Constant-rate blanket applications of fertilizer N can result in both an over and under supply relative to crop utilization on a field-by-field basis. Variable-rate (VR) applications tailored to better meet crop demand can improve N use efficiency on spatially variable soils. The objectives of this study were to compare the response in corn (Zea mays L.) canopy reflectance derived vegetation indices (VI) to varying fertilizer N rates and to determine relationships between resulting VIs acquired using two different sensing platforms. Four fertilizer N rates in 50/50 split at V1-2 and V6-7 leaf stages were applied, from deficient to excessive, to create varying corn nutritional N status. Sensing and biophysical sampling were performed throughout the season for analysis and comparison to calculated VIs. Grain yield plateaued around 135±10 kg N ha -1 across the study. Furthermore, strong relationships between VIs and fertilizer N rates were found, with the strongest using combined indices that incorporate the red-edge wavelength (720 nm). Relationships strengthened at later growth stages. The response models were found to be sensor specific, VI specific, and mostly non-transferable between sensors. Results from this study demonstrate the utility of using remote sensing technologies to predict corn N status more accurately for eventual use in VR prescription development.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
The varying influence of the environment on N supply and demand dictates the need for annually updated fertilizer N recommendations. Currently, crop yield goal (CYG) methods are used by 34 land grant universities, including Mississippi State University, these do not consider environmental variations. This research tested the efficacy of CYG by determining the agronomic optimum N rate (AONR) and the economic optimum N rate for Mississippi corn (Zea mays L.) production. In total, 12 treatments in 2020 and 11 in 2021 were replicated four times over four locations in a randomized complete block design. The optimum N rates were calculated by fitting linear, quadratic, linear plateau, and quadratic plateau models by means of three different goodness of fit measures. Furthermore, differences between the CYG rate calculated from the Mississippi yield goal equation and AONR were compared at different management levels (14 comparisons) (all data combined, both years combined, sites combined by year, and individual sites). Overall, AONR varied from 134 to 301 kg N ha–1 at different management levels. When we compared the AONR to the CYG rate, the CYG rate over‐recommended N in 12 of the 14 possible comparisons, with differences ranging from 69 kg N ha–1 less to 110 kg N ha–1 greater than the AONR. These differences between AONR values highlight variability caused by factors such as the soil, environment, and their interaction with N supply and demand, which are unaccounted for by the CYG method.
In-season sensing can account for field variability and improve nitrogen (N) management; however, opportunities exist for refinement. The purpose of this study was to compare different sensors and vegetation indices (VIs) (normalized difference vegetation index (NDVI); normalized difference red edge (NDRE); Simplified Canopy Chlorophyll Content Index (SCCCI)) at various corn stages to predict in-season yield potential. Additionally, different methods of yield prediction were evaluated where the final yield was regressed against raw or % reflectance VIs, relative VIs, and in-season yield estimates (INSEY, VI divided by growing degree days). Field experiments at eight-site years were established in Mississippi. Crop reflectance data were collected using an at-leaf SPAD sensor, two proximal sensors: GreenSeeker and Crop Circle, and a small unmanned aerial system (sUAS) equipped with a MicaSense sensor. Overall, relative VI measurements were superior for grain yield prediction. MicaSense best predicted yield at the VT-R1 stages (R2 = 0.78–0.83), Crop Circle and SPAD at VT (R2 = 0.57 and 0.49), and GreenSeeker at V10 (R2 = 0.52). When VIs were compared, SCCCI (R2 = 0.40–0.49) outperformed other VIs in terms of yield prediction. Overall, the best grain yield prediction was achieved using the MicaSense-derived SCCCI at the VT-R1 growth stages.
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