A spreadsheet for determining critical soil test values using the modified arcsine-log calibration curve

Authors: Bolster, Carl; CORRENDO, ADRIAN - Kansas State University; PEARCE, AUSTIN - North Carolina State University; SPARGO, JOHN - Pennsylvania State University; SLATON, NATHAN - University Of Arkansas; OSMOND, DEANNA - North Carolina State University

Submitted to: Soil Science Society of America Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/13/2022
Publication Date: 12/8/2022
Citation: Bolster, C.H., Correndo, A.A., Pearce, A., Spargo, J., Slaton, N., Osmond, D. 2022. A spreadsheet for determining critical soil test values using the modified arcsine-log calibration curve. Soil Science Society of America Journal. 87(1):182-189. https://doi.org/10.1002/saj2.20498.
DOI: https://doi.org/10.1002/saj2.20498

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. Yield response to fertilizer is often converted to relative yield (RY) to normalize differences in yield potential based on other factors (e.g., soil physical properties, climate and soil moisture, and pest pressure) inherent in multi-site field experiments. Graphical plots of soil test values (STV) and RY of control treatments usually display a nonlinear trend with a diminishing increase of RY with increasing soil test values until yield response and RY approach a plateau or asymptote. This yield response curve is often interpreted from a sufficiency level approach that identifies 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. Sites with soil test values below the CSTV are assumed to be nutrient deficient, and thus a crop response to added fertilizer is expected. Here, we describe an easy-to-use Microsoft Excel spreadsheet developed for estimating CSTV from soil test correlation data. Here we briefly provide an overview and instructions on using the spreadsheet with several datasets and show an example of why users need to be aware of the influence of a few high soil test values on regression results. By providing an accessible and easy-to-use spreadsheet capable of performing the necessary calculations, we hope to provide a tool that will encourage greater adoption of this method among those involved in research, teaching, and extension with limited access to more sophisticated software packages.

Technical Abstract: Soil 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 which are not 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 values 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.