@article{bolster_correndo_pearce_spargo_slaton_osmond_2022, title={A spreadsheet for determining critical soil test values using the modified arcsine-log calibration curve}, volume={12}, ISSN={["1435-0661"]}, DOI={10.1002/saj2.20498}, abstractNote={AbstractSoil test correlation data are often used to identify a critical soil test value (CSTV), above which crop response to added fertilizer is not expected. Oftentimes, models are used to determine the CSTV from soil test correlation data, yet most commonly used models have inherent assumptions that may not be valid for these data. The arcsine‐log calibration curve (ALCC) was developed in response to the statistical limitations of other commonly used models. A modified ALCC model using standardized major axis regression further improves this model's applicability to soil test correlation data. Here, we describe a Microsoft Excel spreadsheet for calculating CSTV from soil test correlation data using the modified ALCC model. The spreadsheet is available for download providing an accessible and easy‐to‐use tool for those who would like to use this method but who lack the experience with more sophisticated coding programs. The spreadsheet is available for download at http://www.ars.usda.gov/ALCC.}, journal={SOIL SCIENCE SOCIETY OF AMERICA JOURNAL}, author={Bolster, Carl H. and Correndo, Adrian A. and Pearce, Austin W. and Spargo, John T. and Slaton, Nathan A. and Osmond, Deanna L.}, year={2022}, month={Dec} } @article{pearce_slaton_lyons_bolster_bruulsema_grove_jones_mcgrath_miguez_nelson_et al._2022, title={Defining relative yield for soil test correlation and calibration trials in the fertilizer recommendation support tool}, volume={8}, ISSN={["1435-0661"]}, DOI={10.1002/saj2.20450}, abstractNote={AbstractThe 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. Yield response for the nutrient of interest in a particular site‐year 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 but 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, which defines A as the numerically highest treatment yield mean, as the most appropriate 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.}, journal={SOIL SCIENCE SOCIETY OF AMERICA JOURNAL}, author={Pearce, Austin W. and Slaton, Nathan A. and Lyons, Sarah E. and Bolster, Carl H. and Bruulsema, Tom W. and Grove, John H. and Jones, John D. and McGrath, Josh M. and Miguez, Fernando E. and Nelson, Nathan O. and et al.}, year={2022}, month={Aug} } @article{correndo_pearce_bolster_spargo_osmond_ciampitti_2023, title={The soiltestcorr R package: An accessible framework for reproducible correlation analysis of crop yield and soil test data}, volume={21}, ISSN={["2352-7110"]}, DOI={10.1016/j.softx.2022.101275}, abstractNote={The soiltestcorr R package is an open-source software designed to enable accessible and reproducible computation of correlation analyses between crop yield response to fertilization and soil test values. The package compiles a series of functions for analyzing soil test correlation data: (i) Cate & Nelson data partitioning procedure (graphical and statistical versions), (ii) nonlinear regression analysis (linear-plateau, quadratic-plateau, and Mitscherlich-type exponential models), and (iii) the modified arcsine-log calibration curve. The soiltestcorr enables users to correlate crop response to soil nutrient availability and estimate a critical soil test value and visualize results with ggplot without requiring advanced R programming skills. Finally, a web application that facilitates the use of the package is also offered for users with no background in R programming.}, journal={SOFTWAREX}, author={Correndo, Adrian A. and Pearce, Austin and Bolster, Carl H. and Spargo, John T. and Osmond, Deanna and Ciampitti, Ignacio A.}, year={2023}, month={Feb} } @article{lyons_arthur_slaton_pearce_spargo_osmond_kleinman_2021, title={Development of a soil test correlation and calibration database for the USA}, volume={6}, ISSN={["2471-9625"]}, DOI={10.1002/ael2.20058}, abstractNote={AbstractAs part of the Fertilizer Recommendation Support Tool (FRST) project, the FRST database was developed to consolidate and preserve U.S. soil test correlation and calibration data. Legacy phosphorus (P) and potassium (K) soil test data that met a minimum requirement were included in the database. The FRST database initially included over 1,200 individual trials from a range of years, cropping systems, geographic regions, and management practices. The FRST database is being migrated from a Microsoft Excel spreadsheet to a relational database format housed within the USDA‐ARS Agricultural Collaborative Research Outcomes System (AgCROS) to be accessed via the online FRST decision support tool. Data will be continually added to the FRST database through an online submission form following peer review by the FRST team. The FRST database and associated decision support tool will aid researchers, extension associates, consultants, and farmers in improving fertilizer recommendations for crops across the United States.}, number={4}, journal={AGRICULTURAL & ENVIRONMENTAL LETTERS}, author={Lyons, Sarah E. and Arthur, Dan K. and Slaton, Nathan A. and Pearce, Austin W. and Spargo, John T. and Osmond, Deanna L. and Kleinman, Peter J. A.}, year={2021} } @article{slaton_lyons_osmond_brouder_culman_drescher_gatiboni_hoben_kleinman_mcgrath_et al._2021, title={Minimum dataset and metadata guidelines for soil-test correlation and calibration research}, volume={11}, ISSN={["1435-0661"]}, url={https://doi.org/10.1002/saj2.20338}, DOI={10.1002/saj2.20338}, abstractNote={AbstractSoil‐test correlation and calibration data are essential to modern agriculture, and their continued relevance is underscored by the expansion of precision farming and the persistence of sustainable soil management priorities. In support of transparent, science‐based fertilizer recommendations, we seek to establish a core set of required and recommended information for soil‐test P and K correlation and calibration studies, a minimum dataset, building on previous research. The Fertilizer Recommendation Support Tool (FRST) project team and collaborators are developing a national database that will support a soil‐test‐based nutrient management decision aid tool. The FRST team includes over 80 scientists from 37 land‐grant universities, two state universities, one private university, three federal agencies, two private not‐for‐profit organizations, and one state department of agriculture. The minimum dataset committee developed and vetted a robust set of factors fo minimum dataset consideration that includes information on soil sample collection and processing, soil chemical and physical properties, experimental design and statistical analyses, and metadata about the trial, production system, and field management. The minimum dataset provides guidelines for essential information to meet the primary objective of knowledge synthesis, including meta‐analysis and systemic reviews, but permits researchers the flexibility to satisfy local, state, and regional objectives. Ultimately, this consensus‐driven effort seeks to establish a standard that ensures the maximum utility and impact of modern correlation and calibration studies for developing crop nutrition recommendations that improve productivity and profitability for the crop producer, while reducing environmental impacts of nutrient losses.}, journal={SOIL SCIENCE SOCIETY OF AMERICA JOURNAL}, publisher={Wiley}, author={Slaton, Nathan A. and Lyons, Sarah E. and Osmond, Deanna L. and Brouder, Sylvie M. and Culman, Steve W. and Drescher, Gerson and Gatiboni, Luciano C. and Hoben, John and Kleinman, Peter J. A. and McGrath, Joshua M. and et al.}, year={2021}, month={Nov} }