At present, registration and protection of rapeseed (Brassica napus L.) cultivars relies on a small number of morphological traits. As the number of cultivars increases, the ability to distinguish them on the basis of these traits becomes more difficult. New descriptors like molecular markers are required to maintain the efficiency of registration testing. The objectives of this study were to (i) evaluate the discrimination power of 17 amplified fragment length polymorphism (AFLP) primer combinations tested on a collection of 83 spring and winter rapeseed cultivars, (ii) assess the structure of the genetic diversity revealed by AFLPs, and (iii) compare three genetic distance estimators (Jaccard, Sokal, and Michener and Sokal and Michener weighted by the PIC). A total of 324 polymorphic markers were scored, with an average of 19.1 markers per primer combination. The use of only two primer combinations was sufficient to identify uniquely all the cultivars. The analysis of the genetic structure of the diversity by cluster and principal component analyses and AMOVAs (analyses of molecular variance) clearly delineated three significant factors: the cultivar type (winter or spring), the country of origin (France or Germany), and the breeding company. The three measures of genetic distance were highly correlated (P < 0.001, r = 0.96–0.98) and led to similar groupings. AFLPs were shown to be a powerful method for identifying cultivars and analyzing the genetic structure of the diversity in rapeseed.

The French INRA wheat (Triticum aestivum L. em Thell.) breeding program is based on multilocation trials to produce high-yielding, adapted lines for a wide range of environments. Differential genotypic responses to variable environment conditions limit the accuracy of yield estimations. Factor regression was used to partition the genotype-environment (GE) interaction into four biologically interpretable terms. Yield data were analyzed from 34 wheat genotypes grown in four environments using 12 auxiliary agronomic traits as genotypic and environmental covariates. Most of the GE interaction (91%) was explained by the combination of only three traits: 1,000-kernel weight, lodging susceptibility and spike length. These traits are easily measured in breeding programs, therefore factor regression model can provide a convenient and useful prediction method of yield.

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