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

Vista sencilla de ítem
dc.creatorAlimagham, Seyyedmajid
dc.creatorvan Loon, Marloes P
dc.creatorRamirez Villegas, Julian
dc.creatorBerghuijs, Herman N.C.
dc.creatorRosenstock, Todd Stuart
dc.creatorvan Ittersum, Martin K.
dc.date2025-08-01
dc.date2025-11-24T10:31:52Z
dc.date2025-11-24T10:31:52Z
dc.date.accessioned2026-06-27T13:38:02Z
dc.descriptionContext 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. Objective This study aimed to integrate ML with a process-based crop model to produce geographically continuous gridded crop yield projections while reducing uncertainties associated with standalone ML or crop growth models. As a case study, we implemented it to project the climate change impact on water-limited potential yield of maize across SSA. Methods We developed an integrated system that combines ML with eco-physiological processes to estimate sowing dates and thermal times, ensuring that crop phenology is accounted for, thus improving potential rainfed yield simulations under varying environmental conditions. Random Forest and crop model-based algorithms are integrated in three steps: (i) RF1, a Random Forest model integrated with a sowing algorithm, designed to estimate the sowing window and sowing date; (ii) RF2, a Random Forest model combined with a crop model algorithm to estimate cumulative thermal time during the growing season, used to determine the timing of phenological stages; and (iii) RF3, another Random Forest model, trained based on eco-physiological principles applied in phases (i) and (ii), employed to simulate water-limited potential yield. The outcomes of the different steps of the framework under historical conditions were tested against reported data across SSA. Results and conclusions For maize and historical climatic conditions, the framework delivers yields which differ less than 20 % of those simulated with a crop model with high-quality inputs, in 95 % of the cases. Our approach thus shows value for generating crop yield projections in data-scarce regions under historical climate, and under future climatic conditions which already feature today somewhere in SSA and for which the framework has been trained. Significance Our approach can also be applied to other major food crops in SSA, under both current and climate change conditions. It allows testing the effect of adaptation of crop cultivars in terms of maturity group. Thus, it can be used for different crops and with far less data requirements compared to process-based crop models. It has the potential for significant applications in assessing climate change impacts, guiding adaptation strategies, and supporting crop breeding programes and policymaking efforts in SSA.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/178106
dc.identifier.urihttp://hdl.handle.net/123456789/65520
dc.languageen
dc.publisherElsevier
dc.rightsOpen Access
dc.sourceAlimagham, S.; van Loon, M.P.; Ramirez Villegas, J.; Berghuijs, H.N.; Rosenstock, T.S.; van Ittersum, M.K. (2025) Integrating crop models and machine learning for projecting climate change impacts on crops in data-limited environments. Agricultural Systems 228: 104367. ISSN: 0308-521X
dc.subjectmachine learning
dc.subjectadaptation
dc.subjectcrop modelling
dc.subjectclimate change impacts
dc.subjectsowing date
dc.subjectphenology
dc.subjectmaximum sustainable yield
dc.titleIntegrating crop models and machine learning for projecting climate change impacts on crops in data-limited environments
dc.typeJournal Article

Archivos

Colecciones

Alianza Bioversity International y CIAT