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Triticale, a hybrid species between wheat and rye, is one of the newest additions to the plant kingdom with a very short history of improvement. It has very limited genomic resources because of its large and complex genome. Objectives of this study were to generate dense marker data, understand genetic diversity, population structure, linkage disequilibrium (LD), and estimate accuracies of commonly used genomic selection (GS) models on forage yield of triticale. Genotyping-by-sequencing (GBS), using PstI and MspI restriction enzymes for reducing genome complexity, was performed on a triticale diversity panel (n¼289). After filtering for biallelic loci with more than 70% genome coverage, and minor allele frequency (MAF) >0.05, de novo variant calling identified 16,378 single nucleotide polymorphism (SNP) markers. Sequences of these variants were mapped to wheat and rye reference genomes to infer their homologous groups and chromosome positions. About 45% (7430), and 58% (9500) of the de novo identified SNPs were mapped to the wheat and rye reference genomes, respectively. Interestingly, 28.9% (2151) of the 7430 SNPs were mapped to the D genome of hexaploid wheat, indicating substantial substitution of the R genome with D genome in cultivated triticale. About 27% of marker pairs were in significant LD with an average r2>0.18 (P<0.05). Genome-wide LD declined rapidly to r2 < 0.1 beyond 10 kb physical distance. The three sub-genomes (A, B, and R) showed comparable LD decay patterns. Genetic diversity and population structure analyses identified five distinct clusters. Genotype grouping did not follow prior winter vs spring-type classification. However, one of the clusters was largely dominated by winter triticale. GS accuracies were estimated for forage yield using three commonly used models with different training population sizes and marker densities. GS accuracy increased with increasing training population size while gain in accuracy tended to plateau with marker densities of 2000 SNPs or more. Average GS accuracy was about 0.52, indicating the potential of using GS in triticale forage yield improvement. [ABSTRACT FROM AUTHOR]

Oat (Avena sativa L.) is one of the most important forage crops in the Southern Great Plains of the United States. However, it is more sensitive to cold stress than other small grains. In this study, diverse oat germplasm was evaluated for winter survival across multiple years and locations in the region. Field screening started with an observation trial of 1,861 diverse genotypes in the 2012–2013 season and was followed by four seasons of replicated trials from 2013 to 2017. Selection of good winter survivors was started in 2014–2015 season. All trials were laid out in randomized complete blocks with replications of two in 2013–2014 and 2014–2015, four in 2015–2016, and three in 2016–2017. Winter survival was scored in a 1‐to‐9 scale. Data were analysed for each year and location separately. Additive main effects and multiplicative interaction (AMMI) analysis were carried out on combined data of 35 genotypes that were commonly grown in each year and location. Highly significant (p < 0.001) variations were observed among genotypes, environments and genotype‐by‐environment interaction (GEI). The first three interaction principal components (IPCs) were highly significant (p < 0.001), explaining 96% of GEI. Broad sense heritability ranged from 46% to 93%, while heritability for all environments combined was relatively low (24.6%). At the end of the two cycles (2014/2015‐to‐2016/2017) of selection, mean winter survival was improved by more than 38% per cycle compared with the base population mean. Genotypes CIav 4390, CIav 6909 and CIav 7618 showed significantly higher winter survival than the standard checks Okay and Dallas. Genotypes CIav 4390 showed 20% and 35% improvement over the standard checks Okay and Dallas, respectively. Winter survival improvement in oat will remain a difficult task because of high GEI and low heritability. The identified superior genotypes will be used as crossing parents to transfer cold tolerance genes to other elite lines. [ABSTRACT FROM AUTHOR]

Wheat (Thticum aestivum L) has been widely grown for winter forage production across the world. However, improvement of forage yield and nutritive characteristics has not been a major goal of wheat breeding programs, and little is known about genetic diversity in the traits of winter wheat germplasm. A set of 299 hard winter wheat germplasm from the Great Plains was evaluated during two growing seasons in Oklahoma and 15 forage-related traits were evaluated. There were significant (P < 0.0001) genetic variations in all the traits but effects of environment and germplasm x environment interaction were significant for seven and eight traits, respectively. A significant portion of the variation for forage traits was due to genetics, indicating an ability to breed for improved forage traits. Dry matter yield (DMY, kg ha-1) of the germplasm ranged from 1260 for Cheyenne to 4158 for Sturdy2K. Crude protein (CP, g kg-1) ranged from 161 for OK05108 to 268 for Nuplains. When crude protein yield (kg ha-1) was considered, Sturdy2K was the best germplasm, followed by 2180 and OK1068009. State of origin of the germplasm was also a significant source of variation for most of the traits. Heading date was positively and negatively associated with CP and DMY, respectively. This study identified significant genetic variations of the hard winter wheat germplasm for forage traits and the results of this study could provide useful guidelines for winter forage wheat breeding programs. [ABSTRACT FROM AUTHOR]

There has been an increasing use of rye (Secale cereale L.) for winter forage production in the United States because of its good forage yield, nutritive value, and winter hardiness. However, limited information on forage rye is available. To identify variations and germplasm with superior forage yield and/or nutritive value, winter rye germplasm, including elite cultivars and advanced breeding lines, were evaluated during two growing seasons. Effects of germplasm, clipping date, and germplasm by clipping date interactions were significant for dry matter yield (DMY). Clipping date and germplasm by clipping date interactions were significant for crude protein (CP), minerals, and nutritive value, but germplasm effect was not significant. Across the environments, seasonal DMY ranged from 789 kg ha-1 for ThunderGreen at the first clipping to 3733 kg ha-1 for NF97352 at the first clipping. Crude protein concentration varied from 17.7 to 27.9% for ThunderGreen at the third and first clipping, respectively. Cumulative DMY ranged from 5301 kg ha-1 for ThunderGreen to 8114 kg ha-1 for NF95319B. The results of this study identified significant variations in DMY and nutritive value among the rye germplasm, which may provide useful information for rye breeding programs for winter forage production and livestock producers. [ABSTRACT FROM AUTHOR]

Genomic selection (GS) can improve genetic gain of complex traits in plant breeding. Phenotyping agronomic traits of winter wheat (Triticum aestivum L.) for dual‐purpose use is expensive and time‐consuming. In this study, we compared the prediction accuracies of four GS models (RR‐BLUP, GBLUP, GAUSS, and BL) for forage yield (FY), plant height (PH) and heading date (HD) of the hard winter wheat diversity panel (n = 298) using random and stratified sampling methods. In addition, we determined the appropriate training population (TP) size and marker density for GS of the traits. Moderate to high prediction accuracies ranging from 0.66 to 0.69 for FY, 0.46 to 0.49 for PH, and 0.71 to 0.74 for HD were observed for the GS models. However, the sampling method had little or no impact on prediction accuracy. The RR‐BLUP, GBLUP, and GAUSS models produced slightly greater prediction accuracies than BL for all traits studied. Prediction accuracies increased with increasing TP size and marker density in all the GS models tested. However, increase of prediction accuracy started to plateau at nTP = 180 lines and 1,000; 1,500; or 3,000 SNPs suggesting that the minimum TP size and marker density were about 180 lines and 1,000 or more SNPs, depending on the model and trait. The impact of TP size on prediction accuracy was greater for RR‐BLUP, GAUSS, and GBLUP than for BL model. This study suggests that RR‐BLUP, GBLUP, and GAUSS are viable models for selecting the forage agronomic traits during dual‐purpose wheat breeding. [ABSTRACT FROM AUTHOR]

Phenotyping forage quality traits is time‐consuming in forage wheat breeding. In this study, prediction accuracies of three genomic selection (GS) models (ridge regression best linear unbiased prediction [RRBLUP], Gaussian kernel [GAUSS], and Bayesian LASSO [BL, where LASSO stands for least absolute shrinkage and selection operator]) for forage quality traits of winter wheat (Triticum aestivum L.) were compared using two genotype sampling methods. In addition, the impact of training population (TP) size and marker density on prediction accuracy was explored. The study was done using a diversity panel (n = 298) that was genotyped using 90K single nucleotide polymorphisms (SNPs) and phenotyped for forage quality traits including crude protein, acid detergent fiber, neutral detergent fiber, sugars, lignin content, and in vitro true dry matter digestibility. Generally, the three models produced similar prediction accuracies, which ranged from 0.34 to 0.61, for all traits. The sampling method had little effect on accuracy. Crude protein was one of the traits with the highest prediction accuracy, and it required only 1000 markers to attain its highest prediction accuracy value. Increasing TP size and marker density increased accuracies of all traits, and increasing the TP size was more effective than increasing marker density. For this panel, the optimal TP size (nTP) was 150, at which point prediction accuracies of all traits, except for sugars, reached over 90% of the highest value at nTP = 250. However, the sampling method for marker density had no effect on accuracy. The results suggest that GS can be an alternative approach to facilitate selection of forage quality traits during forage wheat breeding. [ABSTRACT FROM AUTHOR]

Plant wax n -alkanes are a major constituent of the leaf and grain surface. In this study, we explored what can be learned from the abundance and carbon isotopic composition (δ 13C) of n -alkanes in historical winter wheat cultivars. We investigated leaf and grain wax n -alkane concentration (Σ alkL and Σ alkG) and carbon isotopes (δ 13C alkL and δ 13C alkG) on C 29 as well as bulk leaf and grain carbon isotopes (δ 13C bulkL and δ 13C bulkG) to assess if these wax components changed across five wheat cultivars released from the 1950s to the early 2010s. Results showed that Σ alkL and grain yield increased, while δ 13C alkL and δ 13C bulkL decreased across the historical wheat cultivars. We found a significant correlation between Σ alkL and shoot biomass at the early growth stage, and a strong correlation between Σ alkL at the grain-filling stage and grain yield. Grain measures, including Σ alkG , δ 13C alkG , and δ 13C bulkG did not correlate with crop production. Although δ 13C alkL and grain yield were not correlated at the flowering stage, they were correlated at the grain-filling stage under dry conditions. Our results indicate that increased Σ alkL has been indirectly selected in breeding efforts to improve crop production in winter wheat, suggesting that greater leaf waxiness confers advantages for crop growth. [ABSTRACT FROM AUTHOR]