Strengthening statistical capacity in ACP countries Lessons learned

Strengthening statistical capacity in ACP countries: Lessons learned and opportunities of harnessing the data revolution Pietro Gennari FAO Statistics Division

Overview of presentation Characteristics of the Agricultural Sector and Implications for Data Collection Ø Level of Development of Agricultural Statistical System Ø Success Stories in Statistical Capacity Development Ø Data revolution and improvement of data collection methods in agricultural statistics Ø The role of Big Data Ø The AGRIS project Ø The Voices of the Hungry Project Ø

Characteristics of the Agricultural Sector in ACP countries: Implications for Data Collection

Common features of ACP countries: • • • Smallholder and subsistence agriculture prevalent form of farm organization High degree of diversification of rural economies in farm & nonfarm activities Multiple and mixed cropping widespread Distinctive characteristics of Small Islands States • • Relatively greater importance of fishery & forestry; urban agriculture; Obesity and quality of the diet more important than food insecurity; Heavy dependence on food imports and agricultural subsidies (migration of smallholder out of agriculture); Vulnerability to shocks, including to volatility of international prices, climate change and natural disasters.

Level of Development of the Statistical System in ACP countries

• Progress in social statistics and MDGs, but poor status of agricultural statistics. • Sporadic farm surveys (less than 50% of African countries have conducted 1 ag census or survey since 2000 and mostly are ag census); admin data/extension workers main data source. • Old/expensive/inefficient methods in agricultural statistics • Agricultural data often collected in institutional isolation (different statistical units & survey instruments; little coordination between Mo. A and NSO and with other sectors; Agriculture not mainstreamed into the NSDS) • Limited policy relevance of the available data (no linkage with socioeconomic dimensions; no link with non-farm activities; poor timeliness; limited access)

• Limited funding for agricultural statistics (poorer countries have the poorest data); • Lack of human resources, limited technical capacity in data collection & analysis • In small islands, Size of statistical institutions too small to reach the necessary critical mass; • Lack of a conducive political/institutional environment (Negative consequences of Conflicts, Fragile States, Authoritarian regimes; statistics office often dependent from Ministry of Planning)

Capacity development in Agricultural Statistics: Success stories in ACP countries

• Integration of different data sources • Linking Agricultural and Population Census (Mozambique, Burkina Faso) • Integrated household surveys with a module on agricultural production (LSMS-ISA in 7 African countries; SPC-led HH survey in the Pacific) • Link statistics to policy-making: Support to monitoring the CAADP results framework (2015 -2025 strategy) & the National Agricultural and Food Investment Plans (NAIFPS) • Designing open data policies in the Nigeria Federal Ministry of Agriculture (FAO AMIS project)

• Use of new technologies for agricultural statistics: • Geo-referencing with handheld GPS or tablets (Gambia, Malawi & Uganda): crop area measurement, geo-positioning survey units and linking to GIS/Google Earth for monitoring and data dissemination. • Satellite images/remote sensing tools: area frames for agricultural surveys (Ethiopia, Rwanda); monitoring land use (forest, water, crops, etc. ); impact of natural disasters on ag. productivity. • Open-source CAPI software for Ag Census (Mozambique) and complex farm surveys. • Mobile devices’ applications (Cameroon: low-cost data collection enabling real-time validation, processing and transmission for simple surveys on prices, pest & diseases, food security.

Data revolution & improvement of data collection methods in Agricultural Statistics

• Big data is not the solution to all current data problems • biased results due to non-representative samples • only indirect measurement of social phenomena (need of a gold standard to compare Big data estimates; need to periodically update statistical models that link. Big data estimates and official statistics) • only trend measurement, not levels • data not openly accessible (proprietary information, confidentiality) • bypassing national institutions (need to build capacity in the NSO to process Big data and to develop statistical models) • Big data can complement/strengthen official statistics • Satellite images and other remote sensing tools • Internet-scraping: compile internet searches to provide information on the current concerns of local populations (food and water shortages, infrastructure failure, spread of diseases, local conflicts). • Need of integrated solutions that combine the use of new technologies with innovative and cost-effective survey methods

Voices of the Hungry Project (Vo. H)

� Prevalence Problems with current Food Security indicators of Undernourishment: ◦ Complex methodology and low quality of basic data ◦ Impossible to obtain sub-national estimates (essential for designing & monitoring national policies) ◦ 2 -3 years time lag � Indicators based on Food consumption/nutritional outcomes: ◦ Indirect measurement of food insecurity, reflecting not only changes in the target variable (health, water/sanitation access) ◦ Sporadic surveys with incomplete country coverage ◦ 3 -5 years time lag ◦ Data collection difficult and costly

Main benefits of the Vo. H � People´s access to adequate food is measured directly, a key dimension of food security for which proper indicators are missing � Enables assessment of the depth of food insecurity (mild, moderate, or severe) => can be used in developed countries � A sound methodology (Item-Response Theory) allows assessment of reliability and precision of the measures � Allows assessment of food insecurity experiences at the individual level, thus proper analysis of gender related food insecurity disparities � Rapid and low cost – enables timely global monitoring � Complements other existing measures of food security � Ideal indicator for the Post-2015 Development agenda (food access target)

Expected Results � Establish a global standard (Food Insecurity Experience Scale - FIES) for measuring the severity of food insecurity that allows comparisons over time, across countries and across social groups: ◦ 8 simple yes/no questions to reveal food-related behaviors and experiences associated with increasing difficulties in accessing food ◦ This standard can be applied in any national HH survey � Provide estimates the prevalence of moderate and severe food insecurity in 150+ countries in 2014 and 2015, and to set a benchmark against which to monitor SDG progress at national level. � Make available the linguistic and cultural adaptation of the questionnaire to any interested user in more than 200 languages. � Make available open source software for the collection and processing of survey data � Promote adoption of the FIES in national food security monitoring systems, by including the module in national household surveys

Agricultural and Rural Integrated Survey Project (AGRIS)

� Lack WHY AGRIS? of reliable data on small-holders (drivers of poverty and hunger eradication): ◦ crop yields, cost of production, farm and non-farm income, farming practices (including use of water, fertilizers, pesticides), use of machinery, women access to land contribution to agriculture. � High quality agricultural data mainly through Agricultural Censuses (only every ten years, no production data) � No regular system of surveys in between two censuses to provide annual production data & forecasts � Specialized surveys: no possibility of linking economic & social data � Agricultural Statistical systems based mainly on reports produced by extension workers through eye estimates or production targets � Objective of AGRIS: to provide a cost-effective and flexible survey tool to regularly produce a minimum set of reliable agricultural data that can be disaggregated by type of farms, geographical areas and population groups

WHAT IS AGRIS? � Standardized multipurpose survey on Agricultural Farms, � with rotating modules = collection of a large number of variables with reduced costs and limited burden (only 1 -2 modules per year) ◦ Core Module (production & socio-demographic data) = every year ◦ Additional Modules for structural data (Type of employment, Cost of production and prices, Use of Machinery, Farming Practices, etc. ) = each module every 3 -5 years � Integrated approach: � Economic data (production, inputs, farm-gate prices, production cost, farming practices, etc. ) � Social data (sex, age, education, type of employment, income) � Environmental data (land use, water use, pesticides, etc. ) � Data collection = use of new technologies, including GPS, CAPI, RS

Modality of Implementation § § Dependent on countries’ statistical programme On-going annual agricultural survey: likely that the annual survey collects only part of the minimum set of core data: ◦ AGRIS modules could be added to the annual survey to cover missing data and survey design could be improved using GS guidelines On-going LSMS-ISA survey: data is likely to be collected only every 3 years and to cover only part of the GS MSCD ◦ AGRIS could complement annual data and the rest of GS minimum set of core data Agricultural Census: ◦ AGRIS could build on the census result to introduce a regular survey No LSMS or annual agricultural survey: ◦ AGRIS will be the vehicle for collecting the minimum set of core data