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An improved method for biometric analysis of soil test – crop response data sets

Maheswaran Rohan , Mark Conyers

Abstract

Context. To increase cereal production, primary producers want to know what amount of fertilizer needs to be applied to achieve high yield. To calculate the critical soil test value (CSTV), especially in Colwell-P, several models were found in the literature. The arcsine-log calibration curve has been commonly used in Australia to estimate the CSTV. However, this method has some mathematical weaknesses, which tend to give underestimated values for CSTV. Aim. In this paper, we describe the mathematical issues and propose a model to overcome these issues. The simplified model proposed allows us to estimate the CSTV and its standard error. Method. We have applied the regression and the delta method to the data used in the ALCC method of Dyson and Conyers (2013). Key Results. Based on the given data, we found that a soil test value of 31.5 mg P per kg soil is required to achieve a 90% relative yield of wheat, which is the middle ground of previously published critical values between the underestimate (21.4 mg/kg) generated by the ALCC algorithm and the overestimate (40 mg/kg) generated by the conventional Mitscherlich method. The advantages of the proposed method are that it is (i) simple and easy to apply to any data set (ii) easy to incorporate other covariates into the models. Implication. The ALCC algorithm needs to be replaced with the proposed method and the required P in current farming practice might need to be updated.

CP24162  Accepted 11 December 2024

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