Improving publicly available corn nitrogen rate recommendation tools with soil and weather measurements

Authors

Curtis J. Ransom, USDA ARS, Columbia, MOFollow
Newell R. Kitchen, USDA ARS, Columbia, MOFollow
John E. Sawyer, Iowa State UniversityFollow
James J. Camberato, Purdue UniversityFollow
Paul R. Carter, Clive, IA
Richard Ferguson, University of Nebraska-LincolnFollow
Fabián G. Fernández, University of Minnesota - Twin CitiesFollow
David W. Franzen, North Dakota State University--FargoFollow
Carrie A.M. Laboski, University of Wisconsin-MadisonFollow
D. Brenton Myers, Corteva Agriscience, Johnston, IA
Emerson D. Nafziger, University of Illinois at Urbana-ChampaignFollow
John F. Shanahan, Soil Health Institute, Lincoln, NE

ORCID IDs

Curtis J. Ransom https://orcid.org/0000-0002-1268-7247

Newell R. Kitchen https://orcid.org/0000-0003-2268-9016

James J. Camberato https://orcid.org/0000-0002-6677-854X

Fabián G. Fernández https://orcid.org/0000-0002-9539-0050

David W. Franzen https://orcid.org/0000-0003-4862-8086

Emerson D. Nafziger https://orcid.org/0000-0002-6578-1624

Document Type

Article

Date of this Version

2021

Citation

Agronomy Journal. 2021;1–23.

DOI: 10.1002/agj2.20627

Comments

U.S. government work

Abstract

Improving corn (Zea mays L.) N fertilizer rate recommendation tools is necessary for improving farmers’ profits and minimizing N pollution. Research has repeatedly shown that weather and soil factors influence available N and crop N need. Adjusting available corn N recommendation tools with soil and weather measurements could improve farmers’ ability to manage N. The aim of this research was to improve publicly available N recommendation tools with site-specific soil and weather measurements. Information from49 site-years ofNresponse trials in theU.S. Midwest was used to evaluate 21 rate recommendation tools for a single (at-planting) and split (at-planting + sidedress) N application. Using elastic net and decision tree algorithms, the difference between each tool’s N recommendation and the economically optimum nitrogen rate (EONR) was modeled against soil and weather measurements. The model’s predicted values were used to adjust the tools. Unadjusted the best performing tool had r² = .24; after adjustment, the best performing tool had r² = .57. Overall tool improvement was modest and sometimes required many additional inputs. Using weather measurements (e.g., evenness of rainfall or abundant and well-distributed rainfall) helped increase N recommendations by accounting for N loss while soil measurements (e.g., pH and total C) helped decrease N recommendations when there was sufficient available soil N. This investigation showed that incorporating soil and weather measurements is a viable approach for improving corn N recommendation tools regionally; but even with adjustments, tools still have room for additional improvement.