Abstract

Crop trials or crop performance trials (CPT), which are among the most important activities associated with plant breeding programs, are commonly used to measure the performance stability of genotypes. Several methods which include variation, regression, and cluster analyses for determination of crop stability have been proposed and are commonly used. However, many of these approaches require the use of normally distributed data. Thus, commonly used statistical tests, like the t- or F-test may not be appropriate when the assumptions of data are violated. In this study, two resampling techniques (jackknife and bootstrapping) were integrated into several crop stability analyses. An upland cotton data set from China was analyzed to demonstrate the utility of these methods in measuring performance stability.

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Apr 29th, 1:00 PM

STATISTICAL TESTS FOR STABILITY ANALYSIS WITH RESAMPLING TECHNIQUES

Crop trials or crop performance trials (CPT), which are among the most important activities associated with plant breeding programs, are commonly used to measure the performance stability of genotypes. Several methods which include variation, regression, and cluster analyses for determination of crop stability have been proposed and are commonly used. However, many of these approaches require the use of normally distributed data. Thus, commonly used statistical tests, like the t- or F-test may not be appropriate when the assumptions of data are violated. In this study, two resampling techniques (jackknife and bootstrapping) were integrated into several crop stability analyses. An upland cotton data set from China was analyzed to demonstrate the utility of these methods in measuring performance stability.