Prediction of productivity of any crop in a season has very important economic importance for a country. For yield improvements in rice, information about suitable management practices is rapidly increasing. The generation of new data through agronomic research methods is insufficient and time-consuming to meet these needs. It is important for a country like India, where productivity of crops in any season may vary greatly depending on the prevailing weather conditions of that season. In recent years, several dynamic crop ...
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Prediction of productivity of any crop in a season has very important economic importance for a country. For yield improvements in rice, information about suitable management practices is rapidly increasing. The generation of new data through agronomic research methods is insufficient and time-consuming to meet these needs. It is important for a country like India, where productivity of crops in any season may vary greatly depending on the prevailing weather conditions of that season. In recent years, several dynamic crop growth simulation models have been developed to help in such a predictive process. Model accuracy in prediction and their sensitivity also help in mid-course correction, so that farmer can adopt a measure to avoid any drop in potential production of any crops. The main goal of a crop simulation model is to estimate crop production, resource use and environmental impact as a function of local weather and soil conditions and crop management. Agricultural system models have untapped potential to help agricultural research and technology transfer in the 21st century.
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