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Modified Arcsine Log Calibration Curve spreadsheet
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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. Estimating CSTVs from soil test correlation data helps farmers determine what fields are expected to respond to fertilizer application. 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 by Dyson and Conyers (2013) in response to the statistical limitations of other commonly used models. The modified-ALCC model using standardized major axis regression developed by Correndo et al. (2017) further improves this model’s applicability to soil test correlation data. This method, however, requires additional calculations that may be challenging to those without the proper experience or background. Here we provide an easy-to-use spreadsheet capable of performing the necessary calculations for using the modified ALCC model 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 (Bolster et al. 2022).

The ALCC spreadsheet is available for download here 

In addition to our Excel spreadsheet, we have developed an R package (soiltestcorr) that fits the ALCC along with several other models to soil correlation data. Soiltestcorr can be downloaded here 

References:

Correndo, A.A., A. Pearce, C.H. Bolster, J.T. Spargo, D. Osmond, and I.A. Ciampitti. 2022. The soiltestcorr R package: An accessible framework for reproducible correlation analysis of crop yield and soil test data. SoftwareX 21. doi.org/10.1016/j.softx.2022.101275

Correndo, A.A., F. Salvagiotti, F.O. García, and F.H. Gutiérrez-Boem. 2017. A modification of the arcsine-log calibration curve for analysing soil test value-relative yield relationships. Crop & Pasture Science 68: 297–304. doi: 10.1071/CP16444.

Dyson, C.B., and M.K. Conyers. 2013. Methodology for online biometric analysis of soil test-crop response datasets. Crop & Pasture Science 64: 435–441. doi: 10.1071/CP13009.