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Machine learning for food security: Principles for transparency and usability

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  • Yujun Zhou
  • Erin Lentz
  • Hope Michelson
  • Chungmann Kim
  • Kathy Baylis

Abstract

Machine learning (ML) holds potential to predict hunger crises before they occur. Yet, ML models embed crucial choices that affect their utility. We develop a prototype model to predict food insecurity across three countries in sub‐Saharan Africa. Readily available data on prices, assets, and weather all influence our model predictions. Our model obtains 55%–84% accuracy, substantially outperforming both a logit and ML models using only time and location. We highlight key principles for transparency and demonstrate how modeling choices between recall and accuracy can be tailored to policy‐maker needs. Our work provides a path for future modeling efforts in this area.

Suggested Citation

  • Yujun Zhou & Erin Lentz & Hope Michelson & Chungmann Kim & Kathy Baylis, 2022. "Machine learning for food security: Principles for transparency and usability," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 893-910, June.
    • Handle: RePEc:wly:apecpp:v:44:y:2022:i:2:p:893-910
    DOI: 10.1002/aepp.13214
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    References listed on IDEAS

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    1. Samuel Bazzi & Robert A. Blair & Christopher Blattman & Oeindrila Dube & Matthew Gudgeon & Richard Peck, 2022. "The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 764-779, October.
    2. Linden McBride & Christopher B. Barrett & Christopher Browne & Leiqiu Hu & Yanyan Liu & David S. Matteson & Ying Sun & Jiaming Wen, 2022. "Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 879-892, June.
    3. Erin Coughlan de Perez & Maarten van Aalst & Richard Choularton & Bart van den Hurk & Simon Mason & Hannah Nissan & Saroja Schwager, 2019. "From rain to famine: assessing the utility of rainfall observations and seasonal forecasts to anticipate food insecurity in East Africa," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 11(1), pages 57-68, February.
    4. Baez,Javier E. & Kshirsagar,Varun & Skoufias,Emmanuel, 2019. "Adaptive Safety Nets for Rural Africa : Drought-Sensitive Targeting with Sparse Data," Policy Research Working Paper Series 9071, The World Bank.
    5. Christopher Yeh & Anthony Perez & Anne Driscoll & George Azzari & Zhongyi Tang & David Lobell & Stefano Ermon & Marshall Burke, 2020. "Using publicly available satellite imagery and deep learning to understand economic well-being in Africa," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    6. Knippenberg, Erwin & Jensen, Nathaniel & Constas, Mark, 2019. "Quantifying household resilience with high frequency data: Temporal dynamics and methodological options," World Development, Elsevier, vol. 121(C), pages 1-15.
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    Cited by:

    1. Backer, David & Billing, Trey, 2024. "Forecasting the prevalence of child acute malnutrition using environmental and conflict conditions as leading indicators," World Development, Elsevier, vol. 176(C).
    2. repec:ags:aaea22:335590 is not listed on IDEAS
    3. Baez, Javier E. & Kshirsagar, Varun & Skoufias, Emmanuel, 2024. "Drought-sensitive targeting and child growth faltering in Southern Africa," World Development, Elsevier, vol. 182(C).
    4. Yi, Jing & Canning, Patrick N. & Ge, Houtian & Rehkamp, Sarah & Gomez, Miguel I., 2023. "A national database of highly perishable fresh produce production with temporal and spatial resolution," 2023 Annual Meeting, July 23-25, Washington D.C. 335590, Agricultural and Applied Economics Association.
    5. Prem Chandra Pandey & Manish Pandey, 2023. "Highlighting the role of agriculture and geospatial technology in food security and sustainable development goals," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(5), pages 3175-3195, October.

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