Mitigating crop modeling uncertainties through machine learning in drylands.

ABSTRACT: Dry farming systems in drylands are sensitive to climate extremes, resulting in large yield variability and posing challenges to food security. Reliable crop modeling approaches are therefore essential, particularly under data-scarce conditions. This study evaluated the performance of the CSM-CERES-Wheat model across selected drylands in Iran using five gridded meteorological datasets. CFS and ERA5-Land provided daily precipitation, temperature and solar radiation, while CHIRPS, IMERG and PERSIANN-CDR supplied only precipitation. A novel clustering-unbiasing-ensembling framework was applied using four machine learning (ML) algorithms. Light Gradient Boosting Machine and Random Forest outperformed other MLs, particularly for precipitation and minimum temperature. These top-performing MLs in

SUBMITTER: Nouri M 

PROVIDER: S-EPMC12663468 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature