Key message Large genetic improvement can be achieved by simultaneous genomic selection for grain yield and protein content when combining different breeding strategies in the form of selection indices. Abstract Genomic selection has been implemented in many national and international breeding programmes in recent years. Numerous studies have shown the potential of this new breeding tool; few have, however, taken the simultaneous selection for multiple traits into account that is though common practice in breeding programmes. The simultaneous improvement in grain yield and protein content is thereby a major challenge in wheat breeding due to a severe negative trade-off. Accordingly, the potential and limits of multi-trait selection for this particular trait complex utilizing the vast phenotypic and genomic data collected in an applied wheat breeding programme were investigated in this study. Two breeding strategies based on various genomic-selection indices were compared, which (1) aimed to select high-protein genotypes with acceptable yield potential and (2) develop high-yielding varieties, while maintaining protein content. The prediction accuracy of preliminary yield trials could be strongly improved when combining phenotypic and genomic information in a genomics-assisted selection approach, which surpassed both genomics-based and classical phenotypic selection methods both for single trait predictions and in genomic index selection across years. The employed genomic selection indices mitigated furthermore the negative trade-off between grain yield and protein content leading to a substantial selection response for protein yield, i.e. total seed nitrogen content, which suggested that it is feasible to develop varieties that combine a superior yield potential with comparably high protein content, thus utilizing available nitrogen resources more efficiently. Electronic supplementary material The online version of this article (10.1007/s00122-019-03312-5) contains supplementary material, which is available to authorized users.

The oat (Avena sativa L.) breeding program at the Eastern Cereal and Oilseed Research Centre of Agriculture & Agri‐Food Canada has the responsibility to breed new oat cultivars for producers in eastern Canada, which includes Ontario, Quebec, and the Atlantic provinces. A 3‐yr multilocation test was conducted to understand the genotype × location interaction patterns and the relationships among test locations in eastern Canada. A genotype + genotype × environment interaction biplot analysis of yield data revealed three distinct oat mega‐environments in eastern Canada: (i) northern Ontario, (ii) southern and eastern Ontario, and (iii) Quebec and Atlantic Canada. To breed for all mega‐environments, initial yield screening must be conducted at locations representing each of these mega‐environments. Based on the relationships among test locations, six essential test locations were identified: three in Ontario, two in Quebec, and one in Atlantic Canada. Testing at all six locations appeared to provide a good coverage of the whole oat‐growing area in eastern Canada. Based on these findings, a breeding and test strategy was developed. This includes conducting initial yield screening at three locations in Ontario, Quebec, and Atlantic Canada, followed by a formal yield test at all six essential test locations. Specifically adapted genotypes selected from this test will then be tested in the Registration Tests in their respectively adapted subregions.

Key messageSimultaneous genomic selection for grain yield, protein content and dough rheological traits enables the development of resource-use efficient varieties that combine superior yield potential with comparably high end-use quality.AbstractSelecting simultaneously for grain yield and baking quality is a major challenge in wheat breeding, and several concepts like grain protein deviations have been developed for shifting the undesirable negative correlation between both traits. The protein quality is, however, not considered in these concepts, although it is an important aspect and might facilitate the selection of genotypes that use available resources more efficiently with respect to the quantity and quality of the final end products. A population of 480 lines from an applied wheat breeding programme that was phenotyped for grain yield, protein content, protein yield and dough rheological traits was thus used to assess the potential of using integrated genomic selection indices to ease selection decisions with regard to the plethora of quality traits. Additionally, the feasibility of achieving a simultaneous genetic improvement in grain yield, protein content and protein quality was investigated to develop more resource-use efficient varieties. Dough rheological traits related to either gluten strength or viscosity were combined in two separate indices, both of which showed a substantially smaller negative trade-off with grain yield than the protein content. Genomic selection indices based on regression deviations for the two latter traits were subsequently extended by the gluten strength or viscosity indices. They revealed a large merit for identifying resource-use efficient genotypes that combine both superior yield potential with comparably high end-use quality. Hence, genomic selection opens up the opportunity for multi-trait selection in early generations, which will most likely increase the efficiency when developing new and improved varieties.Electronic supplementary materialThe online version of this article (10.1007/s00122-019-03386-1) contains supplementary material, which is available to authorized users.

Fusarium head blight of barley (Hordeum vulgare) is a devastating disease in many countries. We undertook a study to identify barley cultivars, if any, that are resistant to Fusarium head blight and deoxynivalenol (DON) accumulation and to determine if DON concentration is correlated with other plant traits in Eastern Canada and China. Barley cultivars were grown in the field under artificial inoculation conditions at two locations (Charlottetown and Ottawa) in Canada during two summers and at Hangzhou in China during two winters. Seed samples were collected for DON analysis from the barley performance trial at five locations in Ontario. None of the 64 barley cultivars were immune to Fusarium head blight infection. Two-row cultivars, however, were significantly more resistant to Fusarium head blight infection and DON accumulation than six-row cultivars. Three cultivars (Island, AC Alberte, and Chevron) were found to be most resistant, as they were consistently low in Fusarium head blight incidence and DON concentration in both Eastern Canada and China. In six-row barley, DON concentration was correlated positively with Fusarium head blight incidence at both Charlottetown and Ottawa, and it was negatively correlated with plant height at Ottawa. DON concentration and heading date were not consistently correlated. Barley yellow dwarf and powdery mildew appeared to have very little effect on Fusarium head blight infection. Susceptibility to DON accumulation did not result in low yield under natural infection conditions in Ontario. Cultivar × location interactions for DON concentration, Fusarium head blight incidence, and heading date were significant.

Winter hardiness is a major constraint for autumn sown crops in temperate regions, and thus an important breeding goal in the development of new winter wheat varieties. Winter hardiness is though influenced by many environmental factors rendering phenotypic selection under field conditions a difficult task due to irregular occurrence or absence of winter damage in field trials. Controlled frost tolerance tests in growth chamber experiments are, on the other hand, even with few genotypes, often costly and laborious, which makes a genomic breeding strategy for early generation selection an attractive alternative. The aims of this study were thus to compare the merit of marker-assisted selection using the major frost tolerance QTL Fr-A2 with genomic prediction for winter hardiness and frost tolerance, and to assess the potential of combining both measures with a genomic selection index using a high density marker map or a reduced set of pre-selected markers. Cross-validation within two training populations phenotyped for frost tolerance and winter hardiness underpinned the importance of Fr-A2 for frost tolerance especially when upweighting its effect in genomic prediction models, while a combined genomic selection index increased the prediction accuracy for an independent validation population in comparison to training with winter hardiness data alone. The prediction accuracy could moreover be maintained with pre-selected marker sets, which is highly relevant when employing cost reducing fingerprinting techniques such as targeted genotyping-by-sequencing. Genomic selection showed thus large potential to improve or maintain the performance of winter wheat for these difficult, costly, and laborious to phenotype traits.