Using a Crop Modeling Framework for Precision Cost-Benefit Analysis of Variable Seeding and Nitrogen Application Rates - McNunn, Gabriel; Heaton, Emily; Archontoulis, Sotirios; Licht, Mark; VanLoocke, Andy (Frontiers in Sustainable Food Systems, 2019)
A key goal of precision agriculture is to achieve the maximum crop yield while minimizing inputs and loses from cropping systems. The challenge of precision agriculture is these factors interact with one another on a subfield scale. The seeding density and the nitrogen (N) fertilizer application rate are two of the most important inputs influencing agronomic, economic and environmental outcomes including yield, return on investment (ROI) and nitrate (NO3-) leaching. A cropping system simulation framework is used to predict site-specific subfield optimum seeding density and (N) fertilizer application rates for the economic optimum (maximum ROI) versus agronomic optimum (maximum yield). The framework couples the process-based APSIM cropping system model with the SSURGO soils database, Daymet weather data service, land grant university estimates of crop production costs and commodity price estimates, and the R statistics software. The framework performance was evaluated using multiple years of precision yield monitor data obtained from a continuous maize (Zea mays L.) cropping system field experiment with varying N-fertilizer rates. Subfield model estimates of crop yield were sensitive to initial conditions related to historical management of the field and had an r2 = 0.65 and a root mean square error of 2356.0 kg ha-1. A site-specific application of the framework comparing economic optimum seeding density and N-fertilizer rates with agronomic optimum values estimated the average ROI benefit to be up to 12.1% with a NO3- leaching reduction of up to 15.2 kg ha-1 at the economic optimum. However, in a minority of cases NO3- leaching was greater at the economic optimum, indicating that managing to maximize ROI rather than yield may not always reduce environmental impacts. Our results suggest that managing cropping systems for the economic optimum is plausible using publicly available data with our framework and may likely lead to improved environmental outcomes.