Strategic Plan for Agriculture and Rural Statistics for Zambia (SPARS-ZAM 2019-2023)

1.1 Introduction This chapter presents basic information on the Global Strategy for Improving Agricultural and Rural Statistics (of which SPARS_ZAM is a part).The chapter also covers the objectives, rationale and methodology for preparing SPARS_ZAM; the policy context and demand for agricultural data; the integration of the SPARS into the NSDS; and the key stakeholders, from all sub-sectors, who participated in the preparation of the SPARS.

1.2 The Global Strategy to Improve Agriculture and Rural Statistics The Global Strategy (GS) to Improve Agricultural and Rural Statistics was adopted in February 2010 by the United Nations Statistical Commission in response to the decline in the availability and quality of agricultural statistics in many countries in recent years and to meet the need for statistics to inform emerging issues in agriculture such as the environment and the use of fuel crops for bio-fuel. The main purpose of the Global Strategy is to provide a framework and methodology that will improve the quality and availability of national and international food and agriculture statistics to guide policy analysis and decision making. This strategy aims at: (i) Strengthening the statistical capacity of developing countries to produce reliable statistics on food security, sustainable agriculture and rural development; and (ii) Encouraging the formation of a long-term vision for the development of agricultural statistical systems in developing countries. The Global Strategy is based on three pillars, namely: Pillar 1: Establishment of a minimum set of core data required to meet current and emerging demands; Pillar 2: I ntegration of agriculture into the National Statistical System (NSS); Pillar 3: Establish suitable governance processes and build the necessary statistical capacity to ensure sustainability of agricultural statistical systems. 1 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA

1.3 Implementation of the Global Strategy A Global Office has been established within FAO, in Rome, to provide the overall technical coordination of GS activities. At the regional level, work on the GS is supported by partners who provide technical and other assistance to countries. Specifically, the partners helping implement the GS in Africa are: (i) The African Development Bank (AfDB), which was assigned the responsibility of providing technical assistance and governance to African countries; (ii) The United Nations Economic Commission for Africa (UNECA), which was assigned the responsibility of providing training; and (iii) The United Nations Food and Agriculture Organisation (FAO), which was assigned the responsibility of conducting research. A regional action plan has been prepared to support the implementation of the GS in Africa . One of the recommendations of this Action Plan is that countries should develop a Strategic Plan for Agriculture and Rural Statistics (SPARS), which should be integrated into the NSDS and serve as a framework for the long-term development of agricultural statistics. It is in pursuit of this goal that Zambia decided to address the weaknesses in its agricultural statistics system through the development of the SPARS_ZAM. Towards this end the Central Statistical Office (CSO) and the Ministry of Agriculture (MoA) requested for technical assistance from the AfDB for the preparation of SPARS_ZAM.

1.4 Policy Context and Rationale for SPARS_ZAM Zambia covers an area of 752,610 sq km. Of this area, 34 percent is used for agriculture, and 7 percent is arable. The population of Zambia is approximately 15,000,000. The Government of the Republic of Zambia attaches great importance to the agricultural sector, as it is the single largest employer and makes a significant contribution to GDP. As such, there is absolute need to be regularly monitoring the performance of this sector, and this requires pertinent statistics. The Agriculture Sector has been recognised as one of the growth sectors in the Revised-Sixth National Development Plan. The sector accounts for about 50 percent of the employed population(R- SNDP, pp 8). 2 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 Figure 2: Percentage Distribution of Employed Persons by Industry The sector also contributes a significant share to the Gross Domestic Product. It has been contributing an average of 9 percent to GDP from 2010 to date (Figure 2). Though the trend suggests a declining share for agriculture in the GDP, this is mainly due to rapid growth in other sectors of the economy. As the government is trying to shift the mainstay of the economy from the mining sector to agriculture, it is at the same time promoting diversification of the latter sector to move away from relying on a handful of crops to a wide variety of agricultural products such as livestock and forest products. Figure 3: Share of Agriculture, Forestry and Fishing to the Total GDP 3 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA The Government has always recognised the importance of the Agriculture Sector, and has over the years developed several policy documents to guide the development of the sector. These documents include: (i) Zambia Vision 2030 (ii) Zambia Agricultural Investment Plan (iii) National Agricultural Policy 2004-2015, and (iv) Strategic Plan for Ministry of Agriculture and Livestock 2014-2016. The development of the Strategic Plan for Ministry of Agriculture and Livestock was motivated by the expiry of the preceding plan, which covered the period 2006 to 2010. The need to cover emerging issues, change in the administrative structure of the Ministry, and the launch of the Comprehensive Africa Agriculture Development Plan (CAADP), all necessitated the development of a new strategy. Implementation of the Zambia Vision 2030 aims at lifting the Zambian Economy so that by 2030, the country will be “A Prosperous Middle Income Nation”. The agriculture sector is expected to play a very significant role in eradicating poverty, especially rural poverty and, as such, play a major role in the attainment of the Zambia Vision 2030. Zambia has since independence carried out only two Censuses of Agriculture, in the 1970/71 agricultural season, and also between the years 1990 and 1992. Plans have been there for a long time to conduct another Agriculture Census, but all these have foundered on account of lack of resources. One of the objectives of the 1990-92 Census of Agriculture was to develop a national capability to build and manage an efficient agricultural statistical system. Findings from the sector assessment show that, 27 years on, this has not been achieved. In recent years, the need for ongoing assessment of the agricultural situation and crop forecasting has assumed greater importance to better manage the uncertain economic conditions. With the Government set to diversify the agriculture sector, there is a need to generate and provide relevant and reliable agricultural statistics in a timely manner to facilitate the monitoring of policy instruments and plans. The Government of Zambia recognizes the importance of the agricultural sector in the economy and its contribution to eradicating hunger and malnutrition in the country. The Government acknowledges the weaknesses and gaps in the current agricultural statistics, as well as the need to improve statistical methodologies in order to provide more reliable and timely statistics. It also recognizes that agricultural data collection work needs to be better coordinated and that the agricultural statistics system needs to be integrated into the national statistics system. The government has welcomed the Global Strategy initiative and has fully supported its activities in Zambia. National stakeholders have actively participated in the IdCA process and this report strongly reflects national requirements and priorities. 4 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023

1.5 SPARS_ZAM Methodology In keeping with the 10 key principles behind the development of SPARS, SPARS_ZAM should be: 1. Backed by political support, nationally led and owned 2. Designed through a sound methodological approach (including M&E mechanisms) 3. Mainstreamed into the NSDS national process 4. Covering the entire agricultural and rural sector 5. Policy and results based with a quality fit for purpose 6. Taking into account what is in place and international commitments 7. Drawing on international statistical standards 8. Setting out an integrated and realistic SCB programme 9. Funded as a priority by governments for its implementation 10. Serving as a coherence framework for external assistance SPARS_ZAM was developed in a fully inclusive and participatory manner. Key players involved in the agricultural statistical system in all sub sectors participated in all the stages, up-to and including the validation of this report. To adequately cover all the sub-sectors of agriculture and the complexity of the sector, five Sectoral Technical Working Groups (STWG) were established to support the development of SPARS_ZAM, as follows: • STWG 1: covering (i) Food crops including horticulture, (iii) Cash crops, (iv) Food Security/safety/ nutrition, and (v) Irrigation and inputs. • STWG 2: covering (i) Livestock, (ii) Dairy, (iii) pastoralist livestock production system, (iv) Apiculture, (v) Emerging Livestock, and (vi) Animal health and Veterinary services. • STWG 3: covering (i) Marine fisheries, (ii) In-land and aquaculture fisheries. • STWG 4: covering (i) Forestry, (ii) Environment/Natural resources (related to agriculture). • STWG 5: covering cross-cutting areas including but not limited to the Central Statistical Office (CSO), Trade, Prices, Statistical Capacity Building (SCB), Legislation /Legal framework/ Institutional development for agricultural statistics, Governance, Resource mobilization and Research. Each STWG could also call upon other structures/institutions to join them to make some contribution on specific questions and issues related to agricultural statistics (e.g. IAPRI/Universities, Zambia Central Bank, Zambia Revenue Authority, Meteorology, key users and Development partners. A National Strategy Coordinator (NSC), based in the CSO, was appointed to coordinate the implementation of the Global Strategy with his Alternative in MoA. Five (5) resource-persons/national consultants and one international consultant were also recruited to support this work. Four of the national consultants were to provide support and guidance to the STWGs (one each for STWG 1 to STWG 4) to produce the relevant reports and sub-sector plans. The fifth national consultant was to 5 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA coordinate the work of the other four consultants and, together with the international consultant, consolidate the sector reports into one overall document, leading to the production of SPARS_ZAM. It was agreed that a Steering Committee on Agricultural and Rural Statistics (SCARS) would be established to officially approve SPARS documents. This was necessary because the CSO is a government department under the Ministry of National Planning and Development and thus does not have a Board of Directors. The SCARS is an adhoc arrangement put in place to cover up for this, with members of the Committee appointed by the Permanent Secretary of the Ministry of Agriculture to perform the functions of a board in respect to the SPARS_ZAM development process. If, in the future, the CSO will be a semi-autonomous agency with a Board of Directors, that Board will legally undertake the function of approving documents on all statistics programmes including SPARS_ZAM. Figure 4: Governance Structure for SPARS_ZAM 6 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 The African Development Bank supported the whole process financially and provided regular monitoring of the process through backstopping missions within the framework of the implementation of the Action Plan for Africa of the Global Strategy. The first task of each STWG consultant was to identify the key stakeholders for the WG and to review relevant documents and literature. Information was then collected from key stakeholders using a structured questionnaire, one to one interviews and group discussions. Each STWG consultant prepared two reports, an assessment report and a planning report, both of which were signed off by the relevant STWG stakeholders. The fifth national consultant and the international consultant consolidated both sets of reports into an In-depth Country Assessment Report and the overall SPARS_ZAM, both of which have also been validated by a wider group of stakeholders. The different phases of the development of the SPARS_ZAM were: • Preparation of the Roadmap for the development of the SPARS_ZAM, 8-19th June 2015 • Initial validation of the Roadmap by stakeholders, 14th August, 2015 • Approval of Roadmap by SCARS, 20th August, 2015; • Launching of SPARS_ZAM and the Assessment phase, 2nd March, 2016 • Preparation of assessment reports by Subsector Technical Working Groups (STWG), August, 2016 • Validation of Assessment reports by STWGs and preparation of the Consolidated Assessment Report, 28th Feb - 3rd March, 2017 • Validation of the Consolidated Assessment Report by all stakeholders, 9th March, 2017 • Adoption of the Consolidated Assessment Report by SCARS, 30th March 2017; • Preparation of the Vision, Mission and Strategic goals and operational strategies of SPARS_ ZAM, Development of implementation plans (action plans with budgets, monitoring and financing plans) by STWGs, April - October, 2017 • Technical review of implementation plans by STWGs and preparation of the Consolidated Strategic Plan (SPARS_ZAM), June 2018; • Validation of draft SPARS_ZAM document by all stakeholders, June 2018; • Adoption of the SPARS_ZAM by SCARS, AAA 2018

1.6 Institutional Environment

1.6.1 Administrative Structure Zambia is a landlocked sub-Saharan country sharing boundaries with eight countries, namely Malawi, Mozambique, Zimbabwe, Botswana, Namibia, Angola, Democratic Republic of the Congo and Tanzania. 7 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA The country is administratively divided into ten (10) provinces, namely: Central, Copperbelt, Eastern, Luapula, Lusaka, Muchinga, Northern, North-Western, Southern and Western. Each province is further divided into districts, and there are 105 districts in all. With the Government now earnestly implementing the Decentralisation Policy, there is a rapidly growing demand for agriculture and rural statistics, especially from the Provincial and District planning departments. Lusaka is the capital city and the seat of government. The economy of the country is largely dependent on mining, but agriculture is a very important sector.

1.6.2 The National Statistical System The National Statistical System (NSS) is the ensemble of statistical organisations and units within a country that jointly collect, process and disseminate official statistics on behalf of national government. While the CSO is responsible for all national censuses and major sample surveys often in collaboration with line ministries, the CSO does not collect all national statistics. Some statistics are compiled by line agencies with support from their own statistics units. These statistics may be complied from administrative records such as the management information systems in many line ministries (e.g. health, education, agriculture) or by carrying out their own surveys. Issues arise where different sources give rise to different estimates. This may reflect different methodologies and definitions or departures from statistical standards and best practice. For example, up to 1992/93 the Ministry of Agriculture and the CSO were carrying out separate and independent crop forecasting surveys, with different methodologies. The two surveys were not coordinated and the results from the surveys were usually at variance. Since 1993/94 the two surveys have been integrated and are carried out jointly by the MoA and CSO.

1.6.3 Legal Framework for the Collection of Statistics The legal mandate for the collection of statistics is set out in Part IV of the 1964 Census and Statistics Act, Chapter 127(previously Chapter 425) of the Laws of Zambia, which established the Central Statistics Office (CSO) as a department within the Ministry of Finance. The Act empowers the Director of the CSO to “generally organize a coordinated scheme of social and economic statistics relating to Zambia”.The Act provides for a centralized statistical service, empowering the Director, with the approval of the Minister, to collect statistics for Zambia or any part thereof with respect to such matters as may be prescribed. Specific to agriculture, the Act sets out guidelines for the collection of statistics including: tenure; area (including fallow, pasture, woodland and land under water); area planted and yield by crop; number of trees, quantity and value of production; soil and water conservation and irrigation; livestock population, mortality, births and deaths, purchases and sales by breed type, age and sex; 8 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 farm buildings and equipment; workers and wages. However, the CSO does not have the capacity to produce all the required statistics and the Act does not provide for an institutional arrangement for the National Statistical System (NSS) to facilitate the efficient and effective production of statistics, consequently current coordination and harmonisation arrangements are either non-existent or rather weak where they exist.

1.6.4 Legal Framework for Agricultural Statistics There are more than 30 other pieces of legislation in the laws of Zambia relating to crops and other agricultural commodities which also implicitly provide for the generation of agriculture information for planning and decision-making purposes. These include the Agriculture Statistics Act (Chapter 229 of the Laws of Zambia) which provides for “the collection of agricultural statistics; to provide for the compilation and publication of statistics; and to provide for matters incidental to the foregoing”. Other Acts which include a component relating to agricultural statistics cover agricultural credit, food reserves, fertiliser and food, commodity specific acts (cotton, coffee, tobacco and dairy produce) and plant seeds, noxious weeds, pests and diseases. In the light of the new proposed Statistics Act, the statistical legislation within these Acts should be considered for amendment or even repeal. In general, the assessment established that the statistical legislation for agricultural statistics is adequate across the sub-sectors (though there are particular issues for fishery statistics, see below). It was however noted that the legislation does not cover any institutionalised arrangements for the collaboration and sharing of data between organisations, making the coordination and harmonisation of agricultural statistics production in the country very weak. The current legal and institutional framework was considered to be outdated by not providing a congenial environment for the development of agricultural statistics and requires ugent repealing to embrace flexibility and dynamism in order to respond to the changing statistical environment. There is a desire to increase penalties relating to compliance to release information, a particular issue for crop statistics relating to stock levels (required monthly for food security discussions) and trade, and to encourage the enforcement of registration and permit systems. This would improve the coverage of the statistical information provided, as a by-product, from these administrative systems. While Fisheries Regulations 2012 (SI No. 24 of 2012) requires the submission of returns of fish catches the larger part of the fishing community in Zambia are subsistence fishers, mostly with low education levels to the point that some are unable to read and write. Consequently, no fish catch returns are submitted by these fishers. There are also limitations in the legal provisions in that aquaculture producers and input suppliers are not required to submit returns on their harvests, fish fingerlings produced, fish feeds and also the cost of the inputs. 9 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA

1.7 Review of National Strategy for the Development of Statistics Zambia’s first National Strategy for the Development of Statistics (NSDS) covers the period 2014- 2018,and follows the implementation of a Strategic Plan for the CSO between 2003 and 2007. The NSDS acknowledges that the1964 Census and Statistics Act is outdated and has overtime become a constraint to statistical development in the country. Against this background, the NSDS proposes a revision of the Statistics Act, to be followed by the transformation of the CSO into a semi- autonomous agency, to be named the “National Bureau of Statistics”, with a Board of Directors. The draft of the new Act spells out the revamped functions of the new institution to: • Act as the principal source and custodian of official statistics in Zambia; • Coordinate and harmonize the production of statistics in the National Statistical System (NSS); • Collect and store, process, analyze and disseminate official statistics; • Develop and maintain a comprehensive socio-economic national database; and • Establish the Statistics Board to oversee the work of the Zambian Statistical Agency. • The revised Statistics Act is still to be passed by Parliament, though the NSDS was approved by Cabinet in May 2014. While some agricultural statistical activities have been included in the NSDS, it is recognized that, due to the underdeveloped nature of statistical sector systems, full integration has not been possible. Sector systems will be gradually integrated as and when they are developed and formulated. As the formulation of the NSDS follows a bottom-up approach, the SPARS_ZAM will be the building-block for agricultural statistics. In this regard, a continuous close collaboration between the SPARS_ZAM and NSDS teams will be always indispensable to ensure integration of the agriculture sector into the national statistics system. Stakeholders saw the implementation of the NSDS and enactment of the new Statistics Act as the way forward by providing a framework for strengthening statistical capacity, collaboration and coordination across the entire NSS. However, while virtually all key informants believed that a strategic planning approach for statistics for the whole country existed, some did not know what it was called. It was also considered that while crops, livestock and fishery statistics were appropriately mainstreamed in the NSDS, development plans for forestry, water and environment statistics were yet to be seen as having been covered in the overall plan. The CSO is currently planning for the development of NSDS2, covering the period 2019 to 2023, the same time frame as SPARS_ZAM. The CSO is fully committed mainstreaming SPARS_ZAM into NSDS2, including all the agricultural sub-sectors. 10 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023

1.8 Statistical Coordination Mechanisms There is no formal institutional arrangement in place to establish User-Producer committees which can foster collaboration and understanding between producers and between users and producers across all the sectors, to ensure statistics are consistent, reliable, relevant and timely. The assessment established that the absence of a proper coordinating mechanism was one of the major weaknesses for the collection of agricultural statistics. The present Statistics Act does not vest the power to coordinate the NSS in any institution, an omission that equally affects the agriculture statistics system. There is no mechanism to oversee the methodologies adopted to carry out statistical activities. This especially applies to statistics that are collected using administrative means. As a result: • Data collection in different subsectors is planned in isolation and there exists disconnect between the producer and user needs; • Consultation does not exist at a multi-sectoral level; • There is a common tendency for individual department to keep their information in their respective databases without sharing it to the users; • The national statistical system is not known and appreciated by many stakeholders. • There are examples of producer-user committees which used to exist - up to 2010, there was a statistics committee chaired by the Economic Management Department in the Ministry of Finance which met quarterly to discuss submissions from sectors on economic statistics. A User-Producer Committee for livestock statistics has existed, comprising government planning departments, civil society and NGOS, which was supported by the Zambia Social Investment Fund (ZAMSIF). Other coordination mechanisms have been ad-hoc, to be disbanded after having played their role e.g. the Inter-Ministerial National Census Committee. There are also examples of collaborations in the collection of statistics, e.g. the Demographic and Health Survey is conducted by CSO in collaboration with the Ministry of Health and the Labour Force Survey is conducted by the CSO with the Ministry of Labour. For agriculture the Crop Forecast Survey and the Post-Harvest & Livestock Surveys are joint exercises conducted by the CSO and MoA while CSO and MFL collaborate on the Fish Catch Assessment Surveys. The Rural Agricultural Livelihoods Survey is an exercise undertaken jointly by the CSO, MoA and IAPRI. While these institutions collaborate on such exercises they will jointly discuss and agree the content and design of the questionnaires together, though usually there are no formal coordination mechanisms in place to encompass the views of other stakeholders. Regional coordination exists for trans-boundary fisheries such as that provided by the Lake Tanganyika Authority (LTA) and a Protocol Agreement between Zambia and Zimbabwe on the management of the fisheries resources of Lake Kariba and trans-boundary waters of the Zambezi River. 11 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA An example was given whereby priorities for the collection of crop statistics are driven by national food security decision making. As a result, commodities of economic importance, such as horticulture (e.g. tomato, onion, cashew nuts) that are not perceived to be directly impacting on food security have low priority. The creation of a User-Producer dialogue would go a long way in reviewing the prioritization of statistics to be collected.

1.9 Stakeholder Analysis The main central institutions concerned with agricultural development and agricultural statistics are shown in Annex 1. Most of the institutions are located in Lusaka.

1.9.1 Central Statistics Office The Central Statistical Office (CSO) is a department under the Ministry of National Development Planning (until 2015 it was under the Ministry of Finance). CSO’s mission statement is to coordinate and provide timely, quality and credible official statistics for use by stakeholders and clients for sustainable development. The CSO is headed by a Director and has four divisions namely Economic and Financial Statistics, Agriculture and Environment Statistics, Social Statistics, and Information, Research and Dissemination, each managed by a Deputy Director. Within the Agriculture and Environment Statistics division, there is an agriculture branch and an environment branch, each headed by a Chief Statistician. Prices statistics, External Trade statistics and National Accounts statistics, all important sources of information on the agricultural sector, are within the Economic and Financial Statistics division, while Labour Statistics and Geographic Information Systems come under the Social Statistics division The CSO has offices in all the provincial headquarters, headed by Regional Statisticians, who carry out ongoing activities, such as consumer price collection. The CSO also has its own permanent field team to carry out data collection activities for censuses and surveys (if required this team can be supplemented by temporary recruits from a reserve list).

1.9.2 Ministry of Agriculture The Ministry of Agriculture (until 2015 this was the Ministry of Agriculture and Livestock) has a mission statement to facilitate and support the development of a sustainable, diversified and competitive agricultural sector that assures food and nutrition security, contributes to job creation and maximises the sector’s contribution to GDP. 12 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 The Department of Policy and Planning is headed by a Director and is divided into two branches, namely a Programme Planning, Monitoring and Evaluation Branch and a Policy Analysis and Statistics Branch. The later branch includes an Agricultural Statistics & Early Warning Section (ASEWS), headed by a Chief Statistician, as one of its two sections. The ASEWS is responsible for: • Conducting crop forecasting and post-harvest surveys (conducted jointly with the CSO); • Provision of early warning data and information for food security and timely planning and decision-making; • Undertaking Vulnerability and Food Needs Assessment; • Producing agricultural statistic bulletins and maintaining the agricultural database. The Ministry chairs the National Stocks Committee which meets to discuss and recommend policy decisions and actions pertaining to levels of food stocks, especially for maize and wheat, held in the country. Members include the CSO, ASEWS, FRA, Zambia National Farmers Union, Millers Association of Zambia, the Grain Traders Association of Zambia, NGOs, UN and international development agencies. The Committee meets whenever there is a felt need, increasing the frequency of meetings when there are large staple food deficits or surpluses in the country. The Agricultural Marketing Information Centre (AMIC) collates the wholesale and retail prices of agricultural commodities and inputs collected by their own staff in the provincial centres.

1.9.3 Ministry of Fisheries and Livestock This Ministry was created in 2015 and has seven departments including Livestock Development, Veterinary Services and Fisheries. The Ministry also has a Policy, Planning and Information Department which includes a small statistics unit though these statisticians undertake various non- statistical tasks. While there are plans to establish an upgraded statistics unit, there is currently no single portal for the collation and management of statistics within the Ministry. For livestock the National Livestock and Epidemiology Information Centre, which is within the Veterinary Services Department, provides a statistical service. A Fisheries Information Management Unit in the Department of Fisheries uses extension staff in the districts to collect data.

1.9.4 Lands, National Resources & Environment The Ministry of Lands Natural Resources and Environmental Protection (MLNREP) is responsible for the formulation and implementation of land administration, forest management and environmental protection policies. The Planning and Information Department has overall responsibility for the development, monitoring and evaluation of policies, legislation and programmes. 13 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA The Forestry Department oversees the management, protection, and conservation of forests to ensure sustainable utilization and management for socio-economic development while the Environment and Natural Resources Management Department is responsible for the coordination of the implementation of multilateral and bilateral environment and natural resources programmes and projects. Zambia Environmental Management Agency is responsible for the formulation of policies on all aspects of the environment and the Climate Change Secretariat, now part of the Ministry of National Development Planning, facilitates the coordination of all climate change activities being undertaken by government, the private sector and cooperating partners in order to achieve the aims and objectives of the National Climate Change Response Strategy. The Water Resources Management Authority, under the Ministry of Water and Energy, has the responsibility to preserve and protect Zambia's ground and surface water resources and regulate the abstraction, allocation, use, development and management of water in a sustainable manner. The Meteorological Department under the Ministry of Transport and Communication maintains a network of surface and upper air stations to monitor various weather elements. This meteorological data is then processed and analysed for use in the planning of economic development and the sustainable exploitation of natural resources.

1.9.5 Indaba Agricultural Policy Research Institute (IAPRI) IAPRI's mandate is to utilize empirical evidence to advise and guide the Government of Zambia and other stakeholders on agricultural investments and policies. This is achieved through three core activities: • Producing trusted, impartial, and high-quality research on agricultural, food, and natural resource policy issues in Zambia and the wider southern Africa region; • Integrating research findings into national, regional, and international programs and policy strategies to promote sustainable agricultural growth and cut hunger and poverty in Zambia; and • Supporting the development and strengthening of capacity for policy research, analysis and outreach of public and private institutions in Zambia. IAPRI also collaborates with the CSO, MoA and MFL to implement the Rural Agricultural Livelihoods Survey, a panel survey providing broader and more detailed information on rural household livelihood activities and outcomes than can be obtained from the PHS and CFS. 14 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023

1.9.6 Stakeholders with other Research, Academic and International Institutions Other policy research institutions consulted include the Policy Monitoring and Research Centre (PMRC) a public policy think-tank established to promote public understanding and to encourage and facilitate debate on social and economic policy issues critical to national development. The objective of the Zambian Agricultural Research Institute is to develop and adapt crop, soil and plant protection technologies and to provide a high quality, appropriate and cost effective service to farmers. Staff from two schools at the University of Zambia, Veterinary Medicine and Agricultural Sciences, were also involved in the consultations. Stakeholders with international organizations included the Centre for Forestry Research (CIFOR), Famine and Early Warning Systems Network (FEWSNET), World Fish (whose mission is to reduce poverty and hunger by improving fisheries and aquaculture), and the United Nations Food and Agricultural Organisation.

1.9.7 Zambia National Farmers Union and Affiliated Organisations The Zambia National Farmers Union (ZNFU) is a membership based organization, with countrywide coverage, representing the agriculture industry. Members are currently categorized into District Famers’ Associations; Commodity-based Associations; Corporate Farming Businesses; the Agribusiness Chamber; and Associate Members. Commodity based associations consulted include the Dairy Association, Poultry Association, Forest Fruits, Small Livestock Association and the Timber Association. The ZNFU is a lobbying organisation on behalf of its members and has a Research and Development section which uses statistics to carry out its function. It has in the past carried out its own agricultural production and stock surveys, but these have been discontinued with the organisation accepting the results from the CSO / MoA surveys.

1.9.8 Other Civil Society Stakeholders Other stakeholders consulted include the Community Based Natural Resource Management (CBNRM) forum (a platform for community based natural resource management discourse, debate and development in Zambia). 15 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA Chapter 2: Evaluation of the National Agricultural Statistics System (NASS)

2.1 Introduction This chapter answers the question ‘Where are we now’? Before embarking on developing this Strategic Plan for Agriculture and Rural Statistics, a detailed assessment was undertaken of the present status of the agriculture and rural statistics. The assessment was carried out through literature reviews, by administering a questionnaire to a number of stakeholders, having face-to-face diagnostic interviews, having group discussions with persons in each of the key sectors identified for purposes of developing this strategic plan. Each STWG came up with its own assessment, and these were consolidated into an overall document, ‘Report of the In-depth Country Assessment for Zambia’ CSO, March 2017, which is a key input into the process of defining the Strategic Goals, Objectives and Activities for the SPARS. This chapter provides an assessment of the current capacity to produce agriculture and rural statistics assesses current data collection activities and reviews user needs and data gaps. Finally the main findings from a SWOT analysis are presented.

2.2 Assessment of Capacity to Produce Agriculture & Rural Statistics

2.2.1 Human Resources Almost all institutions contacted indicated they did not have adequate human resources for statistical work (there is currently a freeze on recruitment of new staff). There was a substantial difference between the establishment register (the number of posts approved by Cabinet), staff assignment (the posts with funding authority from the MoF) and the number of posts currently filled. However, they generally indicated that the staff they had were skilled, experienced and qualified, except for the environment sub-sector. The CSO and MoA both have dedicated agricultural statistics units. The Agricultural Statistics Section in the CSO has 6 professional and 4 support staff, which after a recruitment exercise 2 years ago is considered adequate. The Agricultural Statistics and Early Warning Section in MoA has a staff complement of 10 officers (9 professional) who are responsible for the execution of the Crop Forecast and Post-harvest survey as well as other specialized surveys. The section is also responsible for analysing secondary data such as formal trade data from the ZRA/CSO Asycuda system. For the CFS and PHS surveys the MoA deploys its extension officers to make up 50% of the field team while the CSO calls upon its own permanent field team of data collectors. 16 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 Currently the MFL does not have a statistics unit though there are plans to establish one - there are statisticians currently in the Department of Policy, Planning and Information, but with staff shortages their responsibilities go beyond statistics. The National Livestock Epidemiology and Information Centre (NALEIC) undertakes regular statistical analysis of the disease data from all the districts in the country and generates epidemiological memos and other special reports for policy makers in the MFL. It has a staff establishment of nine, of which only four positions are filled. The Fisheries Information Management Unit in the Department of Fisheries uses extension staff in districts to collect data. The unit has only five positions filled out of nineteen on its establishment register. There are 133 capture fisheries extension staff in the Districts compared to a register of 650 (20%), and 41 aquaculture extension officers compared to an establishment of 493 (8%). Only forestry has a dedicated statistics unit in the natural resources sub-sector; lands, water, environment and meteorology all carry out statistical work using other staff. The numbers of staff engaged in the production of statistics vary from 5 in forestry to 25 in meteorology all of whom have degrees or diplomas. Experience, skills and qualifications were mostly lacking in the Environment sub sector. Insufficient staff numbers adversely affects data collection, particularly for administrative statistics such as market statistics (e.g. sale volumes and prices). In the aftermath of low recruitment levels by government, coupled with staff attrition and retirement, the numbers of extension officers (for both crops and fisheries) and veterinary officers have dwindled. Officers based in districts areoften required to cover large areas and large numbers of households. For instance it is generally stated that the veterinary officer to farmer ratio stands at 1:3,000 farmers compared to the recommended figure of 1:400 farmers. In almost all the districts it is common to find more ‘camps’ that are unmanned as those that are manned.

2.2.2 Infrastructure, Equipment, Information Technology The MoA and CSO have transitioned from the use of paper questionnaires to using tablets for data collection in the field (CAPI-Computer-Assisted Personal Interview). This has huge led to significant savings in time and resources, though challenges remain at the data analysis stage, including the need for additional computers across all sub-sectors. Currently the collection of livestock and fisheries data from field stations is paper-based as personal computers are virtually non-existent. The data is relayed to the district where it is entered into Excel spreadsheets.The move away from hard copies to the use of PCs for data storage is raising data safety/ security issues whenever computers crash or are affected by serious computer viruses. Further the office buildings to not always have Local Area Networks or functional internet facilities. In the natural resources sector all institutions reported their IT equipment to be inadequate while the environment and lands sub-sector had problems acquiring appropriate software. All the institutions had Local Area Networks. 17 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA There are a number of statistical software used by the various institutions, but prominent among these are MS Excel, STATA and SPSS. Other software used include GenStat, RATS, GAMS, Surveybe, ArcGIS, CSPro, SAS and MS Access and some sector specific packages e.g. Pasgear for fisheries,CLICOM, CLIMSOFT, SAIKU, ASPRO and HYDSTRA for natural resources. SPSS is the main software used for data analysis. However, the high cost of purchasing this and other proprietary software was an issue raised by many institutions. The lack of access to analytical software (including GIS software) negatively affects the timely production of quality information. Transportation is an issue across all sectors. For surveys with small sample-sizes, or for routine data collection exercises, the CSO fleet of pool vehicles is usually sufficient. On those occasions when the CSO needs more vehicles, it borrows from other government ministries, or pools resources with the client ministry. The MoA is the main source of transport for data collection during the crop surveys, though this can be a challenge due to competing priorities at the district level. Though it is government policy to provide motor-bikes to all camp staff, not all veterinary officers have motorized transport, making it difficult to access all livestock farmers on a regular basis. In the fisheries sector vehicles, boats, engines and camping equipment are inadequate and are usually shared between extension, research and training units.In the natural resource sub-sector all institutions apart from forestry, who had procured vehicles recently for the national forest assessment, do not have sufficient vehicles to do statistical work. The CSO Head Office has recently moved into newly built offices. The CSO provincial offices are in rented buildings, mostly from the National Pension Scheme Authority (NAPSA), and, in most cases, require some renovation. Officers in the veterinary camps do not have offices to operate from (their houses double as offices), nor office equipment, e.g. computers and printers. Fisheries officers in the districts share their offices with other government departments while offices for staff from the natural resources sub-sector require renovation.

2.2.3 Financial Resources The Crop Forecast Survey is relatively well resourced compared to the Post Harvest and Livestock Survey (the PHS was not carried out in 2015/16 due to resource constraints). For 2017, the CFS survey had an approved budget of USD 700,000. On the other hand, the PHS had a limited budget of USD 80,000 and, as such, implementation was constrained. The Government funds ad-hoc surveys which arise from time to time but does not have enough resources to finance all agricultural data collection. For example, there is need to conduct the Fisheries Catch Assessment Surveys three times in a year and for all natural water bodies, in order to come up with meaningful estimates. However, funds can only suffice for the collection of data from one or two water bodies, once a year. Some of the challenges the CSO faces with funding stem from the fact that, being a department under the Ministry of National Development Planning, it shares 18 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 the budgetary allocation that is given to the parent Ministry with the other departments, and there is usually not enough to go around Figure 5: Total National Budget & Allocation to Agriculture Sector, 2008-2017 There is no dedicated budget line for data collection in the MFL. However, with the establishment of the Department of Policy, Planning and Information, this picture is about to change. Though a National Livestock and Aquaculture Census was included in the 2017 National Budget, the money apportioned for the exercise was only sufficient for a large scale sample survey (the budget approved was USD 5.6m, by way of comparison the 2010 Population and Housing census cost USD 53m). The Department of Fisheries has a dedicated budget for the Fisheries Information Management Unit. In 2016 this was USD $300,000, around 15% of the total budget for the Department. However the actual release is usually only 25% to 55% of the allocated budget. Some fisheries do receive support from projects funded by development partners e.g. Tanganyika which has benefitted from the ADB and Kariba from FAO. Financial resources to support the generation of statistics for natural resources are generally inadequate. The institutions do not have dedicated budget lines for statistical work. Where statistical work is funded it is within the budgets of many different activities e.g.in forestry, the statistics are funded through the budget line of an array of activities covering forest protection and management, and is considered to be less than 20% of requirements. 19 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA

2.2.4 International Support Overall donor support for agricultural statistics has been elusive. The last Census of Agriculture was done in 1991-92, so the holding of another is long over-due. The CSO has a project proposal, though of the donors who have been approached for assistance, the FAO has shown willingness to help with some aspects, others are yet to respond. The Forestry Department has conducted two national forest assessments in 2006 and 2014 under the auspices of the Food and Agriculture Organization (FAO) with financial support of the Government of Finland. A number of CSO staff have attended short-term training in agricultural statistics, under the auspices of UNECA, FAO, SADC and AfDB in particular. One member of staff will go to Tanzania 2017 to study for a Master’s Degree in Agriculture Statistics. The CSO also runs (now occasionally) an In-Service Training Programme for sub-professional officers. The programme accepts participants from the entire National Statistical System. The last two times this programme has been conducted, it was with the support of the ODA (Overseas Development Agency, the predecessor of DFID), in 1994, and JICA (Japanese International Development Agency), in 2007-2008. (the Government also contributed funds for the upkeep of the trainees, in both cases). The trainees in this programme were given grounding in theoretical as well as official statistics. The JICA-supported programme also had a component for training Professionals in Official statistics, and this was appreciated by the trainees.

2.2.5 Agricultural Statistics Capacity Indicator The capacity to produce agricultural statistics is measured by the “Agricultural Statistics Capacity Indicators (ASCI)” calculated biannually by AfDB. The ASCI is a composite indicator which measures the development of the NASS. The ASCI was estimated at 55.4% for Zambia in the 2017 reference year, up from 51.1% in 2015 and 49% in 2013. Zambia’s ASCI rating is currently below the average rating for African nations (57.2% in 2017). • Looking across the dimensions of the ASCI, the following conclusions can be made: • The level of the Prerequisite Dimension (institutional infrastructure) increased from 41.3% to 50.4% between 2015 and 2017, though it is well below the African average of 70.3%. • The level of the Input Dimension (reflecting the availability of resources) fell slightly between 2015 and 2017 from 35.3% to 32.3%; • The level of the Throughput Dimension (reflecting statistical methods and practices) increased from 52.9% to 59.3% between the two reference years; 20 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 • The level of the Output Dimension (reflecting the availability of statistical Information) remained fairly constant (75.0% and 75.8% in 2015 and 2017 respectively) and is the dimension on which Zambia scores the highest.

2.3 Assessment of Current Data Collection Activities Users of the NASS were asked to rate attributes of the available statistics, such as timeliness, relevance, methodological soundness, accuracy and reliability, accessibility, presentation, meta-data and micro-data and to give an overall rating of data quality.

2.3.1 National Agricultural Census Agricultural Censuses have been conducted in 1970/71 and 1991/92. The Housing and Population Censuses of 2000 and 2010 have included a short agricultural module with a number of ‘yes / no’ questions to identify whether the household had been involved in any agricultural activities in the 12 months leading up to census day. These questions were included to establish a new sample frame to provide more accurate estimates of agriculture and livestock production. A few questions on the crops sub-sector have been included on the questionaire for a planned Livestock and Aquaculture Census in 2017.

2.3.2 Crop Statistics The CSO and MoA together conduct two major annual surveys to collect crop and agricultural statistics. These are the Crop Forecasting Survey (CFS), typically conducted in the first quarter of the year (though exact timing depends on the status of the crops) and the Post-Harvest and Livestock Survey (PHS) conducted in the last quarter of the year. These surveys typically run for 4 to 5 months from start to completion and timeliness is a major challenge. The PHS survey is the more detailed survey. The data quality from both surveys is rated ‘good’ by stakeholders for crop statistics. 21 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA Table 1: Published Confidence Intervals for Crop Production Estimates Crop Confidence Interval Production Estimate Coefficient of Variation (000 metric tonnes) (+/- 000 MT) Maize 2,710 2.74% 146 Sorghum 138 11.13% 3 Rice 27 15.72% 8 Millet 30 6.11% 4 Sunflower 59 5.69% 7 Groundnuts 128 4.10% 10 Soya beans 65 9.51% 12 Seed cotton 112 6.71% 15 Irish potato 3 29.40% 3 Virginia Tobacco 3 27.80% 2 Burley tobacco 6 39.05% 5 Mixed beans 45 6.26% 6 Bambara nuts 5 15.98% 2 Cowpeas 10 12.09% 2 Velvet beans <1 2.19% <1 Sweet potatoes 230 6.60% 30 Pineapples 8 39.78% 8 Source CFS 2015/16 An examination of confidence intervals for crop production estimates (Table 1) shows that the survey works well for crops where production is fairly well distributed across the population and less well for other crops. The estimation of production for perennial crops such as cassava has historically been a challenge, as harvesting is a continuous exercise throughout the year. The current methodology for estimating cassava production is to assume a constant yield of 11.7 metric tonnes per hectare throughout the country. This estimate was established by a Root and Tuber Improvement Project research during the 1980’s. While the revision of results that are generated from the CFS and PHS surveys is not possible, Government does sometime use industry generated statistics in place of the survey estimates. This is particularly the case where survey estimates have very wide confidence intervals. The tobacco industry through the Tobacco Board and Tobacco Association generates statistics on national production of Virginia and Burley Tobacco from their administrative records. This data is rated ‘good’. When national production estimates from the Cotton Board of Zambia, based on processing returns from cotton Ginneries (rated ‘good’), are compared to estimates generated by the CFS, it seems likely that, in recent years, the CFS underestimates production. 22 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 For sugar and coffee producers and growers associations exists and maintain records from which production estimates can be made – all rated ‘good’ for quality. While the Zambia Export Growers Association (ZEGA) is the main industry association representing the interests of commercial growers and exporters of horticultural products statistics on production and export only cover its members. Similarly estimates for the production for soya beans made by ZNFU are only collected from ZNFU members.

2.3.3 Livestock Statistics In Zambia, the core list of livestock includes cattle, sheep, goats, pigs, horses, donkeys and poultry (i.e chickens, ducks, quails, rabbits, turkey, guinea fowls). While the core list of livestock products includes meat, milk, eggs, skins, hides and horns. It is estimated that by far the most important current source of livestock statistics are administrative records (accounting for about 71%), followed by surveys (27%) and censuses (2%). For MFL, the administrative approach entails a 100% enumeration of all the households in each veterinary camp by a government camp extension officer. Data collectedincludes outbreaks of livestock diseases and other livestock health-related indicators; livestock population; production of livestock and livestock products; trade in live animals and livestock products. However not all veterinary camps are manned and most veterinary camps have vast catchment areas (for example, Chilanga district, with an approximate area of 1,500 km2is manned by only 5 camp extension officers). It is unrealistic to expect a camp extension officer to canvass each and every livestock raising household in the veterinary camp. This leads to lack of precision of the generated estimates. As a result data qualty assessments for administative data are a combination of ‘good’ and ‘fair’. Some livestock statistics are available from the PHScarried out annually at the end of each agricultural season. However this survey is based on a sampling frame of the general population and the resulting livestock estimates, particularly for cattle, have large confidence intervals (there may also be issues around the accuracy of farmer responses as the cattle numbers produced by the survey seem to be underestimates).Survey data from the PHS is rated ‘fair’ for livestock statistics. The last Census of Agriculture was done in 1991/1992 whilst no Livestock Census has been done since independence. A smaller National Sample Livestock Census was done in 2010 and covered two districts only, namely Monze and Namwala. Preparations are underway for carying out the first ever Livestock and Aquaculture Census in Zambia. The Governmemnt has dedicated funds and other development partners have pledged to contribute resources. 23 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA

2.3.4 Fisheries Statistics Administrative data includes the registration of boats, fishers and fish farmers, catch returns from Kapenta (a type of sardine), semi industrial fishing and sport fishing and internal and external trades. These statistics are rated either ‘poor’ or ‘fair’ reflecting incomplete recording due to lack of resources (staff and financial –the omission of estimates based on illegal activities also contributes to under reporting). Statistics on formal imports and exports of fish and fish products are rated ‘good’ but informal cross border trade goes unrecorded – it is known that large volumes of fish imported into Zambia is informally exported to Democratic Republic of Congo (DRC) by cross border traders. Fisheries frame surveys are undertaken to create sampling frames for capture fisheries and for aquaculture. A Frame Survey is a census-based approach in which data is collected on all fishing gear and crafts, fishing villages/camps and also aspects of the demography of the fishing community. The overall objective is to provide information on the composition, magnitude and distribution of fishing effort; facilities and services in fishing villages to guide development and management of the resources of a fishery. Frame Surveys are conducted every five years for each fishery and have been on completed on schedule in recent years. The data provided rated ‘good’ when conducted though the frequency of these surveys needs to be improved. Catch Assessment Surveys (CAS) have been undertaken on all the major fisheries in Zambia since 1978. The survey is used to estimate the catch by species with a corresponding fishing effort. The data from CAS is used to derive important indicators used to evaluate the performance of a fishery. The changing fish species composition over seasons and years as well as fish sizes also helps managers guide and plan for management measures focused on fish conservation and sustainable exploitation. Catch Assessment Surveys are expensive to conduct. The frequency of carrying out these surveys is low and though the data can be of good quality, sometimes the coverage is incomplete due to inadequate resources both human and financial.

2.3.5 Forestry and Environment Subsector: The existing data sets are generally for the use of the subsectors institutions and are sourced from administrative records. The data sources are assessments and /or sample measurements mainly due to the vastness of natural resources. Most of the data collected in these sub-sectors is of good or acceptable quality. In terms of collection methods, most institutions use the questionnaires, key informant or focus groups. They adhere to internationally accepted standards and recommendations from the United Nations. In Forestry for example, the national forest inventory incorporates the REDD+ parameters necessary to report to the United Nations Framework Convention on Climate Change (UNFCCC). 24 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 Control of data quality control is an important part of the process in the collection of data by some institutions. In the water subsector, data verification exercise is done by hydro-technicians when they receive data from the field. In forestry assessments, a quality assurance team, composed of experts, follow the field assessment teams to carry out independent measurements to compare with the data collected and correct any errors. Generally, the subsectors in the land, water, forestry and environment sub-sectors do not have a solid infrastructure for data and information sharing and dissemination. Periodical publications and reports appear to be the common format of dissemination largely covering their respective mandates.

2.3.6 Prices There are three levels of price collection for agricultural commodities - producer prices (farm-gate); wholesale prices and retail prices. Data quality for price collection carried out by the CSO is rated ‘good’ or ‘fair’ ; the quality of price statistics in the MOA is rated ‘poor’ due to lack of staff and finance. The only national source of farm-gate prices is the PHS. The information is collected for commodities that the sampled farmers indicate that they have sold over the year. The data collected includes ‘month of sale’, therefore it is possible to retrospectively construct a monthly producer price table. Wholesale prices are supposed to be collected at district level by district marketing development officers (DMDO) from the MoA. The officers should visit key sample markets once per week to collect the prices. The data is then entered into the price database. Districts that have these officers have been collecting this data, however, aggregating this data into a national database has proved to be a challenge. The MOA disseminates weekly wholesale price statistics based on price collection in 2-3 markets within each of the 10 Provincial centres. The CSO collects retail price data every month from selected districts of the country. Aggregate national price changes for selected commodities of wide interest are included in the monthly publication. Retail price statistics for food commodities are an important input in monitoring national food security. Weekly and monthly food price movements are used at the local, national and regional level to inform policy makers on availability and access.

2.3.7 Trade Statistics & Informal Cross Border Trade Zambia uses the Harmonized Commodity Description and Coding System (HS) of tariff nomenclature for formal trade statistics. This data is generated by the Zambia Revenue Authority (ZRA) and the database is shared with the CSO on a monthly basis. The CSO provides the agricultural trade data to the MoA for further analysis on specific agricultural trade indicators. Stakeholders considered the quality of this data, captured through the formal declaring of import and exports to be ‘good’. However there was concern, expressed by stakeholders across the agricultural sectors, that informal 25 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA border trade was significant and this was not being captured, hence an overall grading of ‘fair’ for trade data for agricultural commodities. FEWSNET and the World Food Programme (WFP) established a system for collecting informal cross- border trade statistics for food commodities such as maize, wheat flour, beans in the COMESA region. This activity has been formally handed over to ACTESA, a COMESA affiliate that currently appears to be non-operational.

2.3.8 Data Processing, Data Management and Quality Controls The CSO and the MOA have recently completed a roll out of tablets to replace paper questionnaires for the CFS using the World Bank Survey Solutions CAPI software. This has greatly reduced the time required to collect data and has also eliminated the need for data entry. The data is transmitted to Lusaka for data cleaning and consistency checks using SPSS. Once the data cleaning and verification is concluded and finalized, the National Food Balance Sheet (NFBS) is generated by the ASEWS, in collaboration with CSO. A Stocks Committee chaired by the Permanent Secretary in the MoA reviews all the different data inputs required for generation of the food balance sheet, including submissions from industry stakeholders. Once consensus is reached, the food balance sheet is finalized and a press statement is released by the Minister of Agriculture. Survey reports for the PHS are available from the CSO. Production of reports for the 2011/12 to 2014/15 crop seasons was initially delayed by lack of funds, but these are now available. The use of electronic equipment (PDAs, tablets, smart-phones, etc.) to collect administrative agricultural data in the field is very limited. Primary data collection is usually on paper and the information is then captured using desktop or laptop computers at a central point, usually at district or provincial level. This data, sually in Excel, may then partially compiled to give some basic summary indicators. In order to obtain national indicators, the district or provincial level datasets are transmitted to the national office for aggregation, analysis and dissemination. The challenge is that some districts and field stations do not submit all the data required and this creates serious statistical gaps during analysis at national level. Raw data has also been lost in some districts (computer crashes, viruses and occasionally theft).

2.3.9 Dissemination Most organisations disseminate their results through paper publications, bulletins and brochures. The CSO releases survey results through workshops and media events.That notwithstanding, a lot of statistical data is kept in raw form within organisations and only a small portion of the data collected is processed into indicators that can be monitored over time. There is very limited documentation of important data collection outputs despite the considerable amount of resources used to collect the information. 26 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 Field programme reports, quarterly and annual reports are used for dissemination of administrative data, though these may not be accessible to the public. This lack of dissemination is largely due to inadequate staff numbers coupled with poor skills and equipment required for data processing and archiving. A few organisations use their website such as www.iapri.org.zm, www.zari.gov.zm, www.agriculture. gov.zm , www.zamstats.gov.zm‘ and the CountrySTAT website, as well as the Data Portal (http:// zambia.africadata.org or http://zambiainfohighway.knoema.com ) for dissemination. The Data Portal was developed with the help and support of the AfDB under the Africa Information Highway (AIH) programme. The CSO also has a Dissemination Unit supplying hard or soft copies of reports. The Dissemination Unit also coordinates the production and release of special request items /data from users (such as specific tables that may not have been produced in any report).

2.3.10 Challenges Challenges faced by stakeholders with regards to the data they produce include: Table 2: Issues and Challenges of Statistics Data Collection Issue Challenges Strategic (leadership, Policy, legal advocacy) Limited commitment from top management Lack of suitable organization structure and system Insufficient appreciation of statistics by managers and decision makers Unwillingness by some respondents to share information. Capacity(human, Physical and ICT) Inadequate numbers and inadequately trained personnel; this adversely affects the collection of administrative statistics where such officers are required to be present on the ground most of the time for enforcement, registration or data collection; Lack of funds for training and capacity building; Lack of a website which would avail an online information plat- form; Inadequate expertise in analytical tools Statistics Production (data collection, processing, analysis, report Low sampling intensity Lack of adequate budget to support data collection affecting both surveys and administrative data collection (including the late release of funds from Treasury). This includes a lack of trans- port, camping equipment, computers (including tablets for data capture in the field); Absence of disaggregated data at the lowest level of administra- tion Lack of analytical tools (such as GIS software) Dissemination Interpretation of technical data for general public Inadequate manpower Inadequate resources to support dissemination Inadequate communication infrastructure 27 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA One technical issue encountered is the representativeness of the CSO/MoA sampling frame for the sub-sectors of the agriculture sector. The sampling frame seems to represent the crops subsector better than it does the livestock sector. As such, the PHS statistics on crops tend to be more reliable than those on livestock.

2.4 Assessment of User Needs & Data Gaps In assessing their satisfaction with the NASS, users were asked how statistics were used in their operations, and whether the statistics they needed were available. This identified significant data gaps in virtually all sectors. Users want statistics on a number of variables that are not currently being produced. This would suggest the existence of a hunger for data. A summary, by sub-sector, of the main findings is given below. Information needs identified by the cross cutting working group have been included under the appropriate subsector.

2.4.1 User Needs – Crops subsector The main user needs for crop statistics in Zambia relate to: Production, Prices, External trade and to a lesser extent consumption statistics. Crop statistics are used to make national food security decisions. The National Food Balance Sheet utilizes various types of statistics namely, production statistics, statistics on physical stock levels, consumption statistics, post-harvest loss statistics and trade statistics. It needs input data from the CFS and also utilizes information from other sources such as CSO population growth estimates, industrial utilization of staples, historical per capita consumption patterns etc. The Food Reserve Agency concludes its purchase schedule for the marketing season after the final crop forecast and food balance information is finalized. Detailed household level consumption statistics for agricultural commodities are generated by the Living Conditions Monitoring Survey (LCMS). The survey uses the diary method to record consumption of individual commodities, including food, at the household level. The National Food Balance sheet converts this information into kilo-calories to derive the requirements for maize, the main cereal consumed in Zambia. The status quo method, which looks at imports and exports, is used to measure apparent consumption for wheat, rice, and potatoes that are also included in the national food balance sheet. Retail price statistics for food commodities are an important input in monitoring of national food security. Weekly and monthly food price movements are used at the local, national and regional level to inform policy makers on availability and access. At the macro-level, crop production statistics are used in National Accounts for the calculation of Gross Domestic Product (GDP). 28 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023

2.4.2 User Needs - Livestock Subsector With the diversity and multiplicity of stakeholders’ interests, endless data could be collected. However, scarce resources entail that the collection of livestock statistics should be prioritised and cover the following indicators / datasets): a) Livestock inventory; the collection of livestock numbers b) Livestock slaughters divided into two categories: I.) Primary products, coming directly from the slaughtered animals, including meat, offals, raw fats, fresh hides and skins. II.) Processed products, derived from the processing of primary products and include sausages, lard and salted hides. c) Production-related indicators (i.e meat, milk, eggs, hides, etc) divided into two categories: I.) Products from slaughtered animals (i.e. meat, hides) II.) Products from live animals (i.e. milk, eggs) d) Trade-related indicators:data on the imports and exports of Livestock and Livestock Products (LLPs). Variables include the type and quantity of LLPs as well as the trading partners (countries). e) Price-related indicators capture data on the prices of LLPs across the country. f) Livestock disease-related indicators which capture data on the prevalence and incidence of livestock diseases in the country. g) Staff and resources refers to the human resource base dedicated to statistical production in the livestock sub-sector as well as the fiscal and other resources dedicated for the same.

2.4.3 User Needs - Fisheries Subsector Fisheries data and statistics are used by various organizations including government, non- governmental and academia for among others the following: • Monitoring the performance of the fisheries in terms of catches, size and species of fish caught to assess the status of the stocks in order to make informed decision on either to increase or reduce fishing effort. • Monitoring the development of aquaculture for the purposes of planning input supplies and also making cultured fish accessible to markets. • Monitoring the water quality and limnology of catchment areas, lakes, rivers and swamps. This will guide planners and managers on aquaculture investment in water bodies and prevent unregulated discharge into aquatic systems. The data is shared with universities and other learning and research institutions as well as industries or entities wanting to invest in fishery areas. • Used by scientists and academic institutions for publications to increase knowledge on fisheries • Important in the review and development of fisheries management plans which provide a policy framework to guide the sustainable utilization of exploitable fish stocks. 29 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA • Important in meeting the Department of Fisheries reporting obligations to international fisheries bodies such as the FAO. • Sharing information with countries Zambia shares trans-boundary fisheries with such as Kariba with Zimbabwe, Tanganyika with Burundi, DR Congo and Tanzania, Mweru-Luapula with DR Congo and the Upper Zambezi with Botswana, Namibia and Zimbabwe. • Important in development of national food balance sheets and used by other state agencies in estimation of fisheries contribution to the national economy. • Preparation of national reports

2.4.4 User Needs - Forestry and Environment Subsector The statistics for Land, Environment, Forestry, Water and Natural resources are generally used for planning and policy formulation. However, it is important that the information from the sub-sectors are used in an holistic way in order to achieve integrated development. Land statistics are available at the Commissioner of Lands, Deeds & Registry and Survey departments. Land records have now been incorporated into one database called the Zambia Land Management Information System (ZLMIS). In Forestry, statistics collected by the Forestry Department relate to production, consumption, spatial distribution, trade imports, exports and development of wood and non-wood forest resources for particular locations. Statistics on both wood and non-wood forest products are used to quantify the socio-economic benefits from forests and are used in the compilation of the subsector's contribution for Gross Domestic Product (GDP). Other uses include capital budgeting, demand and supply forecasts, production scheduling, resource allocation and optimal control. National Forest Monitoring are used to analyse the forest degradation and deforestation rates. Environmental statistics are used to assess progress on the state of the environment and combines data from various subsectors including forestry, meteorology, water and climate change. The Meteorological Department utilizes the data collected from their weather station to develop trend analysis for monitoring seasonal forecast as well as floods. The Water Resources Management Agency (WARMA) collects hydrological and rainfall data to determine the water situation in the country. On the other hand, the Zambia Environmental Management Agency (ZEMA) collects data on water quality and pollution. The Climate Change Secretariat under the Ministry of National Development Planning collects data from on-going projects involved in climate mitigation projects, including those promoting SMART agriculture. 30 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023

2.4.5 Data Gaps In summary the data gaps identified by stakeholders include: Censuses: to carry out censuses of • Livestock and aquaculture to provide basic baseline data and a frame for more detailed surveys. • Dairy processors. • Milk collection centres. • Beehive / apiary keepers. Household Surveys: to carry out additional regular surveys covering • Cost of production for individual agricultural produce. • Horticulture (vegetables and flowers) and fruit production; • Species level fish catch surveys; • Aquaculture production; • Non-timber products (Forest Livelihoods and Economic Survey); • Consumption of individual agricultural commodities. • Income from agricultural activities • Labour and employment in agriculture Economic Surveys: to cover • Agro-input suppliers and agro-processors. • Industrial wood processing companies. Expansion of existing surveys: to cover • Farmers practicing climate change adaptation measures; • Type of pesticide used; • Labour costs Price collection: regular collection of farm gate and wholesale prices of agricultural commodities. Informal cross-border trade: to restore and expand the cross border trade survey with more border points and more commodities. Administrative systems: to cover • Agro chemicals • Water use (quality & quantity) • Rainfall & temperature • Crop stock and storage statistics • Livestock slaughter slabs and abattoirs, number of slaughters, carcass weights • Livestock disease outbreaks, vaccinations, • Veterinary and livestock extension officers 31 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA • Land for development in customary areas • Timber products Other • Soil surveys • Distribution maps of livestock, livestock infrastructure • Ecosystem services data

2.5 Main Findings: Strength, Weaknesses, Opportunities and Threats (SWOT) for NASS A SWOT analysis has been carried out for the overall agricultural statistical system in Zambia and for each of the component sub-sectors, A SWOT is a useful tool to assess the existing agricultural statistical system to identify the current weaknesses and strengths, the opportunities for improvements and factors that represent threats to the implementation of any improvements. The SWOT for NASS is shown overleaf (Table 3). The main strengths of the NASS are seen to be the existing statistical capacity (with its established systems of field organisation, registers and geographic sampling frame) which adheres to professional and international standards, to make available relevant data for agricultural policy making. The weaknesses identified fit into three broad groups. One set of weaknesses reflect governance and coordination and the perceived under-development of the agricultural statistical system (low profile of CSO in Government hierarchy; inadequate statistical legislation; limited coordination and collaboration in agricultural statistical production). Another group of weakness reflect inadequate financial and human resources; the later in terms of both numbers and skills (inability to attract and retain staff; agriculture census and survey work programmes not prioritised in Government budgets; non-existence of in-service training; inadequate analytical skills; lack of resources for data processing, analysis and dissemination). The last group of weaknesses are around statistical systems - under developed Sector Management Information Systems; failure to update the geographical frame; incomplete coverage of agro-business entities in the business register; difficulty in accessing data. The final weakness in this group - users of agricultural statistics have limited knowledge of national and international development processes and statistical standards – reflect the lack of ability of producers to educate users and to provide outputs which are user friendly. The opportunities identified include the increased demand for agricultural data and increased government/ political commitment to development of statistics. The existence of international partnerships for statistical development and professional statistical standards/frameworks, e.g. SPARS, with the possibility to share experiences, resources and facilities in SADC, FAO, COMESA, AU, the African region and beyond. Other opportunity were seen to be provided by advances in IT leading to improved quality of data collected and timely dissemination of results and statistics as a commodity for business opportunities 32 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 The limited budgetary allocation for agricultural statistics given the high cost of capacity development, the relatively high cost of censuses and surveys and the high cost of statistical software were seen as the main threats. Another set of threats were identified around the low level of appreciation and understanding of agricultural statistics, the perceived undue interference with agricultural statistical production and release and the lack of commitment to coordination, collaboration, networking and information sharing. Finally the non-cooperation and fatigue of data survey respondents and inadequate data storage and security were recorded as threats. Table 3: SWOT for National Agricultural Statistics System - Zambia Strengths Weaknesses a Established statistical systems i.e. field organisa- a Users of agricultural statistics have limited knowledge of national tion; business register and geographical frame and international development processes and statistical stan- dards; b Existence of core statistical competencies b Under-development of the agricultural statistical system c Available data relevant for agricultural policy c Inadequate Statistical legislation makers d Compliance with professional and internation- d Inadequate financial and human resources constraints; al standards in the compilation of agricultural e Low profile of CSO in Government hierarchy; statistics f Limited coordination and collaboration in agricultural statistical production; g Under developed Sector Management Information Systems; h Non-existence of in-service training; i Inability to attract and retain staff; j Lack of resources for data processing, analysis and dissemina- tion; k Difficulty in accessing data; l Failure to update the geographical frame; m Incomplete coverage of agro- business entities in the business register; n Agriculture census and survey work programmes not prioritised in Government budgets. o Inadequate analytic skills Threats a Increased government / political commitment to a Low level of appreciation and understanding of agricultural development of statistics statistics b Increased demand for agricultural data b Perceived undue interference with agricultural statistical produc- tion and release c Possibilities to share experiences, resources and c Lack of commitment to coordination, collaboration, networking facilities in SADC,FAO, COMESA, AU, the African and information sharing region and beyond d Existence of international professional statistical d Limited budgetary allocation for agricultural statistics standards/frameworks, e.g. SPARS e International partnerships for statistical develop- e Inability to attract and retain staff ment f Advances in IT leading to improved quality of data f Relatively high cost of censuses and surveys collected and timely dissemination of results. g Statistics are a commodity for business opportu- g High cost of Statistical Software nity h Inadequate data storage and security i High cost of capacity development 33 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA Chapter 3: Strategic Planning

3.1 Introduction This Chapter presents the Vision and Mission statements for the national agricultural statistics system. There is a summary of the challenges facing the NASS linking to the Strategic Goals (SG) and Operational Strategies (OS) identified for the SPARS. The activities defined under each OS are outlined, including a calendar of the main data collection exercises. Finally a financing strategy, a monitoring and evaluation plan and an advocacy and communication plan are described.

3.2 Summary of Challenges The main challenges reflect the lack of structures for collaboration and coordination, data gaps and resource constraints (human, IT, physical and financial). More specifically: 1. The current Statistics Act does not provide for an institutional arrangement for the National Statistical System (NSS) to facilitate the efficient and effective production of statistics. Consequently coordination and harmonization arrangements are either non-existent or rather weak. When adopted the new proposed Statistics Act will address these issues. In the interim there is a need to establish networks which can foster collaboration and understanding between producers and between users and producers across all the sectors, to ensure statistics are consistent, reliable, relevant and timely. 2. In particular there is no mechanism to oversee that appropriate data collection methodologies are adopted to carry out statistical activities. 3. There are more than 30 pieces of legislation relating to crops and other agricultural commodities which also implicitly provide for the generation of agriculture statistics. In the light of the new proposed Statistics Act, the statistical legislation within these Acts should be considered for amendment or even repeal. 4. While some agricultural statistical activities are included in the NSDS, full integration has not been possible (especially for forestry, water and environmental statistics). Close collaboration between the SPARS_ZAM and NSDS teams is needed to ensure the full integration of the agriculture sector into the next generation NSDS. 5. The NSDS process needs to be more widely owned as, while virtually all key informants believed that a strategic planning approach for statistics for the whole country existed, some did not know what it was called. 6. Almost all institutions indicated they did not have adequate human resources for statistical work, 34 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 with an inability to recruit and retain staff. In particular not all institutions have a statistics unit e.g. the MFL does not have a statistics unit (though there are plans to establish one) and only forestry has a dedicated statistics unit in the natural resources sub-sector. 7. However, the institutions indicated that the staff they had were generally skilled, experienced and qualified, except for the environment sub-sector. There was a lack of funding for training and capacity building (there is no in-service training programme) leaving staff with inadequate analytical skills. 8. Insufficient staff numbers adversely affects data collection, particularly for administrative statistics such as market statistics (e.g. sale volumes and prices). Extension officers (for both crops and fisheries) and veterinary officers are required to cover large areas and large numbers of households. Administrative data for livestock and fisheries are rated either ‘poor’ or ‘fair’ reflecting incomplete completion of records and reports due to lack of resources (staff and financial). 9. Technological advances provide an opportunity to improve the quality of data collected and the timely dissemination of results. The MoA and CSO are now using tablets for data collection in the field with significant savings in time and resources, though challenges remain at the data analysis stage and in providing timely reports. 10. The use of electronic equipment (PDAs, tablets, smart-phones, etc.) to collect administrative agricultural data in the field is very limited and currently the collection of livestock and fisheries data from field stations is paper-based. In the natural resources sector all institutions reported their IT equipment to be inadequate. 11. There is a lack of resources for data processing and analysis. The high cost of proprietary software is an issue with the lack of access and inadequate expertise in analytical software (including GIS software) negatively affecting the timely production of data. 12. Lack of transportation is an issue across all sectors. 13. While the CSO Head Office has recently moved into newly built offices, officers in veterinary camps do not have offices nor office equipment. Fisheries officers in the districts share their offices with other government departments while offices for natural resources staff require renovation. 14. The existing statistical capacity within CSO & MoA to carry out annual surveys is seen as a strength. However, the sampling frame represents the main food crops better than it does the other crops and the livestock sector. Where confidence intervals are large the Government may use industry generated statistics in place of the survey estimates. 15. Priorities for crop statistics are driven by national food security decision making. As a result 35 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA commodities of economic importance, such as horticulture (e.g. tomato, onion, cashew nuts) that are not perceived to be directly impacting on food security have low priority for the resources, limiting the information that is available. 16. Statistics on formal imports and exports of agricultural products are rated ‘good’ but stakeholders, across the agricultural sectors, felt that informal border trade, which is significant, was not being captured, and consequently graded trade data overall ‘fair’ for agricultural commodities. 17. The only national source of farm-gate prices is the Post-Harvest Survey. Wholesale prices are collected at district level by district marketing development officers (DMDO) where such officers are in post. The CSO collects retail price data every month from selected districts of the country. 18. A lot of statistical data is kept in raw form within organisations and only a small portion of the data collected is processed into indicators that can be monitored over time. In general Sector level Management Information Systems are under-developed. Inadequate data storage and security may compromise data. There is very limited documentation of important data collection outputs despite the considerable amount of resources used to collect the information. 19. There is a lack of websites to provide online platforms to disseminate and share information. In particular the land, water, forestry and environment sub-sectors do not have any infrastructure for data and information sharing and dissemination. The lack of resources to support dissemination and communication has led to inadequate interpretation and presentation of technical data to the general public. 20. The Government funds some surveys, especially the Crop Forecast Survey, but does not have enough resources to finance all agricultural data collection. 21. While there is an increased demand for agricultural data there is limited commitment from top management to provide resources for data collection; agriculture censuses and surveys are not prioritised in Government budgets. 22. As the CSO is a department in the Ministry of National Development Planning, it has a low profile and shares the budgetary allocation that is given to the parent Ministry with the other departments.There is no dedicated budget line for data collection in the MFL. Financial resources to support statistics for natural resources are generally inadequate and the institutions do not have dedicated budget lines for statistical work. 23. Overall donor support for agricultural statistics has been elusive. The last Census of Agriculture was done in 1991-92, so the holding of another is long over-due. Preparations are underway for carying out the first ever Livestock and Aquaculture Census . The Governmemnt has dedicated funds and other development partners have promossed to contribute resources. 36 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 24. International partnerships exist and are being used for statistical development (e.g. UNECA, FAO, COMESA) While institutions follow internationally set standards and frameworks for data collection, many users of agricultural statistics have limited knowledge of these processes and there is insufficient appreciation of statistics by managers and decision makers.

3.3 Vision and Mission Statements The Vision of the Zambia SPARS, reflecting the future image of the national agricultural statistical system is: An efficient, responsive and coordinated source of agricultural and rural statistics’. The Mission of the Zambia SPARS, reflecting what the national agricultural statistics system should deliver, is: ‘To provide quality agricultural and rural statistics for evidence-based policy and decision making for enhanced user satisfaction’.

3.4 Strategic Plan

3.4.1 Goals & Strategies For the development of SPARS_ZAM, five Strategic Goals have been elaborated to meet the challenges (listed in section 3.2) which have been identified by stakeholders in the various sub- sectors of agriculture. These Strategic Goals, which also link to the specific Goals of the NSDS, establishing the close alignment of the SPARS and the NSDS, are: SG1: Improved coordination & management of agriculture statistics SG2: Meet the national and international demand for agriculture statistics SG3: Strengthen statistical operations & processes SG4: Implement a comprehensive human resource strategy SG5: Develop a funding strategy for statistical operations. Table 4 shows the Strategic Goals, the challenges each addresses (using the numbering as in section 3.2) and the operational strategies that have been agreed in order to implement SPARS_ZAM. 37 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA Table 4: Strategic Goals, Challenges addressed and Operational Strategies STRATEGIC GOAL PURPOSE & CHALLENGES ADDRESSED OPERATIONAL STRATEGIES SG1: Improved coordination & manage- To ensure the production of agriculture OS1.1: Improve coordination, collaboration ment of agriculture statistics statistics is coordinated and harmonised and networking among stakeholders OS1.2: Improve technical coordination Challenges Addressed: 1, 2, 3, 4, 5, 24 among data producers SG2: Meet the national and international To ensure user needs are increasingly met. OS2.1: Adopt an integrated census and demand for agriculture statistics survey programme Challenges Addressed: 8, 14, 15, 16, 17. OS2.2: Expand the compilation, analysis and reporting of data including from ad- ministrative records SG3: Strengthen statistical operations & The collection, processing, reporting and OS3.1 Provide appropriate and adequate processes dissemination of agriculture statistics is physical infrastructure efficient and effective. OS3.2 Improve data access, knowledge management and communication Challenges Addressed: 9, 10, 11, 12, 13, 18, 19. OS3.3 Monitor and improve data quality SG4: Implement a comprehemsive human Establish a cadre of skilled professional OS4.1 Establish and maintain a critical resource strategy statistical personnel mass of professional agricultural statistics personnel Challenges Addressed: 6, 7, 8. OS4.2 Establish a training programme to develop the capacity of agricultural statis- tics personnel SG5: Develop sustainable funding for sta- Availability of funds leads to timely imple- OS5.1 Enhance and strengthen collabo- tistical operations mentation of planned statistical activities. rations to support sustainable funding for agricultural statistics activities Challenges Addressed: 20, 21, 22, 23. OS5.2 Advance the awareness of the im- portance of agricultural statistics SG1: Improved coordination & management of agriculture statistics OS1.1: Improve coordination, collaboration and networking among stakeholders This includes supporting participation in NSS technical sub-committees on agricultural statistics to provide valuable feedback to data producers on user needs and the relevance of statistics currently produced. Though the legal necessity to establish such committees awaits the passing of the new Statistics Act, their immediate inception will be beneficial to the NSS and all its partners. In particular it is expected improved coordination will lead to the full integration of all sub-sectors of agriculture into the next generation of the NSDS. Links with national, regional and global agricultural associations will improve statistical operations by facilitating the exchange of views, giving exposure to new methodologies and best practice and bringing support to introduce new operations and programmes. 38 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 OS1.2: Improve technical coordination among data producers The entire production of agricultural statistics, from collection to analysis, should utilize effective and innovative tools. Best practice, methods and international standards (suitably adapted) should be identified and promoted to support coordination amongst data producers, with particular attention to adopting appropriate data collection methodologies and harmonizing indicator definitions. SG2: Meet the national and international demand for agriculture statistics OS2.1: Adopt an integrated census and survey programme The census and survey programme, complimenting information from administrative records and other sources, will be designed to meet data needs and fill data gaps. OS2.2: Expand the compilation, analysis and reporting of data including from administrative records As with the census and survey programme, the focus of this operational strategy will be to meet user needs and fill data gaps by promoting data collection and improving reporting and analysis. SG3: Strengthen statistical operations & processes OS3.1 Provide appropriate and adequate physical infrastructure Dedicated offices, appropriately equipped, are needed for agricultural statistics units at all levels to improve statistical operations. The use of ICT has become a fixture in statistical work. This includes the need to strengthen data transfer, data storage of data and the use of advanced statistical software packages for data analysis. OS3.2 Improve data access, knowledge management and communication This operational strategy will develop and implement data management policies for data access and confidentiality. This also involves the establishment of web sites and data warehouses, linking to the CSO MIS system, with the appropriate capacity and skills to facilitate the integration and analysis of data for decision making – an area of weakness identified in the SWOT. This operational strategy will also develop and implement an Information, Knowledge Management and Communication (IKMC) strategy for designing and producing appropriate dissemination products. 39 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA OS3.3 Monitor and improve data quality The M&E system which will underpin the implementation of SPARS_ZAM is an example of the frameworks to be set up to ensure that agricultural statistical activities are accountable, that the data collected is of good quality, and to show that the resources made available have been wisely used. SG4: Implement a comprehensive human resource strategy OS4.1 Establish and maintain a critical mass of professional agricultural statistics personnel The availability of adequate numbers of skilled and trained personnel to undertake statistical work is an important requirement for the development of any statistical system. This is one of the weaknesses identified in the assessment, particular the need to establish dedicated statistics units in some MDAs. OS4.2 Establish a training programme to develop the capacity of agricultural statistics personnel It is important that the NASS institutions identify their training needs and ensure that these are being met through training programmes offered across the NSS or through other outlets. SG5: Develop a funding strategy for statistical operations. OS5.1 Enhance and strengthen collaborations to support sustainable funding for agricultural statistics activities The identification of the weaknesses in statistical systems and the requirements for improvements, as identified in the SPARS_ZAM process, serves as a basis to discuss the additional resources required to ensure the continued supply of much needed data policy making and planning. OS5.2 Advance the awareness of the importance of agricultural statistics This will be achieved through a targeted advocacy and communication for decision–makers and other relevant stakeholders and by using social media to increase awareness and provide education. 40 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023

3.4.2 Activities Some of the activities associated with each Strategic Goal are described below: SG1: Improve coordination and management of agricultural statistics; • Review and amend the legal frameworks for collecting statistics in the sub-sectors • Participate in NSS technical meetings, NASS subsector technical meetings & development of next generation NSDS; • Identify and maintain links with stakeholders - national and international; • Develop a metadata handbook to harmonise statistical definitions across the sub-sectors; • Develop a compendium of best practice methods for collecting statistics across the sub-sectors; • Disseminate metadata & methodology handbooks and raise awareness SG2: Address the national & international demand for agricultural statistics • Conduct Census of Agriculture and survey programme to meet user needs and fill data gaps; • Strengthen and support routine data collection activities; • Monitor producer, farm gate and market prices of agricultural commodities • Develop data collection instruments for environment and climate change and mobilize the subsector for the collection of data; • Enhance the analysis of data including updating the food balance sheet, developing natural resource accounting and utilising more satellite imagery. SG3: Strengthen statistical operations and processes • Acquire / refurbish and equip offices for agricultural statistics activities; • Procure transport equipment for agricultural data collection; • Procure IT equipment and software; • Develop and implement data access and confidentiality policies; • Establish and maintain data centres and web portals; • Develop and implement an Information, Knowledge Management and Communication (IKMC) strategy; • Train agricultural statistics staff and stakeholders to monitor statistical activities; • On-going monitoring of statistical activities; • Annual data quality workshops and reports. 41 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA SG4: Implement a comprehensive human resource strategy • Set up and strengthen agricultural statistics units; • Recruit and deploy adequately qualified personnel; • Undertake training needs assessments; • Develop partnerships with training providers; • Develop training curriculum; • Undertake training. • SG5: Develop a sustainable funding for statistical operations • Organise advocacy meetings to lobby for funds for statistical activities; • Organise workshops to prepare funding proposals; • Hold sensitization events for stakeholders and policy makers; • Use media to increase awareness and provide education

3.4.3 Calendar of Main Data Collection Activities As part of the process filling data gaps in the agricultural and rural statistics system, a list of data collection activities has been identified for implementation over the 5 years of the Action Plan. The frequency of each data collection exercise, together with the responsible lead agencies is shown in Table 5. The inclusion of an agricultural module with the Housing & Population Census will provide a sampling frame for follow-on Agriculture and Livestock & Aquaculture large scale sample surveys. 42 Table 5: Calendar of main data collection activities Data Collection Activity Frequency Responsibility 2019 2020 2021 2022 2023 Population & Housing Census – agricultural module 10 years MoA, CSO Agriculture Census (large scale sample survey) 5 yearly MoA, CSO Crop Forecast Survey Annual MoA, CSO Special Crop specific survey Annual MoA, CSO Post Harvest Survey Annual MoA, CSO Crop Monitoring (administrative data) Annual MoA Agricultural Inputs Price Survey 6 monthly MoA Market Price Survey 6 monthly MoA Cost of Production Survey Annual MoA, CSO Informal Cross Border Trade Survey Continuous IAPRI Livestock & Aquaculture Census (large scale survey) Every 3 years MFL, CSO Apiculture Census Every 3 years MFL, CSO Survey of livestock diseases Annual MFL, CSO Inventory of livestock& veterinary facilities 5 yearly MFL. CSO Survey of toxins and residues (food safety issues) Annual MFL. CSO Fisheries & Livestock Monthly Market Price Collection Monthly MFL Fish Frame Surveys Annual programme DoF, CSO Catch Assessment Surveys 3 times / year DoF, CSO Gillnet surveys Annual Programme DoF, CSO Hydro acoustic surveys Annual Programme DoF, CSO, PPID Aquaculture Production Survey Annual DoF, CSO Collection of fish marketing statistics Monthly DoF, CSO National Forest Inventory 5 yearly MLNR-FD Climate adaptation Annual MoA, MLNR, MoH, MoE, MTC, MWDSEP, MID Forest Livelihood survey 3 yearly CSO, FD Environmental outlook assessment 2 yearly ZEMA Soil Productivity Assessments 2 yearly MoA, FD STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 43 2019 - 2023 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA

3.5 Financing Strategy The Action Plan with a detailed annual budget by activity level is set out in Annex 5. The total budget for all activities under SPARS_ZAM over the 5 Year Period is US $57.9m. Each STWG has set out its priorities for financing. These have been combined into one list which is presented in Table 6. This table shows, for each activity, the amount of funding identified as available (code 1). This can either be from routine government allocations to the various institutions or be commitments from other sources (such as donors) or projects. Any additional funding required to complete activities is then identified either as high priority (code 2) or other funding required (code 3). One problem that has been noted is the fact that budgetary allocations are rarely released in their entirety. The government is implored to ensure that money set aside for these programs is released as planned. We would suggest ring-fencing such funds, as this is one way to ensure the successful implementation of the SPARS-ZAM. This will in turn generate a wealth of statistics for monitoring the development of the sector and also SDGs. 44 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 Table 6 Financing Strategy (US $000) Funding Priority for Other funding Serial available Funding required Activity Total No. -1 -2 -3

1.1.1 Review and revise agricultural statistics legal & programme frameworks 40 120 55 215

1.1.2 Participate in NSS meetings including technical working groups on agricultural statistics and development of next generation NSDS 78 104 95 276

1.1.3 Improve networking with stakeholders - national and international 100 339 362 801

1.2.1 Develop a metadata handbook to harmonise statistical definitions for the agriculture sector 10 210 20 240

1.2.2 Develop a compendium of best practice methods for collecting agricultural statistics 15 150 40 205

1.2.3 Disseminate metadata & methodology handbooks and raise awareness 67 164 152 383 Total SG1 310 1,087 724 2,120

2.1.1 Integrated census & survey programme - crops[1] 13,600 4,350 250 18,200

2.1.2 Integrated census & survey programme - livestock[2] 7,850 2,350 0 10,200

2.1.3 Integrated census & survey programme - fisheries 1,543 3,596 1,386 6,525

2.1.4 Integrated census & survey programme - forestry & environment 285 1,000 80 1,365

2.2.1 Develop data collection Instruments for environment and climate change 200 35 0 235

2.2.2 Train & sensitise staff to collect statistics from administrative processes 100 240 240 580

2.2.3 Support routine data collection and reporting 615 732 360 1,707

2.2.4 Enhance the analysis of data 69 380 56 505 Total SG2 24,262 12,683 2,372 39,317

3.1.1 Acquire/refurbish and equip offices for agricultural statistics activities 890 783 0 1,673

3.1.2 Procure transport equipment for data collection (vehicles, motor cycles, boats) 866 835 240 1,941

3.1.3 Procure computer and other specialised survey equipment for data collection / processing 1,402 1,378 566 3,346

3.1.4 Pilot and introduce new initiatives for data collection / processing 200 350 150 700

3.2.1 Develop & implement data quality, data access and confidentiality policies 300 105 40 445 45 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA Funding Priority for Other funding Serial available Funding required Activity Total No. -1 -2 -3

3.2.2 Develop and implement an Information, Knowledge Management and Communication (IKMC) Strategy 0 300 300 600

3.2.3 Establish and maintain data centres and web portals for agricultural statistics 186 340 20 546

3.3.1 Train agricultural statistics staff and stakeholders to monitor agricultural statistics activities 253 363 0 615

3.3.2 Ongoing monitoring of statistical activities 225 270 0 495

3.3.3 Annual data quality workshops and reports 105 170 75 350 Total SG3 4,427 4,894 1,391 10,711

4.1.1 Set up / strengthen agriculture statistics unit 110 155 0 265

4.1.2 Recruit and deploy adequately qualified personnel 1,410 0 0 1,410

4.2.1 Training of agricultural statistics personnel 790 2,092 408 3,290 Total SG4 2,310 2,247 408 4,965

5.1.1 Organise advocacy meetings to lobby for funds for agriculture statistics activities 120 210 40 370

5.1.2 Organise workshops to prepare funding proposals 55 155 55 265

5.2.1 Hold sensitization events for stakeholders & policy makers 180 125 70 375

5.2.2 Use media to increase awareness and provide education 275 75 55 405 Total SG5 630 565 220 1,415 TOTAL SPARS_ZAM 31,938 21,475 5,115 58,528 Of the total budget of US $58.5m for SPARS_ZAM activities over a half (54%) is anticipated from government funding or has been committed by others. Of the remaining US $26.6m to be found, US $21.5m is identified for priority activities. However, budgeted allocations and budgeted releases do not always match and releases can be significantly lower than budgets. The situation is made more complex by the lack of separate and specific budget lines for statistical activities. While multilateral bodies such as FAO, UNECA, AfDB, currently provide support to build capacity in agricultural statistics, through the global fund, the adoption and implementation of SPARS is an important step to raise the profile of agricultural statistics and increase resource mobilisation. 46 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 Figure 5: Financing Strategy – SPARS_ZAM 47 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA Table 7 shows the total budget required and financing strategy for each of the main data collection activities to be undertaken during the 5 year period of this Plan. Table 7: Financing Strategy for Main Data Collection Exercises High Priority for Other funding Funds Available TOTAL $000 Data Collection Activity funding required -1 -2 -3 Census of Population & Housing - agriculture module 0 500 0 500 Census of Agriculture (large scale sample survey) 6,300 700 0 7,000 Crop Forecast Survey 5,000 0 0 5,000 Special Crop specific survey 100 400 0 500 Post Harvest Survey 2,000 2,000 0 4,000 Crop Monitoring (administrative returns) 125 125 0 250 Agricultural Inputs Price Survey 0 100 0 100 Market Price Survey 50 50 0 100 Cost of Production Survey 50 300 150 500 Cross border informal trade survey 100 300 100 500 Livestock & Aquaculture Census (large scale survey) 5,700 300 0 6,000 Apiculture Census 200 200 0 400 Survey of Livestock diseases 1,000 1,500 0 2,500 Inventory of Livestock & Veterinary Facilities 300 0 0 300 Survey of toxins and residues (food safety issues) 450 300 0 750 Fisheries and Livestock Monthly Market Price Survey 400 150 0 550 Fisheries Frame Surveys 184 300 256 740 Fisheries Catch Assessment Surveys 440 2,000 1,130 3,570 Fisheries Gillnet surveys 332 458 0 790 Fisheries Hydro Acoustic Surveys 87 288 0 375 Aquaculture Production Survey 300 450 0 750 Fish marketing statistics 200 250 0 450 National Forest Inventory 85 500 0 585 Climate adaptation 20 90 0 110 Forest Livelihood Survey 100 80 30 210 Environmental outlook assessment 0 100 0 100 Soil productivity assessments 0 100 0 100 Total budget for main data collection activities 23,523 11,541 1,666 36,730 48 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 Figure 6: Financing Strategy: Main Data Collection Exercises In addition, other data activities across the sub-sectors are budgeted at US $2.6m. This gives a total budget of $39.3m for all agricultural data collection activities (68% of the total SPARS_ZAM budget). Most of the resources will come from Government. But where there are funding gaps, these may be filed by securing finances from cooperating partners and multilateral bodies. One way this can be done is at meetings convened to sell the SPARS_ZM to the potential donors.

3.6 Monitoring & Evaluation

3.6.1 Introduction Monitoring and evaluation is an important phase in gauging the progress of implementation of SPARS. There will be a continuous process of collecting and analyzing information, assess the efficiency and effectiveness in implementing the Strategic plan. The NSS Technical Sub-committee on Agricultural Statistics (composed of representatives of structures producing agricultural and rural statistics), will be responsible for producing the monitoring and evaluation reports of the implementation of the Strategic Plan. These reports will be passed to the Steering Committee on Agricultural and Rural Statistics (SCARS) in its capacity as the decision-making body for SPARS_ZAM for endorsement (see Figure 3 on page 14). If in the future, the CSO will be a semi-autonomous agency with a Board of Directors, that Board will legally undertake the function of approving documents on all statistics programmes including SPARS_ZAM. 49 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA

3.6.2 Monitoring Tools The indicators in the detailed logical framework will be used in tracking the implementation and achievements of the SPARS_ZAM. In addition, for successful implementation of SPARS, activities shall be prioritized in the annual work plans and aligned to implementing institutions budgets and performance contracts.

3.6.3 Monitoring, Evaluation & Reporting Each subsector will, on a regular basis, provide status reports on activities being implemented, along with key performance indicators. The NSS Technical Sub-committee on Agricultural Statistics, through the SCARS secretariat, will consolidate the subsector reports on progress of SPARS implementation. The following reports will be produced:- Bi-annual report & Annual report Mid-term review report End of plan period evaluation report

3.6.4 Feedback Mechanism The reports generated will be communicated through management and stakeholder meetings, workshops and review-seminars, among others. The progress reports will prompt formulation of corrective measures where appropriate.

3.6.5 Results Based Logical Framework A Logical Framework for Monitoring has been developed to provide the Results-Chain giving the Impact, Outcomes and Outputs of SPARS_ZAM together with performance indicators (Baseline and Targets), means of verification, risks and mitigation measures. The Logical Framework is given in Annex 3 of this Strategic Plan.

3.7 Advocacy & Communication Plan In Zambia, Agricultural and Rural Statistics are generated and disseminated by different entities, including the Ministry of Agriculture (especially active on crop statistics), Ministry of Fisheries and Livestock, and the Central Statistical Office. Each of the entities is involved in the generation of agricultural statistics and has its own ways of disseminating the results; there is no single place where one can go to find all the agriculture and rural statistics. The new Census and Statistics Act provides for dissemination of all data collected using public resources. This provision requires that a well-defined and forward-looking dissemination strategy and 50 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 policy are formulated. The SPARS_ZAM, being a subset of the National Strategy for the Development of Statistics, will benefit from the provision of the over-arching NSDS. The SPARS communication plan aims at reaching the entire spectrum of agriculture statistics stakeholders through an all- inclusive and participatory approach. Under the NSDS, there will be statistics units formed in all the subsectors. Focal point persons from these units, with clearance from their supervisors, will be required to submit all official statistics they generate to a central repository managed by CSO. This repository or database will be accessible to all stake holders generating various official statistics, thus addressing the present problem where stakeholders have difficulty accessing official statistics and data. Agriculture and Rural statistics will benefit from this arrangement, as it will also increase its visibility if it is implemented the way it is planned. In addition to this channel, a SPARS Advocacy and Communication Plan will be developed, with, among others, the following objectives: • to increase stakeholder awareness of agricultural statistics; • to enhance stakeholder buy-in and goodwill for SPARS activities; • to create a platform for dialogue and advocacy in agricultural and rural statistics; and • to promote knowledge- sharing and adoption of new methodologies, tools and good practices in the field of agricultural statistics.

3.7.1 Implementation approach The SPARS shall guide implementation of sector statistical activities, thereby enhancing production of agricultural and rural statistics in the country. Implementation of the advocacy and communication plan will be through a holistic and integrated approach, involving the use of more than one channel or method to communicate the identified SPARS output. This will necessitate the creation of a multi- agency secretariat with membership drawn from organisations that are also implementing the NSDS. Thus, the following are (proposed) members of the secretariat: • Central Statistical Office (CSO) • Ministry of Agriculture (MoA) • Ministry of Fisheries and Livestock (MFL) • Ministry of Lands, Natural Resources and Environmental Protection (MLNREP) The tools and approaches that may be used to communicate SPARS outputs and also raise public awareness may include: Websites: All data producers in the NSS will be encouraged to establish websites containing pages that are dedicated to the statistical information they produce. The websites will be cross referenced to the websites of other data producers in the NSS. A specific website for SPARS will be developed. There is no doubt that with a dedicated website for SPARS, a larger audience will be reached and conveying key messages of SPARS will be easy. 51 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA Increasing visibility through advocacy materials: Advocacy materials (i.e. brochures, leaflets, posters, folders, T-shirts, etc.) are useful for raising awareness of SPARS outputs. Materials such as these are best disseminated at large public gatherings such as at the International Trade Fare, and the Agriculture and Commercial Show. These events are attended by tens of thousands of people. Preparation of SPARS-themed publications and statistics bulletins, convening special seminars and workshops at which SPARS-related topics/outputs are discussed are all good communication channels. Increasing SPARS outreach activities: such as issuance of e-bulletins and newsletters, use of public mass media (such as Community Radio Stations): The regular issuance of newsletters or e-bulletins will help increase outreach to specific audience groups with the objective of keeping them informed on activities, events, developments and reports and publications. Placing advertisements or running paid-for programmes on Community Radio Stations would also enhance the visibility of SPARS and its outputs. Meetings/Workshops: Arranging meetings and workshops with Politicians, especially Members of Parliament, Ministers and other key stake holders who can influence policy. Presenting SPARS outputs at Constituency and Ward levels would unfailingly win the support of this group of persons.

3.7.2 Stakeholder Mapping and Prioritization Matrix To effectively deliver its goals and objectives, the communication secretariat will as a first priority undertake a mapping of stakeholders' key to the delivery of the SPARS. This will assist in directing efforts in the weak areas for optimal results. The next crucial step will be to develop a priority matrix for categorization of the stakeholders into four categories according to their interest and influence in agricultural statistical activities. The structure of a Priority Matrix is given in Figure 7 below 52 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 Figure 7: Stakeholder Priority Matrix The groups of stakeholders (Audience/ target groups) and the key messages for each group are given in the table below. Table 8: Audiences and Key Messages 53 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA The SPARS shall guide implementation of sector statistical activities, thereby enhancing production of agricultural and rural statistics in the country. It will facilitate prioritization of agricultural sta- tistical programmes for informed planning, monitoring and evaluation, policy design and decision making. The SPARS communication plan aims at reaching the entire spectrum of agricultural statistics stakeholders through an all-inclusive and participatory approach. The Communication plan will be underpinned by an integrated and holistic approach. An integrated approach will encompass use of various methodologies/approaches in communicating the identified outputs. A multi-agency secre- tariat drawn from the implementing institutions and counties and will be anchored within the NSDS framework. 54 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023

Annex 1: Stakeholders Consulted It was agreed at the beginning of the Assessment phase that, to avoid causing respondent fatigue, each consultant was to visit a given number of institutions/ministries. Questionnaires from insti- tutions that were of common interest to more than one consultant were to be shared between the concerned persons. Thus, for example, the questionnaire from the Zambia National Farmers Union (ZNFU) was to be shared between the consultant covering crops, and the one doing Cross-Cutting Issues. a) Crop statistics Stakeholders Ministry/ Institute Division/ Department Function IAPRI (indaba Agricultural Policy Research and Outreach IAPRI’s mandate is to utilize empirical evidence to Research Institute) advise and guide the Government of the Republic of Zambia and other stakeholders on agricultural investments and policies. The overarching goal of IAPRI’s policy analysis and outreach efforts is to identify policies and investments in the agricultural sector that can effectively stimulate inclusive economic growth and poverty reduction. This is achieved through three core operational activities: • Producing trusted, impartial, and high-quality research on agricultural, food, and natural resource policy issues in Zambia and the wider southern Africa region; • Integrating research findings into national, regional, and international programs and policy strategies to promote sustainable agricultural growth and cut hunger and poverty in Zambia; and • Supporting the development and strengthening of capacity for policy research, analysis and outreach of public and private institutions in Zambia. Central Statistical Office Information and Research Collection, Analysis and Dissemination of Official Statistics Zambia National Farmers Union Promote and safeguard members interests. Also collects agric statistics. ZARI (Zambia Agricultural Re- Research Agricultural Research search Institute) Ministry of Agriculture Policy & Planning Unit University of Zambia School of Agriculture Offering Higher Education F.A.O Aims at eradicating hunger, food insecurity, etc. [Website more informative] FEWSNET (Famine Early Warn- Provision of Early Warning and Analysis on acute food ing Systems Network) insecurity. 55 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA b) Livestock statistics Stakeholders Ministry/Institute Division/Department Function Ministry of Fisheries and Livestock Veterinary Services To prevent, control and contain livestock disease vectors and pests in order to enhance livestock production and productivity Livestock Development To enhance livestock production and productivity Policy Planning and To plan, monitor and evaluate the implementation of ministerial and Information sector policies and programmes University of Zambia School of Veterinary To train human resource for the livestock sub-sector Medicine School of Agricultural To train human resource for the agricultural sector Sciences IAPRI To undertake agricultural policy research and outreach Central Statistical Office Dairy Association of Zambia To promote and safeguard the interests of the dairy industry Poultry Association of Zambia Enhance and support sustainable growth of the poultry industry whilst maintaining prosperity and profitability Zambia National Farmers Union To promote and safeguard the interests of the agricultural industry Forest Fruits To promote the marketing of honey Small Livestock Association of To undetake and promote the marketing of small livestock Zambia c) Fishery statistics stakeholders Ministry / Dept Division /Dept Function Dept of Fisheries MFL Capture fisheries The Department of Fisheries of the Zambian Ministry of Fisheries and Livestock oversees the implementation of national programs in Aquaculture capture fisheries and aquaculture development. It is responsible for the enforcement of relevant laws and policies related to aquatic resources Fisheries data & Information and carrying out regulatory functions. The Dept. promotes sustainable utilization of fisheries resources by employing research, extension strategies, training programs and promoting public/private partnerships at all levels of development and management. One of the Dept.’s major responsibilities is the collection, storage and analysis of data in collaboration with the Central Statistics Office IAPRI (indaba Agricul- Agricultural research, consul- IAPRI’s mandate is to utilize empirical evidence to advise and guide the tural Policy Research tancy and policy GZM and other stakeholders on agricultural investments and policies. The Institute) overarching goal is to identify policies and investments in the agricultural sector that can stimulate inclusive economic growth and reduce poverty. This is achieved through three core operational activities: • Producing trusted, impartial, and high-quality research on agricultural, food, and natural resource policy issues in Zambia and the wider southern Africa region; • Integrating research findings into national, regional, and international programs and policy strategies; and • Supporting the development and strengthening of capacity for policy research, analysis and outreach of public and private institutions . PMRC Research, training and policy A public policy Think Tank, PMRC collaborates directly with Government, development Media, Private Sector, Academia, Civil Society Organizations and general citizenry in it's work. WorldFish Fisheries research WorldFish is an international, non-profit research organization strengthening livelihoods and enhancing food and nutrition security by improving fisheries and aquaculture. WorldFish focuses on sustainable aquaculture, resilient small-scale fisheries and enhancing the contributions of fish to nutrition of the poor. It is is a member of the Consultative Group for International Agricultural Research (CGIAR) and leads the CGIAR FISH Research Program with its HQ in Penang, Malaysia &regional offices in Bangladesh, Cambodia, Egypt, Myanmar, Solomon Islands and Zambia (Africa Regional Office). National Assembly (Com- mittee on Agriculture) 56 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 d) Forestry & Environment Statistics Stakeholders Ministry/ Institute Division / Department Function Ministry of Lands Formulation and implementation of Land administration, Natural Resources and Forest Management and Environmental Protection Policies Environmental Protection Forestry Department Management, Protection, and conservation of forests to (MLNREP) ensure sustainable utilization and management for socio- economic development Environment and Natural Resources Coordination of the implementation of multilateral and Management Department bilateral environment and natural resources programmes and projects Planning and Information Department Development, monitoring and evaluation of policies, legislation and programmes on land natural resources and environment Zambia Environmental Formulation of policies on all aspects of the Environment Management Agency and sustainable management of the environment Timber Producers Promotion of sawmilling and value addition among the Association of Zambia timber industry players Community Based Natural Promoting the creation of secure livelihoods among Resources Forum communities for sustainable utilization of natural resources that include forestry, fisheries, water, agriculture land and wildlife. Centre for International Zambia Field Office Providing research on the most pressing challenges of Forestry Research forest and landscape management using multidisciplinary approach in order to improve the well-being of humans, protect the environment and increase equity Ministry of National Central Statistics Office To collect and analyze official data on economic and planning and development social indicators, disseminated on a timely basis as well as coordinating and providing quality and credible official statistics for use by stakeholders and clients for sustainable development Climate Change Secretariat Facilitate the coordination of all climate change activities being undertaken by government, the private sector and cooperating partners in order to achieve the aims and objectives of the National Climate Change Response Strategy Ministry of Transport and Meteorological Department Establishment and maintenance of a network of surface Communication and upper air stations for the purpose of observing various weather elements; Organization of telecommunications system for the rapid collection and dissemination of meteorological data a To process and analyze meteorological data for use in the planning of economic development and rational exploitation of natural resources Ministry of Water and Water Resources Management Authority To preserve and protect Zambia's ground and surface Energy water resources and regulate abstraction, allocation, use, development and management of water in a sustainable manner 57 e) Cross – Cutting Statistics Stakeholders Ministry/ Institute Division/ Department Function IAPRI (indaba Research and Outreach IAPRI’s mandate is to utilize empirical evidence to advise and guide the Agricultural Policy GZM and other stakeholders on agricultural investments and policies. The Research Institute) overarching goal is to identify policies and investments in the agricultural sector that can stimulate inclusive economic growth and reduce poverty. This is achieved through three core operational activities: • Producing trusted, impartial, and high-quality research on agricultural, food, and natural resource policy issues in Zambia and the wider southern Africa region; • Integrating research findings into national, regional, and international programs and policy strategies; and Supporting the development and strengthening of capacity for policy research, analysis and outreach of public and private institutions . Central Statistical Office Information and Research Collection, Analysis and Dissemination of Official Statistics Zambia National Farmers Promote and safeguard members interests. Also collects agric statistics. Union ZARI (Zambia Research Agricultural Research Agricultural Research Institute) Ministry of Agriculture Planning Unit University of Zambia School of Agriculture Offering Higher Education F.A.O Aims at eradicating hunger, food insecurity, etc. FEWSNET (Famine Provision of Early Warning and Analysis on acute food insecurity. Early Warning Systems Network) STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023

Annex 2: List of Participating Persons/Institutions Technical and Financial Support African Development Bank (AfDB) International Consultants Mr. Enock Ching’anda, AfDB Mr. Enock Ngendakuman, AfDB Dr. Jeremiah Banda, AfDB Mr. Richard Harris, AfDB National Strategy Coordinator Mr. Patrick Mwendalubi Chuni National Consultants Mr. Bwalya Chendauka, National Consultant, Forestry Subsector Mr.Patrick Ngalande, National Consultant, Fisheries Subsector Mr. Daniel Daka, National Consultant, Cross-cutting Issues Mr. Dingiswayo Banda, National Consultant, Crops Subsector Dr. Mataa Liywali, National Consultant, Livestock Subsector Central Statistical Office Staff who participated in the development of SPARS_ZAM Mr.Frank Kakungu Mrs. Sheila Mudenda Mr. Antony Silungwe Mrs. Nelly Nsamwa Mbewe 59 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA

Annex 3: Results Framework RESULTS CHAIN PERFORMANCE INDICATORS Means of Verification Risks & Mitigating Actions Indicator Baseline Target IMPACT High quality Agricultural 55.40% 80.00% Calculated and agricultural Statistics Capacity -2017 -2023 published biennially statistics covering Indicator African Development all subsectors are Bank in ‘Progress on produced in an the Capacity of African efficient, timely Countries to Produce and sustainable Timely, Reliable and manner and made Sustainable Agricultural available to all Statistics’ users EFFECTS 1. The production Integration of Partial Full Natonal Strategy for Best practice in data collection of agriculture agriculture sector in integration integration the Development of is not followed. statistics is NSDS (NSDS 2014- (NSDS 2019- Statistics for Zambia Mitigating action: coordinated and 18) 23) Use co-ordination harmonised mechanisms and NSDS process to create peer pressure to maintain standards across the sub- sectors 2. User needs are Percentage 75% IdCA (Annex 4), SPARS_ Stakeholders unwilling or increasingly met of data needs -2023 ZAM mid-term and end lack capacity to use data for reported as met by of plan reports decision making. stakeholders Mitigating action: Increase appreciation and development Agricultural 75.80% 96% Published by African of monitoring frameworks in Statistics Capacity -2017 -2023 Development Bank (as agricultural sector plans Indicator – above) Dimension 4 (Availability of Statistical Information) 3. The collection, Percentage of 48% 80% IdCA (Annex 3), SPARS_ Calendar of processing, assessments of -2017 -2023 ZAM mid term and end data collection activities is not reporting and supply of statistics of Plan reports. implemented. dissemination rated of good quality of agriculture by stakeholders statistics is efficient and effective. Agricultural 59.30% 73% Published by African Mitigating action: An advocacy Statistics Capacity -2017 -2023 Development Bank (as and communication strategy Indicator – above) will be developed for SPARS_ Dimension 3 ZAM which together with (Statistical Methods the full implementaion of & Practices) NSDS2 will raise the profile of agricultural statistics. Some of the stages in the statistics production cycle not funded Mitigating action: Ensure all stages are funded before embarking on activity 60 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 RESULTS CHAIN PERFORMANCE INDICATORS Means of Verification Risks & Mitigating Actions Indicator Baseline Target IMPACT 4. Human Number of 20 35 IdCA (section 5.3), High staff turnover. resource capacity statistician posts -2017 -2023 SPARS_ZAM mid term Mitigating action: Development for agricultural in agriculture and end of Plan reports. of capacity building and statistical activities statistics units filled training programme. strengthened by professionaly Inadequate resources directed qualified staff at Human Resource Capacity Building. Mitigating action: Agricultural 42.10% 53% Published by African Ensure government buy-in Statistics Capacity -2017 -2023 Development Bank (as HRCBP through advocacy; let Indicator – above) Gvt see value in having trained Dimension 2 staff. (Resources – average of human resources, staffing & training) 5. Availability All funds provided 55% 80% SPARS_ZAM mid term Funds are not available for of funds leads or available as -2018 and end of Plan reports. priority activities. to timely a percentage of Mitigating action: An advocacy -2023 implementation of total funding to and communication strategy planned statistical implement SPARS_ will be developed for SPARS_ activities. ZAM ZAM which together with the full implementaion of Agricultural 41.70% 88.30% Published by African NSDS2 will raise the profile of Statistics Capacity -2017 -2023 Development Bank (as agricultural statistics Indicator – above) Dimension 2 (Resources – financial resources) PRODUCTS 1.1: Improved Number of 0 4 Minutes of NSS Data management policies are coordination, NSS technical -2017 2022) technical sub-committee not implemented collaboration and sub-committee meetings on agriculture Mitigating action: networking among meetings on stakeholders agricultural statistics per year with representation from all stakeholders 1.2:Improved Compendium of No Compendium Minutes of NSS technical Use co-ordination technical data collection compendium published sub-committee on mechanisms and NSDS coordination methodologies agricultural statistics process to create peer among data for agricultural pressure to maintain producers statistics standards across the sub- sectors Metadata handbook No handbook Handbook Minutes of NSS for agricultural published technical sub-committee statistics on agricultural statistics 61 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA RESULTS CHAIN PERFORMANCE INDICATORS Means of Verification Risks & Mitigating Actions Indicator Baseline Target IMPACT 2.1 An integrated Percentage of 0 90% Minutes of NSS technical Data gaps not filled. census and survey reports available for sub-committee on Mitigating action: An advocacy programme is census and survey agricultural statistics and communication strategy implemented activities listed in will be developed for SPARS_ the Calendar of ZAM which together with Data Collection the full implementaion of Activities NSDS2 will raise the profile of 2.2 The Percentage of 0 75% Minutes of NSS technical agricultural statistics compilation, reports available sub-committee on analysis and for administrative agricultural statistics reporting of data data activities listed including from in the Calendar administrative of Data Collection records is Activities expanded 3.1 Agricultural Agricultural By 2023 Minutes of NSS technical Management do not react to statistics statistics units sub-committee on monitoring reports and do not units have an have approriate agricultural statistics take appropriate and office space and Annual reports of CSO. remedial action where adequate physical equipment MoA, MFL, MLNR, necessary. infrastructure MWSEP, WARMA, ZEMA Mitigating action: 3.2 Data Availability of 70.70% By 2019 IT reports to CSO, MoA, The proposed new Statistics dissemination and Web portals for -2017 MFL, MLNR, MWSEP, Act (when passed) will give access is improved dissemination WARMA, ZEMA the new National Bureau of of agricultural Statistics a specific function to statistics oversee the NSS. Agricultural 100% Published by African Adequate office space not Statistics Capacity -2023 Development Bank (as provided for statistical units. Indicator – above) Dimension 4 (Output Mitigating action: Government dimension – Data to build new office blocks accessability) 3.3 Agricultural Percentage of 0 75% Minutes of NSS technical statistics activities activities in the -2023 sub-committee on are regulartly Calendar of Data agricultural statistics monitored Collection Activities submiting annual monitoring report to NSS technical sub-committee on agricultural statistics 62 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 RESULTS CHAIN PERFORMANCE INDICATORS Means of Verification Risks & Mitigating Actions Indicator Baseline Target IMPACT 4.1 Number of Percentage of 0 100% Minutes of NSS technical Qualified staff do not apply for professional new statistician -2017 sub-committee on vacancies. level posts for posts identfied agricultural statistics Mitigating action: Development agricultural within agricultural of capacity building and statisticians statistics units Annual reports of CSO, training programme for increased that are added MoA, MFL, MLNR, existing staff. to establishment MWSEP, WARMA, ZEMA Trained staff do not have register opportunities to apply their skills. Mitigating action: Promotion of secondary analysis of datasets. Local Universities do not offer training in Agriculture Statistics, let alone official Statistics 4.2 Training Training programme None Training Annual reports of CSO, Mitigating action: Utilise programmes Trainee feedback -2017 programme MoA, MFL, MLNR, regional training centres such to develop the satisfaction rating published MWSEP, WARMA, ZEMA as East African Statistics capacity of annually Minutes of NSS technical Training Centre agricultural >80% sub-committee on statistics agricultural statistics personnel operational 5.1 Collaborations Percentage of 0 100% (2023) Minutes of NSS technical Funding for agricultural to support NSS technical sub-committee on statistics activities is not sustanable funding sub-committee agricultural statistics incresased. for agricultural meetings on Mitigating action: statistics activities agricultural An advocacy and enhanced and statistics with communication strategy will strengthened representation from be developed for SPARS_ZAM at least 1 DP which together with the full implementaion of NSDS2 will 5.2 Awareness of Percentage Not collected >80% Annual reports of CSO, raise the profile of agricultural the importance stakeholder MOA, MFL, MLNR, statistics of agricultural feedback rating MWSEP, WARMA, ZEMA statistics increased sensitisation event highly 63 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA

Annex 4: Risk & Mitigation Serial No. RISKS PROBABILITY IMPACT MITIGATION 1 Stakeholders unwilling Low High There is high appreciation and or lack capacity to development of monitoring use data for policy frameworks in agricultural and decision making, sector plans – implementation of monitoring and SPARS_ZAM will strengthen these evaluation. frameworks. 2 Financial resources are Medium High An advocacy & communication not available strategy will be developed for SPARS_ZAM which, together with full integration into NSDS2, will raise the profile of agricultural statistics for government & Development Partners. 3 CSO and line ministries Medium Medium Build on the current coordination are unable to establish mechanisms such as the SCARS, and implement STWGs & NSDS steering committee co-ordination and collaboration mechanisms 4 Data management Medium Medium Use the co-ordination mechanism policies are not and NSDS to create peer pressure to implemented maintain standards across the sub- sectors. 5 Management do not Low Medium The proposed Statistics Act (when react to data quality passed by Parliament) will give the reports and do not take new National Bureau of Statistics a remedial action where specific function to oversee the NSS. necessary. 6 High staff turnover Medium Medium Development of capacity building & training programme 7 Staff do not have the Low Medium Promotion of secondary analysis of opportunity to build and datasets. apply their skills. 64 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023

Annex 5: Detailed Action Plan with Budget US $000 Activities Responsibility 2018 2019 2020 2021 2022 Total SG1: IMPROVE COORDINATION & MANAGEMENT OF AGRICULTURAL STATISTICS OS1.1: Improve coordination, collaboration and networking among stakeholders 1.1.1 Review and revise agricultural MOA, CSO, MFL, MLNR, 95 75 25 10 10 215 statistics legal & programme MWSEP, MNDP, MOJ frameworks 1.1.2 Participate in NSS meetings MoA, CSO, MFL, MLNR, 67 62 54 54 39 276 including technical working MWSEP groups on agricultural statistics and development of next gener- ation NSDS 1.1.3 Improve networking with MoA, CSO, MFL, MLNR, 133 293 120 120 135 801 stakeholders - national and MWSEP international OS1.2: Improve technical coordination among data producers 1.2.1 Develop a metadata handbook to MoA, CSO, MFL, MLNR, 150 50 0 40 0 240 harmonise statistical definitions MWSEP for the agriculture sector 1.2.2 Develop a compendium of best MoA, CSO, MFL, MLNR, 90 60 0 55 0 205 practice methods for collecting MWSEP agricultural statistics 1.2.3 Disseminate metadata & meth- MoA, CSO, MFL, MLNR, 44 144 105 50 40 383 odology handbooks and raise MWSEP awareness Total SG1 579 684 304 329 224 2,120 SG2: MEET THE NATIONAL & INTERNATIONAL DEMAND FOR AGRICULTURE STATISTICS OS2.1: Adopt an integrated census and survey programme 2.1.1 Integrated census & survey MoA,CSO, MFL, 2,340 2,840 2,340 2,340 8,340 18,200 programme – crops[1] 2.1.2 Integrated census & survey MFL, CSO 700 1,200 6,700 700 900 10,200 programme – livestock[2] 2.1.3 Integrated census & survey MFL, CSO 1,255 1,275 1,305 1,335 1,355 6,525 programme – fisheries 2.1.4 Integrated census & survey MLNR, CSO, MoE, MTC, 255 200 555 305 50 1,365 programme - forestry & envi- MWDSEP, MID ronment OS2.2: Enhance the compilation, analysis and reporting of data from administrative records 2.2.1 Develop data collection Instru- MLNR, MWSEP, CSO 100 75 50 10 0 235 ments for environment and climate change 2.2.2 Train & sensitise staff to collect MoA, CSO, MFL, MLNR, 90 80 160 100 150 580 statistics from administrative MWSEP processes 2.2.3 Support routine data collection MoA, CSO, MFL, MLNR, 340 332 335 345 355 1,707 and reporting MWSEP 65 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA US $000 Activities Responsibility 2018 2019 2020 2021 2022 Total 2.2.4 Enhance the analysis of data MoA, CSO, MLNR, MWSEP, 145 145 65 65 85 505 MNDP, ZEMA, WARMA Total SG2 5,225 6,147 11,510 5,200 11,235 39,317 SG3: STRENGTHEN STATISTICAL OPERATIONS & PROCESSES OS3.1: Provide appropriate and adequate physical infrastructure 3.1.1 Acquire/refurbish and equip MoA, MFL, MLNR, 1,117 319 124 74 39 1,673 offices for agricultural statistics MWSEP,ZEMA,WARMA, MTC activities 3.1.2 Procure transport equipment for MoA, MFL, MLNR, MWSEP, 900 640 121 190 90 1,941 data collection (vehicles, motor MNDP, ZEMA, WARMA, MTC cycles, boats) 3.1.3 Procure computer and other MoA, MFL, MLNR, MWSEP, 1,713 575 395 275 388 3,346 specialised survey equipment MNDP, ZEMA, WARMA, MTC 3.1.4 Pilot and introduce new MoA, CSO, MLNR, MWSEP, 120 170 120 120 170 700 initiatives for data collection / MNDP, ZEMA, WARMA, MTC processing OS3.2: Improve data access, knowledge management and communication 3.2.1 Develop & implement data qual- MoA, CSO, MFL, MLNR, 205 60 50 50 80 445 ity, data access and confidential- MWSEP ity policies 3.2.2 Develop and implement an CSO, MFL 100 70 130 150 150 600 Information, Knowledge Man- agement and Communication (IKMC) Strategy 3.2.3 Establish and maintain data MoA, CSO, MFL, MLNR, 172 206 56 56 56 546 centres and web portals for MWSEP agricultural statistics OS3.3: Monitor & improve data quality 3.3.1 Train agricultural statistics staff MoA, CSO, MFL, MLNR, 225 120 110 75 85 615 and stakeholders to monitor MSWEP agricultural statistics activities 3.3.2 Ongoing monitoring of statistical MoA, CSO, MFL, MLNR, 115 110 100 90 80 495 activities MSWEP 3.3.3 Annual data quality workshops MoA, CSO, MFL, MLNR, 70 70 70 70 70 350 and reports MSWEP Total SG3 4,737 2,340 1,276 1,150 1,208 10,711 SG4: IMPLEMENT A COMPREHENSIVE HUMAN RESOURCE STRATEGY OS4.1: Establish and maintain a critical mass of skilled agricultural statistics personnel 4.1.1 Set up / strengthen agriculture CSO, MFL, MLNR, MSWEP 135 55 25 25 25 265 statistics unit 4.1.2 Recruit and deploy adequately CSO, MFL, MLNR, MSWEP 300 270 280 280 280 1,410 qualified personnel OS4.2: Establish a training programme to develop the capacity of agricultural statistics personnel 66 STRATEGIC PLAN FOR AGRICULTURE & RURAL STATISTICS SPARS_ZAMBIA 2019 - 2023 US $000 Activities Responsibility 2018 2019 2020 2021 2022 Total 4.2.1 Training of agricultural statistics MoA, CSO, MFL, MLNR, 934 646 523 652 535 3,290 personnel MWSEP, ZEMA, WARMA Total SG4 1,369 971 828 957 840 4,965 SG5: DEVELOP SUSTAINABLE FUNDING FOR STATISTICAL OPERATIONS OS5.1: Enhance and strengthen collaborations to support sustainable funding for agricultural statistics activities 5.1.1 Organise advocacy meetings to MoA, CSO, MFL, MLNR, 105 70 70 50 75 370 lobby for funds for agriculture MWSEP, ZEMA, WARMA statistics activities 5.1.2 Organise workshops to prepare MoA, CSO, MFL, MLNR, 100 35 60 35 35 265 funding proposals MWSEP OS5.2: Advance the awareness of the importance of agricultural statistics 5.2.1 Hold sensitization events for MoA, CSO, MFL, MLNR, 87 82 77 67 62 375 stakeholders & policy makers MWSEP 5.2.2 Use media to increase aware- MoA, CSO, MFL, MLNR, 81 81 81 81 81 405 ness and provide education MWSEP, ZEMA, WARMA Total SG5 373 268 288 233 253 1,415 TOTAL SPARS_ZAM 12,283 10,410 14,206 7,869 13,760 58,528 [1] Includes budget provision to include an agriculture module in Population & Housing Census [2] Includes budget provision for a joint Livestock and Aquaculture census conducted as a large scale sample survey 67 ACTION PLAN TO IMPROVE STATISTICS FOR FOOD SECURITY, SUSTAINABLE AGRICULTURE AND RURAL DEVELOPMENT IN AFRICA

Annex 6: Cited Literature 1. National Strategy for the Development of Statistics (NSDS) for Zambia. 2014 – 2018. 2. The National Strategy for the Development of Statistics (NSDS) for Zambia. Situational Analysis and Statistical Needs Assessment Report. Lusaka.2008. 3. Statistics South Africa. Statistical quality considerations that can be useful for the production of the innovation survey. Presentation to workshop on the review of innovation measurement in South Africa. 9 November 2011. 4. CSO. Report of the 2010/2011 Agricultural Season Post Harvest Survey. 2012. 5. FAO. Livestock Data Collection Manual: Concepts, definitions and classifications. Rome. 2011. 6. GRZ.ASIP - A New Approach to Agriculture in Zambia. MAFF. 1995. 7. Kaplinsky and Morris. A Handbook for Value Chain Research. IDRC.2001. 8. SADC. Animal Health Year Book 2010. Gaborone. 2011. 9. Redman TC. Data Quality. The Field Guide. Boston. Digital Press. 241. 2001. 10. MAL. Role of mobile technology in livestock disease surveillance. A review of the Digital Pen Technology in Zambia. 2012. 11. ADB. Report of the Regional Training Workshop for the Development of SPARS for En- glish-speaking Countries. Zambia, 2015. 12. FAO. SPARS Guidelines. Rome. 2014. 13. Mataa L. Reporting Livestock Disease Information in Zambia. Constraints and Challenges. Tropicultura. 2005. 14. MAL. National Agricultural Policy 2004 - 2015. Lusaka. 2004. 15. MAL. Monitoring and Evaluation Manual. Lusaka. 2013. 16. Strategic plan For Ministry of agriculture and Livestock ,2014 - 2016 17. National Agricultural Policy, 2004-2005 18. Revised Sixth National Development Plan (R-SNDP) 19. Zambia National Agriculture Investment Plan (NAIP) 2014-2018 68