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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #353398

Research Project: Sustainable Intensification of Grain and Biomass Cropping Systems using a Landscape-Based GxExM Approach

Location: Cropping Systems and Water Quality Research

Title: Utilizing weather, soil, and plant condition for predicting corn yield and nitrogen fertilizer response

Author
item Kitchen, Newell
item YOST, M - Utah State University
item RANSOM, C - University Of Missouri
item BEAN, G - University Of Missouri
item CAMBERATO, J - Purdue University
item CARTER, P - Dupont Pioneer Hi-Bred
item FERGUSON, R - University Of Nebraska
item FERNANDEZ, F - University Of Minnesota
item FRANZEN, D - North Dakota State University
item LABOSKI, C - University Of Wisconsin
item NAFZIGER, E - University Of Illinois
item SAWYER, J - Iowa State University

Submitted to: International Conference on Precision Agriculture Abstracts & Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 6/5/2018
Publication Date: 6/24/2018
Citation: Kitchen, N.R., Yost, M.A., Ransom, C.J., Bean, G.M., Camberato, J.J., Carter, P.R., Ferguson, R.B., Fernandez, F.G., Franzen, D.W., Laboski, C.A., Nafziger, E.D., Sawyer, J.E. 2018. Utilizing weather, soil, and plant condition for predicting corn yield and nitrogen fertilizer response. 14th International Conference on Precision Agriculture, June 24-27, 2018, Montreal, Quebec, Canada. Paper No.5109.

Interpretive Summary: Improving corn nitrogen (N) fertilizer rate recommendation tools should increase farmer’s profits and help mitigate N pollution. One way may be to include more detailed weather and soil information, which repeatedly has been shown to influence crop N need. The objective of this research was to improve publicly-available N recommendation tools by modifying them with additional site-specific soil and weather information. Using various regression techniques, soil and weather information was used to develop models that helped to better explain the economical optimum N rate. A weather measurement found to be especially helpful for adjusting N rate decision tools was the evenness of rainfall from planting to the date of side-dress N application. With soil the pH was found helpful in adjusting the recommendation tools. All tools showed improvement with soil and weather adjustment. The greatest improvement in tool performance was with including both soil and weather information with the late-spring soil nitrate test, canopy reflectance sensing, and maximum return to N (MRTN). This analysis demonstrated that incorporating soil and weather information can help improve N recommendations, and thus could help farmers to make better N fertilizer decisions. Better N management will increase farmer profits and help reduce fertilizer over-applications.

Technical Abstract: Improving corn (Zea mays L.) nitrogen (N) fertilizer rate recommendation tools should increase farmer’s profits and help mitigate N pollution. Weather and soil properties have repeatedly been shown to influence crop N need. The objective of this research was to improve publicly-available N recommendation tools by adjusting them with additional soil and weather information. Four N recommendation tools were evaluated across 49 N response trials conducted in eight U.S. states over three growing seasons. Tools were evaluated for split (planting+side-dress) fertilizer applications. Using an elastic net algorithm the difference between each tool’s N recommendation and the economically optimum N rate (EONR) was regressed against soil and weather information, then the elastic net regression coefficients were used to adjust the tool’s N recommendation. The evenness of rainfall calculated from planting to the date of side-dress and soil pH (0-0.30 m) were the most frequently identified parameters for adjusting tools. All tools showed improvement with adjustment (+r^2 = 0.09). The greatest improvement in tool performance was with including soil and weather information with the Late-Spring Soil Nitrate Test (LSNT), canopy reflectance sensing, and MRTN. This analysis demonstrated that incorporating soil and weather information can help improve N recommendations.