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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #410032
Research Project: Innovative Cropping System Solutions for Sustainable Production on Spatially Variable Landscapes

Location: Cropping Systems and Water Quality Research

Title: Corn yield variance in response to nitrogen fertilization

Author
item CORRENDO, ADRIAN - Kansas State University
item LACASA, JOSEFINA - Kansas State University
item HEFLEY, TREVOR - Kansas State University
item CLARK, JASON - South Dakota State University
item Ransom, Curtis
item CIAMPITTI, IGNACIO - Kansas State University

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 11/1/2023
Publication Date: 11/1/2023
Citation: Correndo, A.A., Lacasa, J., Hefley, T., Clark, J., Ransom, C.J., Ciampitti, I.A. 2023. Corn yield variance in response to nitrogen fertilization [abstract]. 2023 ASA-CSSA-SSSA International Annual Meeting, October 29-November 1, 2023, St. Louis, Missouri. Available: https://scisoc.confex.com/scisoc/2023am/meetingapp.cgi/Paper/150062

Interpretive Summary:

Technical Abstract: While the effects of fertilizer management strategies (rate, source, time, and placement) on expected yield have been widely assessed in corn, their impact on yield variability has been commonly overlooked. The aim of this work is to study the effect of (i) the nitrogen (N) fertilizer rate, and (ii) the timing of fertilization on the variance of corn yield. The public database “Performance and Refinement of N Fertilization Tools” (PRNT) consisting of 49 site-years across eight states of the US Midwest Region was used for this study. Treatments included a control (0 kg N ha-1) plus combinations between seven N rates (45, 90, 135, 179, 225, 270, and 315 kg N ha-1) and two fertilization timings (all at planting or split, with 45 kg ha-1 at planting and the rest of the N fertilizer at the 9th leaf developmental stage (V9). Both expected value and variance were jointly modeled with Bayesian generalized additive models using regression splines due to their shape flexibility.