A modification of the arcsine–log calibration curve for analysing soil test value–relative yield relationships
Adrián A. Correndo A D , Fernando Salvagiotti B , Fernando O. García A and Flavio H. Gutiérrez-Boem C
+ Author Affiliations
- Author Affiliations
A International Plant Nutrition Institute (IPNI), Latin America Southern Cone Program, Av. Santa Fe 910, Acassuso, Buenos Aires, Argentina.
B EEA INTA Oliveros, Departamento de Agronomía, Oliveros, Santa Fe, Argentina.
C University of Buenos Aires, College of Agronomy, INBA-CONICET, Buenos Aires, Argentina.
D Corresponding author. Email: correndo@agro.uba.ar
Crop and Pasture Science 68(3) 297-304 https://doi.org/10.1071/CP16444
Submitted: 2 December 2016 Accepted: 17 February 2017 Published: 21 March 2017
Abstract
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.
Additional keywords: bivariate model, correlation, standardised major axis regression.
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