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Improving Corn Nitrogen Rate Recommendations Through Tool Fusion
1C. J. Ransom, 1N. R. Kitchen, 2J. J. Camberato, 3P. R. Carter, 4R. B. Ferguson, 5F. G. Fernandez, 6D. W. Franzen, 7C. A. Laboski, 8E. D. Nafziger, 9J. E. Sawyer, 10J. Shanahan
1. Univ. of Missouri-USDA ARS-Columbia, MO
2. Purdue Univ.-Lafayette IN
3. DuPont Pioneer-Johnston, IA
4. Univ. of Nebraska-Lincoln NE
5. Univ. of Minnesota-St. Paul MN
6. North Dakota State Univ.-Fargo ND
7. Univ. of Wisconsin-Madison WI
8. Univ. of Illinois-Urbana IL
9. Iowa State Univ.-Ames IA
10. Fortigen, Lincoln, NE

 Improving corn (Zea maysL,) nitrogen (N) fertilizer rate recommendation tools can improve farmer’s profits and help mitigate N pollution. One way to improve N recommendation methods is to not rely on a single tool, but to employ two or more tools. Thiscould be thoughtof as “tool fusion”.The objective of this analysis was to improve N management by combining N recommendation tools used for guiding rates for an in-seasonN application. This evaluation was conductedon 49 N response trials that spanned eight states and three growing seasons. An economical optimal N rate (EONR) was calculatedfor N treatments receiving  45 kg N ha-1applied at-planting  and the remaining fertilizer N applied at the V9 corn developmental stage. A yieldgoal approach, the Iowa Late-Spring Nitrate Test (IA LSNT) , and canopy reflectance sensing were the three recommendation toolsused to evaluate the tool fusion concept. Tools were fused using either an elastic net or decision tree approach. Using the elastic net approachtools, were fusedwith all combinations of mainand two- or three-way interaction terms regressed against EONR. The decision tree was developed using only the maineffects compared against EONR. Regardless of the method used to combine tools, any combination of two or three N recommendation tools together improved performance compared to using any one tool alone. The best elastic net based tool fusion occurred when all three recommendation tools and all possible interactions were includedin the model which helped explain 42% of the variation around EONR, a 75% increase over the best tool alone. Additionally, the root-mean-squareerror (RMSE) improved from 68 kg N ha-1(besttool used alone) to 55 kg N ha-1. However, the best combination occurred when using the three N recommendation tools in a decision tree. The decision tree method explained 45% of the variation in EONR and had a RMSEvalue equal to 53 kg N ha-1. This analysisdemonstrated that combining tools is a valid way to improve N recommendations,and thus could aid farmers in better managing N than using a single tool by itself. 

Keyword: Nitrogen recommendation tools, Nitrogen fertilizer, Elastic Net, Decision Tree, Canopy reflectance sensing, EONR, corn, LSNT, PSNT, and Tool fusion