Researchers working on a USDA-sponsored research project are currently conducting approximately one hundred large-scale, on-farm agronomic field trials in seven countries and seven US states. Each experiment randomizes input application rates on full fields no less than 35 hectares in area. The methodology of their experiments is to use precision technology to design and conduct the trials; farmers can implement the trials with very little bother. Previous studies of the economic value of variable rate application technology have always assumed farmers have perfect knowledge about yield response functions at the sub-plot level within a field. This article relaxes this assumption and recognizes the economic value of gaining information about yield response through on-farm agronomic experiments.
We report results of Monte Carlo simulations to begin to gauge the practicality of this idea of using precision technology to increase the information needed to increase the demand for precision technology. We begin by using the APSIM crop growth model to simulate crop growth on cornfields in the US Midwest. The APSIM field characteristics matrix parameters are derived from soil samples taken on an actual Illinois cornfield. Simulated data on daily weather events are taken from historical weather data for the field’s county. We simulate trials by dividing the field into a grid of plots of sizes consistent with the plot sizes in the actual USDA-sponsored trials, and assigning randomizing nitrogen fertilizer and seeding rates to each plot. Then we conduct an APSIM simulation in each plot in each “year” of the experiment. Having generated simulated field trial data, we then use spatial econometric methods to estimate reduced-form yield response functions with simulated data. The econometric analyses provide “information,” which when statistically and economically analyzed can be used to provide farmers with profitable input management recommendations. Increasing the number of years of experimentation improves how well the estimated reduced yield response function mimics the actual outcomes of the APSIM model. These procedures allow us to assign economic costs and benefits to the experiments. Key simulation results were: (1) By using site-specific variable rate technology instead of uniform rate technology, a farmer who knew the true response function of every block in his/her field could expect to be able to increase annual net revenues by approximately two dollars per hectare. (2) Information about yield response functions was worth much less to the uniform rate farmer than to a site-specific farmer. (3) For the farmer with only partial information (anywhere from four to thirty years of field trial data), net revenues were higher under uniform technology than under site-specific technology, even when the differences in the costs of production under these technologies were not accounted for.
