Sensor-Based Algorithm for Mid-Season Nitrogen Application in Corn

Abstract

Applying insufficient nitrogen (N) in a highly responsive crop, such as corn, results in lower grain yield, quality, and profits. On the other hand, when nitrogen is applied in excess of crop needs, profit is reduced and negative environmental consequences are likely. The objective of this study was to develop and employ a sensor-based algorithm to determine the mid-season N requirements for deficit-irrigated corn in Coastal Plain soils. The algorithm was developed using varied prescription rate N plot on two soil types. The test plots received nine different rates of N fertilizer, replicated 5 times in plots of each soil type using a Randomized Complete Block design. A 6-row GreenSeeker optical sensor was used to measure plant NDVI, between the V6 to V8 growth stages. The sensor readings were used to develop an algorithm to be used in the estimation of side-dress N application in corn. The NDVI sensor readings were collected at the V6 to V8 growth stage during the 2015 and 2016 growing seasons correlated with actual corn yields (R 2 > 0.68, p < 0.001). In-Season Estimated yield (INSEY) was used along with the actual yield to produce a yield potential for each growing season for deficit-irrigated corn crop. In summary, the algorithm developed from the NDVI readings reduced N application rates by 21% and 34% in soil types 1 and 2, respectively, compared to the normal grower practice (226 kg N/ha) with no reduction in corn yields.

A scoping review of side-dress nitrogen recommendation systems and their perspectives in precision agriculture

“…Using local N-rich calibration strips (a small part of the field with no N limitation from sowing) the response of crop yield to N is estimated, so that the yield gap is calculated and the N rate defined. Initially calibrated for winter wheat in the USA (Raun et al, 2005), it has been also calibrated for maize in the USA (Teal et al, 2006), then generalized (Solie et al, 2012) and tested for several crops (Porter, 2010) and cropping systems (Virginia Corn Algorithm, Thomason et al, 2011; North Dakota State University maize algorithms, Franzen et al, 2014; Clemson University algorithm, Khalilian et al, 2017). The various calibration equations and modifications of the original algorithm are a reason for its wide diffusion.…”

Cotton Response to Variable Nitrogen Rate Fertigation through an Overhead Irrigation System

“…Two Nitrogen Rich Strips (NRS) were created in the test field by applying a high N rate (168 kg/ha) such that N would not be limited throughout the optical sensing period during both growing seasons. in cotton following the same methodology as described in Khalilian et al 2017 [12]. The sensor readings were then used to calculate N requirements for the optical sensor based NDVI treatments (TRT 3-6).…”

In-Season Assessment of Cotton Nitrogen Status from a Handheld Smartphone and an Unmanned Aerial System

“…More recently, there has been increased interest in the use of unmanned aerial vehicles (UAVs) to collect spectral information to determine crop N status (Ballester et al, 2017). Fertilizer application algorithms have been developed based on commercially available ground-based sensors (Arnall et al, 2008;Khalilian et al, 2017), but there is little information regarding the suitability of these algorithms to UAVcollected information. Tremblay et al (2009) noted the non-transferability of application algorithms from one ground-based sensor to another, which could be extended intuitively to sensors on different platforms.…”