Crop Growth Simulation Modeling

Abstract

Agricultural crops include various plant species grown on the farm for food and fiber. Increase in the world population demands increase in the agricultural production as well as efficient management of resources in the form of precision agriculture through crop growth models to facilitate measured amounts of inputs to obtain desired quantity and quality of crop output. Crop growth simulation models integrate crop physiology, weather parameters, soil parameters, and management practices to simulate growth and yield of crops. These models compute growth values on a day to day basis, using relationships among input such as nutrients, water, weather parameters, etc. to predict values of crop growth parameters. Crop-specific model design results in poor modularity and prevents model sharing. A generic model uses common crop physiological processes. Validating and fine tuning the crop model is an important step before using it for actual prediction tasks. A number of crop growth models have been developed since 1980s. Future crop models should rely on improving the mechanism of interaction with environment, breeding programs, and microscale studies on individual crop growth components such as nutrients dispersion, CO₂ diffusion, etc. The models should be able to predict crop yields under site‐specific soils, input, and weather conditions.