Journal of Taiwan Agricultural Research

Author:Hsiu-Ying Lu

Abstract:

A high adaptable crop variety would be the one with relatively low sensitivity in the poor environments and high sensitivity in the favorable environments. That is, an ideal genotype should have nonlinear responses to environment. The conventional linear regres­ sion for stability analysis cannot detect such varieties, and an unstable variety might be wrongly regarded as stable, because the computation of the linear regression over all the environments has the masking effect on the detection of ideal genotypes, if they exist in the population. The purpose of this study was to compare the validity of the piecewise linear regression model proposed by Verma and Chahal (1978) and the conventional regression model in stability analysis. ased on hypothetical models of maize and rice, two hundred sets of simulated data generated from each hypothetical model were analyzed by each of the conventional regression and piecewise linear regression. The result showed the piecewise linear regression is useful to measure the separate responses of varieties to the defferent ranges of environments, and reduce the risk of wrongly identification. A SAS program for the piecewise linear regression analysis of stability was presented. This program runs on personal computers. The procedure was illustrated through an example of maize regional trial data in Taiwan. It is useful to agricultural researchers in application of piecewise linear regression for stability analysis. The relationship among stability in poor environments, stability in favorable environments and mean performance for each genotype was shown by a three-dimensional graph. This graph provides a direct and easy method of screening genotypes with the performance above the mean and high adaptability.

Key words:Stability, Piecewise linear regression, Nonlinear responses, Simulation experiment, Hypothetical model, SAS program.

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