A modification of the arcsine-log calibration curve for analysing soil test value-relative yield relationships

作者：Correndo Adrian A^*; Salvagiotti Fernando; Garcia Fernando O; Gutierrez Boem Flavio H

来源：Crop and Pasture Science, 2017, 68(3): 297-304.

DOI：10.1071/CP16444

• bivariate model
• correlation
• standardised major axis regression

This article aims to discuss the arcsine-log calibration curve (ALCC) method designed for the Better Fertiliser Decisions for Cropping Systems (BFDC) to calibrate relationships between relative yield (RY) and soil test value (STV). Its main advantage lies in estimating confidence limits of the critical value (CSTV). Nevertheless, intervals for 95% confidence level are often too wide, and authors suggest a reduction in the confidence level to 70% in order to achieve narrower estimates. Still, this method can be further improved by modifying specific procedures. For this purpose, several datasets belonging to the BFDC were used. For any confidence level, estimates with the modified ALCC procedures were always more accurate than the original ALCC. The overestimation of confidence limits with the original ALCC was inversely related to the correlation coefficient of the dataset, which might allow a relatively simple and reliable correction of previous estimates. In addition, because the method is based on the correlation between STV and RY, the importance to test it for significance is emphasised in order to support the hypothesis of a relationship. Then, the modified ALCC approach could also allow a more reliable comparison of datasets by slopes of the bivariate linear relationship between transformed variables.

• 出版日期2017