Generic crop model
Crop models that accurately capture physiological knowledge and predict the phenotypic outcomes of genotype-by-environment-by-management (G×E×M) interactions offer invaluable insights for decision-making in the agricultural industry. However, the inherent complexity of these models often limits their practicality in crop improvement. Currently, there is a gap between existing crop models and the integration of genetic information while maintaining simplicity to represent G×E×M interactions. These complex models, tailored for agronomists familiar with gathering specific inputs, fall short in their broader applications for enhanced agricultural decision-making. For example, they may not meet the needs of breeding programs seeking to explore G×E×M interactions or integrate with climate and land models.
To bridge this gap, a new approach has emerged: the concept of a simple yet comprehensive core model that can simulate essential processes and be easily incorporated into larger integrated models or customized for specific crops and genetic varieties. By accommodating variations in biological process algorithms and leveraging advances in coding and computational techniques, these models hold great potential to serve as the foundation for the next generation of crop models, driving advancements in crop improvement technologies.
We are currently working on the development of such a crop modeling tool in a Python environment.