Ground‐Based Canopy Reflectance Sensing for Variable‐Rate Nitrogen Corn Fertilization

Abstract

Nitrogen available to support corn (Zea mays L.) production can be highly variable within fields. Canopy reflectance sensing for assessing crop N health has been proposed as a technology to base side‐dress variable‐rate N application. Objectives of this research were to evaluate the use of active‐light crop‐canopy reflectance sensors for assessing corn N need, and derive the N fertilizer rate that would return the maximum profit relative to a single producer‐selected N application rate. A total of 16 field‐scale experiments were conducted over four seasons (2004–2007) in three major soil areas. Multiple blocks of randomized N rate response plots traversed the length of the field. Each block consisted of eight treatments from 0 to 235 kg N ha−1 on 34 kg N ha−1 increments, side‐dressed between the V7–V11 vegetative growth stages. Canopy sensor measurements were obtained from these blocks and adjacent N‐rich reference strips at the time of side‐dressing. Within fields, the range of optimal N rate varied by >100 kg N ha−1 in 13 of 16 fields. A sufficiency index (SI) calculated from the sensor readings correlated with optimal N rate, but only in 50% of the fields. As fertilizer cost increased relative to grain price, so did the value of using canopy sensors. While soil type, fertilizer cost, and corn price all affected our analysis, a modest ($25 to $50 ha−1) profit using canopy sensing was found. These results affirm that, for many fields, crop‐canopy reflectance sensing has potential for improving N management over conventional single‐rate applications.

Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

“…Early studies on corn focused on the ability of reflectance measurements to distinguish N fertilizer rate treatments (Walburg et al, 1982;McMurtrey et al, 1994;Blackmer et al, 1994;Ma et al, 1996), with subsequent studies focused on the development of N rate recommendations (Bausch and Duke, 1996;Kitchen et al, 2010;Dellinger et al, 2008;Schmidt et al, 2009;Scharf and Lory, 2009;Tubaña et al, 2008;Bausch and Delgado, 2003;Solari et al, 2010). Most of the earlier studies were based on measurements of reflected sunlight, while most of the later studies are based on reflectance of pulsed light originating from the sensor.…”

“…The most widely published approach for developing N rate recommendations for corn has been to measure the EONR and reflectance across a range of environments, then regress EONR vs. reflectance (Kitchen et al, 2010;Dellinger et al, 2008;Schmidt et al, 2009;Scharf and Lory, 2009;Barker and Sawyer, 2010). This regression relationship can then be used to predict EONR in the future.…”

“…Nitrogen rate prediction models, such as Adapt-N (Melkonian et al, 2008) and Maize-N (Setiyono et al, 2011) can incorporate localized soil properties, producer management, and weather conditions up to and during the growing season to provide refined predictions of EONR. In-season assessment of crop N status is a promising approach that can improve N use efficiency over some more traditional methods (Raun et al, 2002(Raun et al, , 2005Kitchen et al, 2010;Scharf et al, 2011). Systems using active crop canopy sensors rely on the growing plant to provide an estimate of N supply-from pre-plant fertilizer as well as soilsupplied N-up to the time of sensing.…”

Using an unmanned aerial vehicle to evaluate nitrogen variability and height effect with an active crop canopy sensor

“…As a result, non-destructive techniques have focused on remote sensing to correlate with and quantify canopy chlorophyll content. Remote sensing is used to monitor relative crop response to applied N in order to evaluate different in-season N management strategies (Scharf et al 2011;Zillmann et al 2006;Dellinger et al 2008;Raun et al 2008;Holland and Schepers 2010;Kitchen et al 2010; Thompson et al 2015).…”

Quantifying Greenhouse Gas (CO2, CH4, and N2O) Fluxes from Soil in a Pasture

“…Details for sensor mounting and operating procedures are similar to that described previously. 30 The timing of this in-season N fertilization was between the two centerpivot lagoon effluent applications. It was presumed the crop had taken up N from the first slurry application, and that crop canopy sensing would detect differences as the boundary between no-slurry and slurry was crossed.…”