Skip to main content
ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #369545
Research Project: Sustainable Intensification of Cropping Systems on Spatially Variable Landscapes and Soils

Location: Cropping Systems and Water Quality Research

Title: Integrating remote sensing and crop growth models for an improved regional corn nitrogen recommendation

Author
item RANSOM, CURTIS - University Of Missouri
item Kitchen, Newell
item CAMBERATO, JAMES - Purdue University
item CARTER, PAUL - Retired Non ARS Employee
item FERGUSON, RICHARD - University Of Nebraska
item FERNANDEZ, FABIAN - University Of Minnesota
item FRANZEN, DAVID - North Dakota State University
item LABOSKI, CARRIE - University Of Wisconsin
item NAFZIGER, EMERSON - University Of Illinois
item SAWYER, JOHN - Iowa State University

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 10/25/2019
Publication Date: 11/10/2019
Citation: Ransom, C., Kitchen, N.R., Camberato, J.J., Carter, P.R., Ferguson, R.B., Fernandez, F.G., Franzen, D.W., Laboski, C.A., Nafziger, E.D., Sawyer, J.E. 2019. Integrating remote sensing and crop growth models for an improved regional corn nitrogen recommendation [abstract]. ASA-CSSA-SSSA Annual International Conference, November 10-13, 2019, San Antonio, Texas. Available: https://scisoc.confex.com/scisoc/2019am/meetingapp.cgi/Paper/118781

Interpretive Summary:

Technical Abstract: Corn N fertilizer recommendations should take into account environmental growing conditions, crop genetics, and management decisions (E x G x M). Crop growth models have the potential for incorporating all of these factors when determining a site-specific N recommendation. However, models often provide inaccurate assessments of crop N demand and therefore need approaches for increasing accuracy with in-season plant growth measurements. One method for doing this is to use proximal and remote sensing technologies (e.g., near-canopy reflectance sensors and aerial imagery) as a means of calibrating and adjusting crop growth models for improved site-specific N recommendations. Data was collected on corn N response trials using a standardized research protocol across 49 sites, ranging in productivity potential, in eight Midwest states from 2014 to 2016. Crop, soil, and weather measurements were collected at each site necessary to run crop model simulations. Canopy reflectance and aerial imagery data were collected near the time of in-season fertilization. In this presentation we evaluate and compare different methods of utilizing crop growth models in conjunction with aerial imagery and/or canopy reflectance sensing for making corn N recommendations across the U.S. Corn Belt.