|Scharf, Peter - UNIVERSITY OF MISSOURI|
|Palm, Harlan - UNIVERSITY OF MISSOURI|
|Shannon, Kent - UNIVERSITY OF MISSOURI|
Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: June 2, 2008
Publication Date: October 8, 2008
Citation: Kitchen, N.R., Sudduth, K.A., Drummond, S.T., Scharf, P.C., Palm, H., Shannon, K.D., Vories, E.D. 2008. Components of an Optimal Algorithm For Canopy-Sensed Corn Nitrogen Rate [abstract]. ASA-CSSA-SSSA Annual Meeting Abstracts. ASA-CSSA-SSSA Annual Meeting. October 5-9, 2008, Houston, TX. Paper No. 084437. Technical Abstract: Documented variable nitrogen (N) need within- and between corn (Zea mays L.) fields supports the use of active crop-canopy reflectance sensing for deciding how much N fertilizer to apply. These sensors detect variations in chlorophyll content and yield potential during mid-vegetative growth stages and are typically attached to in-season N fertilization equipment for real-time, automated, decision and application operations. The decision rules (or algorithm) used require optimization considering many factors such as growth stage, soil color, sensor referencing, grain and fertilizer prices, yield potential, and stand anomalies. The objective of this research was to use 15 field-scale (400 to 800 m in length) experiments conducted on different farmers’ fields over four growing seasons (2004-2007) to evaluate the relative importance of these factors in developing an optimal algorithm. The fields represent three different major soil areas of Missouri: river alluvium, deep loess, and claypan. Multiple blocks of N rate response plots were arranged in a randomized complete block design traversing the length of each field. Each block consisted of 8 N treatments from 0 to 235 kg N/ha on 34 kg N/ha increments top-dressed between vegetative growth stage V7 and V12. Crop canopy reflectance sensor measurements were obtained from N response blocks and adjacent N-rich reference strips at the time of N application. The results of the algorithm optimized for canopy sensors were compared to results with the conventional N rate used by the producers operating these fields.