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Scaling up the LSMS-ISA to Monitor Progress on CAADP Indicators

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Alberto Zezza
Development Data Group
World Bank

2018 ReSAKSS Annual Conference
Addis Ababa, 24-26 October 2018

Published in: Government & Nonprofit
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Scaling up the LSMS-ISA to Monitor Progress on CAADP Indicators

  1. 1. Scaling up the LSMS-ISA to Monitor Progress on CAADP Indicators Alberto Zezza Development Data Group World Bank 2018 ReSAKSS Annual Conference Addis Ababa, 24-26 October 2018
  2. 2. Outline • The LSMS & LSMS-ISA programs and related survey initiatives • Data collection • Methodological research • Capacity development • Role of surveys in measuring development outcomes (CAADP, but also SDGs) • The relevance and potential for [biofortification] analysis • Yields • Food consumption • Varietal identification, technology adoption • What next for a closer collaboration?
  3. 3. LSMS-ISA and scaling up of ag surveys • LSMS, est. 1980 • LSMS-ISA: Africa and ag focus, 8 countries, 28+ surveys • Understand not just monitor • Sustainability of approach • Value for money by integrating different domains (e.g. ag, food consumption, nutrition), technology • Within national statistical system • Scaling-up: The 50 by 2030 initiative • FAO’s AGRISurvey program
  4. 4. LSMS-Integrated Surveys on Agriculture: Working on 3 Fronts • Collecting & disseminating multi-topic household survey data with a focus on agriculture in 8 African countries • Improving methods in agricultural statistics based on rigorous experimentation and tool development • -- land, soil, labor, yields, variety, food… • -- sourcebooks • Conducting and promoting policy research in agriculture and rural development
  5. 5. SDG Indicators by Goal and Tier • 77 indicators in total identified as coming from household surveys • Goal 3 with highest number followed by goals 16, 8, 5, 7, 1 and 2 • About 80% are either Tier I or Tier II, 13 of the indicators are Tier III Mitra and Walsh, 2017 By Goal: Tier I Tier II Tier III Mixed Total Goal 1: Poverty 2 2 2 0 6 Goal 2. Hunger 4 0 1 0 5 Goal 3. Health 8 9 1 0 18 Goal 4. Education 1 3 1 2 7 Goal 5. Gender equality 2 7 0 0 9 Goal 6. Water and sanitation 2 0 0 0 2 Goal 7. Energy 2 0 0 0 2 Goal 8. Decent work 6 2 1 0 9 Goal 9. Infrastructure 1 0 0 0 1 Goal 10. Inequality 1 0 3 0 4 Goal 11. Cities 1 1 1 0 3 Goal 16. Justice 1 6 3 0 10 Goal 17. Partnership 1 0 0 0 1 Total 32 30 13 2 77
  6. 6. LSMS potential for monitoring and understanding CAADP • Poverty, inequality • Productivity • Post Harvest Losses • Nutrition: Stunting • Jobs for youth in agriculture • Women in agribusiness • Resilience Household • Expenditures – Food & Nonfood • Education • Health • Labour • Nonfarm Enterprises • Durable Assets • Anthropometry • Food Security • Shocks Agriculture • Plot Details • Inputs – Use & Access; Labor & Non-Labor Alike • Crops – Cultivation & Production • Implements & Machinery • Extension services • Livestock, Fisheries • Forestry Community • Demographics • Services • Facilities • Infrastructure • Governance • Organizations & Groups • Prices
  7. 7. How can LSMS-ISA data contribute to the CAADP agenda: Focus on biofortification • Food consumption data: • Design: Potential for biofortification • Who consumes biofortified foods? • Productivity measures • Innovation in land and labor data collection • Technology adoption • Varietal identification
  8. 8. Uganda: Validating remotely-sensed maize yields Source: Lobell, D. B., Azzari, G., Burke, M., Gourlay, S., Jin, Z., Kilic, T., and Murray, S. (2018). “Eyes in the sky, boots on the ground: assessing satellite- and ground-based approaches to crop yield measurement and analysis in Uganda.” World Bank Policy Research Working Paper No. 8374.
  9. 9. Varietal identification • Partnership with CGIAR Standing Panel on Impact Assessment (SPIA) to improve data and survey methods on varietal adoption • Status quo: Farmer’s self-reporting, expert opinions • Survey experiments assessing the accuracy of prevailing subjective approaches to data collection vis-à-vis DNA fingerprinting • Maize in Uganda (completed) • Sweet Potato in Ethiopia (completed) • Cassava in Malawi (completed) • Sorghum in Mali (on-going) • Banana in Uganda (on-going) • Sub-objective: Remote sensing • Integrate at scale, guidebooks
  10. 10. Farmer-Reporting vs. DNA Fingerprinting Correspondence: Uganda Maize Varietal Identification Farmer-Reporting DNA Fingerprinting Source: Ilukor, J., Kilic, T., Stevenson, J., Gourlay, S., Kosmowski, F., Kilian, A., Sserumaga, J., and Asea, G. (Forthcoming). “Blowing in the Wind: The Quest for Accurate Crop Variety Identification in Field Research, with an Application to Maize in Uganda.”
  11. 11. Farmer-Reporting vs. DNA Fingerprinting Correspondence: Ethiopia Sweet Potato Varietal Identification Farmer-ReportingDNA Fingerprinting Source: Kosmowski, F., Aragaw, A., Kilian, A., Ambel, A., Ilukor, J., Yigezu, B., and Stevenson, J. (2018). “Varietal identification in household surveys: results from three household-based methods against the benchmark of DNA fingerprinting in Southern Ethiopia.” Experimental Agriculture, 1-15.
  12. 12. Final thoughts • The agricultural data landscape is changing, but much remains to be done • LSMS-ISA: Engagement, take up by researchers and country policy analysts - weak interaction with regional programs • Scaling-up opportunities going forward: 50 by 2030 initiative Can we leverage ‘data smart agriculture’ to achieve nutrition- smart agriculture?
  13. 13. Scaling up the LSMS-ISA to Monitor Progress on CAADP Indicators Alberto Zezza Development Data Group World Bank 2018 ReSAKSS Annual Conference Addis Ababa, 24-26 October 2018
  14. 14. LSMS-Led Research: Cross-Country Gender & Agriculture • Partners: IFAD, Africa Gender Innovation Lab, IFPRI, FAO • World Bank Policy Research Working Papers • World Bank-ONE Campaign Report – Leveling the Field • Agricultural Economics Special Issue Nutrition & Agriculture • Partners: BMGF, IFPRI • World Bank Policy Research Working Papers • Journal of Development Studies Special Issue Agriculture in Africa: Telling Facts from Myths • Partners: AfDB, World Bank Africa CE, Yale, Cornell, Maastricht • World Bank Policy Research Working Papers • Food Policy Special Issue
  15. 15. Selected (LSMS & Non-LSMS) Peer-Reviewed Uganda NPS-Based Research Welfare • World Bank Poverty Assessment • Inequality in Uganda • Poverty Dynamics • Is Poverty Reduction Overstated? • Combining Satellite Imagery and Machine Learning to Predict Poverty • Measuring Poverty for Food Security Analysis • Household Income Portfolios • Rise of Middle Class and Food System Transformation • Staple Food Consumption and Undernourishment • Food Price Seasonality • Targeting for Development Programs Nutrition • Household Income and Child Nutrition • Livestock Ownership and Child Nutrition • Agricultural Commercialization and Nutrition
  16. 16. Selected (LSMS & Non-LSMS) Peer-Reviewed UNPS-Based Research (3) Agriculture • Agricultural Input Use • Agricultural Input Credit • Agricultural Intensification • Inorganic Fertilizer Profitability • Agricultural Factor Markets and Market Failures • Smallholder Access to Land • Gender Differences in Agricultural Productivity • Women’s Contribution to Agricultural Labor • Post-Harvest Losses • Adoption of Modern Varieties and Welfare • Technological Change in Agriculture Sector • Improved Spatially-Disaggregated Livestock Measures • Socio-environmental Drivers of Forest Change • Coffee Certification and Welfare
  17. 17. LSMS-Led Research: Sector/Topic-Specific • Agricultural Productivity • Land: Guesstimates to GPStimates, Missing(ness) in Action, Sourcebook • Debunking IR: Ethiopia, Uganda • Agricultural Productivity and Poverty: Nigeria, Malawi • ADePT Crop Module (Automated Analysis, Data Files, including Uganda) • Soil quality: Ethiopia, X-Country, including Uganda • Livestock • Livestock Data for Evidence-Based Policies (Uganda, Tanzania) • Livestock Ownership, Animal Source Food Consumption and Child Nutrition (Uganda) • Milk Off-Take in Extensive Livestock Systems (Niger) • Livestock Module Guidebook (Uptake: ISA countries, IFPRI) • ADePT Livestock Module (Automated Analysis, Data Files)

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