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

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

Agricultural Systems, 2025, vol. 228, issue C

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.

Keywords: Adaptation; Phenology; Potential climate change impact; Sowing date (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:agisys:v:228:y:2025:i:c:s0308521x25001076

DOI: 10.1016/j.agsy.2025.104367

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