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 includingseven 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.

SummaryThis paper provides estimates of varieties × environments and plot error variances based on more than 1000 trials of varieties of oats, barley, wheat, perennial and Italian ryegrass, timothy, cocksfoot, potato, sugar beet, swedes, autumn kale, forage maize and field beans. Tables of critical differences and acceptance probabilities are presented to guide in planning future series of trials.

SummaryThis paper presents the results of analyses of yield variability in more than 500 trials of varieties of spring oats, spring wheat, spring barley and winter wheat. A table of critical percentage yield differences has been constructed for guidance in planning future series of trials.

No abstract