INTERPRETING GENOTYPE-X ENVIRONMENT INTERACTION IN WHEAT BY PARTIAL LEAST-SQUARES REGRESSION

The partial least squares (PLS) regression method relates genotype x e nvironment interaction effects (GEI) as dependent variables (Y) to ext ernal environmental (or cultivar) variables as the explanatory variabl es (X) in one single estimation procedure. We applied PLS regression t o two wheat data sets with the objective of determining the most relev ant cultivar and environmental variables that explained grain yield GE I. One data set had two field experiments, one including seven durum w heat (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 M arch as well as maximum temperature in March were related to the facto r 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 t o the factor that explained the largest portion (>41%) of GEI, The sec ond data set had eight bread wheat cultivars evaluated in 21 low relat ive humidity (RH) environments and 12 high RH environments. For both l ow and high RH environments, results indicated that relative performan ce of cultivars is influenced by differential sensitivity to minimum t emperatures during the spike growth period, The PLS method was effecti ve in detecting environmental and cultivar explanatory variables assoc iated with factors that explained large portions of GEI.