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 (NO 3-N) losses, soil nitrous oxide (N 2 O) 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 N 2 O and NO 3-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 N 2 O 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 N 2 O emissions. However, the potential for unmanned aerial vehicle to evaluate water quality appears much more limited because NO 3-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.

Split‐nitrogen application with cover cropping reduces subsurface nitrate losses while maintaining corn yields

“…The greater annual NO 3 -N loss in 2018 compared with 2019 and 2020 from the control treatment (17.5, 9.3, and 9.2 kg N ha −1 , respectively) was mostly due to accumulated residual soil N from the previous year (2017), which was attributed to the combination of high N application rate and below-average precipitation later in the growing season. Corn received 303 kg N ha −1 in the 2017 growing season, and despite high grain yields and N removal in portions of the field (Preza Fontes et al, 2019), postharvest soil N was high across all 16 plots, ranging from 27 to 56 kg NO 3 -N ha −1 (Supplemental Table S4). As discussed above, aboveaverage precipitation during spring 2018 resulted in high soil N losses with drainage discharge.…”

Determining nitrogen deficiencies for maize using various remote sensing indices

“…Red-edge NDVI is computationally similar to GNDVI except it uses reflectance in the red-edge rather than the green band. Red-edge NDVI was found to have a stronger relationship with maize grain yield than NDVI, particularly when reflectance data were collected later in the season (Preza Fontes et al, 2019;Sharma et al, 2015). Additionally, RENDVI is more sensitive to chlorophyll concentration than traditional NDVI because NDVI is determined by the ratio of exposed leaf to soil surface (Sharma et al, 2015).…”

“…Using remote sensing technology, nitrogen, phosphorus, potassium and sulfur deficiencies have been successfully identified in wheat (Mahajan et al, 2014), and the association of various vegetation indices with nitrogen supply and plant chlorophyll content is well documented (Dellinger et al, 2008;Hunt, et al, 2013;Maresma et al, 2018;Preza Fontes et al, 2019). Furthermore, remote sensing-based nitrogen recommendations for maize have been suggested in prior studies (Clay et al, 2006;Dellinger et al, 2008;Schmidt et al, 2009).…”

“…Of the following indices assessed, normalized difference vegetation index (NDVI) is one of the oldest and has been used extensively for a myriad of applications (Rouse et al, 1974). Because NDVI is associated with plant health, it has a significant relationship with early-season N 2 O emissions when low crop demand exacerbates N losses (Preza Fontes et al, 2019). Due to its relationship with leaf area index (LAI), NDVI may lose predictive power once the canopy has closed around V9 (9 true, collared leaves; Teal et al, 2006).…”

Identification of Infiltration Features and Hydraulic Properties of Soils Based on Crop Water Stress Derived from Remotely Sensed Data

“…Haberle et al [27,28] show that soil properties can change abruptly, and consequently, the use of interpolation methods provides results with limited accuracy. A suitable approach for assessing soil hydraulic properties is offered by remote sensing (RS), which has considerable potential, especially for precision agriculture and, by extension, for protecting surface waters from eutrophication [29,30]. Although the spatial variability of soil hydraulic properties is very important, RS data have received relatively little attention in this field.…”