TY  - JA
AU  - Vargas,M.
AU  - Crossa,J.
AU  - Sayre,K.D.
AU  - Reynolds,M.P.
AU  - Ramírez,M.E.
AU  - Talbot,M.
TI  - Interpreting genotype X environment interaction in wheat by partial least squares regression
SN  - 1435-0653
PY  - 1998///
CY  - USA
PB  - CSSA, Wiley
KW  - Genotype environment interaction
KW  - AGROVOC
KW  - Research projects
KW  - Soft wheat
KW  - Statistical methods
KW  - Triticum turgidum
KW  - Varieties
KW  - Triticum
KW  - Triticum aestivum
KW  - Wheat
KW  - CIMMYT
N1  - Peer review; Peer-review: Yes - Open Access: Yes|http://science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=0011-183X
N2  - The partial least squares (PLS) regression method relates genotype × environment interaction effects (GEI) as dependent variables (Y) to external environmental (or cultivar) variables as the explanatory variables (X) in one single estimation procedure. We applied PLS regression to two wheat data sets with the objective of determining the most relevant cultivar and environmental variables that explained grain yield GEI. One data set had two field experiments, one including seven durum wheat (Triticum turgidum L. var. durum) cultivars and the other, seven bread wheat (Triticum aestivum L.) cultivars, both tested for 6 yr. In durum wheat cultivars, sun hours per day in December, February, and March as well as maximum temperature in March were related to the factor that explained more than 39% of GEI, while in bread wheat cultivars, minimum temperature in December and January as well as sun hours per day in January and February were the environmental variables related to the factor that explained the largest portion (>41%) of GEI. The second data set had eight bread wheat cultivars evaluated in 21 low relative humidity (RH) environments and 12 high RH environments. For both low and high RH environments, results indicated that relative performance of cultivars is influenced by differential sensitivity to minimum temperatures during the spike growth period. The PLS method was effective in detecting environmental and cultivar explanatory variables associated with factors that explained large portions of GEI
UR  - https://hdl.handle.net/20.500.12665/732
DO  - https://doi.org/10.2135/cropsci1998.0011183X003800030010x
T2  - Crop Science
ER  -