A Case Study of Environmental Benefits of Sensor-Based Nitrogen Application in Corn.

Crop canopy reflectance sensors make it possible to estimate crop N demand and apply appropriate N fertilizer rates at different locations in a field, reducing fertilizer input and associated environmental impacts while maintaining crop yield. Environmental benefits, however, have not been quantified previously. The objective of this study was to estimate the environmental impact of sensor-based N fertilization of corn using model-based environmental Life Cycle Assessment. Nitrogen rate and corn grain yield were measured during a sensor-based, variable N-rate experiment in Lincoln County, MO. Spatially explicit soil properties were derived using a predictive modeling technique based on in-field soil sampling. Soil NO emissions, volatilized NH loss, and soil NO leaching were predicted at 60 discrete field locations using the DeNitrification-DeComposition (DNDC) model. Life cycle cumulative energy consumption, global warming potential (GWP), acidification potential, and eutrophication potential were estimated using model predictions, experimental data, and life cycle data. In this experiment, variable-rate N management reduced total N fertilizer use by 11% without decreasing grain yield. Precision application of N is predicted to have reduced soil NO emissions by 10%, volatilized NH loss by 23%, and NO leaching by 16%, which in turn reduced life cycle nonrenewable energy consumption, GWP, acidification potential, and eutrophication potential by 7, 10, 22, and 16%, respectively. Although mean N losses were reduced, the variations in N losses were increased compared with conventional, uniform N application. Crop canopy sensor-based, variable-rate N fertilization was predicted to increase corn grain N use efficiency while simultaneously reducing total life-cycle energy use, GWP, acidification, and eutrophication.