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ARS Home » Plains Area » Lincoln, Nebraska » Agroecosystem Management Research » Research » Publications at this Location » Publication #228695

Title: An Active Sensor Nitrogen Application Algorithm for Corn Using a Chlorophyll Meter Based Sufficiency Index Concept

Author
item Shanahan, John
item SOLARI, FERNANDO - MONSANTO
item FERGUSON, RICHARD - UNIVERSITY OF NEBRASKA
item SCHEPERS, JAMES - ARS-RETIRED COLLABORATOR

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 6/1/2008
Publication Date: 10/5/2008
Citation: Shanahan, J.F., Solari, F., Ferguson, R., Schepers, J.S. 2008. An Active Sensor Nitrogen Application Algorithm for Corn Using a Chlorophyll Meter Based Sufficiency Index Concept [abstract]. ASA-CSSA-SSSA Annual Meeting, October 5-9, 2008, Houston, Texas. 2008 CDROM Proceedings of 2008 Joint Annual Meetings of ASA. Abstract No. 43330.

Interpretive Summary:

Technical Abstract: Traditional N fertilizer management schemes for U.S. corn production systems have resulted in low N use efficiency, reduced water quality, and considerable public debate regarding N use in crop production. We have built a prototype high clearance N applicator configured with active sensors, controller, and nozzle/valve system designed to deliver spatially variable rates of N fertilizer in lieu of uniform at-planting applications to address these issues. This paper reports on the development of a sensor algorithm to be used for translating active sensor readings into N application rates, and efforts to validate the algorithm in small plot field studies. The active sensor used in our work is the Crop Circle ACS-210 manufactured by Holland Scientific of Lincoln, NE. The algorithm was developed by combining results from a long term field study showing chlorophyll meter readings can be used to assess corn N status and determine rate of N application along with results showing active sensor readings are highly correlated with chlorophyll meter assessments of canopy N status. The resulting algorithm is proposed as a means for converting active sensor readings into corrective in-season N application rates that maintain grain yields. The algorithm was validated in a field study involving small plots receiving different amounts and timings of N. Sensor readings were acquired on the dates N applications were made and the algorithm used to convert sensor estimated N deficiency. Grain yields were also determined for these same plots. The relationship of relative grain yields versus sensor estimated N requirements was described by a quadratic plateau regression model with a coefficient of determination of 0.64, indicating the sensor algorithm would provide a reasonably accurate assessment of in-season N requirements for maintaining grain yields.