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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #416418
Research Project: Innovative Cropping System Solutions for Sustainable Production on Spatially Variable Landscapes

Location: Cropping Systems and Water Quality Research

Title: Improving corn nitrogen recommendations through on-farm research

Author
item WANG, YIZHENG - University Of Missouri
item LORY, JOHN - University Of Missouri
item Ransom, Curtis
item SHANNON, KENT - University Of Missouri
item ANDERSON, STEPHEN - University Of Missouri

Submitted to: Symposium Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 6/20/2024
Publication Date: 6/20/2024
Citation: Wang, Y., Lory, J., Ransom, C.J., Shannon, K., Anderson, S. 2024. Improving corn nitrogen recommendations through on-farm research [abstract]. 2024 Digital Agriculture Symposium, June 20-21, 2024, Columbia, Missouri.

Interpretive Summary:

Technical Abstract: Soil nitrogen (N) is a major limiting factor in corn production. Applying the optimal N fertilizer rate maximizes corn yield while minimizing N loss to the environment. Understanding factors that affect corn yield response to N is essential for providing effective N management advice. Our objective is to identify and calibrate spatial and temporal factors that explain differences in nitrogen response within and among Missouri corn fields. Up to 40 on-farm field-scale nitrogen response trials will be conducted across central and North Central Missouri. Farmers will apply five or six nitrogen rates to 76-m long plots replicated to fill the entire field using their fertilizer application equipment (typically 30 to 40 plots per field). Farmers harvest the field using their combine reporting yields based on their yield monitor. Prior to implementing each field experiment, we collect cover crop biomass (when present), landscape indices derived from digital elevation maps, SSURGO, soil electrical conductivity, and deep soil cores. Additionally, we gather surface soil (0-15 cm) data using a stratified grid design (0.4-ha resolution) to obtain spatial information for soil fertility, soil health indices, and texture. Additional data being collected includes weather and spectral data derived from UAVs and satellite imagery (in-season and historical). Strategies such as crop modeling, machine learning and probabilistic statistical methods will be used to identify and calibrate key factors controlling N response within and among Missouri farmer fields. Preliminary findings provided valuable practical experience implementing these technically demanding designs with farmer equipment and confirm the complexity of corn N response across Missouri fields.