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Informing Strategic Investments in Enhancing Agricultural Technology Development and Use: The Role of Agricultural Stati

Informing Strategic Investments in Enhancing Agricultural Technology Development and Use: The Role of Agricultural Statistics. Stanley Wood Senior Research Fellow, International Food Policy Research Institute (IFPRI) Co-Principal Investigator, Harvest Choice.

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Informing Strategic Investments in Enhancing Agricultural Technology Development and Use: The Role of Agricultural Stati

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  1. Informing Strategic Investments in Enhancing Agricultural Technology Development and Use: The Role of Agricultural Statistics Stanley Wood Senior Research Fellow, International Food Policy Research Institute (IFPRI) Co-Principal Investigator, HarvestChoice Contribution of Partners in the Development of Agricultural Statistics in Africa Twentieth Session, African Commission on Agricultural Statistics Algiers, Algeria, 10-13th December 2007

  2. Overview • Reinvigorated engagement in agricultural development in Africa • What is HarvestChoice? • Need for improved agricultural statistical data to support strategy/policy/ investment analysis • HarvestChoice/FAO initiatives related to agricultural statistics in Africa

  3. Re-engagement in Agriculture(some examples) • NEPAD’s explicit strategy on the role of agricultural growth in economic growth→ CAADP→ReSAKSS→National Strategic (planning, design, M&E) Information System & Analysis Capacity • World Bank:Multi-Country Agricultural Productivity Program (MAPP), Rural Infrastructure, [Re-emphasis: World Bank Assistance to Agriculture in Sub-Saharan Africa: An IEG Review. 2007. World Development Report. 2007] • Bill and Melinda Gates Foundation:Agricultural Development Program

  4. BMGF Schematic of the Agricultural Development Program

  5. What is HarvestChoice? • A BMGF-sponsored ($4M, 39 month) effort co-managed by IFPRI and U. of Minnesota to compile, generate, harmonize, and disseminate public-goods information on the potential payoffs from improved crop production technologies and practices. • Focus on poor farm households in SSA and S. Asia, but embedded in a perspective of national (social) welfare, and international flows of knowledge, technology, and trade • Institutionally-neutral portal supported and accessible to a growing number of R&D partners: FAO (Statistics Division), CIMMYT, CIAT, IRRI, ICRISAT, & Universities (Pretoria, VT, Georgia, Davis), World Bank.

  6. What is HarvestChoice? • Partner/user programs: HarvestPlus, Generation Challenge Program, USAID/IPM-CRSP, USAID/IEHA, (AGRA/PASS,WB/MAPP, Howard Buffet Foundation, Sainsbury Family Trust) • Regional partners and processes, e.g. CAADP (ReSAKSS), ASARECA, (SADC, CORAF)

  7. Some Strategic Questions • Where are the poor and what is their welfare status? • On what cropping systems do the poor most depend? • What are the constraints to the productivity of those systems? • What existing or potential technologies might best • address those constraints? Under what scenarios? • What is the magnitude and distribution of potential payoffs to the • poor from different investment targeting strategies? • by, e.g., districts, AEZs, production systems, crops, constraints, technologies..

  8. Aligning Location -specific (geo- referenced) data Analytical (largely economic) tools Harmonizing (Spatial) Thematic Data Thematic Layers

  9. 1. Macro Trends: Human Welfare & Crop Systems 2. Micro Linkages: Human Welfare & Crop Systems 3. Crop Systems Evaluation Platform (Physical) a. Baseline distribution & performance of crop systems b. Distribution & severity of key productivity constraints c. Potential responses to change (tech., man., climate.) 4. Technology Landscape 5. (Economic) Evaluation Other data (e.g, prices, investments, market, technology spillover insights) Dialogue with Stake-holder/User Groups on Scenarios 6. Commer- cialization Prospects Constraint-Scale Evaluation Technology-Scale Evaluation • 7. Outreach • (e.g., country delivery • CountrySTAT) HarvestChoice Activities

  10. Fixed Geographies of Analysis Flexible Geographies of Analysis Market/Policy Analysis Macro Scale, Usually aggregate, Geo-political units e.g., IMPACT/WATER, GTAP derivatives e.g., DREAM, MM models informs Household Characterization Micro Scale Region Urban/Rural Income tercile Consumption Production Inputs informs Change (e.g., climate, technologies) Change (e.g., policy) Infrastructure/Market Access Production System Production System Analysis Meso Scale, Pixels as Units of Analysis Aggregation By Commodity Ecosystem Services

  11. Inequality Infant Mortality Children Underweight Poverty CBS et al. 2003 Hunger Task Force/CIESIN 2005 Alderman et al 2002 Prepared by CIAT from WHO data Where are the Africa’s poor and what is their welfare status? Compiling and harmonizing available, sub-national datasets on Expenditure, poverty, undernourishment, child mortality and undernourishment, Micronutrient deficiency, selected DALY’s Hunger Task Force/CIESIN 2005

  12. Consumption: g. per cap. per day RWANDA, 2000 On what cropping systems do the poor most depend? CONSUMPTION g. per cap. per day Rwanda, 2000

  13. Crop Consumption • (1st Admin * U/R * Expend. Class * M/F Headed) • For 17 countries in SSA • Includes 73 % of SSA population • All but 2 AGRA/PASS countries • Testing extrapolation using country typology HarvestPlus (CIAT & IFPRI), maps prepared by Glenn Hyman

  14. Overview of Spatial Allocation Initial Representation Final Representation

  15. Av. Maize Output (kg per hh) Uganda 1999-2000 Maize Area (1st Level Admin) Maize Area (2nd Level Admin) Least Poor Quintile Poorest Quintile West North Central East On what cropping systems do the poor most depend? PRODUCTION For 20+ major crops at 10km resolution “Plausible” assessment of the spatial distribution of production systems and performance of crops. Complemented by available data on technology adoption, market participation, land holding structure, land tenure & new data on input use/costs (FAO)

  16. New Tools forDistributing & Validating Crop Data • SPAM Results Web Accessible through Google Earth

  17. Evaluating the Payoffs to Crop Improvement for the Poor • Economic benefits of technical change arising from; higher (on- and off-farm) productivity, lower unit costs, lower variance of output, quality price premiums, commercialization constraints and opportunities (using 2+ stage assessment) • Share of benefits to poor producers and poor consumers • - Spatial incidence of benefits • - Implications for nutrition and incomes • Potential sources of benefit – Local, spillins • Economic implications of time lags, (e.g. R&D, regulation, • commercialization, adoption)

  18. Increase in Potential Maize Yield per Kg N Kg maize / Kg N What if...? Baseline What yield response to N Application? • Maize Yield Response to Fertilizer • kg[Maize Yield] / kg[N Fertilizer] • Maize in Year 2000 (medium maturity) • 0.5-degree grid (about 50 km) • 0 and 50 kg[N]/ha N fertilization ?

  19. “Site”-Specific Response: Ghana *

  20. Initial Ideas from Data Strategy • Brainstorming (Oct 2007) • Regional network of harmonized (national) panel datasets • Strengthening national agricultural statistical services (especially ag. census and expenditure/welfare indicators) • Use of “new” technologies/approaches to data collection (satellite, GPS, PDA,..) BMGF: An (Unofficial) Guide to Selected Investments, and Strategy Ideas with Potential Linkages to Agricultural Statistics Capacity in Africa

  21. HarvestChoice/FAO activities related to agricultural statistics in Africa • Compilation and harmonization of agricultural census data (including capture/digitization of older data when necessary to better understand past trends) • [e.g., holdings, production systems, land tenure, cropping patterns, technology and input use, labour use, productivity, access to services, market participation] • Standardized analysis of national consumption and expenditure data • [e.g., household characteristics, expenditure/income, consumption of agricultural goods, food security]

  22. HarvestChoice/FAO activities related to agricultural statistics in Africa • Production system characterization • (e.g., orientation, output and input mixes, technologies, management practices, cropping patterns, rotations/fallow use, natural resource needs/impacts, productivity) • Cost of production database • (to support more detailed productivity and profitability analysis - particularly in the light of potential change, e.g., increased investment or policy change) • NB All processed data generated will be made available in digital format and, wherever feasible, made available for national CountrySTAT implementations

  23. HarvestChoice/FAO activities Learning/Partner Hopes from AFCAS • Gather and consolidate information on the status of on-going and planned nationally representative survey and census activities of participating countries • Identify opportunities for “data rescue” of past census/ survey data • Start to identify potential synergies between country statistical service development plans and potential funding options of relevance to the Gates Foundation portfolio • Find partner countries to help develop and test the Cost of Production survey instrument to be administered by FAO/ESSD • Communicate new opportunities for investment in statistical and monitoring systems at country level

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