Chapter 15 - Spatializing crop models for sustainable agriculture and environment

Abstract

Agriculture is heavily influenced by weather and climate. Modeling is a potentially useful instrument used by researchers, planners, and stakeholders in agricultural science, crop management technology, and in policy decisions. The main objective of developing crop simulators is to predict the growth, production, and yield of agricultural crops, thereby reducing the need for multisite field experiments. Studies on food security in evolving climates highlight a growing appetite for simulations of large-scale crop development. The numerical crop models (NCMs) were inherently area-specific, with some soil characteristics and geography. It is intended that spatializing them would increase their applicability across broad areas. As the simulation scale increases, it becomes difficult to conduct a thorough model validation with field measurements or grid datasets. Major advances have been made in the modeling and creation of crop growth using mechanistic models, but simulation on a larger spatial scale with conventional models is not feasible as it consumes a lot of time. To a very limited degree, the integration of remote sensing data with the NCM was successful. In CLAMs, multiparameterization enables us to incorporate enormous crop management data, soil characters, and others. With Noah-MP-Crop we can simulate the crop growth at global level. The simulation can be done in a versatile time step in the Noah-MP-Crop model, whereas conventional NCMs can only simulate in a daily time step.