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The Data-Intensive Farm Management (DIFM) project is a collaborative agronomic research initiative that utilizes precision technology to design and execute randomized field trials on commercial farm fields. The aim of this study is to provide data-driven farm input management guidance, which has the potential to yield economic and environmental benefits. Various rates of seeds and nitrogen were used in the trial to determine the economically optimal nitrogen and seed rates. By implementing machine learning algorithms, will help confirm the minimal impact of nitrogen on yield in similar environmental conditions and seeding rates can be optimized based on detailed soil characteristics, leading to profit-maximizing strategies even with the complexity of variable inputs.