Fusing Corn Nitrogen Recommendation Tools for an Improved Canopy Reflectance Sensor Performance.

Nitrogen (N) rate recommendation tools are utilized to help producers maximize corn grain yield production. Many of these tools provide recommendations at field scales but often fail when corn N requirements are variable across the field. Canopy reflectance sensors are capable of capturing within-field variability, although the sensor algorithm recommendations may not always be as accurate at predicting corn N needs compared to other tools. Therefore, the fusion of within-field canopy reflectance sensor with field-scale N recommendation tools may help account for yield variability from N applications, and improve N rate recommendations by utilizing the strengths of multiple tools. Research was conducted on 49 N response trials over eight Midwest states to determine which N rate recommendation tool was most effective at recommending economical optimal N rates (EONR) under varying soil and weather conditions. Field-scale tools that were evaluated included pre-plant soil nitrate test, pre-sidedress soil nitrate test, maximum return to N (MRTN), yield goal based calculations, and the Maize-N crop growth model. A second objective was to determine if the Holland and Schepers canopy reflectance sensor algorithm could be improved by integrating the best performing N recommendation tools that were previously evaluated. Tools were integrated by replacing the base N rate in the algorithm, the farmer’s N rate, with the N recommendation from the best performing tools. Results showed the canopy reflectance sensor underestimated EONR but was improved by using better performing tools as the base N rate and adjusting the recommendation using a management zone scaling factor. The management zone scaling factor could be estimated using soil or weather information.