Integrating crop models and machine learning for projecting climate change impacts on crops in data-limited environments - Alimagham, Seyyedmajid; van Loon, Marloes P; Ramirez Villegas, Julian; Berghuijs, Herman N.C.; Rosenstock, Todd Stuart; van Ittersum, Martin K. (Elsevier, 2025)
Context 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.