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Selection of Superior Cultivars of Oats by Using Regression Coefficients 1
Author(s) -
Eagles H. A.,
Hinz P. N.,
Frey K. J.
Publication year - 1977
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci1977.0011183x001700010029x
Subject(s) - avena , regression , regression analysis , statistics , linear regression , selection (genetic algorithm) , biology , mathematics , cultivar , yield (engineering) , cube root , straw , geometric mean , agronomy , computer science , materials science , geometry , artificial intelligence , metallurgy
Regression on environmental mean was investigated for measuring production stability of grain and straw yields of 80 lines of oats ( Avena sativa L.) tested in 24 field environments. Mean squares for heterogeneity among regressions suggested that the regression parameter was not heritable for grain yield, but may be heritable for straw yield. The regression lines for straw yield tended to converge at an environmental yield level below that normally used for oat production. Therefore, selection with use of mean yields alone would save cultivars that are superior at all yield levels. This situation was attributed to a high correlation between regression coefficients and mean yields. Analyses were conducted on direct and cube‐root scales of measurement. The cube‐root scale was chosen to reduce the heterogeneity of error mean squares obtained from the 24 individual environments, but also was found to reduce the significance of the mean squares for heterogeneity among regressions. The effect of a transformation of the data on heterogeneity and usefulness of regression coefficients is discussed.