The economics of precision farming in the Texas High Plains

This study evaluates the profitability of whole-field and precision farming in cotton, corn, grain sorghum, and peanut production. Optimal levels of nitrogen fertilizer for a 10-year planning horizon are derived for both precision farming and traditional whole-field farming management practices. Different pricing scenarios for irrigation water, nitrogen fertilizer, and output were used in the experiments.; A dynamic optimization model was developed to evaluate the relationships between optimal nitrogen application levels and other soil characteristics. The optimization model was solved for three scenarios, the precision farming scenario, the naïve whole-field farming scenario, and the conventional whole-field farming scenario. The precision farming scenario was solved for every GPS location in the field according to the specific soil and location characteristics to achieve optimal conditions. The naïve whole-field farming scenario represents the optimal conditions under conventional whole-field farming where average soil and location characteristics were used. The final scenario used the optimal input application prescribed in the naïve scenario for each individual location according to the specific soil and location characteristics.; Overall, this analysis revealed that precision farming does have the potential to improve crop yield, net present value of returns, and spatially utilize nitrogen more efficiently as compared to conventional whole-field farming. The increases in yield, nitrogen application, and net present value of returns were found to be specific to the commodity and experiment. It is shown that individual locations may have great differences depending on the management practice. This is important to note because of the potential for the establishment of management zones within these locations.; The optimal results for the field were mapped with MapInfo Software to allow for visual interpretation of the results. This allows for identification of key management zones in precision farming. Each location in the field can be represented visually according to a color scale where red areas indicate higher levels. Depending on the specific field and crop, as well as the input-output prices used, an increase in net revenues can be expected. This study attempts to provide practical results for a producer trying to decide whether or not to invest in precision farming technology.