Spatial autocorrelation and spatial heterogeneity are taken into account using a spatial error model and a groupwise heteroskedasticity model. A partial budget is used to calculate uniform rate and VRT returns. Soil Teq, a subsidiary of Ag Chem Corporation, supported this review. The economic trade off between grid soil sampling and laboratory tests, or soil sensor data is the choice between relatively accurate information at a few points, or less accurate information at many points. The analysis focuses on an automated soil sampling and sensing system for pH. Compared to 2.5-acre grids, the estimated cost savings from this system are slightly more than one dollar per acre.
Estimates indicate that the value of yield lost due to sensor measurement error is small.
In the example field, most of the benefit of the pH sensor was linked to the greater spatial density of pH information. This study was partially supported by the United Soybean Board. Results suggested that on-farm precipitation data could be useful, but only one gauge on a farm, not multiple gauges, would be necessary to make management decisions and model corn yield at the farm scale. They also illustrated that spatial precipitation variability is an important factor of spatial yield variability. Precipitation variability by phenological phase at the farm scale.