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ARS Home » Northeast Area » University Park, Pennsylvania » Pasture Systems & Watershed Management Research » Research » Publications at this Location » Publication #219738
Title: Evaluating Multiple Indices from a Canopy Reflectance Sensor to Estimate Corn N Requirements

Author
item SRIPADA, RAVI - CANAAN VALLEY INST
item Schmidt, John
item DELLINGER, ADAM - USDA-NRCS
item BEEGLE, DOUGLAS - PENN STATE UNIV

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/13/2008
Publication Date: 11/1/2008
Citation: Sripada, R.P., Schmidt, J.P., Dellinger, A.E., Beegle, D.B. 2008. Evaluating Multiple Indices from a Canopy Reflectance Sensor to Estimate Corn N Requirements. Agronomy Journal. 100(6):1553-1561.

Interpretive Summary: With the increasing cost of fertilizer nitrogen (N), there is a renewed emphasis on developing new technologies for quantifying in-season N requirements for corn (Zea mays L.). The objectives of this research are (i) to evaluate different vegetative indices (based on remote sensing) derived from an active sensor in estimating in-season N requirements for corn, and (ii) to consider the influence of N:Corn price ratio on the economic optimum N rate (EONR) developed using these indices. Field studies were used to evaluate corn yield response to N fertilizer applied when the corn was 40 cm tall. The EONR was determined for N:Corn price ratios ranging from zero to 14:1. An evaluation of the relationship between EONR and more than 20 vegetative indices indicated that Relative Green Difference Normalized Vegetation Index (RGNDVIR) was the best predictor of EONR (R2 of 0.79). A relationship was developed so that EONR estimates derived using an active sensor could easily be adjusted based on the current N:Corn price ratio. For a given value of RGNDVIR, the EONR estimate is higher at lower price ratios and vice versa.

Technical Abstract: With the increasing cost of fertilizer N, there is a renewed emphasis on developing new technologies for quantifying in-season N requirements for corn. The objectives of this research are (i) to evaluate different vegetative indices derived from an active reflectance sensor in estimating in-season N requirements for corn, and (ii) to consider the influence of N:Corn price ratio on the economic optimum N rate (EONR) developed using these indices. Field experiments were conducted at eight site-years in central Pennsylvania. A two-way factorial experiment was implemented as a split-plot randomized complete block (four blocks) design, with different rates of N applied at planting (NPL) to create a range of N supply, corn color, and radiance; and (ii) at V6 (NV6) to measure yield response to NV6. Canopy reflectance measurements were obtained using a Crop Circle (Holland Scientific, Lincoln, NE) sensor just before NV6 applications. The EONR at V6 at 6:1 price ratio ranged from 0 to 221 kg per ha among the eight site-years, with a mean of 69 kg per ha. Better prediction of EONR was obtained by indices calculated relative to a high N plot than absolute indices. Relative Green Difference Normalized Vegetation Index (RGNDVIR) was the best predictor of EONR at V6 when expressed as a linear-floor model (R2 of 0.79). A relationship was developed so that EONR estimates derived using the Crop Circle sensor can easily be adjusted based on the current N:Corn price ratio. For a given value of RGNDVIR, the EONR estimate is higher at lower price ratios and vice versa.