|RANSOM, C. - University Of Missouri|
Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 9/6/2017
Publication Date: 10/22/2017
Citation: Ransom, C., Kitchen, N.R. 2017. The best and worst of corn nitrogen rate recommendation tools used in the Midwest [abstract]. ASA-CSSA-SSSA Annual Meeting, October 22-25, 2017, Tampa, Florida. Available: https://scisoc.confex.com/crops/2017am/recordingredirect.cgi/id/29881.
Technical Abstract: Publicly-available nitrogen (N) rate recommendation tools are utilized to help maximize yield in corn production. These tools often fail when N is over-applied and results in excess N being lost to the environment, or when N is under-applied and results in decreased yield and economic returns. The performance of a tool is often based on the specific soil and weather conditions of a growing season. Research is needed to determine which tools are the most effective at recommending economical optimal N rates (EONR) under varying soil and weather conditions across the Corn Belt. Nitrogen response trials were conducted across eight Midwest states from 2014 to 2016 resulting in a range of production sites and resulting corn response to N. The performance of publicly-available N recommendation tools: pre-plant soil nitrate tests, pre-sidedress soil nitrate tests (PSNT), maximum return to N (MRTN), crop canopy sensor, and the Maize-N crop growth model are contrasted in this presentation. Tools used for a split N application performed better than tools used for an all at planting recommendation. Of the tools evaluated, the IA PSNT, MRTN, and Nebraska yield goal performed the best as a split N application with 37 and 45 % of the sites having N recommendation within 30 kg N/ha of EONR and a RMSE of less than 80 kg N/ha. In contrast, the three poorest performing tools included three of the five yield goal recommendations evaluated with less than 18% of the sites’ N recommendations were within 30 kg N/ha and have RMSE values between 114 to 125 kg N/ha. Additionally, the Maize-N crop growth model and canopy reflectance sensor algorithm tested performed poorly and on average underestimated EONR.