Combining mobile proximal soil sensors and a crop model to produce high spatial resolution yield prediction maps

Abstract Purpose Precision agriculture requires detailed knowledge of the within-field variation of yield forming factors and the productivity potential of each area of the field. The goal of this work was to use a case study to test all the steps of the process of creating a crop model-based yield map from soil mobile soil sensors and determine the impact of uncertainties and inaccuracies on the results. Methods Soil texture maps (0- 90 cm of depth) of a field were derived from mobile sensors and used as input for the process based deterministic crop growth model HERMES to produce a high-resolution yield map.Results Compared to actual yield maps, the simulated yield map successfully identified the major differences in productivity within the field, although some spatial variation was lost during the simulation, mostly at the point of translating soil texture maps into soil water retention parameters. The model also showed a tendency to overestimate yield across the entire field. A crop model simulation based on measured soil parameters resulted in a yield prediction accuracy of about 10% higher than a simulation based on estimated (mapped) soil parameters. Conclusion The loss of spatial variability, although measurable, occurred at a scale that might not have a significant impact on the site-specific management plan. Most yield map inaccuracies can be attributed more to model calibration than to the mapping process itself.