Publication

Abstract : Most crop models were developed and tested in homogeneous field conditions. However, these crop models are increasingly applied beyond the field scale for larger regions. Inadequate representation of the spatial variability at a larger scale introduces significant errors in the models’ predictions, yet attention to this topic is lacking. The selection of optimal crop models and their inputs when moving from the field to a regional scale must be performed carefully using strict guidelines while considering uncertainty propagation. This paper reviews crop modeling applications and their constraints in large-scale studies. The discussion focuses on the core issues that arise when applying crop models to a range of spatial scales: (i) parameterization and calibration of model inputs beyond field scale; (ii) constraints in the selection of model inputs at various scales; and (iii) retrieval and integration of remotely sensed crop variables into the crop model. Further, this review highlights cutting-edge approaches, namely scalable yield modeling, semi-empirical crop models, and global modeling initiatives, which can be used in a multi-scale assessment of agricultural systems.