Abstract
Non-normality in the distribution of individual observations of production and quality traits in forest tree breeding may cause inaccurate selection and overestimation of predicted selection gain. The distribution of individual observations of traits such as height, diameter, branch diameter, branch angle and number of branches per whorl is not always normal. We investigated how the observations were distributed and to what degree it is possible to improve normality, homogeneity of error variance and additivity by using empirical power transformations. Computer simulations showed that a seriously skewed distribution impairs selection efficiency and exaggerates selection gain expectations. If the distribution is heavily skewed, transformation might be worthwhile. It does not seem possible to offer any general advice about which varities should be transformed, but in most cases there seems to be no need of any transformation.
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Communicated by P. M. A. Tigerstedt
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Jansson, G., Danell, Ö. Needs and benefits of empirical power transformations for production and quality traits in forest tree breeding. Theoret. Appl. Genetics 87, 487–497 (1993). https://doi.org/10.1007/BF00215095
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DOI: https://doi.org/10.1007/BF00215095