Statistical Determination of Barley Varietal Adaptation<sup>1</sup>

Immer, F. R.; Hayes, H. K.; Powers, LeRoy

doi:10.2134/agronj1934.00021962002600050008x

Cited by 37 publications

(13 citation statements)

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“…Note that there is a single observation for each variety/location/year combination representing the total barley yield for that combination. These data were first introduced by Immer, Hayes, and Powers (1934), and variations of this dataset have been used by Fisher (1971), Cleveland (1993), and Venables and Ripley (2002).…”

Section: Examplesmentioning

confidence: 99%

Simultaneous Factor Selection and Collapsing Levels in ANOVA

2009

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Summary. When performing an Analysis of Variance, the investigator often has two main goals: to determine which of the factors have a significant effect on the response, and to detect differences among the levels of the significant factors. Level comparisons are done via a posthoc analysis based on pairwise differences. This paper proposes a novel constrained regression approach to simultaneously accomplish both goals via shrinkage within a single automated procedure. The form of this shrinkage has the ability to collapse levels within a factor by setting their effects to be equal, while also achieving factor selection by zeroing out entire factors. Using this approach also leads to the identification of a structure within each factor, as levels can be automatically collapsed to form groups. In contrast to the traditional pairwise comparison methods, these groups are necessarily non-overlapping so that the results are interpretable in terms of distinct subsets of levels. The proposed procedure is shown to have the oracle property in that asymptotically it performs as well as if the exact structure were known beforehand. A simulation and real data examples show the strong performance of the method.

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Section: Examplesmentioning

confidence: 99%

Simultaneous Factor Selection and Collapsing Levels in ANOVA

2009

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“…Various workers (e.g. Immer, Hayes, andPowers (1934), Salmon (1951), Homer and Frey (1957), and Sandison and Bartlett (1958) have discussed some of the methods and problems of comparing varietal performance in several environments for several years. In their work, the variety x location or variety x season interactions were the basic measures of adaptability.…”

Section: Introductionmentioning

confidence: 99%

The analysis of adaptation in a plant-breeding programme

Finlay¹,

Wilkinson²

1963

Aust. J. Agric. Res.

2,597

1,890

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SummaryThe adaptation of barley varieties was studied by the use of grain yields of a randomly chosen group of 277 varieties from a world collection, grown in replicated trials for several seasons at three sites in South Australia. For each variety a linear regression of yield on the mean yield of all varieties for each site and season was computed to measure variety adaptation. In these calculations the basic yields were measured on a logarithmic scale, as it was found that a high degree of linearity was thereby induced. The mean yield of all varieties for each site and season provided a quantitative grading of the environments; and from the analysis described, varieties specifically adapted to good or poor seasons and those showing general adaptability may be identified.The study of the adaptation of the whole population of varieties was facilitated by the use of a two-dimensional plot (scatter diagram), with mean yield and regression coefficient as coordinates for each variety. Though wide variation was evident in both mean yield and sensitivity to environment as characterized by the regression coefficient, the variation in sensitivity was proportionately less among varieties with higher mean yield, and the varieties with highest mean yield exhibited, within very narrow limits (regression cocfficientts close to 0.8), a similar degree of adaptation to all environments over the wide range, especially of seasonal conditions, typical of the South Australian cereal belt.

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“…These changes, observable as genotype x environment interactions in a biometrical analysis, have long been recognised as an important source of phenotypic variation (Immer, Hayes and Powers, 1934;Yates and Cochran, 1938;Mather, 1949). Little is known of the inheritance and physiology of these changes, but it has been found empirically that a linear relation frequently exists between phenotype and environment when the latter is measured by its effect on the character under study (Finlay and Wilkinson, 1963;Eberhart and Russell, 1966;Perkins and Jinks, 1968;Breese, 1969).…”

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confidence: 99%