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Integrating crop models and machine learning for projecting climate change impacts on crops in data-limited environments

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Listed:
  • Alimagham, Seyyedmajid
  • van Loon, Marloes P.
  • Ramirez-Villegas, Julian
  • Berghuijs, Herman N.C.
  • Rosenstock, Todd S.
  • van Ittersum, Martin K.

Abstract

Accurately projecting crop yields under climate change is essential for understanding potential impacts and planning of agricultural adaptation in sub-Saharan Africa (SSA). Crop growth models and machine learning (ML) are often used, but their effectiveness is limited by data availability, precision, and geographic coverage in SSA.

Suggested Citation

  • Alimagham, Seyyedmajid & van Loon, Marloes P. & Ramirez-Villegas, Julian & Berghuijs, Herman N.C. & Rosenstock, Todd S. & van Ittersum, Martin K., 2025. "Integrating crop models and machine learning for projecting climate change impacts on crops in data-limited environments," Agricultural Systems, Elsevier, vol. 228(C).
    • Handle: RePEc:eee:agisys:v:228:y:2025:i:c:s0308521x25001076
    DOI: 10.1016/j.agsy.2025.104367
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    References listed on IDEAS

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