Creating new genetic variation by crossing two or more parents is the initial and often most important step when developing new crop varieties. Hence, several mate selection indices have been suggested to support the planning of crosses in genomic breeding pipelines that have been established in many breeding programmes in recent years. The corresponding index weights are however difficult to determine objectively, and these indices often feature weights determined by laborious grid searches or rules of thumb. The aim of this study was to compare methods that employ the latter approach with mate selection indices based on desired gains in simulations and an empirical Fusarium head blight experiment for winter wheat. The results indicated that the suggested desired gain indices outperform routinely used methods in terms of reaching a favourable balance between the short-term selection gain, long-term selection gain, as well as the genetic diversity. They might have beyond that a high prospect for making a broader spectrum of genetic diversity accessible in the framework of germplasm exchange between breeding programmes.

Changes in entries’ market classes and genetic improvements within classes—not environmental changes—enhanced yields over thirty-one years of wheat trials. Correlations between yields and ancestries drove genomic prediction accuracies. Increasing crop yields is important for enhancing farmers’ livelihoods, meeting market demands, and reducing the environmental impact of agriculture. We analyzed the yield trends of Ontario winter wheat variety trials between 1988 and 2018. Over this period, wheat yields steadily increased by 38 kg ha−1 yr−1, or 0.68% yr−1 relative to the mean. While fungicide treatment of trials contributed a one-time 670 kg ha−1 yield increase, yields were otherwise unaffected by long-term changes in agronomic practice, climate, or other non-genetic factors. Genetic improvement entirely accounted for yield improvement. Market class changes over the 31 year span accounted for some yield improvement. More importantly, genetic improvement occurred within each market class. Entry yield estimates calculated from genomic prediction models strongly correlated with field estimated yields with a mean r of 0.68. Genomic prediction accuracies were high because yields differed across genetically distinct subpopulations. Despite environmental changes, genetic improvement will likely increase Ontario winter wheat yields into the future.

Training populations for pedigree and genomic prediction in plant breeding programmes are largely updated with superior genotypes from multi-environment trials, where they are tested with the goal of variety development. Such a selective phenotyping has, however, a negative impact on prediction abilities, especially when only a subset of breeding lines can be tested, as for laborious and costly to phenotype traits. This study focused, thus, on investigating the impact of selective phenotyping in the training population of an applied wheat breeding programme, and assessing the potential to mitigate this impact by pedigree and genomic multi-trait prediction models as well as bi-directionally selected training populations for several baking quality parameters. Combining both pedigree and genomic information in multi-trait prediction models with pre-existing phenotypic information for protein content and sedimentation value compensated for the observed loss in prediction ability, while entering few inferior breeding lines into the training population further mitigated the impact of selective phenotyping and even led to a slight increase of prediction ability in comparison to a randomly chosen training population.

The availability of cost-efficient genotyping technologies has facilitated the implementation of genomic selection into numerous breeding programs. However, some studies reported a superiority of pedigree over genomic selection in line breeding, and as, aside from systematic record keeping, no additional costs are incurring in pedigree-based prediction, the question about the actual benefit of fingerprinting several hundred lines each year might suggest itself. This study aimed thus on shedding some light on this question by comparing pedigree, genomic, and single-step prediction models using phenotypic and genotypic data that has been collected during a time period of ten years in an applied wheat breeding program. The mentioned models were for this purpose empirically tested in a multi-year forward prediction as well as a supporting simulation study. Given the availability of deep pedigree records, pedigree prediction performed similar to genomic prediction for some of the investigated traits if preexisting information of the selection candidates was available. Notwithstanding, blending both information sources increased the prediction accuracy and thus the selection gain substantially, especially for low heritable traits. Nevertheless, the largest advantage of genomic predictions can be seen for breeding scenarios where such preexisting information is not systemically available or difficult and costly to obtain.

'Hilliard' (Reg. no. CV-1163, PI 676271), a soft red winter (SRW) wheat (Triticum aestivum L.) developed and tested as VA11W-108 by the Virginia Agricultural Experiment Station, was released in March 2015. Hilliard was derived from the cross '25R47'/'Jamestown'. Hilliard is widely adapted, from Texas to Ontario, Canada, and provides producers with a mid-season, medium height, awned, semi-dwarf (Rht2) cul tivar that has very high yield potential, good straw strength, and intermediate grain volume weight and quality. It expresses moderate to high levels of resistance to most diseases prevalent in the eastern United States and Ontario. In the 2016-2018 USDA-ARS Uniform SRW Wheat nurseries, Hilliard ranked first in grain yield in the southern nursery across all 3 yr (5,147-5,758 kg ha(-1)). In the uniform eastern nursery, it ranked first for grain yield in 2016 (6,159 kg ha(-1)) and 2017 (5,633 kg ha(-1)) and second in 2018 (5,515 kg ha(-1)). Grain volume weights of Hilliard were similar to overall trial averages in the uniform southern (73.4-75.2 kg hl(-1)) and eastern (70-75.8 kg hl(-1)) nurseries. Hilliard has soft grain texture with flour softness equivalent values varying from 58.1 to 61.7 g 100 g(-1). Straight grade flour yields on a Quadrumat Senior mill varied from 66.8 to 68.4 g kg(-1). Flour protein concentration varied from 7.0 to 9.1 g 100 g(-1) and gluten strength from 108 to 128 g 100 g(-1), as measured by lactic acid solvent retention capacity. Cookie spread diameter varied from 18.3 to 18.6 cm.

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.

Simultaneous 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. Selecting 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.

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. 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.

Twenty-five hard red spring wheat (Triticum aestivum) lines, including three known cultivars used as checks, were grown in seven locations across Eastern Canada. The objective of this multi-location experiment was to evaluate selected Eastern Cereal and Oilseed Research Centre advanced lines (ECAD lines) from the Spring Wheat Breeding Program in order to identify the best lines for performance and grower trials. The lines from this trial performed very well compared to the check varieties, especially at the Ontario locations. Overall, the ECAD lines were on a par with or superior to the checks in terms of several attributes, including yield, protein content, and Fusarium head blight resistance.

Tamburic-Ilincic, L., Brinkman, J. M. P., Sparry, E. and Hooker, D. C. 2015. Optimizing inputs for winter durum wheat in Ontario. Can. J. Plant Sci. 95: 361–368. Best management practices need to be determined for a new wheat class in Ontario: winter durum. The objectives of this study were to determine optimal nitrogen rates (75, 100, and 125 kg N ha −1 ), seeding rates (400, 440, and 480 seeds m −2 ), and fungicide applications on the grain yield, grain protein, and leaf disease control of ‘OAC Amber’ winter wheat durum at five field locations in Ontario. Seeding rates between 400 and 480 seeds m −2 did not impact performance. Overall, fungicide applications at flag leaf and flowering increased grain yield by an average of 0.52 Mg ha −1 , increased seed weight and test weight, reduced powdery mildew [Blumeria graminis (DC.) Speer f. sp. tritici emend. É.J. Marchal] and septoria leaf blotch [Mycosphaerella graminicola (Fuckel) J. Schröt.] in the canopy, but decreased grain protein from 128 to 126 g kg −1 . Grain yields did not increase with N rates higher than 100 kg N ha −1 , and the response to N rate did not depend on the application of fungicides. Grain protein concentrations increased with N rates up to 125 kg N ha −1 , which was the highest N rate investigated in this study. An economic analysis is needed to determine the impact of agronomic management strategies specific to winter durum wheat in Ontario.

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.