Interpreting genotype by environment interaction using weather covariates

Das, R R and Anil Kumar, V and Rakshit, S and Maraboina, R and Panwar, S and Savadia, S and Rathore, A (2012) Interpreting genotype by environment interaction using weather covariates. Journal of Statistics and Applications, 10 (1-2). pp. 45-62.

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Understanding genotype by environment interaction (G*E) has a lways been a challenge to statisticians and plant breeders. Recen tly site regression analysis has emerged as a powerful analysis tool to understand G*E, speci fic and general adaptability of genotypes and grouping of environments into mega-environments . This paper attempts to enhance power of site regression by using environmental co variates in tandem to explain G*E better. In this present study, performances o f eighteen genotypes were investigated across five environments during the year 2008 rainy se ason. Three traits, namely grain yield, harvest index and dry fodder yield were us ed for analysis purpose. Biplot analysis identified two major groups of environments , first group of environments included Karad and Coimbatore and second group consisted Udaipur, Palem and Surat. SPH 1615 and SPH 1609 were identified as winning genotypes for firs t mega- environment whereas SPH 1596, SPH 1611 and CSH 16 were winners fo r second mega- environment for grain yield. High yielding genotypes, SPH 1606, SP H 1616 and CSH 23 performed consistently well across all environments and sh ould be considered for general adaptability. Genotype SPH 1596 was identified for both specif ic and general adaptability. By superimposing GGE biplots for different trai ts, genotypes SPH 1596 and CSH 23 were identified as stable for all three traits. C limatic data on average maximum temperature and minimum temperature at early (June-July) a nd late phase (August) of plant growth was incorporated to study G*E by using factorial regression. Average maximum temperature and minimum temperature at early phas e and average minimum temperature during late phase were found significantly affec ting genotype performance.

Item Type: Article
Uncontrolled Keywords: AEA (Average Environment Axis); biplot; Factorial regression ; G*E (Genotype by Environment interaction); GGE (Genotype plus Genotype by Environment); MET (Multi-Environment Trial); PCA (Principal Component Analysis); stability, Site regression; SVD (Singular Value Decomposition).
Subjects: Others > Agriculture-Farming, Production, Technology, Economics
Depositing User: Mr Siva Shankar
Date Deposited: 16 Sep 2013 06:17
Last Modified: 16 Sep 2013 06:17
Official URL:
Acknowledgement: UNSPECIFIED
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