BackgroundGenomic selection (GS) can accelerate genetic gains in breeding programmes by reducing the time it takes to complete a cycle of selection. Puccinia coronata f. sp lolli (crown rust) is one of the most widespread diseases of perennial ryegrass and can lead to reductions in yield, persistency and nutritional value. Here, we used a large perennial ryegrass population to assess the accuracy of using genome wide markers to predict crown rust resistance and to investigate the factors affecting predictive ability.ResultsUsing these data, predictive ability for crown rust resistance in the complete population reached a maximum of 0.52. Much of the predictive ability resulted from the ability of markers to capture genetic relationships among families within the training set, and reducing the marker density had little impact on predictive ability. Using permutation based variable importance measure and genome wide association studies (GWAS) to identify and rank markers enabled the identification of a small subset of SNPs that could achieve predictive abilities close to those achieved using the complete marker set.ConclusionUsing a GWAS to identify and rank markers enabled a small panel of markers to be identified that could achieve higher predictive ability than the same number of randomly selected markers, and predictive abilities close to those achieved with the entire marker set. This was particularly evident in a sub-population characterised by having on-average higher genome-wide linkage disequilibirum (LD). Higher predictive abilities with selected markers over random markers suggests they are in LD with QTL. Accuracy due to genetic relationships will decay rapidly over generations whereas accuracy due to LD will persist, which is advantageous for practical breeding applications.Electronic supplementary materialThe online version of this article (10.1186/s12863-018-0613-z) contains supplementary material, which is available to authorized users.

The expression of elevated water‐soluble carbohydrate (WSC) concentrations in perennial ryegrass (Lolium perenne L.) cultivars selected for high forage WSC concentration can be highly variable across environments. Our aim was to determine whether N application rate influences the expression of the high WSC phenotype. Cultivars AberDart (selected for high WSC concentration) and Fennema (control) were evaluated across four fertilizer N application rates (0, 40, 80, and 120 kg ha−1 per harvest) over four replicates and 2 yr at Grange, Ireland, and Særheim, Norway. Plots were managed for silage production with four cuts per year in Ireland and three cuts per year in Norway. Nine forage traits were measured: WSC, dry matter digestibility, crude protein, buffering capacity, dry matter, ash, dry matter yield, N use efficiency, and apparent N recovery. The response of AberDart and Fennema to N application rate was predominantly similar within and over years and locations for all traits. Differences between cultivars in WSC concentration were largely consistent across N application rates, years, and locations. AberDart had mean WSC concentrations 8 to 12% higher than Fennema depending on harvest. Present results suggest that the evaluation and selection of perennial ryegrass for high concentrations of WSC in cool‐temperate maritime climates may be conducted across a wide range of N application rates and, by extension, soil N supply rates.

Spatial analyses of yield trials allow adjustment of cultivar means for spatial variation, improving the statistical precision of yield estimation. While the relative efficiency of spatial analysis has been frequently reported in several yield trials, its application to long‐term Lolium spp. forage yield trials has not been characterized. The objective of this study was to evaluate the trend analysis, nearest‐neighbor analysis (NNA), and correlated error (CE) models for their ability to account for spatial variability in 138 Lolium spp. forage yield trials. This case study was performed on data from five locations and 11 yr (2001–2011) using randomized complete block design (RCBD) trials conducted by the Department of Agriculture, Food and Marine (DAFM) in Ireland. The relative efficiencies of trend, NNA, and CE models compared with RCBD models were 129, 143, and 193% for analysis by trial × year, and 121, 125, and 171% for analysis by trial, respectively. When the top one, two, three, four, or five cultivar(s) were compared between CE and RCBD models, the agreement between two models to find common cultivars varied from 66% for the top cultivar to 28% for the top five cultivars. Using CE models, four replicates were sufficient to detect mean yield differences between cultivars of 7% of the mean and 80% power. Spatial analysis should be added to the routine DAFM testing programs, not only to improve the precision of yield estimates, but also to reduce the risk of missing potential candidate cultivars, given the existence of spatial variation.

Forage dry matter yield (DMY) is a high‐priority trait in breeding perennial ryegrass (Lolium perenne L.). However, determining dry matter concentration is highly labor intensive. For a similar level of resources, indirect selection based on fresh matter yield (FMY) would allow a greater number of replicates, genotypes, or both to be evaluated. Our objective was to estimate the efficiency of indirect selection for DMY based on FMY of pure perennial ryegrass sward plots. Over a 14‐yr period, replicated trials, containing perennial ryegrass genotypes of similar ploidy and maturity category, were sown in Ireland and assessed for DMY and FMY at each harvest over two consecutive years. Forage was generally surface dry when harvested. The estimated efficiency of indirect selection based on two replicates and comparable selection intensity was high (≥0.80). Simulation models indicated that resources would be used more efficiently by evaluating more genotypes than by increasing the number of replicates. For example, doubling the number of plots to increase the number of replicates from two to four indicated an increase in the efficiency of indirect selection from a mean 0.88 to 0.94. However, doubling the number of plots and including more genotypes, facilitating greater selection intensity, indicated an increase in the efficiency of indirect selection from a mean 0.88 to 1.04. This study indicates that FMY can be used successfully as an indirect selection method of increasing DMY in perennial ryegrass swards.