Title: Will Variable-Rate Nitrogen Fertilization Using Corn Canopy Reflectance Sensing Deliver Environmental Benefits? Authors
Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 25, 2009
Publication Date: November 13, 2009
Citation: Roberts, D.F., Kitchen, N.R., Scharf, P.C., Sudduth, K.A. 2010. Will Variable-Rate Nitrogen Fertilization Using Corn Canopy Reflectance Sensing Deliver Environmental Benefits? Agronomy Journal. 102:85-95. Interpretive Summary: Nitrogen (N) fertilizer and manure sources of N are generally inefficiently used by crops (efficiencies only 30 to 60%). With costs of N fertilizer dramatically increasing in recent years, farmers are seeking improved management practices that enhance N use efficiency. However for this or any other management consideration, the best practices for one location within a field may not be the best for another location within the same field. This is because soil conditions vary within fields and N has the potential for leaving the soil through different pathways (e.g., leaching, runoff, or gaseous emissions), depending on these varying soil conditions. The purpose of this research was to investigate if light reflectance sensing of corn plants to determine on-the-go variable-rate N applications would decrease N losses from fields and thereby deliver environmental benefits. In this investigation on 16 farmers’ fields from 2004-2007 we found the amount of N the corn crop needed varied within most fields by over 100 lbs per acre. This finding reinforces the need for precision technologies that will enable variable-rate N fertilizer applications. Compared to the amount the farmers had been applying to these same farm fields, we found the amount of N fertilizer needed was less and could be site-specifically estimated by light reflectance sensing. The amount of N saved ranged from 10 to 50 lbs /acre, depending on soil type and fertilizer and grain prices. Additionally the study illustrated that using canopy sensing for N fertilization would likely: 1) improve yield efficiency (i.e., bushel per unit of N), 2) give higher N fertilizer recovery in the crop, and 3) decrease soil nitrate at the end of the growing season. This investigation supports the idea that sensor-based N application can deliver environmental benefits to rural citizens and the general public, likely in the form of reduced nitrate losses to rivers and streams and reduced nitrous oxide emissions into the atmosphere.
Technical Abstract: Within-field variability of corn N need calls for development of site-specific fertilizer application strategies. One approach many are investigating is in-season canopy reflectance sensing. Justification for this strategy partly rests with the premise that it will improve N use and in turn reduce N loss from fields. The objective of this study was to determine whether N fertilization using corn canopy reflectance sensing could deliver environmental benefits. On 16 field-scale sites, multiple blocks of randomized N rate plots (0 to 235 kg N/ha) traversing the field length were top-dressed between V7-V11 growth stages. Canopy sensor measurements were obtained from these plots and adjacent N-rich reference strips at top-dressing. Environmental indicators were examined at the determined optimal N rate and the N rate the producer used. A partial N mass balance on response blocks within fields highlighted how variable optimal N rate likely resulted in multiple and different N loss pathways. For many fields, optimal N rate was less than the producer N rate and the observed trends were as expected: higher yield efficiency, higher N fertilizer recovery efficiency, lower un-accounted for N, and less post-harvest soil inorganic N. For a measurement examining canopy sensor-based N applications, N savings of 10 to 50 kg N/ha could be expected in most situations, but savings varied by reflectance readings, soil type, and fertilizer and grain prices. In some situations sensor-based N would be greater than the producer N rate. Given that sensor information can be processed into an N rate that approximates the optimal N rate, the results support the premise that sensor-based N applications will deliver environmental benefits.