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ARS Home » Midwest Area » Bowling Green, Kentucky » Food Animal Environmental Systems Research » Research » Publications at this Location » Publication #392704

Research Project: Developing Agronomically and Environmentally Beneficial Management Practices to Increase the Sustainability and Safety of Animal Manure Utilization

Location: Food Animal Environmental Systems Research

Title: Defining relative yield for soil test correlation and calibration trials in the fertilizer recommendation support tool

Author
item PEARCE, AUSTIN - North Carolina State University
item SLATON, NATHON - University Of Arkansas
item LYONS, SARA - North Carolina State University
item Bolster, Carl
item BRUULSEMA, TOM - Plant Nutrition Canada
item GROVE, JOHN - University Of Kentucky
item JONES, JOHN - University Of Wisconsin
item MCGRATH, JOSHUA - University Of Kentucky
item MIGUEZ, FERNANDO - Iowa State University
item NELSON, NATHAN - Kansas State University

Submitted to: Soil Science Society of America Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/11/2022
Publication Date: 5/28/2022
Citation: Pearce, A., Slaton, N., Lyons, S., Bolster, C.H., Bruulsema, T., Grove, J., Jones, J., McGrath, J., Miguez, F., Nelson, N. 2022. Defining relative yield for soil test correlation and calibration trials in the fertilizer recommendation support tool. Soil Science Society of America Journal. https://doi.org/10.1002/saj2.20450.
DOI: https://doi.org/10.1002/saj2.20450

Interpretive Summary: Properly correlated and calibrated soil tests are the foundation of crop fertilizer recommendations that seek to maximize economic yields and minimize environmental losses. The goal of soil-test correlation experiments is to relate crop yield response from increasing fertilizer rate treatments with the extracted soil-test concentration of the nutrient of interest. This yield response curve is often used to estimate a critical soil test value (CSTV), above which the soil is assumed to be nutrient sufficient and a crop response to added fertilizer is thereby unlikely. The yield response to fertilizer is often converted to relative yield (RY) to normalize for the variability of other factors inherent in multi-site field experiments. Several methods exist in the literature for calculating RY; however, the effect of method choice on soil test correlation outcomes is undocumented. Here, we compare six different methods for calculating RY using five published correlation datasets and compare the resulting CSTVs. Understanding RY definitions and how they influence soil test correlation results can help improve researcher and end-user confidence in soil test correlation and calibration outcomes.

Technical Abstract: The Fertilizer Recommendation Support Tool (FRST) will perform correlations between soil nutrient concentrations and crop response to fertilization from user-selected datasets in the FRST national database of soil test correlation and calibration trials. Site-year yield response for the nutrient-of-interest control treatment is presented as relative yield (RY), a ratio of unfertilized yield to the maximum attainable yield (A). Several methods exist in the literature for estimating A and calculating RY; however, the effect of method choice on soil test correlation outcomes is undocumented. We used six published methods to calculate RY from site-year yield data for five published correlation datasets, and fit a generalized linear plateau (LP) model to each. The critical soil test value (at the LP join point) and RY intercept coefficients were not significantly affected by RY method for any of the datasets, and RY plateau was significantly affected by method for only one dataset. The top options after robust group discussions were the so-called MAX and FITMAX methods. We selected the MAX method that defines A as the numerically highest-yielding treatment as the best suited method for FRST because MAX represents maximal yield in responsive sites, is inclusive of trial data having a range of treatment numbers, limits RY to 100% which allows options for transforming data, and is simpler to implement than FITMAX, which requires a decision tree to calculate RY for diverse trials.