Location: Cropping Systems and Water Quality Research
Title: Integrating remote sensing and crop growth models for an improved regional corn nitrogen recommendationAuthor
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RANSOM, CURTIS - University Of Missouri |
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Kitchen, Newell |
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CAMBERATO, JAMES - Purdue University |
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CARTER, PAUL - Retired Non ARS Employee |
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FERGUSON, RICHARD - University Of Nebraska |
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FERNANDEZ, FABIAN - University Of Minnesota |
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FRANZEN, DAVID - North Dakota State University |
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LABOSKI, CARRIE - University Of Wisconsin |
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NAFZIGER, EMERSON - University Of Illinois |
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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. |