar and São Tome & Príncipe have the lowest score (below 25%) for this dimension. These countries have low GDP per capita and relatively low agricultural value added (Agric. VA) in Africa. Such countries need both financial and technical assistance to effectively establish their institutional infrastructure in order to fully operate their NASS. High- performing countries like Namibia, Rwanda, and Mauritius could also assist by sharing their best practices with countries where the NASS institutional framework is weak. (ii) Input Dimension: Resources Capacity For the year 2015, a total of 10 countries recorded an average score of between 40% and 60% in resources provision to run their NASS activities. Resources in this context include not only finance, but also human resources (staffing and training) and the physical infrastructure to run an effective and efficient NASS. Botswana, Ethiopia, Mauritius, Swaziland are the only countries to record scores above 50% for this dimension in the year By contrast, countries such as Comoros, Congo Republic, Democratic Republic of Congo, Equatorial Guinea, Guinea, Liberia, Somalia, South Sudan, and the United Republic of Tanzania recorded scores below 20% for this dimension. Countries with high GDP per capita, like Equatorial Guinea, should be encouraged to fund their NASS through the national budget. Those with low resources may wish to learn from the best practices of high-performing countries in raising resources to run their NASS; for example, Ethiopia and Rwanda both have low GDP per capita. (iii) Throughput Dimension: Statistical Methods and Practices The Statistical Methods and Practices Dimension covers the entire operating agricultural statistical system, namely the collection, management, and dissemination of agricultural statistical data. Ethiopia remains the regional country with the highest capacity for this dimension, with an impressive 82.9% recorded for Even though this country is amongst the poorest on the continent, its statistical methods and practices can serve as a model to be emulated by other countries that are making slower progress under this dimension. (iv) Output Dimension: Availability of Statistical Information The Availability of Statistical Information Dimension responds directly to the first pillar of the Action Plan, as it assesses the Minimum Set of Core Data (MSCD) requirements, as determined by the Global Strategy. In total, 31 countries have surpassed the 70% score to deliver on this dimension; in other words, they are succeeding in making available their MSCD data to users in a timely fashion. The period from 2013 to 2015 witnessed an improvement in data supply to users in countries like Burundi, Gambia, Lesotho, Madagascar, Malawi, Mozambique, Senegal, Tanzania, Rwanda, and Uganda, among others. On the other hand, Equatorial Guinea and Botswana, which have the capacity to fund their agricultural statistical activities and infrastructure, have seen a reduction in their capacity to make statistical information readily available to users in the period 2013 to Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years xiii

16 xiv Executive Summary

17 1. BACKGROUND Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 1

18 1.1. Introduction The implementation of the Action Plan for Africa for Improving Statistics for Food Security, Sustainable Agriculture, and Rural Development, which spans the period from 2011 to 2018, is ongoing and activities are well advanced in the execution. The Action Plan for Africa was developed under the auspices of the African Development Bank, jointly with the United Nations Economic Commission for Africa (UNECA), the Food and Agriculture Organization of the UN (FAO) and in close collaboration with the African Union Commission (AUC). The Global Strategy is a comprehensive framework for improving the availability and use of agricultural and rural data, which are necessary for evidence-based decision-making to improve the lives and livelihoods across the continent. The Global Strategy s overarching aim is to improve countries statistical capacities to produce timely, reliable, and sustainable agricultural and rural statistics. Africa was the first region to develop and implement its Action Plan in order to ascertain the status of agricultural statistical capacity in African countries. To this end, a standardized tool for the measurement of the performance of National Agricultural Statistics Systems (NASS) was developed within the framework of the Global Strategy. This tool collected basic data to be used to generate standard objective indicators at the start of the implementation period of the Action Plan. Thereafter, these evolved into monitoring and evaluation (M&E) indicators to track the statistical capacities of African countries to produce agricultural data of the required quality and quantity for policy formulation and decision-making. In short, the Country Assessment (CA) process served to establish the baseline indicators and to measure the evolution of National Agricultural Statistics Systems (NASS) in African countries. It also facilitated the development of an appropriate program to address national needs in terms of technical assistance, training, and research. The CA process was scheduled to be carried out in three cycles during the implementation period of the Action Plan. It was conducted first for the reference year 2013 to provide baseline data. The second and third cycles of the CA were scheduled to be undertaken for the reference years 2015 and 2017, to measure the ongoing performance of African countries, and determine the real impact of the Action Plan on their capacity to produce agricultural statistics. The first CA process for the reference year 2013 was conducted in 2014 to procure baseline data. For this exercise, the standard (global) questionnaire was tailored to the African context and field-tested in three countries. The CA process covers economic, social, and environmental dimensions for agricultural statistical activities. These dimensions represent the Minimum Set of Core Data (MSCD) internationally agreed upon during the development process of the Global Strategy. The methodology used for data collection and for the generation of Agricultural Statistics Capacity Indicators (ASCIs), as well as the results themselves, were discussed and owned by the African countries. The report on the ASCI 2013 was disseminated widely and commended by partners, as well as the Regional Steering Committee (RSTC) Objectives of the 2015 Country Assessments in Africa As stated above, the Country Assessments for the reference year 2015 were conducted during 2016 to measure how well the Action Plan for Africa was being implemented in terms of its effectiveness, using only the requisite variables to compute the related ASCI. In addition, the CAs provide data to assist the compilation of indicators which, in turn, determine the technical assistance and training components of the Action Plan for Africa. In later sections of this report, a comparison of the 2015 CA results with those of the baseline data (2013 CA results) at both dimensional and elemental levels of the ASCIs are given. The aim is to assess the progress that has been made in the capacity of National Agricultural Statistical Systems (NASS) to produce the required agricultural data in a sustainable manner Background and scope of the Country Assessments The scope of CAs covers all the data items and elements needed to compute all four dimensions of the Agricultural Statistics Capacity Indicators (namely: Prerequisites Dimension, Input Dimension, Throughout Dimension, and Output Dimension). The data items are encapsulated under the following three modules: i) Module I An overview of the National Statistical System (NSS), which covers the institutional environment and core data availability to assess the status of the Minimum Set of Core Data (MSCD) across regional countries; 2 Chapter 1 BACKGROUND

19 ii) iii) Module II A review of the ongoing statistical activities and critical constraints in agriculture statistics system at the level of the National Bureau of Statistics; and Module III information on the subsectors of agriculture, at the level of concerned line ministries. This covers their main statistical activities and critical constraints they may face in meeting national and international agricultural statistical requirements. The activities referred to in Modules II and III include data collection, processing, and dissemination of statistics not only through censuses and surveys, but also through other available sources, such as administrative data sources. Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 3

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21 2. DESIGN AND METHODOLOGY Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 5

22 2.1. Preparation of Country Assessment instruments The CA questionnaire for the 2015 survey was streamlined to provide a lighter, more user-friendly version for participants. This Light Country Assessment (LCA) questionnaire featured data items corresponding to the variables used for computing the ASCIs in Africa. The revised questionnaire s format, however, retained the original numbering of the original, lengthier 2013 version. This was to ensure that the questions match with the original coding of the ASCI computations for the dimensions and elements. The LCA questionnaire was then converted into a user-friendly Excel template with four objectives geared to facilitate its use by regional countries: (i) to ease and enhance its completion and data checking by individual countries, (ii) to prevent and/ or minimize data input errors, (iii) to enable an easy data validation before processing the results, and (iv) to enable an automatic generation of ASCI results, including related charts. The Excel template enables the automatic uploading of the completed questionnaires into the LCA system; this allows ASCI results, as well as related charts, to be generated instantly. This system exhibits all the dimensions and elements of the ASCI and compares results with the baseline results for the year In order for countries to appropriately capture data into the system, a User Manual was also developed by the AfDB to assist countries to complete the CA questionnaire in the Excel template format Data collection The 2015 LCA process was launched and conducted through a regional workshop titled Launching of the Light Country Assessment of National Agricultural Statistics Systems in Africa, which was held in Entebbe, Uganda, from May 16 to 20, The workshop was attended by representatives from Ministries of Agriculture and National Statistical Offices. Among the participants were the National Strategy Coordinators and their alternates from all the participating African countries, with the exception of Eritrea and the Central African Republic. The workshop focused on the overview of the 2013 CA process and lessons learnt, as well as the generation of results (ASCIs). Discussion topics also included selected country experiences, the contents of the LCA (i.e., the contents of Modules I, II and III of the Excel template) and the User Manual. During the workshop, countries completed and submitted the 2015 LCA questionnaire and reviewed the 2015 ASCI preliminary results. To assist the process, most updated basic data for ASCI of 2013 reference year were provided Evaluation and analysis of the status and trend of data reporting Figure 1 reveals that the 2013 CA survey for the region attained the highest response rate to date of 96.3% surpassing the biennial surveys conducted in 2007 and 2009 by the FAO through the African Commission on Agricultural Statistics (AFCAS). The 2015 LCA response rate was only slightly lower, short of one country only (namely Libya) compared to 2013 reporting Data verification and validation, plus endorsement of preliminary results During the May 2016 workshop conducted in Entebbe, African countries were guided on how to report on and check their CA data, using the LCA Excel template. After the workshop, countries were encouraged to continue reporting missing data to AfDB. Where possible, missing and/ or recently updated data for the reference year 2013 were also reported. During this period, the AfDB conducted follow-ups with concerned countries to rectify and/or confirm any inconsistencies that had been observed or flagged in their data. Figure 1 Trend of country responses to survey by year PERCENT YEAR 6 Chapter 2 DESIGN AND METHODOLOGY

23 Further analysis was conducted by comparing the 2015 results with those of 2013, and any inconsistencies/discrepancies observed in both results were again highlighted and shared with countries to assess and confirm the results. Indeed, several interactions with concerned countries were carried out, by phone and/or to discuss and review missing and questionable basic data. This iterative process helped to improve the quality of the 2013 and 2015 results. During the Regional Workshop on the Validation of the MSCD in African countries, which was held in Dar es Salaam, Tanzania, from November 21 to 25, 2016, meetings with individual country representatives were arranged. This provided them with an opportunity to review once again their respective ASCI preliminary results. At this time, they were given detailed clarification or justification of the results with regards to specific changes that may have occurred in their National Agricultural Statistics Systems (NASS) since the 2013 baseline survey/assessment. Some of these clarifications have been well documented in this report (see the explanations provided by individual country representatives in the Boxes) to showcase country best practices toward improving their NASS. This demonstrates that countries owned the entire process and methodology of the CAs as well as subsequent results. The summary of the ASCIs for reference years 2013 and 2015 were presented to the Regional Steering Committee (RSTC) during the 5 th meeting in January 25-27, 2017 in Dakar, Senegal, where they were endorsed by the Committee. The RSTC recommended thereafter that, in addition to the main report of the ASCI results, country capacity profiles should be produced and disseminated. The stated aim of such profiles is for countries to reach an in-depth understanding of the status and/or performance of their respective NASS to inform policymaking and resources allocation going forward How to measure country capacity to produce timely, reliable, and sustainable agricultural statistics The measurement process for the ASCIs has been set out in the standard guidelines titled Guidelines for Assessing Country Capacity to Produce Agricultural and Rural Statistics, published by the Global Office, FAO in June The Guidelines indicate the variables that constitute each element of the ASCI and the formulae to measure them appropriately. This approach has been adapted to the specificities of the Africa context, while still ensuring the comparability of the results to other global regions. The full methods and instruments used for the 2013 CA surveys have been well documented in the first report. 4 There are a total of 23 elements of the ASCIs grouped under four dimensions, which objectively assess countries ability to produce agricultural statistics in a sustainable manner. These 4 The report on the 2013 survey for the Africa region was prepared and published by the AfDB in 2014 and can be viewed online at: Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 7

24 dimensions are the (ii) Prerequisite Dimension (Institutional Infrastructure), (ii) Input Dimension (Resources), (iii) Throughput Dimension (Statistical Methods and Practices), and (iv) Output Dimension (Availability of Statistical Information). The four dimensions are aggregated into a Composite Indicator to measure each country s overall capacity to produce agricultural statistics, hence its level of NASS development. The elements jointly constituting each dimension are presented in Table 1 below Data tabulation and analysis The LCA Excel template produced the tables and charts that are presented in this report. For comparative analysis of agricultural statistics activities and to assess performance of the Action Plan for Africa, the tabulation plan follows a similar pattern to that of 2013 CA results presentation. For the same reason, this report follows a similar pattern to the 2013 baseline report. This is to determine the changes that may have occurred over the two-year period of the Global Strategy s implementation in Africa. It is important to note that the agricultural data have improved in terms of quality and completeness as countries have achieved a better understanding of the logical process and implications of the subsequent results. Table 1 Elements of ASCI by Dimension Capacity Dimensions I. Institutional Infrastructure (PREREQUISITES) II. Resources (INPUT DIMENSION) III. Statistical Methods and Practices (THROUGHPUT DIMENSION) IV. Availability of Statistical Information (OUTPUT DIMENSION) Elements 1.1 Legal Framework 1.2 Coordination in the National Statistical System 1.3 Strategic Vision and Planning for Agricultural Statistics 1.4 Integration of Agriculture in the National Statistics System 1.5 Relevance of data 2.1 Financial Resources 2.2 Human Resources: Staffing 2.3 Human Resources: Training 2.4 Physical Infrastructure 3.1 Statistical Software Capability 3.2 Data Collection Technology 3.3 IT infrastructure 3.4 General Statistical Infrastructure 3.5 Adoption of International Standards 3.6 General Statistical Activities 3.7 Agricultural Market and Price Information 3.8 Agricultural Surveys 3.9 Analysis and Use of Data 3.10 Quality Consciousness 4.1 Core Data Availability 4.2 Timeliness 4.3 Overall data quality perception 4.4 Data Accessibility 8 Chapter 2 DESIGN AND METHODOLOGY

25 2.6. Dissemination strategy of the LCA results The following approach was adopted for dissemination of the LCA results: i) This report presents briefly the methodology and instruments used for the entire LCA process, as well as the main ASCI results for the years 2013 and 2015, including country rankings and groupings. ii) A second report will present the country profiles (based on their respective detailed ASCI results), pointing out changes that may have occurred between 2013 and 2015 at the country level. iii) In addition to the production of hard copies, which will be distributed to stakeholders across the continent, the report will be disseminated electronically, through flash discs, the AfDB internet/website, as well as in the form of ebooks for easy access and wider distribution. Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 9

26 10 Chapter 2

27 3. EXPERIENCES, LESSONS LEARNT, AND CONSTRAINTS Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 11

28 3.1. Experiences and lessons learnt Developing the LCA Excel template As mentioned earlier, the LCA questionnaire was transformed from a Word to an Excel template to facilitate the collection, checking, and processing of the data, as well as the review and validation of the survey results. The questionnaire was also customized to suit the statistical system pertaining to each country, with special attention being given to the composition of the line ministries. This was further linked to specific computation, as stipulated in the ASCI guidelines. Indeed, countries appreciated the ease of reporting their data and the instant preliminary results which allowed them to see the outcome of their responses. It also gave countries the opportunity to improve on the quantity and quality (in minimizing data input errors) of information provided The usefulness of the regional workshop in launching and conducting the LCA The launching workshop of 2016 (see section 2.2) was strategically organized to assemble countries at a single sitting to complete all the templates and submit preliminary completed LCA questionnaires for onward processing. The workshop was attended by key officials involved in the production of national data from National Statistics Offices and Ministries of Agriculture. It was an ideal opportunity for countries to share best practices in compiling CA basic data. This enhanced the quality and the coverage of data reporting by countries, using the LCA Excel template Validation and country ownership of the LCA process and the ASCI results In the process of validating the results, comparison was made with the baseline results (2013) and any cases of inconsistency were brought to the attention of the respective countries. Countries were given the opportunity to assess their individual results on a case-by-case basis. This allowed them to make amendments/updates to both 2013 and 2015 CA information which they had earlier submitted to the AfDB. This amendment stage was a direct result of countries gaining a better understanding of the whole process and the related purposes/ impact of the results. As indicated previously, further clarifications were provided by concerned countries to enhance the accuracy of the LCA for both 2013 and As a consequence, the quality of the data has improved in comparison to the 2013 baseline results, owing to the provision of additional metadata (country clarifications of their results). In brief, countries have owned the entire process, including the validation of the results of the two cycles of the CAs which have so far taken place Documentation of country best practices In clarifying and explaining their respective ASCI levels, selected countries have provided facts and showcased best practices in developing their capacity to produce reliable agricultural statistics. This will assist other, lower-performing countries to learn lessons and build their capacity in the future Constraints The results of the LCA are totally reliant on the data provided by countries; hence the importance of accuracy and the completeness of the basic data cannot be overemphasized. The level of reporting needs to be further improved in the future to ensure that the three non-reporting countries are brought on board. In that way, the ASCI averages calculated for Africa as an entire region will be based on data provided by all 54 regional countries. in addition, there is a need to explore the best way to accelerate the processes of data collection/compilation, checking, and validation so that the results are published as soon as possible, thereby ensure data quality and timeliness. 12 Chapter 3 EXPERIENCES, LESSONS LEARNT, AND CONSTRAINTS

29 Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 13

30 14 Chapter 3

31 4. AGRICULTURAL STATISTICS CAPACITY INDICATORS (ASCIs), 2013 AND 2015 Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 15

32 4.1. Composite Indicator of all four dimensions The Composite Indicator measures the overall capacity of a given country to produce the required quantity and quality of agricultural data. It is an aggregated indicator of all four ASCI dimensions, namely, (i) Institutional Infrastructure, (ii) Resources, (iii) Statistical Methods and Practices, and (iv) Availability of Statistical Information. In other words, the Composite Indicator is a measure of the development of the National Agricultural Statistics System (NASS) as a whole. The level of this Composite Indicator for the year 2015 reveals that there has been a general improvement in the performance of African countries in terms of strengthening their agricultural statistical capacities since the 2013 baseline CA. The implementation of the Action Plan for Africa to improve agricultural and rural statistics has had a noticeable and positive impact on the continent, as shown in Figures 2 and 3. However, although progress in the Africa region was made across all four dimensions from 2013 to 2015, the Composite Indicator remained stubbornly low, at 52.9% in Figure 2 ASCI levels in Africa by dimension, 2013 and 2015 PERCENT 80 Figure 3 Change in agricultural statistics capacities in Africa between 2013 and PERCENT Institutional Infrastructure Resources Statistical Methods and Practices Availability of Statistical Information Composite Indicator DIMENSIONS OF ASCI The improvement registered for all four dimensions ranges between 4.3% and 7.8%, according to individual countries. This indicates that countries are gradually taking cognizance of the importance of agriculture statistics. However, the Resources Dimension, comprising the elements of human staffing and training, physical infrastructure, and funding, registers the lowest level of increase (4.3%), from 26.1% to 30.4%. This indicates that efforts are still needed to strengthen African countries NASS. Tracking the progress of these indicators will enable ministries to take stock, make the right policy decisions, and draw up strategic programs for sustainable growth in the agricultural and rural sector. These, in turn, will help to improve the lives and livelihoods of people across the continent Ranking countries under the Composite Indicator In assessing the overall performance of countries using the Composite Indicator, it is observed that in 2015, the majority of countries (6 out of every 10) scored at least 50% for general capacity to produce agricultural statistics, compared to about 5 of every 10 countries in 2013 (see Figure 4). It is also important to note when comparing the 2015 results with those of 2013, that about 8 of every 10 countries registered various levels of improvement in the two-year interval ). On the other hand, some countries such as Somalia, Equatorial Guinea, and São Tome and Príncipe among others, failed to register any significant improvement in their capacity to produce statistical data over the period Institutional Infrastructure Resources Statistical Methods and Practices Availability of Statistical Information Composite Indicator DIMENSION Figure 4 shows Ethiopia to be the regional country with the highest level (78.8%) of agricultural statistical capacity to produce the required data, while Equatorial Guinea has the lowest capacity (19.2%) over the same period. See Boxes 1, 2, 3, 4, 5, and 6 for selected country explanations on what prevailed and the factors underlying their individual performances. 16 Chapter 4 AGRICULTURAL STATISTICS CAPACITY INDICATORS (ASCIS), 2013 AND 2015

33 Figure 4 Composite ASCIs by country, 2013 and 2015 Ethiopia South Africa Ghana Namibia Egypt Rwanda Mauritius Uganda Botswana Tunisia Mali Mozambique Kenya Niger Liberia Senegal Burkina Faso Nigeria Morocco Tanzania Algeria Malawi Benin Cameroon Lesotho Cabo Verde Sierra Leone Zambia Mauritania Sudan Côte d Ivoire Togo Swaziland Angola Gambia Gabon São Tomé & Principe Guinea Djibouti South Sudan Burundi Seychelles Congo, Rep. Chad Zimbabwe Somalia Comoros Congo, Dem. Rep. Equat. Guinea Madagascar Guinea-Bissau Libya PERCENT Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 17

34 Box 1 Ethiopia: explanatory factors contributing to the national ASCI level The following clarifications are given with respect to the major achievements and/or changes in the overall statistical system of Ethiopia and of agricultural statistics in particular. 1. As has occurred for the past three decades up to the present, the Central Statistical Agency of Ethiopia (CSA) has continued performing the Agricultural Sample Survey on an annual basis, with timely dissemination of the survey results thereby maintaining the data quality and reliability. 2. The CSA introduced CAPIs (Computer-Assisted Personal Interviewing) and tablets as a means of improving its data collection methods. 3. Every three years, the CSA used to revise the survey methodologies, which included the scope & contents of the survey, the method of data collection, and estimation procedures among others, in line with the available world standard statistical techniques and procedures. The introduction of area frames and mobile crop-cutting exercises are at the pilot stage. 4. The CSA has a well-established infrastructure with 25 branch offices located in different parts of the country to administer survey field operations and to undertake frequent field supervisions and follow-ups in order to ensure the quality of data collected. 5. Nearly 95% of the Branch Office Heads are M.Sc. & B.A. holders. 6. The CSA has increased the salaries of its staff as means to reduce staff turnover. 7. In addition to undertaking its own surveys using government funds, which shows an incremental trend over time, the CSA collaborates and technically assists NGO s and development partners such as the World Bank, the U.S. Department of Agriculture (USDA),the International Maize and Wheat Improvement Center (CIMMYT) and governmental institutions such as Ethiopian Institute of Agricultural Research (EIAR), the Ministry of Agriculture, etc. to undertake surveys of their interests. 8. The CSA used allocations from the government budget and NGO allocated funds, which had shown an incremental trend over time, to annually undertake agricultural and related sample surveys. 9. The CSA is on the verge of finalizing the cartographic work of the fourth round Housing & Population Census fully with CAPIs & tablets scheduled for use in data collection at the census time. All the above-mentioned improvements to the national statistical system of the country as a whole, and to national agricultural statistics in particular, raised the country level of the composite capacity indicator to 78.8% in 2015, compared to the level of 66.5% in Habekiristos Beyene Haile, Director, Central Statistical Agency, Ethiopia 18 Chapter 4 AGRICULTURAL STATISTICS CAPACITY INDICATORS (ASCIS), 2013 AND 2015

35 Box 2 Kenya: explanatory factors contributing to the national ASCI level Kenya s Agriculture Statistics Capacity Indicators (ASCIs) have recorded a remarkable improvement which is reflected in the results of the country assessment carried out in The composite ASCI rose from 56.6% to 68.3% in 2013 and 2015, respectively. This was mainly occasioned by the development of the National Agricultural Statistics System in general, and the implementation of the Strategic Plan for Agriculture and Rural Statistics-Kenya (SPARS-KE) in particular, which have provided the framework for the implementation and coordination of agriculture statistics activities in the country. The usage of Computer-Assisted Personal Interviews (CAPI) in data collection coupled with the commencement of the development of the National Strategy for Statistics Development in Kenya, under which Agriculture as a sector has highly contributed to this achievement. With the lessons learned, we are planning to extend the same spirit and initiative to other sectors in the economy. Enhancement of the agriculture statistics budget coupled with implementation of SPARS will further improve Kenya s ASCI level. James Theuri Gatungu Mathenge, Director Production Statistics, National Bureau of Statistics, Kenya Box 3 Ghana: explanatory factors contributing to the national ASCI level Ghana s relatively high overall ASCI score (64.1% in 2013 and 63.5% in 2015) is mainly attributable to the robust institutional infrastructure, application of enhanced statistical methods and practices, and high availability of statistical information. In fact, the inclusion of the Ministry of Food and Agriculture in Ghana s first National Strategy for the Development of Statistics (NSDS) ensured that planned agricultural statistics activities were catered for under the first phase of the Ghana Statistics Development Project, which received funding from a government-initiated International Development Association (IDA) credit and a grant from the Statistics for Results Facility Catalytic Fund in February This funding, together with the annual budgetary allocation, made it possible for the annual Ghana Agricultural Production Survey (GAPS) and other routine sample surveys and administrative data collection activities to be regularly carried out, as demonstrated by the significant increase in general statistical activities (from 71.4% in 2013 to 100.0% in 2015). Staff of the Statistics, Research, and Information Directorate (SRID) of the Ministry also benefitted from targeted training and technological advancement. These training programs were designed to bridge capacity gaps and improve analytical skills and data collection processes, with the emphasis on switching to the use of new technologies such as the computer-aided personal interviewing (CAPI) system, so as to promote the timely release and use of agricultural data. Awareness of the Minimum Set of Core Data (MSCD) identified for the country under the Global Strategy further ensured that, where possible, these indicators were included in routine data collection activities. This may also account for the slight increase in the availability of such data from 90.7% in 2013 to 91.4% in Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 19

36 Box 3 Ghana: explanatory factors contributing to the national ASCI level, cont. Strong participation in the activities of the National Statistics System (NSS) has brought the Ministry of Food and Agriculture into closer collaboration and integration within the system (54.5% in 2013 vs 63.6% in 2015) and also encouraged some strategic direction setting for agricultural statistics (16.7% in 2013 vs 50.0% in 2015). However, the slight decline in the Composite ASCI over the assessment period reflects the adverse impact of the general financial crisis experienced by the country in 2014 and 2015 on the annual budgetary allocation to all sectors of the economy, including the NSS. The dwindling and irregular releases of government subvention to support administrative activities in the NSS, as well as some statistical activities not covered by project funds, such as the Agricultural Census, may have resulted in the poor performance for the Resources Dimension index. Added to this is the fact that, between 2013 and 2014, there was a sharp decline in the number of professional, sub-professional, technical, and support staff of the Ministry of Food and Agriculture as a result of the promulgation of Legislative Instrument 1961, which requires all staff of the Regional and District Agricultural Development Units to be ceded to the Local Government Service (MoFA, 2015) 5. Dr. Philomena Nyarko, Government Statistician, Ghana Statistical Service Box 4 Côte d Ivoire: explanatory factors contributing to the national ASCI level The Agricultural Statistics System in Côte d Ivoire has long been marked by a lack of human and material resources and the poor coordination of statistical production activities. This has had a negative impact on the quantity and quality of agricultural statistics. However, capacity indicators for agricultural statistics improved significantly over the period At the national level, this situation can be attributed to: 1. the adoption of the new statistical law (2013), which remains a reference framework for production despite the absence of implementing legislation; 2. the implementation of the National Development Plan (PND ) and the National Agricultural Investment Program (PNIA ), which emphasized the availability of statistics in general and agricultural statistics in monitoring and evaluation; and 3. the completion of the third national census of agriculture in Côte d Ivoire, which led to improvements in methodologies and material resources. Improving the capacity indicators for agricultural statistics is also reflected in the technical and material support provided by the sub-regional and international institutions, which have experienced an increase since the post-crisis of the country. The development process of the Strategic Plan for Agricultural and Rural Statistics (PSSAR), as part of the Action Plan for Africa, was a catalyst for strengthening the operations of the National Agricultural Statistics System (SNSA). Koffi Gabriel Kouame, Assistant Director, Directorate of Statistics, Documentation and Informatics, Ministry of Agriculture and Rural Development (Minader), Côte d Ivoire 5 Ghanaian Ministry of Food and Agriculture (2015). Agriculture in Ghana. Facts and Figures, Statistics, Research and Information Directorate. September, Chapter 4 AGRICULTURAL STATISTICS CAPACITY INDICATORS (ASCIS), 2013 AND 2015

37 Box 5 Mozambique: explanatory factors contributing to the national ASCI level Some clarifications are given below on the main achievements in the National Statistical System of Mozambique as a whole, and in particular in the provision of agricultural statistics. INSTITUTIONAL INFRASTRUCTURE For this dimension in 2013, Mozambique achieved a score of 65.2% and in 2015 this increased to 70.6%. This was due to improvements in infrastructure and better coordination between the National Statistics Institute (INE) and the Ministry of Agriculture and Food Security (MASA). In the period under analysis, the Strategic Plan for Agrarian Statistics was implemented, integrating data collection systems. The following manuals have been approved: > > Manual of Procedures for Statistical Production in the National Statistical System; > > Manual of Procedures for Approval Technical Operations of Statistics of the National Statistical System; and > > Manual of Code of Conduct for production of Official Statistics. Statistics are increasingly considered a priority sector in the country s governance. RESOURCES For this dimension in 2013, we had a score of 35.3% and in 2015 it increased to 45.0%. After approval of the Agrarian Statistics Master Plan, there was greater flexibility in the availability of funds for statistical activities. There was also an increase in the number of trained technicians. With the integration of data collection systems, the sample grew, consequently, there was an increase in data collection agents in the field. Although resources have increased, there is still a need to increase financial and human resources, so that we can produce agrarian statistics up to the district level, which is the basis of development. STATISTICAL METHODS AND PRACTICES For this dimension in 2013, the score was 58.6% and it rose to 65.1% in Data collection technologies and computer systems have improved. Data collection in the field was introduced via CAPI (Computer Aided Personal Interviewing), which allowed improvement to the quality of the statistics and the timely availability of them. With the integration of the collection systems, the quality of data has also improved. AVAILABILITY OF STATISTICAL INFORMATION For this dimension, there was timely availability in accordance with the established timetables, using various methods for the dissemination and publication of information. The use of web pages, brochures, and yearbooks contributed to the increased availability of information. The National Statistical System of the country as a whole and the agricultural statistical system in particular benefited from the improvement of the dimensional indicators described above. This significantly raised the country s ASCI capacity and composition indicator from 56.6% in 2013 to 65.6% in Delfina Cumbe, Head of Department of Sectoral Statistics, National Institute of Statistics, Mozambique Aurelio Mate, Head of the Department of Agrarian Statistics, Ministry of Agriculture and Food Security, Mozambique Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 21

38 Box 6 Democratic Republic of the Congo (DRC): explanatory factors contributing to the national ASCI level Since 2013, the DRC has seen its capacity for statistical activities boosted by the deployment of the necessary resources (financial resources, human resources recruitment, training), the provision of physical infrastructures, and the adoption of new technologies. Improvements have been recorded in the following areas: 1. The DRC has significantly improved the Resources indicators by 11.5%, from 6.3% in 2013 to 17.8% in 2015, due to the focus on staff training, and the supply of IT equipment. 2. The International Institute for Agricultural Policy Research (IFPRI) has strengthened the capacity of the National Agricultural Statistics Service (SNSA) in the area of data collection within the baseline study of the Government s Crop Year Program and Extension Survey. 3. The use of Computer-Assisted Personal Interviewing (CAPI) and tablet methodology in the data collection of these two studies enabled the NSSA to improve the process of data collection in the field. 4. The introduction of the use of GPS in agricultural surveys (measurement of cultivated areas, sampling of the geographical coordinates of sample villages) helped to improve the data collection process. 5. In the meantime, the SNSA was equipped with about 15 new computers in order to strengthen the capacity of its data center in the processing of survey data. 6. The Statistical Methods and Practices Dimension also showed a significant increase of 30.6%, from 12.7% in 2013 to 43.3% in 2015, for some of the reasons mentioned above. Thus, the use of new technologies in the collection of data and appropriate software for data processing (e.g. Stata) has had a significant impact on the capacity of the country s National Agricultural Statistics System (SNSA) to undertake statistical activities in a professional and cost-effective manner. 7. This improvement in the situation is also due to the effective integration of agriculture into the National Statistical System. It is demonstrated by the involvement of the National Agricultural Statistics Service (SNSA) in national surveys, such as the survey with a unified questionnaire with basic indicators of well-being in the DRC (E-QUIBB / DRC1) National Institute of Statistics (INS). 8. In order to contribute to the production capacity of agricultural statistical information and data, these surveys contain an important module on Agriculture and Food Security. 9. The prospect of the forthcoming completion of a General Agricultural Census (RGA 2020) with the World Bank and FAO, and the implementation of a Strategic Plan for Agricultural and Rural Statistics (PSSAR) with the African Development Bank (AfDB) will certainly contribute to the effective revitalization and improvement of the National Agricultural Statistics System. Robert Ngonde Nsakala, Director of the National Agricultural Statistics Service (SNSA), Ministry of Agriculture, Democratic Republic of Congo 22 Chapter 4

39 Country groupings under the Composite ASCI Grouping countries according to their capacity to produce agricultural statistical data helps to quickly identify those that share common characteristics. It differentiates the high-level performers from those who need more assistance to scale up their results. It also provides a platform to establish a level of south south cooperation among countries, to enhance the implementation of the program across the entire region. Table 2 shows how the grouping of countries has been made based on the score or strength of the composite ASCI. This table is further used in grouping country performances, with regards to the capacity to produce agricultural statistical data, under each of the four dimensions of the ASCI. Table 2 Scale of dimension score of ASCI Range of ASCI Group Capacity level 0 =< ASCI < 20 A Very weak 20 =< ASCI < 40 B Weak 40 =< ASCI < 60 C Average 60=< ASCI < 80 D Strong 80=< ASCI =<100 E Very strong Based on the dimension score, the grouping of countries under the composite ASCI has been generated comparing the number of countries per class interval at baseline (2013) with the recent situation in Figure 5 therefore demonstrates the changes that have occurred over the two-year period in the proportion of countries that have the capacity to produce statistical data. It shows a decline in the number of countries in the weaker groups (groups A and B) and a shift to the stronger groups (groups C and D) from 2013 to This further strengthens the main finding from the LCA that improvement is effectively and gradually taking place in different aspects of the agricultural statistical systems in Africa. Table 4 identifies the specific countries involved in the migration from weaker to stronger groupings due to improved agricultural statistical capacity since The table presents the situation as at 2015, it also portrays the countries that have failed to perform as well. Moreover, in Table 4 the agricultural statistical capacity of each country is assessed against the level of GDP per capita 6 and agriculture value added (% of GDP) following the pattern of 2013 (see the scale of GDP per capita and Agriculture Value Added in Table 3 below). The objective of these 3-dimensional groupings (i.e. GDP per capita, agric. VA, and ASCI) is to identify (a) countries that may have the capacity to finance their agricultural statistics activities but would need technical assistance; and (b) those that would need both financing and TA. Figure 5 Proportion of countries per group by Composite ASCI, 2013 and 2015 % OF COUNTRIES Group A Group B Group C Group D Group E Table 4 shows that a total of 19 out of 51 countries (37.3%) improved their capacity to produce agricultural statistics over the period. The table also shows that some countries such as Malawi, Mozambique, Senegal, and Tanzania, even though have the lowest GDP per capita and correspondingly low agriculture VA, recorded improved statistical capacity from average to strong dimensions over this period. Some countries (like Senegal and Tanzania) have had some technical support (e.g. for SPARS development) to strengthen their ability to produce the relevant statistical data within the margins of the Global Strategy. On the other hand, a country like Equatorial Guinea has experienced a decrease in its capacity to produce the same, even though it has the highest capacity to finance their activities. This type of country would require advocacy for them to allocate and secure the needed resources for the development of their National Agricultural Statistical Systems (NASS). 6 South Sudan and Somalia did not have GDP per capita and agriculture VA, hence were not featured in this table. Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 23

40 Table 3 Scale of score of GDP per capita and agriculture value added (% of GDP) Grade GDP per capita grouping Agriculture VA (% of GDP) grouping Lowest Low Average High Highest Prerequisites Dimension level of institutional infrastructure in Africa This indicator measures the basis for the effective running of the NASS. It is the amalgamation of five main elements of the institutional infrastructure of the country capacity to produce agriculture statistics, as demonstrated in Figure 6. Comparing the performance of Africa since the last Country Assessment in 2013, it is observed that there has been an overall improvement of 5.7% in the status of this dimension in Africa (see Figure 3). Figure 6 shows in more detail that Table 4 Country Groupings by Agricultural Statistical Capacity, GDP per Capita, and Agriculture VA (as % of GDP) 2015 GDP per Capita Agric. VA (% of GDP) Very Weak Agricultural Statistics Capacity Weak Agricultural Statistics Capacity Average Agricultural Statistics Capacity Strong Agricultural Statistics Capacity Very Strong Agricultural Statistics Capacity Countries that migrated to higher groupings due to improved capacity since 2013 Countries that migrated to lower groupings due to reduced capacity since 2013 Lowest % Zimbabwe; Lesotho Senegal Senegal Lowest per capita Low % Madagascar; Comoros Burundi; Chad; Gambia; Congo, Dem. Rep.; Guinea; Niger; Burkina Faso; Benin Uganda; Malawi; Tanzania; Mozambique; Rwanda Burundi; Chad; Gambia; Congo, Dem. Rep.; Guinea; Malawi; Tanzania; Mozambique Average % Guinea-Bissau Sierra Leone; Togo Mali; Ethiopia Mali High % Liberia Highest % Lowest % Congo, Rep. of; Zambia Congo, Rep. of Low per capita Low % Average % São Tomé & Principe Mauritania; Cameroon; Côte d Ivoire Ghana; Kenya Kenya High % Highest % 24 Chapter 4 AGRICULTURAL STATISTICS CAPACITY INDICATORS (ASCIS), 2013 AND 2015

41 Table 4 Country Groupings by Agricultural Statistical Capacity, GDP per Capita, and Agriculture VA (as % of GDP) 2015, cont. GDP per Capita Average per capita High per capita Highest per capita Agric. VA (% of GDP) Lowest % Very Weak Agricultural Statistics Capacity Weak Agricultural Statistics Capacity Average Agricultural Statistics Capacity Djibouti; Swaziland Strong Agricultural Statistics Capacity Tunisia; Egypt; Cabo Verde; Morocco Very Strong Agricultural Statistics Capacity Countries that migrated to higher groupings due to improved capacity since 2013 Djibouti; Tunisia; Cabo Verde; Morocco Low % Sudan Nigeria Nigeria Average % High % Highest % Lowest % Low % Average % High % Highest % Lowest % Low % Average % High % Highest % Equat. Guinea Gabon; Angola; Algeria Namibia; Botswana; South Africa Gabon Countries that migrated to lower groupings due to reduced capacity since 2013 Seychelles Mauritius Seychelles Equat. Guinea (i) there has been a significant increase in the strategic vision and agricultural statistics planning from 49.0% in 2013 to 60.8% on 2015 in the region; (ii) there has also been an enhancement in the integration of agriculture in the National Statistical Systems from 57.3% in 2013 to 64.2% in 2015; (iii) a similar pattern pertains for the relevance of data, as this element reveals an increase from 41.5% in 2013 to 49.3% in This reflects the impact of the implementation of Strategic Plans for Agriculture and Rural Statistics (SPARS) on the continent, as they serve as the platform for long-term sustainable development of agricultural and rural statistics Ranking countries using the Institutional Infrastructure Dimension The performance of countries according to the 2013 level of institutional infrastructure was ranked to identify those that had achieved higher scores. This signifies that countries with a lower performance, i.e. less capacity under this dimension, may learn from the higher achievers best practices. It is instructive to compare performances over time since the implementation of the Global Strategy in Africa. Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 25

42 Figure 6 Level of Institutional Infrastructure by element in Africa, 2013 and 2015 RELEVANCE OF DATA INTEGRATION OF AGRIC. IN NSS LEGAL FRAMEWORK COORDINATION IN NSS STRATEGIC VISION AND AGRIC. STAT PLANNING Figure 7 illustrates the changes that have occurred since This chart has been ordered in descending order from the maximum to the minimum score, according to performances observed for the 2013 reference year. It shows that countries such as Botswana, Cabo Verde, Cameroon, Comoros, Congo Republic, Côte d Ivoire, Democratic Republic of Congo, Egypt, Ethiopia, Ghana, Guinea-Bissau, Kenya, Senegal, Seychelles, Sierra Leone, South Africa, Tanzania, and Zimbabwe, among others, have improved tremendously over the two-year period in strengthening their respective institutional infrastructure in their National Agriculture Statistical Systems. Factors accounting for such improvements are explained by first-hand accounts of country representatives in Boxes 7, 8, 9, 10, and 11 (for selected countries). It is important to note that the SPARS development process was completed in six countries by year-end These countries were Benin, Burundi, Cabo Verde, Côte d Ivoire, Kenya, and Senegal. In addition, nine countries received technical assistance on their SPARS development process, namely Burkina Faso, Cameroon, Chad, Congo Republic, Ethiopia, Ghana, Niger, Rwanda, and Zambia. As a further capacity-building measure, two regional workshops were held in 2015 for French- and English-speaking countries respectively, to train agricultural statistics experts on the use of the standard guidelines for the development of SPARS. The aim was to accelerate the spread of the SPARS approach, thereby strengthening the institutional infrastructure among African countries. The workshops and guidelines have had a significant impact on the results of the 2015 LCA, as shown in Figure 3 above as well as the rest of the charts in this report. Box 7 Cabo Verde: explanatory factors contributing to the national ASCI level Cabo Verde significantly improved its composite ASCI from 50.9% in 2013 to 67.9% in 2015 due to the strengthening of the institutional infrastructure, the use of improved statistical methods, the availability of statistical information, and good governance in the country. Statistics are considered a priority sector in the governance of the country. Based on the recommendations of the Global Strategy, Cabo Verde has put in place its Strategic Plan for the development of agricultural statistics, food security and rural development, with technical and financial support from the AfDB. The Plan was an essential instrument for decision-making, the definition of sectoral policies and the monitoring and evaluation of its implementation. Cabo Verde is in the phase of implementing its Strategic Plan through the realization of the General Census of Agriculture. It is also important to emphasize the role that the National Statistical Council (CNEST) has had in supporting the development of agricultural statistics, hence the improvement of the ASCI indicators. Indeed, CNEST works fully with other delegated bodies of the National Statistics Institute (INE) to that end. Maria Auxiliadora da Cruz Fortes, Director, Ministry of Agriculture and Environment, Cabo Verde 26 Chapter 4 AGRICULTURAL STATISTICS CAPACITY INDICATORS (ASCIS), 2013 AND 2015

43 Figure 7 Level of Institutional Infrastructure by Country, 2013 and 2015 Namibia Uganda Rwanda Liberia Mali Mauritius Nigeria Benin Tunisia Cameroon South Africa Ethiopia Cabo Verde Tanzania Angola Burkina Faso Lesotho Togo Mozambique Kenya Mauritania Malawi Sudan Senegal Botswana Somalia Ghana Algeria Egypt Morocco Djibouti Gambia Burundi Côte d Ivoire South Sudan Congo, Dem. Rep. Sierra Leone Guinea Chad Equat. Guinea Zambia Congo, Rep. of Swaziland São Tomé & Principe Gabon Madagascar Zimbabwe Seychelles Comoros Guinea-Bissau Libya PERCENT Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 27

44 Box 8 Botswana: explanatory factors contributing to the national ASCI level Botswana recorded a moderate improvement in the Composite ASCI from 60.8% in 2013 to 64.5% in 2015, mainly due to the strengthening of the institutional infrastructure. The rating for the institutional infrastructure improved significantly from 59.3% to 85.9% during the period under review. The Statistics Act of 2009 has since become fully operational. In accordance with the Global Strategy for Improving Agricultural and Rural Statistics, an Agricultural Sector Strategic Plan (ASSP) was developed under the guidance of the National Statistics Office and with technical assistance from the African Development Bank. The ASSP has been incorporated into the National Strategy for the Development of Statistics (NSDS), and consequently, the collection, analysis, and dissemination of official agricultural statistics are now properly coordinated. Notably, the ASSP was developed by a technical working group with wide representation in the sector. The 2016 Light Country Assessment was carried out by the technical working group and hence provided a more reliable and accurate measure of the country s strengths on the ASCI dimensions. Stilwell Keabewa Dambuza, Manager, Agriculture & Environment, Statistics Botswana Box 9 Tanzania: explanatory factors contributing to the national ASCI level In his opening address to the regional workshop titled Validation of the Agricultural Minimum Set of Core Data (MSCD) in African Countries, which took place in Dar es Salaam from November 21-25, 2016, the Director General of the National Bureau of Statistics, Dr. Albina Chuwa, clearly pointed out that the United Republic of Tanzania is one of the many African countries that had benefited greatly from the Action Plan of the Global Strategy for Improving Agricultural and Rural Statistics in Africa. Through this Action Plan, Tanzania has developed the Strategic Plan for Agricultural and Rural Statistics (SPARS), which spearheaded the coordination among all the Agricultural Sector Lead Ministries (ASLMs) in the country. It is important to note that the SPARS in Tanzania covers the five-year period 2014/ /19 and was developed by the National Team members from ASLMs, with technical assistance from the FAO and AfDB. The United Republic of Tanzania also reported that the Composite ASCI, which covers the four dimensions of institutional infrastructure, resources, statistical methods and practices, as well as the availability of statistical information, improved from 54.1% in 2013 to 61.6% in This may be due in part to the implementation of the SPARS, which stipulates all activities to be carried out in the specified period, as well as the distribution of the resources according to the activities outlined in the strategy. 28 Chapter 4 AGRICULTURAL STATISTICS CAPACITY INDICATORS (ASCIS), 2013 AND 2015

45 Box 9 Tanzania: explanatory factors contributing to the national ASCI level, cont. The National Team is responsible for data collection, validation, and updating from difference sources in the country. The work is being conducted in a participatory manner and the National Bureau of Statistics coordinates the whole process of data collection, analysis, dissemination, and archiving. Other countries in Africa can also draw on the United Republic of Tanzania s positive experiences, to enhance the reporting process of their required indicators at regional and international levels. Titus Titus Mwisomba, Manager, Agriculture Statistics, National Bureau of Statistics United Republic of Tanzania Box 10 Burkina Faso: explanatory factors contributing to the national ASCI level The National Statistical System (NSS) of Burkina Faso has undergone major institutional progress since 2013 with the creation in each ministry of a technical department in charge of statistics. The direct consequence of this institutional reform has been the recruitment of additional statisticians for each of these new directorates. In particular, the ministries responsible for agricultural and rural statistics benefited from the arrival of new contingents of young statisticians, which had the effect of improving statistical human resources in both quantity and quality. The second positive effect of this institutional evolution has been the improvement of the working conditions by providing suitable premises for the new structures as well as the provision of rolling stock. However, this Government effort must be pursued in order to substantially improve the level of production of agricultural and rural statistics, which is confronted with other types of constraints such as a low level of ICT use, a low level of capacity building techniques, and insufficient harmonization of methods. To this end, the SPARS being finalized will be able to contribute, through its effective implementation, to boosting the level of production and dissemination of agricultural and rural statistics. Lassina Pare, Director of Sector Statistics, Ministry of Agriculture and Hydraulic Development, Burkina Faso Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 29

46 Box 11 Madagascar: explanatory factors contributing to the national ASCI level Madagascar s Agriculture Statistics Capacity Indicators (ASCIs) recorded a moderate increase of approximately 10 points between 2013 and 2015, from 23.4% to 34.2%. This result stems from the strengthening across three dimensions, namely resources (9.9% to 28.0%), statistical methods and practices (14.6% to 33.0%), as well as the availability of statistical information (48.4% to 62.3%). The only dimension exhibiting a declining performance was institutional infrastructure (30.2% to 18.8%). The regression of the institutional infrastructure dimension results from the cancelation of the Trust Fund project, including a component on the development of the National Strategy for Agricultural Statistics. In addition, efforts have been made with the support of some donors (WFP, FAO, Africa Rice, etc.) within the following frameworks: > > the annual assessment of the crop year and food security; > > > the application of the Dot sampling methodology to estimate rice area in a few target areas; and > the completion of the national framework survey with regard to fisheries. Joceline Julie Solonitompoarinony, Chief of Agricultural and Livestock Statistics Service, Madagascar Grouping countries under the Institutional Infrastructure Dimension The dimension scale is used here also to identify and group countries that have similar characteristics in institutional infrastructure for the production of agricultural statistics. This is done in two stages. The first stage is demonstrated in Figure 8, which shows the proportion of countries that improved their institutional infrastructure capacity from 2013 to The figure indicates a shift of countries from the weaker groups (groups B, C, and D) to the strongest group (group E) with respect to the strengthening of the institutional infrastructure of NASS in Africa. statistic, as their level was already relatively satisfactory in Table 5 also indicates that countries such as Madagascar, Djibouti, and Equatorial Guinea have reduced in strength for this capacity from average group scale to weak group scale over the same period. This signals the fact that this group of countries do need assistance to develop and/or strengthen their institutional infrastructure so that their respective NASS can operate efficiently. Figure 8 Proportion of countries grouped by institutional infrastructure scores, 2013 and 2015 The second stage of the grouping of country capacity in Institutional Infrastructure is carried out for GDP per capita and agriculture value added. It is acknowledged that some countries have improved significantly from average to strong levels since 2013, irrespective of their position in GDP per capita and agriculture value added on the grouping scale. This is illustrated in Table 5 below. Table 5 shows that 16 out of 51 countries (31.4%) have improved significantly since their 2013 original position/scale of strength through to Some other countries such as Uganda, Rwanda, Namibia, and Mauritius have not undergone any significant change in institutional infrastructure capacity for agricultural % OF COUNTRIES Group A Group B Group C Group D Group E Chapter 4 AGRICULTURAL STATISTICS CAPACITY INDICATORS (ASCIS), 2013 AND 2015

47 Table 5 Country groupings by institutional infrastructure, GDP per capita, and agriculture VA (as % of GDP), 2015 GDP per Capita Lowest per capita Low per capita Average per capita High per capita Highest per capita Agric. VA (% of GDP) Lowest % Low % Average % High % Highest % Lowest % Low % Average % High % Highest % Lowest % Very Weak Agricultural Statistics Capacity Madagascar Weak Agricultural Statistics Capacity Chad São Tomé & Pr. Swaziland; Djibouti Average Agricultural Statistics Capacity Zimbabwe; Lesotho Comoros; Gambia; Guinea; DRC; Burundi Guinea-Bissau Congo, Rep. of; Zambia Mauritania Morocco Strong Agricultural Statistics Capacity Malawi; Burkina Faso; Mozambique; Benin Togo; Sierra Leone Côte d'ivoire; Ghana; Kenya Egypt Very Strong Agricultural Statistics Capacity Senegal Tanzania; Niger; Uganda; Rwanda Ethiopia; Mali Liberia Cabo Verde Tunisia; Cameroon Low % Sudan Nigeria Average % High % Highest % Lowest % Low % Average % High % Highest % Lowest % Low % Average % High % Highest % Equat. Guinea; Seychelles Gabon; Algeria Angola Botswana; South Africa; Namibia Mauritius Countries that migrated to higher groupings due to improved capacity since 2013 Senegal Comoros; Tanzania; Niger Sierra Leone; Ethiopia Congo, Rep. of Côte d'ivoire; Ghana; Cabo Verde Egypt; Tunisia; Cameroon Gabon; Botswana; South Africa Countries that migrated to lower groupings due to reduced capacity since 2013 Benin; Madagascar; Chad Mauritania Djibouti Equat. Guinea Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 31

48 4.3. Input Dimension resources availability in Africa The Input Dimension measures the ability of a country to deploy sufficient resources to carry out statistical activities. It is a combination of four elements, which are: financial; human resources training; human resources staffing; and physical infrastructure as indicated in the 2013 ASCI report by AfDB, 7 as well as in the Standard Guidelines for computing the ASCI, which was published by the Global Office in Resources are generally lacking or low in African countries for the production of agricultural statistics. Thanks to the series of advocacy opportunities (through workshops and missions to countries) carried out in the framework of the Action Plan for Africa, more attention has been given to the production of agricultural statistics in the region. This has been reflected in the level of data reported by countries in the 2015 Light Country Assessment. Figure 9 demonstrates the changes that have occurred in the Resources Dimension over the two-year period There has been a slight increase in the provision of the following: financial resources, human resources in terms of staffing, and physical infrastructure. However, there has been a minor reduction of about 2.2% in conducting training programs to continue to improve the skills and expertise of the agricultural statistics workforce Ranking countries under the Input (Resources) Dimension In Figure 10 countries have been ranked in descending order according to resources made available for the production of agricultural statistics for policymakers and other users. This allows the swift identification of countries that are providing sufficient funding, engaging the services of professionals, providing adequate training to improve the skills of the staff on the use of appropriate statistical methods, and providing the requisite physical infrastructure such as office equipment and other logistics for carrying out national agricultural statistics programs. In this regard, comparison was made with the 2013 ASCI results, as shown in Figure 10, to observe the changes that have occurred since the implementation period of the Global Strategy in the Africa region. Figure 10 reveals a very interesting trend among countries. Some countries such as Swaziland, Niger, and Zimbabwe, which had exhibited a very low resources level for agricultural statistics activities in 2013, more than doubled their performance by Boxes 12 and 13 provide explanations of the key factors that have contributed to this success in two selected countries. On the other hand, a number of other countries have witnessed a reduction in the provision of resources for the production of agricultural statistics. These include Cabo Verde, Comoros, Côte d Ivoire, Egypt, Ghana, Malawi, Mauritius, Namibia, Nigeria, Senegal, South Africa, South Sudan, Tanzania, and Zambia. Figure 9 Level of resources by element in Africa, 2013 and 2015 PHYSICAL INFRASTRUCTURE FINANCIAL RESOURCES HUMAN RESOURCES: STAFFING Figure 10 also depicts that the majority of countries recording low resources for agricultural statistics production in 2013 registered an increase for this dimension by By contrast, most of the countries that enjoyed a higher level of resources in 2013 experienced a decrease in Mauritius is an illustration of this contrast; this country used to be the highest (66.1%) in resources provision for agricultural statistics production in 2013; however, its score dropped to 58.8% in 2015, when it ceded its premier position to Botswana (58.9%). On the other hand, some countries (such as Angola, Sudan, and Guinea-Bissau) did not register any significant change in resources provision over the two-year period HUMAN RESOURCES: TRAINING 7 The report on the 2013 survey for the Africa region published by the AfDB in 2014 can be viewed online at: Documents/Publications/AfricaCountryAssessment_ASCI_Report_Final_Web_11_2014.pdf 32 Chapter 4 AGRICULTURAL STATISTICS CAPACITY INDICATORS (ASCIS), 2013 AND 2015

49 Figure 10 Capacity level of resources by country, 2013 and 2015 Mauritus Botswana South Africa Zambia Ghana Malawi Cabo Verde Namibia Rwanda Uganda Angola Senegal Egypt São Tomé & Principe Mozambique Côte d Ivoire Ethiopia Kenya Nigeria Gabon Cameroon Lesotho Sudan Swaziland Mali Tunisia Burkina Faso Guinea-Bissau Seychelles Tanzania Algeria Benin Morocco Mauritania South Sudan Comoros Sierra Leone Togo Guinea Congo, Rep. of Djibouti Zimbabwe Burundi Chad Madagascar Niger Liberia Somalia Equat. Guinea Congo, Dem. Rep. Libya Gambia PERCENT Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 33

50 Box 12 Morocco: explanatory factors contributing to the national ASCI level In terms of the Resources Dimension indicator, the following points have contributed to the improvement recorded by Morocco: 1. Realization of the General Census of Agriculture in 2015 made it possible to mobilize important resources, including finance as material for the well-conducted execution of the operation. (Ref in Module 3: Budget for new statistical projects). 2. The establishment of the new provincial statistical offices in January 2015 has helped to increase the number of professionals working in agricultural statistics (Ref in Module 3). 3. The realization of the General Census in 2015 is an innovative experience in the field of collection technology by geometry transmission. This enabled the acquisition of new high-resolution satellite servers and satellite imagery for the cartographic delimitation of the listed agricultural holdings. Mustapha Abderrafik, Agro-economist Engineer, Directorate of Strategy and Statistics, Ministry of Agriculture and Fisheries, Morocco Box 13 Comoros: explanatory factors contributing to the national ASCI level Following the scoring of the 2013 and 2015 country assessments, five broad headings require explanations for the fluctuations in the ratings for Comoros. These cover the institutional framework, resources, information technologies, statistical methods and practices, and consumer prices of agricultural products. 1. INSTITUTIONAL INFRASTRUCTURE For this indicator, we note that the score rose from 25.3% in 2013 to 44.7% in This situation is explained by the fact that although in 2013 the country established the first law (Statistical Act 2011) to govern national statistics, it was not until 2015 that this law was put into place. Therefore it was only in 2015 that: (i) the decree establishing the National Institute of Statistics was signed; (ii) the decrees for the establishment of the National Statistical Council (the main decision-making body of the NSS) and the Board of Directors of the National Statistical Institute were signed; (iii) the institutional framework of the National Statistical Institute was elaborated and validated; and (iv) the NSDS Action Plan was developed and validated; etc. 2. RESOURCES For the Resources Dimension, the score decreased from 16.1% in 2013 to 10% in 2015.This can be explained by the fact that the Comorian state, apart from the salaries of civil servants, does not allocate resources for the realization of statistical activities. Between 2010 and 2014, statistical activities were financed by the AfDB through the institutional capacity building project (PRCI). In 2015 (after the end of the first phase of the project), there was no longer funding for statistical activities. Indeed, since then, the financial resources allocated to the national statistical system have reduced considerably. 34 Chapter 4 AGRICULTURAL STATISTICS CAPACITY INDICATORS (ASCIS), 2013 AND 2015

51 Box 13 Comoros: explanatory factors contributing to the national ASCI level, cont. 3. INFORMATION TECHNOLOGY Until 2013, the National Statistical Office did not have a website to house official statistics of the country, nor did it have a database of these statistics. It was only in late 2014 and early 2015 that these two tools became available at the National Institute of Statistics. Thus, the score significantly increased from 12.5% in 2013 to 62.5% in STATISTICAL METHODS AND PRACTICES Indicators for this dimension reveal that in 2015 as in 2013, the technologies used to collect statistical data and the number of software and other computer systems used in the statistics office remained unchanged. However, the number of computers used in the office increased in 2015 compared to INFORMATION ON THE AGRICULTURAL MARKET AND PRICES The Consumer Price Index, which until 2014 was reporting separately the indices of agricultural products used for direct consumption, ceased to do so after 2014 due to a lack of funding. This resulted in a sharp drop in the score for this dimension, from 10% in 2013 to 0% in It should be recalled that Comoros s information on the consumer prices of agricultural products is generally provided by the Consumer Price Index Service of the NSO, which conducted two-weekly collections between 2010 and 2014 in specific markets of the country. During the period 2015, this service experienced financing problems due to the completion of the PRCI project, which had financed the activities of the CPI. That explains the drop in the score for this indicator in Youssouf Mahdy, Manager of Agricultural Statistics and National Coordinator of the Global Strategy, INSEED, Comoros Grouping countries under the Input (Resources) Dimension In order to identify countries that have similar characteristics in resources capacity, a grouping of scores is made using the same scale of dimension. The grouping is again conducted in two stages. The first stage is to ascertain the proportion of countries per class interval in 2013 and 2015, as indicated in Figure 11. This figure indicates that, even if the resources provision level remains low in Africa, there was a gradual improvement from 2013 to 2015, as the proportion of countries in the very weak group A decreased over the two-year period, while the proportion increased in groups B and C. or vice-versa, due to improved or reduced resources capacity over the period. Figure 11 Proportion of countries grouped by level of resources, 2013 and 2015 % OF COUNTRIES The second stage of grouping under this dimension is carried against GDP per capita and agriculture value added, as shown in Table 6. This is to assess whether country incomes are contributing to funding the production of agriculture statistics in a reasonable manner. This methodology also highlights the possible migration of countries from a weaker to a stronger group Group A Group B Group C Group D Group E Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 35

52 As at 2015, 15 of the 51 participating countries (29.4%) had shown an improvement in their resources capacity. Among them, countries such as Zimbabwe, Burundi, Gambia, and Madagascar exhibited improvements in terms of funding, human staffing and training, as well as physical infrastructure. This improvement was achieved despite their low GDP per capita and relatively low agriculture value added. On the other hand, countries such as Namibia and Mauritius, with a high Table 6 Country groupings by resources, GDP per capita, and agriculture VA (as % of GDP), 2015 GDP per Capita Lowest per capita Low per capita Average per capita High per capita Highest per capita Agric. VA (% of GDP) Lowest % Low % Average % High % Highest % Lowest % Low % Average % High % Highest % Lowest % Low % Average % High % Highest % Lowest % Low % Average % High % Highest % Lowest % Low % Average % High % Highest % Very Weak Resources Comoros; Guinea; Tanzania; DRC Liberia Congo, Rep. of Equat. Guinea Weak Resources Zimbabwe; Senegal; Lesotho Burundi; Gambia; Madagascar; Chad; Niger; Uganda; Burkina Faso; Benin; Malawi Sierra Leone; Guinea-Bissau; Togo Zambia Cote d'ivoire; Mauritania; São Tomé &Pr.; Kenya Tunisia; Djibouti; Egypt; Cameroon; Morocco Sudan; Nigeria Namibia; Algeria; Gabon; Angola Average Resources Rwanda; Mozambique Mali; Ethiopia Cabo Verde; Ghana Swaziland South Africa; Botswana Strong Resources Very Strong Resources Countries that migrated to higher groupings due to improved capacity since 2013 Zimbabwe Burundi; Gambia; Madagascar; Chad; Niger; Mozambique Sierra Leone; Togo; Mali; Ethiopia Mauritania Djibouti; Morocco; Swaziland Countries that migrated to lower groupings due to reduced capacity since 2013 Malawi Zambia Namibia Seychelles Mauritius Mauritius 36 Chapter 4 AGRICULTURAL STATISTICS CAPACITY INDICATORS (ASCIS), 2013 AND 2015

53 GDP per capita, reduced resources provision for agricultural statistical activities in the two-year period. To sum up, since 2013, the majority of the countries still have weak resources capacity to produce the needed agricultural statistics data, irrespective of their strength in terms of GDP per capita Throughput Dimension availability of statistical methods and practices in Africa The Throughput Dimension indicator reflects countries capacity to undertake statistical activities in a professional and cost-effective manner. It comprises nine elements of the ASCIs, which cover the use of information technology focusing on statistical software capability, data collection technology, and information technology infrastructure. This Throughput Dimension also assesses the status of the general statistical infrastructure (such as agriculture surveys and agricultural market and price information, among others). The adoption of international classifications and standards, the analysis and use of data collected, and quality consciousness are all encapsulated under this dimension. Figure 12 demonstrates the status of statistical methods and practices among African countries in 2013 and It shows that there has been an improvement (11.3% increment) in the use of technology for data collection, such as Computer Assisted Personal Interviewing (CAPI). There has also been an improvement of 12.7% in carrying out agricultural surveys. There has been an improvement of 13.2% in adopting international classifications and standards in Africa. This signifies that there is now a stronger foundation for data consistency and the harmonization framework, as well as for data exchange protocols. It is also important to note that there has been very slight improvement (0.2%) in the information technology infrastructure on the continent. This measures the level to which the statistical offices and staff are equipped to process, analyze, disseminate, and archive information through the use of computers/tablets, servers, and the availability of internet connections. There has also been an improvement in the analysis and use of data in Africa (5.4%), which suggests that the level of analytic programming, the use of data for policymaking, and the satisfaction of data user needs have been enhanced since Ranking countries under the Throughput Dimension Comparing performances in African countries over the period, Figure 13 illustrates the changes that have occurred in the use of statistical methods and practices between 2013 and Countries such as Angola, Cabo Verde, Chad, Democratic Republic of Congo, Guinea, Kenya, Madagascar, Malawi, Mali, and Togo showed significant improvements in this dimension between 2013 and It is important to note that the majority of countries with scores below 50% (i.e. 0 to 50%) for this statistical capacity indicator in 2013, recorded some level of improvement in the range of increments between 11% and 66.6% in Box 14 below illustrates some factors contributing to such an improvement in South Africa. Figure 12 Level of statistical methods and practices by element in Africa, 2013 and 2015 QUALITY CONSCIOUSNESS ANALYSIS AND USE OF DATA STATISTICAL SOFTWARE CAPABILITY DATA COLLECTION TECHNOLOGY INFO. TECHNOLOGY INFRASTRUCTURE Some countries such as Liberia and Namibia maintained the same capacity level for this dimension between the years 2013 and By contrast, others such as Botswana, Ghana, Niger, Somalia, Sierra Leone, Swaziland, and Tunisia saw their capacity levels decline under this dimension in the same period. These countries generally have scores above the 50% range. AGRICULTURAL SURVEYS AGRIC. MARKET AND PRICE INFO. GENERAL STATISTICAL ACTIVITIES ADOPTION OF INTERNATIONAL STANDARDS Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 37

54 Figure 13 Level of statistical methods and practices by country, 2013 and 2015 Egypt Ethiopia Ghana Botswana Mozambique South Africa Sierra Leone Tunisia Uganda Kenya Rwanda Morocco Niger Tanzania Namibia Algeria Senegal Liberia Swaziland Lesotho Burkina Faso Malawi Mauritius Mali Cameroon Zambia Gambia Nigeria South Sudan Benin Cabo Verde Sudan Gabon Côte d Ivoire Togo Congo, Rep. of São Tomé & Principe Mauritania Guinea Seychelles Djibouti Comoros Burundi Zimbabwe Chad Somalia Libya Angola Equat. Guinea Madagascar Congo, Dem. Rep. Guinea-Bissau PERCENT 38 Chapter 4 AGRICULTURAL STATISTICS CAPACITY INDICATORS (ASCIS), 2013 AND 2015

55 Box 14 South Africa: explanatory factors contributing to the national ASCI level Statistics South Africa (Stats SA) has been actively participating in the Action Plan and attending regular meetings/ workshops/conferences organized by the African Development Bank (AfDB), the United Nations Economic Commission for Africa (UNECA) and the FAO since the implementation of the Action Plan. This has enabled South Africa to enhance its capacity to develop and implement SPARS through learning from other countries experiences and international best practices. For the first time in South Africa, households were asked in Census 2011 about the kind of agricultural activity they were involved in (if any) and to indicate how many (range) livestock the household owned (for those in livestock farming). This enabled the country to develop a list of agricultural households which could be used as a sampling frame for future agricultural surveys. Also, in 2012 Statistics South Africa (Stats SA) and the Department of Agriculture, Forestry & Fisheries (DAFF) developed a draft strategy of agricultural statistics. It was envisaged that the successful implementation of the strategy would depend on the following: 1. Establishment of a proactive National Agricultural Statistics Coordinating Committee (NASCC) made up of key departments; 2. Development and implementation of an integrated data collection program (using both surveys and administrative sources); 3. Statistical capacity building, including the training of agricultural statisticians and economists, and extension officers to be involved in data collection; and 4. Mobilization of funding from the National Treasury, DAFF, and other key departments. Although there is no official forum, there is more communication and improved interaction with the other key departments on agricultural statistics (institutional infrastructure). This has led to: > > Stats SA piloting surveys on the forestry and fisheries industries; > > The inclusion of more questions on agricultural households on questionnaires for the inter-census survey (Community Survey 2016) and general household surveys; and > > Budget allocation (to be confirmed) to conduct a large sample survey (census) of agriculture in 2017 and This will include smallholder and subsistence farming in every corner for the first time ever in the South African history. In brief, the following constitute the improvements made under each dimension: 1. Institutional infrastructure > > Develop a list of agricultural households which could be used as a sampling frame for future agricultural surveys, > > Draft an agricultural statistics strategy (still to be adopted by all key departments), and > > More communication and improved interaction with the other key departments on agricultural statistics 2. Statistical methods and practices > > Improved coverage of the forestry and fisheries industries, > > Inclusion of questions on agricultural households in the population census, inter-census and general household survey questionnaires, and > > Funding (to be confirmed) by the National Treasury to conduct a large sample survey (census) of agriculture which will only cover commercial farming but will for the first time include smallholder and subsistence farming. Itani Godfrey Magwaba, Chief Director, Statistics South Africa Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 39

56 Grouping countries under the Throughput Dimension To determine countries that share similar features according to the Throughput Dimension, the scale of grouping has been applied again in two stages. Stage one is to determine the proportion of countries in each group in 2015, compared to the same groups in 2013, as shown in Figure 14. This shows signs of improvement in statistical methods and practices in Africa from 2013 to 2015, as the number of countries under the weaker groups A and B decreased, by migration to higher capacity groups C and D. Stage two of the grouping was to identify the specific countries involved in the migration from weaker to stronger levels due to improved capacities in statistical methods and practices, as shown in Table 7. The table indicates that 28 out of 51 countries (54.9%) registered significant improvements in this capacity dimension from 2013 to It is important to note that although the majority of these countries have low GDP per capita and agriculture value added, yet their statistical capacities improved substantially. Table 7 further demonstrates that Ethiopia, which is among the countries with the lowest GDP per capita and average agriculture value added, improved significantly between 2013 and 2015, and registers now a very strong capacity under this dimension. This is an indication of the existence of a hub of best practices in that country, which could be adopted by other weaker countries in the subregion, like Guinea-Bissau and Equatorial Guinea. Such south south cooperation should be encouraged among countries to fast-track the process of program implementation, hence contributing to achievement of the Global Strategy s overarching aim, namely to improve the quality and quantity of agricultural and rural statistics on the continent for poverty reduction, increased livelihoods, and improved food security in the region Output Dimension availability of statistical information in Africa The Output Dimension measures the availability of and accessibility to agricultural data by users at both national and international levels for policy formulation and decision- making. This determines whether countries have the requisite Minimum Set of Core Data. The timely delivery of data by countries to users is equally essential for planning and executing critical tasks, according to their various capacities or needs. The dimension comprises four elements which are: core data availability; timeliness; overall data quality perception; and data accessibility. The 2013 and 2015 results are demonstrated in Figure 15. This reveals some general improvement by African countries in making data more accessible to users between 2013 and More focus has been given to timeliness in data provision to relevant users, compared to the three other elements of this Output Dimension Ranking countries under the Output Dimension Figure 16 demonstrates the progress achieved in making statistical information available to users between 2013 and Figure 14 Proportion of countries grouped by level of statistical methods and practices, 2013 and 2015 Figure 15 Level of availability of statistical information by element in Africa, 2013 and 2015 % OF COUNTRIES 80 CORE DATA AVAILABILITY DATA ACCESSIBILITY TIMELINESS Group A Group B Group C Group D Group E OVERALL DATA QUALITY PERCEPTION Chapter 4 AGRICULTURAL STATISTICS CAPACITY INDICATORS (ASCIS), 2013 AND 2015

57 Table 7 Country groupings by statistical methods and practices, GDP per capita, and agriculture VA (as % of GDP), 2015 GDP per Capita Lowest per capita Low per capita Average per capita High per capita Highest per capita Agric. VA (% of GDP) Lowest % Low % Average % High % Highest % Lowest % Low % Average % High % Highest % Lowest % Very Weak Statistical Methods and Practices Guinea- Bissau Weak Statistical Methods and Practices Zimbabwe Madagascar; Burundi; Gambia Congo, Rep. of São Tomé & Pr. Cameroon Average Statistical Methods and Practices Senegal; Lesotho Comoros; Chad; DRC; Niger; Benin; Burkina Faso; Guinea; Uganda; Rwanda Sierra Leone; Togo Liberia Zambia Mauritania; Côte d'ivoire Swaziland; Djibouti; Tunisia Strong Statistical Methods and Practices Tanzania; Malawi; Mozambique Very Strong Statistical Methods and Practices Countries that migrated to higher groupings due to improved capacity since 2013 Madagascar; Comoros; Chad; DRC; Niger; Benin; Burkina Faso; Guinea; Uganda; Rwanda; Tanzania; Malawi; Mozambique Mali Ethiopia Togo; Mali; Ethiopia Ghana; Cabo Verde; Kenya Egypt; Morocco Mauritania; Côte d'ivoire; Cabo Verde; Kenya Djibouti; Morocco Low % Sudan Nigeria Sudan; Nigeria Average % High % Highest % Lowest % Low % Average % High % Highest % Lowest % Low % Average % High % Highest % Equat. Guinea Angola; Gabon; Algeria; Namibia; Botswana Seychelles; Mauritius South Africa Angola; Gabon; South Africa Seychelles Countries that migrated to lower groupings due to reduced capacity since 2013 Gambia Cameroon Botswana Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 41

58 Figure 16 Availability of statistical information by country, 2013 and 2015 Morocco Ghana Burkina Faso Ethiopia Algeria Mali Egypt South Africa Niger Benin Mauritania Tunisia Namibia Liberia Guinea Zambia Nigeria Zimbabwe Senegal Kenya Tanzania Djibouti Sierra Leone Mauritius Seychelles Cameroon Mozambique Gabon Rwanda São Tomé & Principe Côte d Ivoire Botswana Malawi Chad Sudan Togo Uganda Burundi Gambia Lesotho Cabo Verde Angola Madagascar Congo, Dem. Rep. Cong, Rep. of Guinea-Bissau Comoros Swaziland Equat. Guinea South Sudan Somalia Libya PERCENT 42 Chapter 4 AGRICULTURAL STATISTICS CAPACITY INDICATORS (ASCIS), 2013 AND 2015

59 The majority of countries that scored between 40% and 70% in 2013 registered significant improvements in Reasons for the improvements under this dimension are provided in Boxes 15, 16, and 17 below, which present country cases. By contrast, there was a decline in the number of countries that scored above 70% in 2015, such as Egypt, Ghana, Burkina Faso, and Morocco. Progress stalled in the two-year period for Mauritius and Guinea. It is worth noting that a number of countries, such as Algeria, Burkina Faso, Ghana and Morocco, underwent some form of economic recession over the same period. Equatorial Guinea is by far the worst performer due mainly to the fact that the importance of agricultural statistics in the country is minimal. Box 15 Zambia: explanatory factors contributing to the national ASCI level Agriculture in Zambia has been identified as one of the potential main drivers of gross domestic product (GDP) in the near future. Currently, the agriculture sector contributes at least 10% to GDP, absorbs about 67% of the labor force, and supports the livelihoods of more than 70% of the population. Therefore, the strategic role of agriculture and the rural sector to the national economy cannot be overemphasized. Given its abundant natural resources fit for agricultural activities, the Zambian government has set out strategies to benefit farmers with the aim of increasing crop and livestock production in the country and potentially becoming the subregional food basket. Given the importance of agriculture in Zambia, there has also been a demand for quality and timely statistics by the government, the private sector, and the nation at large. Based on the country assessments conducted by the AfDB in 2013 and 2015, there has been an improvement in the timely dissemination and in the quantity and quality of agriculture statistics. This has been achieved with technical assistance from organizations like the AfDB and FAO through the Action Plan for Africa of the Global Strategy for Improving Agricultural and Rural Statistics. From the inception of the Action Plan for Africa, the AfDB, FAO, and other development partners have held country assessment and training workshops setting out a Minimum Set of Core Data. Based on the training sessions, presentations and indeed interactions with other countries, Zambia has improved on both the quantity and quality of statistics by refining its data collection methods and use of modern data collection and dissemination tools. New technologies such as the use of Computer-Assisted Personal Interviewing (CAPI) have been embraced in Zambia since 2015 and this has greatly improved the quality and timely release of data. The Zambia data portal and the new CountryStat platform are being used as vital and faster tools of data dissemination. Additionally, with financial and technical assistance from the AfDB, Zambia is in the process of developing a Zambian-tailored Strategic Plan for Agricultural and Rural Statistics dubbed SPARS_ZAM. This will help to set out the institutional, organizational, and methodological requirements to improve the quality of the agricultural statistics. Patrick Mwendalubi Chuni, Principal Statistician, Central Statistical Office, Zambia Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 43

60 Box 16 Burundi: explanatory factors contributing to the national ASCI level The improvement in the score of indicators relating to the collection of statistics in Burundi is the result of a combination of the following factors: > > The initiation of the national agricultural survey, which has made available annual data on agricultural production since 2012; > > The contribution of technical assistance (TFP) to the implementation of the agricultural survey and the development of the strategic plan for the development of agricultural and rural statistics (PSABU); > > Establishment of structures and bodies responsible for collecting agricultural statistics (CNSA & GT); > > Secondment of statisticians to the Ministry of Agriculture and Livestock; > > The support of the PROSANUT Project (Food and Nutrition Security Program) in the equipment and publication of statistical data; and > > Efficient collaboration with the National Institute of Statistics. Jean Pierre Madebari, Advisor, Ministry of Agriculture and Livestock, Burundi Box 17 Togo: explanatory factors contributing to the national ASCI level The composite ASCI for Togo rose from 42.5% in 2013 to 58.3% in 2015 due to a combination of factors. Togo has continued to improve its statistical production in general and that of the agricultural sector in particular, thanks to the following circumstances: 1. In 2008, Togo joined the CILSS (Inter-State Committee for the Fight against Drought in the Sahel) through the DSID (Directorate of Agricultural Statistics, Informatics and Documentation) in charge of the production of statistical information on agriculture and the rural sector. This integration within the CILSS allows the country to continue improving the production of agricultural and environmental statistics on food security through tools such as the Harmonized Framework, the House Economic Analysis, and many others. It thus enables the agricultural statistical services to continue benefiting from certain modern tools used in the production and archiving of agricultural statistical data (tablets, servers, etc.). 2. In 2012, Togo carried out its fourth Census of Agriculture. This operation made it possible to develop a wide range of indicators on the rural sector and, at the same time, to equip the agricultural statistical services with rolling stock (e.g. liaison vehicles for the supervision of data collection, motorcycles for census agents), and with GPS (global positioning system) to measure areas and geo-reference farms. 3. In addition, the agricultural and environmental sector has benefited from major investments since 2013 through the PNIASA program for the development of the sector. The establishment of the baseline for this program has resulted in the development of a large number of indicators on the agricultural and environmental sector that had never been produced before. 44 Chapter 4 AGRICULTURAL STATISTICS CAPACITY INDICATORS (ASCIS), 2013 AND 2015

61 Box 17 Togo: explanatory factors contributing to the national ASCI level, cont. 4. Assistance from GIZ (German Cooperation), which is particularly interested in certain specific sectors of the Togolese agricultural system, is currently helping to improve Togolese agricultural statistical production. Specific training courses are organized on the use of ICTs in the production of agricultural statistics. The Agricultural Statistical Service is currently benefiting from German cooperation, software, and GPS to improve the production of agricultural statistics. In short, this continuous improvement in the production of agricultural statistics has been possible thanks to the support received from technical and financial partners, namely the FAO, AfDB, EU, World Bank, ECA, CILSS, GIZ, etc. Dokodjo Kodjo, Chief Agricultural Statistics Division, Ministry of Agriculture, Livestock and Water Resources, Togo Box 18 Malawi: explanatory factors contributing to the national ASCI level Malawi s composite Agricultural Statistics Capacity Indicator (ASCI) increased from 53.2% in 2013 to 61.1 in 2015%. This was attributable to the development and implementation of the Strategic Plan for Agriculture and Rural Statistics (SPARS), also known as the Malawi Agricultural Statistics Strategic Master Plan (MASSMP). MASSMP aligns to the new approach for developing National Strategies for Development of Statistics (NSDS) promoted by the OECD/PARIS21, as well as to the Global Strategy to Improve Agriculture and Rural Statistics. The objective of MASSMP is to guide Malawi s agricultural statistics subsector to improve the quality, accessibility, and timely release of agriculture statistics in the country. Malawi s ASCI improvement is largely due to the implementation of the prioritized activities in the Strategic Plan in the first years of implementation. These activities focused on (a) strengthening the information management system for improved accessibility to statistical information and (b) improvements in crop production estimates based on piloting the use of remote sensing and satellite imagery technologies in crop production estimation in the 2014/15 agricultural season. A number of useful lessons were learnt from the pilot methodologies which were adopted and are being implemented. Emmanuel Jofilisi Mwanaleza, Principal Statistician, Ministry of Agriculture, Irrigation and Water Development Malawi Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 45

62 Grouping countries under the Output Dimension Here again, countries have been grouped in two stages. The first stage produced the proportion of countries with similar capacities regarding the availability of statistical information in 2013 and 2015, as demonstrated by Figure 17. This figure demonstrates the improvement in data supply to users, as the number of countries under groups A, B, C, and D decreased in 2015 by migration to group E, which has the highest capacity for this dimension. Figure 17 Proportion of countries grouped by availability of statistical information, 2013 and 2015 % OF COUNTRIES The second stage of the grouping matches country scores for the availability of statistical information dimension against their GDP per capita and agriculture value added. Table 8 allows us to assess those countries involved in the migration and identify their financial and technical standing to run their respective NASS and make available statistical information in particular. The table indicates a general improvement in 20 out of 51 countries (39%) in their capacity to produce and disseminate data in a timely manner. Table 8 also depicts that apart from Equatorial Guinea and Comoros, which have very weak capacity in making statistical information available to users, the other participating African countries have at least average strength for these tasks, with at most average GDP per capita and low agricultural VA. Attention should be given to countries such as Botswana, Djibouti, Egypt, Equatorial Guinea, and Sierra Leone, which experienced diminished reduction to execute this function effectively from Group A Group B Group C Group D Group E Chapter 4 AGRICULTURAL STATISTICS CAPACITY INDICATORS (ASCIS), 2013 AND 2015

63 Table 8 Country groupings by availability of statistical information, GDP per capita, and agriculture VA (as % of GDP) 2015 GDP per Capita Lowest per capita Low per capita Average per capita High per capita Highest per capita Agric. VA (% of GDP) Lowest % Very Weak Availability of Statistical Information Weak Availability of Statistical Information Average Availability of Statistical Information Low % Comoros DRC Average % Sierra Leone; Guinea-Bissau Strong Availability of Statistical Information Zimbabwe Burundi; Madagascar; Chad; Gambia; Uganda; Guinea; Niger; Benin Very Strong Availability of Statistical Information Lesotho; Senegal Malawi; Mozambique; Tanzania; Burkina Faso; Rwanda Mali; Togo; Ethiopia Countries that migrated to higher groupings due to improved capacity since 2013 Lesotho; Senegal Burundi; Madagascar; Gambia; Uganda; Malawi; Mozambique; Tanzania; Rwanda High % Liberia Liberia Highest % Lowest % Low % Average % High % Highest % Lowest % Congo, Rep. of Swaziland; Djibouti Zambia Sao Tome&Pr.; Cabo Verde; Cote d'ivoire; Mauritania Cameroon; Egypt Ghana; Kenya Tunisia; Morocco Countries that migrated to lower groupings due to reduced capacity since 2013 Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years Togo Cabo Verde; Kenya Swaziland; Tunisia Low % Sudan Nigeria Sudan; Nigeria Average % High % Highest % Lowest % Low % Average % High % Highest % Lowest % Low % Average % High % Highest % Equat. Guinea Botswana Gabon; Angola Mauritius; Seychelles Algeria; South Africa; Namibia Angola; Namibia Sierra Leone Djibouti; Egypt Botswana Equat. Guinea 47

64 48 Chapter 4

65 5. CONCLUSIONS Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 49

66 5.1 Improved results and enabling factors Based on the results of the ASCI 2015, as well comparisons made with the results of the ASCI 2013, together with the explanations given by countries themselves on their performances (via the Boxes in this report), it can be concluded that the implementation of the Action Plan for Improving Statistics for Food Security, Sustainable Agricultural and Rural Development in Africa has made a positive impact in the Africa region, consonant with the objectives of the Global Strategy. This is confirmed by the 6.4% increase in the composite ASCI for Africa in the period According to country justifications, this was due in no small part to activities undertaken within the framework of the implementation of the Action Plan, as well as other similar ongoing initiatives. Enabling factors that have contributed to this improvement include the following: advocacy opportunities offered within several regional and national workshops; backstopping/technical assistance missions to countries; SPARS development process; availability of newly developed, costeffective methods; training sessions of national experts etc. These activities/opportunities have contributed both directly and indirectly to improvements in all aspects of the National Agricultural Statistical Systems (NASS) on the continent. 5.2 The way forward There is a crucial need now to focus more on the weaker areas, by strengthening efforts to increase the availability of the resources for undertaking data production processes. This should take cognizance of available cost-effective methods, hence contributing to further strengthening NASS in Africa. In brief, this report has confirmed that the implementation process of the Action Plan for Africa of the Global Strategy for Improving Agricultural and Rural Statistics is on track. However, this report has also highlighted those capacity areas and countries that require further strengthening to register further significant impact by the end of the implementation period of the first phase of the Global Strategy. The experiences and best practices garnered in conducting the 2013 and 2015 CA cycles will be used in preparing and undertaking the next/third cycle for the 2017 reference year, so that a final report can be made published in 2018 (before the end of the first phase of the Strategy). This will allow the measurement of impacts/achievements in the Africa region vis-à-vis the objectives/expected targets of the Global Strategy. 50 Chapter 5 CONCLUSIONS

67 Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 51

68 52 Chapter 5

69 6. ANNEXES Progress on the Capacity of African Countries to Produce Timely, Reliable, and Sustainable Agricultural Statistics Agricultural Statistics Capacity Indicators (ASCIs) for the 2013 and 2015 reference years 53