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Predicting high-magnitude, low-frequency crop losses using machine learning: an application to cereal crops in Ethiopia

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  • Michael L. Mann

    () (The George Washington University)

  • James M. Warner

    (International Food Policy Research Institute)

  • Arun S. Malik

    (The George Washington University)

Abstract

Timely and accurate agricultural impact assessments for droughts are critical for designing appropriate interventions and policy. These assessments are often ad hoc, late, or spatially imprecise, with reporting at the zonal or regional level. This is problematic as we find substantial variability in losses at the village-level, which is missing when reporting at the zonal level. In this paper, we propose a new data fusion method—combining remotely sensed data with agricultural survey data—that might address these limitations. We apply the method to Ethiopia, which is regularly hit by droughts and is a substantial recipient of ad hoc imported food aid. We then utilize remotely sensed data obtained near mid-season to predict substantial crop losses of greater than or equal to 25% due to drought at the village level for five primary cereal crops. We train machine learning models to predict the likelihood of losses and explore the most influential variables. On independent samples, the models identify substantial drought loss cases with up to 81% accuracy by mid- to late-September. We believe the proposed models could be used to help monitor and predict yields for disaster response teams and policy makers, particularly with further development of the models and integration of soon-to-be available high-resolution, remotely sensed data such as the Harmonized Landsat Sentinel (HLS) data set.

Suggested Citation

  • Michael L. Mann & James M. Warner & Arun S. Malik, 2019. "Predicting high-magnitude, low-frequency crop losses using machine learning: an application to cereal crops in Ethiopia," Climatic Change, Springer, vol. 154(1), pages 211-227, May.
  • Handle: RePEc:spr:climat:v:154:y:2019:i:1:d:10.1007_s10584-019-02432-7
    DOI: 10.1007/s10584-019-02432-7
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    References listed on IDEAS

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    1. Arnaud Costinot & Dave Donaldson & Cory Smith, 2016. "Evolving Comparative Advantage and the Impact of Climate Change in Agricultural Markets: Evidence from 1.7 Million Fields around the World," Journal of Political Economy, University of Chicago Press, vol. 124(1), pages 205-248.
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    3. Paul Evangelista & Nicholas Young & Jonathan Burnett, 2013. "How will climate change spatially affect agriculture production in Ethiopia? Case studies of important cereal crops," Climatic Change, Springer, vol. 119(3), pages 855-873, August.
    4. Bachewe, Fantu Nisrane & Yimer, Feiruz & Minten, Bart, 2016. "Agricultural prices during drought in Ethiopia: An updated assessment using national producer data (January 2014 to June 2016)," ESSP working papers 97, International Food Policy Research Institute (IFPRI).
    5. Diego Lindoso & Juliana Rocha & Nathan Debortoli & Izabel Parente & Flávio Eiró & Marcel Bursztyn & Saulo Rodrigues-Filho, 2014. "Integrated assessment of smallholder farming’s vulnerability to drought in the Brazilian Semi-arid: a case study in Ceará," Climatic Change, Springer, vol. 127(1), pages 93-105, November.
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    Cited by:

    1. Hirvonen, Kalle & Sohnesen, Thomas Pave & Bundervoet, Tom, 2020. "Impact of Ethiopia’s 2015 drought on child undernutrition," World Development, Elsevier, vol. 131(C).

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