Modernization of Food & Agriculture Statistics in support of SDG2 Pietro Gennari Chief Statistician, FAO Enhancing the evaluability of Sustainable Development Goal 2 IFAD, November 2015 Theme 3: National M&E systems and data availability – building on the progress made and addressing existing (capacity) gaps.

SDG process & New data requirements

SDG indicators will drive the international statistical agenda for the next 15 years UN Statistical Commission responsible for developing SDG monitoring framework IAEG-SDG: 28 countries as members, IOs as observers RBAs jointly prepared the proposal for SDG 2. This proposal was agreed by all Chief Statisticians of the UN Electronic discussion platform active in July-October 2 nd meeting of the IAEG-SDG on October Recommendations on the list of indicators: – Green indicators: accepted; to be finalized by end of November – Grey indicators: need further work, hopefully before the UNSC Formal adoption UNSC March 2016 Selection of the 2030 SDA Indicators 3

New data requirements (1) – Global, regional, national & thematic monitoring an indicator architecture – A comprehensive and complex agenda with 169 multidimensional targets around 230 global indicators many new indicators, no established methodology, data not currently produced some indicators produced outside of the national statistical system – A universal agenda, for both developed & developing countries different indicators for rich and poor countries

New data requirements (2) – An ambitious agenda: not only reducing, but eliminating hunger accuracy of indicators for values close to 0 (thresholds) – Emphasis on monitoring inequalities within countries disaggregated data for territorial areas and vulnerable groups of the population – A policy-oriented agenda that can help guide interventions, identify results chains & drivers of change need for real time data inclusion of instrumental targets (MoI) possibility to establish links between outcome targets

Indicators to monitor SDG 2

Target 2.1: Ensure food access by all people ….. Ind Prevalence of Undernourishment (PoU) Ind Prevalence of population with moderate or severe food insecurity, based on Food Insecurity Experience Scale (FIES) Target 2.2: End all forms of malnutrition…. Ind Prevalence of Stunting in children Ind Prevalence of Wasting in children Target 2.3: Double agricultural productivity & incomes of small-scale food producers …. Ind Volume of production per labour unit by classes of farming/pastoral/forestry enterprise size Target 2.4: Ensure sustainable food production systems … Ind % of agricultural area under sustainable agricultural practices 7 List of green & grey indicators for SDG2

Target 2.5: Maintain genetic diversity …. Ind Ex-situ crop collections indicator Ind Percentage of local breeds classified as being at-risk, not-at- risk, & unknown risk of extinction Target 2.a: Increase investment …. Ind. 2.a.1 Agriculture Orientation Index for Government Expenditures Target 2.b: Correct trade distortions in world agricultural markets …. Ind. 2.b.1 Agricultural Export Subsidies / OECD Producer Support Estimates Ind. 2.b.2 % change in Import and Export tariffs on agricultural products Target 2.c: Proper functioning of food commodity markets …. Ind. 2.c.1Indicator of (food) Price Anomalies (IPA) 8 List of green & grey indicators for SDG2

Level of development of National Agricultural Statistical Systems

Progress in social statistics and MDGs, but poor status of agricultural statistics No regular system of surveys in place between two censuses; Admin data/extension workers main data source (“eye estimates”) Specialization of surveys often conducted on “ad hoc” basis Old/expensive/inefficient methods Limited policy relevance of the available data (no linkage with socio-economic dimensions; no link with non-farm activities; poor timeliness; limited data access) Current status of Ag Statistics

Limited funding for agricultural statistics (agricultural statistics not a priority; poorer countries have the poorest data) Lack of human resources, limited technical capacity in data collection & analysis Agricultural data often collected in institutional isolation (different methods & survey instruments; Agriculture not mainstreamed into the NSDS) Lack of a conducive political/institutional environment (MoA main producer of Ag. Stat.; little coordination between MoA and NSO; Conflicts, Fragile States, Authoritarian regimes) Lack of capacity to regularly produce basic agricultural data Lack of capacity to respond to emerging data needs (SDGs) Causes & Consequences

Modernization of Agricultural Statistics

New technologies/New data sources Geo-referencing with handheld GPS or tablets: crop area measurement, geo-positioning survey units and linking to GIS & Google Earth for monitoring and data dissemination. Remote sensing data for building area frames for agricultural surveys; measuring crop areas and monitoring land use (forest, water, etc.) Open-source CAPI software for the collection of complex farm/household surveys. Mobile devices’ application enabling real-time validation, processing and transmission for simple surveys on prices, pest & diseases, food security Crowd-sourcing for low-cost data collection

The Global Strategy to Improve Agricultural Statistics has developed and published 20 new technical reports/guidelines/handbooks including : Linking Agricultural and Population Censuses Methods to develop and use Master Sampling Frames for Agricultural Surveys Methods for estimating crop area, yield and production under mixed, repeated and continuous cropping Improved methods for Crop Forecasting Methods for estimating Cost of Production Methods for estimating Stocks Methods for measuring Post-Harvest Losses of specific crops through the entire supply chain Methods for estimating Livestock production and productivity Improving the quality and use of data from Administrative sources for agricultural statistics New Guidelines

Coordination of surveys Multiple indicators generated by the same survey Promote an increased coordination among HH surveys, international sponsored (MICS, DHS, LSMS, LFS) or national (HIES): – Standardization of definitions & classifications – Standardization of questionnaires – Coordination of the timing in carrying out the surveys Promote the use of multipurpose HH and Farm surveys, especially in poor countries: add standard modules for collecting data on multiple SDG indicators Promote the implementation of a multiyear programme of surveys: indicators updated every 3-5 years

Food Insecurity Experience Scale (FIES)

New indicator of food access for global and national monitoring required by SDG Target 2.1 Existing only in few countries. Global Monitoring cannot be based on national sources in the short-term For the 1 st time FAO to produce a global food access indicator through direct data collection (Voices of the Hungry Project) Established the Food Insecurity Experience Scale (FIES), a metric for the severity of food insecurity for households or individuals Since 2014 annual FIES estimates for about 150 countries, through the Gallup World Poll Technical assistance provided to countries to introduce the FIES in national household surveys and eventually take over What is the FIES? 17

It provides a direct measure of people’s ability to access food 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 A new metric for both households and individuals, thus proper analysis of gender related food insecurity disparities The short questionnaire (9 yes/no questions) can be easily applied in virtually any household or individual survey Rapid and low cost – enables timely global monitoring Governments can use the indicator for targeted intervention, and monitoring/measuring impact of policies/programmes FIES: main benefits 18

Agricultural and Rural Integrated Survey (AGRIS)

Standardized multipurpose survey on Agricultural Farms 10 yr programme with rotating modules = collection of many variables with reduced costs & burden (1-2 modules per year) – Core Module with production + socio-demographic variables = every year – Additional Modules (Type of employment, Cost of production and prices, Use of Machinery, Production methods, etc.) = each module every 3 yrs 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 What is AGRIS?

AGRIS: Expected Results Provide countries with an integrated programme of agricultural surveys – for collecting annual and structural agricultural data – for collecting data on the economic, social and environmental dimensions of the farms Provide a tool for testing new cost-effective methodologies for agricultural statistics developed under the Global Strategy Build country capacity to collect the minimum set of core data Provide estimates on the productivity of small holders and other SDG indicators at national & international levels Make available standard modules for collecting agricultural & data in national farm surveys

Global Survey Hub Establishment of a Global Survey Hub (WB-FAO-IFAD-Bank of Italy) – a one-stop shop for the implementation of Integrated Agricultural Surveys – knowledge centre for methodological documentation and micro- data archive Tackle relevant methodological challenges for harmonizing LSMS-ISA and AGRIS approaches (Harmonization of core modules; harmonization of definitions & classifications) Pilot AGRIS in limited number of countries Improved linkages to other data sources e.g. Big Data, Geo-spatial Long-term objectives Develop methodological and operational guidelines Expand the use of integrated agricultural survey data in LDCs Institutionalize Integrated Agricultural Surveys in the national Statistical Master Plan

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