Combining Environmental Monitoring and Remote Sensing Technologies to Evaluate Cropping System Nitrogen Dynamics at the Field-Scale

Abstract

Nitrogen (N) losses from cropping systems in the U.S. Midwest represent a major environmental and economic concern, negatively impacting water and air quality. While considerable research has investigated processes and controls of N losses in this region, significant knowledge gaps still exist, particularly related to the temporal and spatial variability of crop N uptake and environmental losses at the field-scale. The objectives of this study were (i) to describe the unique application of environmental monitoring and remote sensing technologies to quantify and evaluate relationships between artificial subsurface drainage nitrate (NO3-N) losses, soil nitrous oxide (N2O) emissions, soil N concentrations, corn (Zea mays L.) yield, and remote sensing vegetation indices, and (ii) to discuss the benefits and limitations of using recent developments in technology to monitor cropping system N dynamics at field-scale. Preliminary results showed important insights regarding temporal (when N losses primarily occurred) and spatial (measurement footprint) considerations when trying to link N2O and NO3-N leaching losses within a single study to assess relationship between crop productivity and environmental N losses. Remote sensing vegetation indices were significantly correlated with N2O emissions, indicating that new technologies (e.g., unmanned aerial vehicle platform) could represent an integrative tool for linking sustainability outcomes with improved agronomic efficiencies, with lower vegetation index values associated with poor crop performance and higher N2O emissions. However, the potential for unmanned aerial vehicle to evaluate water quality appears much more limited because NO3-N losses happened prior to early-season crop growth and image collection. Building on this work, we encourage future research to test the usefulness of remote sensing technologies for monitoring environmental quality, with the goal of providing timely and accurate information to enhance the efficiency and sustainability of food production.