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Subject: Statistics × Author: Deepankar × Start date: 2022 × Type: Thesis ×
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    ThesisItemOpen Access
    Resampling Techniques For Evaluating G × E Interaction In Oilseed Crops
    (Chaudhary Charan Singh Haryana Agricultural University hisar, 2022-09) Deepankar; Hooda, B. K
    In multi-environment trials (METs), a set of genotypes is grown simultaneously in different set of environments. The major objective of METs is identification of genotypes which consistently perform across a wider range of environments. To assess the stability of genotype, in literature there exists various parametric and non-parametric measures. But researcher faces conundrum of choosing appropriate stability measure before moving to main objective of MET. To ease researcher in this dilemma, we developed majority approaches where the results of various parametric and nonparametric stability measure were combined. Under majority approaches, we evaluated i) rank sum of parametric and non-parametric stability measures, ii) modal approach, iii) A new weighted-normalized index and iv) a composite measure using TOPSIS algorithm. A statistical distribution is a mathematical function that describes how the results of an experimental trial are likely to occur at random. The stability measures are a complex function of observed values therefore it is difficult to develop theoretical framework to predict their sampling distribution. Hence bootstrap technique has been used to determine their sampling distributions of stability measures. In METs, for studying GEI, additive main effects and multiplicative interactions (AMMI) and genotype and genotype x environment interaction (GGE) models are frequently used by researchers. In both models after removing the additive effect, singular value decomposition is used to partition genotype x environment interaction into ordered sum of multiplicative terms. Researchers usually retain first two multiplicative terms for biplot analysis without giving much thought in checking the significance of multiplicative terms for retention. The resampling techniques such as bootstrap and cross-validation have been used to test the significance of the multiplicative terms by approximating p value for each multiplicative term. Only those multiplicative terms have been retained in model which are found to be significant i.e., p value < 0.05 or 0.01.