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Interpreting genotype by environment interaction using weather covariates

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Relation http://oar.icrisat.org/7103/
http://ssca.org.in/journal.html
 
Title Interpreting genotype by environment interaction using weather covariates
 
Creator Das, R R
Anil Kumar, V
Rakshit, S
Maraboina, R
Panwar, S
Savadia, S
Rathore, A
 
Subject Agriculture-Farming, Production, Technology, Economics
 
Description 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.
 
Publisher Society of Statistics, Computers and Applications
 
Date 2012
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
Rights —
 
Identifier http://oar.icrisat.org/7103/1/paper4.pdf
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.