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A crop simulation model is used to estimate crop production as a function of weather conditions, soil parameters, and plant related inputs. These crop simulation models are extensively used by farmers, corporations and policy makers for agronomical planning and decision making. CornSoyWater is one such application which provides irrigation recommendation for soy and corn farmers using hybrid maize and soy sim models. As this is a simulation technology, the accuracy of results depends on the quality of data provided to it. One such important input parameter is weather data. CornSoyWater simulates field and crop conditions by retrieving the updated weather data from nearest weather station for that field. However, the closest weather station could be far enough that the simulation model cannot rely on that weather station’s data. Currently, in CornSoyWater twenty miles is considered as the threshold distance beyond which the nearest weather station’s data might not represent the field conditions. A significant number of fields which produces corn and soybean does not have access to weather station within the threshold distance, which arises the necessity to optimize the existing models to work for real-world scenarios. In this thesis, we solved this problem using a new approach which uses quantification of the shape, distance, and position of weather stations to choose the optimal ones and performs inverse distance weighing on them. The results demonstrate that this approach works well for those fields which don’t have access to weather stations within the threshold distance.
Advisor: Jitender Deogun