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found no genetic improvement in performance under lowfertility conditions during that century. Market prices and environmental concerns favor low-input wheatThis situation may result from breeding that has been (Triticum aestivum L.) production systems. This study assesses the conducted, either under high-or low-input levels, includefficiency of low-input vs. high-input selection environments to iming N fertilizer and fungicides. In France, wheat breedprove wheat for low-input environments. Three standard cultivars, 11 parents, and 270 lines bred in INRA Mons-Pé ronne were investi-ing has been mainly conducted under a high level of gated for 2 yr (1998, 1999) in France at three INRA locations. Four inputs. Genetic gain measured under low-input levels agronomic treatments combining two levels of fungicides with two seen in the target environments was then due to indirect levels of nitrogen (N) were applied. Because of seed supply, only 10 selection. The relative gain of indirect versus direct seyear ϫ treatment ϫ location combinations were conducted. Broadlection, considering equal selection intensities, depends sense heritabilities for grain yield (GY) ranged from 0.18 at low N on heritabilities at both input levels and genetic correlawithout fungicide to 0.90 at high N without fungicide. Heritability tion between input levels (Falconer, 1974). Heritabilities estimates were higher at high N than at low N level. This was due to are generally lower under low-input level or in stressed both an increase in error variance and a decrease in genetic variance environments than under high-input level (Ud-Din et al., at low nitrogen level. Heritabilities in treatments without fungicide 1992; Calhoun et al., 1994; Bä nziger et al., 1997; Bertin

International audienceThe exponential development of molecular markers enables a more effective study of the genetic architecture of traits of economic importance, like test weight in wheat (Triticum aestivum L.), for which a high value is desired by most end-users. The association mapping (AM) method now allows more precise exploration of the entire genome. AM requires populations with substantial genetic variability of the traits of interest. The breeding lines at the end of a selection cycle, characterized for numerous traits, represent a potentially useful population for AM studies. Using three elite line populations, selected by several breeders and genotyped with about 2,500 Diversity Arrays Technology markers, several associations were identified between these markers and test weight, grain yield and heading date. To minimize spurious associations, we compared the general linear model and mixed linear model (MLM), which adjust for population structure and kinship differently. The MLM model with the kinship matrix was the most efficient. Finally, elite lines from several breeding programs had sufficient genetic variability to allow for the mapping of several chromosomal regions involved in the variation of three important traits

Five genomic prediction models were applied to three wheat agronomic traits—grain yield, heading date and grain test weight—in three breeding populations, each comprising about 350 doubled haploid or recombinant inbred lines evaluated in three locations during a 3-year period. The prediction accuracy, measured as the correlation between genomic estimated breeding value and observed trait, was in the range of previously published values for yield (r = 0.2–0.5), a trait with relatively low heritability. Accuracies for heading date and test weight, with relatively high heritabilities, were about 0.70. There was no improvement of prediction accuracy when two or three breeding populations were merged into one for a larger training set (e.g., for yield r ranged between 0.11 and 0.40 in the respective populations and between 0.18 and 0.35 in the merged populations). Cross-population prediction, when one population was used as the training population set and another population was used as the validation set, resulted in no prediction accuracy. This lack of cross-population prediction accuracy cannot be explained by a lower level of relatedness between populations, as measured by a shared SNP similarity, since it was only slightly lower between than within populations. Simulation studies confirm that cross-prediction accuracy decreases as the proportion of shared QTLs decreases, which can be expected from a higher level of QTL × environment interactions.Electronic supplementary materialThe online version of this article (doi:10.1007/s11032-014-0143-y) contains supplementary material, which is available to authorized users.

Price reduction and environmental concerns advocate a lower use of nitrogen (N) fertilizer on the wheat (Triticum aestivum L.) crop. It is a common hypothesis that hybrids would be more valuable in stressed environments such as limited fertilizer conditions. The objective of this study was to assess heterosis and combining ability at two N levels. Seven winter wheat cultivars were used to produce a 7 × 7 diallel cross without the reciprocals. The 21 F1 hybrids and parental lines were tested in replicated plots over 2 yr without N fertilizer (N0) or with 150 kg N ha−1 (N+). The diallel analysis was conducted according to Griffing with year, genotype, and treatment as fixed effects. Mid‐parent heterosis for grain yield was +12.2%** at N0 and +8.9%** at N+ in 1997 and +1.7%ns at N0 and −0.4%ns at N+ in 1998. This was directly related to high mid‐parent heterosis for above‐ground dry weight in 1997 (+11.2%** at N0 and +10.9%** at N+) and low heterosis in 1998 (+1.2%ns at N0 and +0.0%ns at N+). General (GCA) and specific (SCA) combining ability effects were always significant. The GCA/SCA ratio ranged from 3.6 to 14.8. The GCA × N level interaction was generally significant indicating different parental contributions at low or high N levels. The SCA × N level interaction was never significant. There was a tendency toward higher GCA/SCA ratio at N0 than at N+. The choice of parents will be dependent upon the N level under which the new hybrids will be grown.