Soil salinity is a growing problem in world production agriculture. Continued improvement in crop salt tolerance will require the implementation of innovative breeding strategies such as marker-assisted selection (MAS) and genomic selection (GS). Genetic analyses for yield and vigor traits under salt stress in alfalfa breeding populations with three different phenotypic datasets was assessed. Genotype-by-sequencing (GBS) developed markers with allele dosage and phenotypic data were analyzed by genome-wide association studies (GWAS) and GS using different models. GWAS identified 27 single nucleotide polymorphism (SNP) markers associated with salt tolerance. Mapping SNPs markers against the Medicago truncatula reference genome revealed several putative candidate genes based on their roles in response to salt stress. Additionally, eight GS models were used to estimate breeding values of the training population under salt stress. Highest prediction accuracies and root mean square errors were used to determine the best prediction model. The machine learning methods (support vector machine and random forest) performance best with the prediction accuracy of 0.793 for yield. The marker loci and candidate genes identified, along with optimized GS prediction models, were shown to be useful in improvement of alfalfa with enhanced salt tolerance. DNA markers and the outcome of the GS will be made available to the alfalfa breeding community in efforts to accelerate genetic gains, in the development of biotic stress tolerant and more productive modern-day alfalfa cultivars.

Developing drought-resistant alfalfa (Medicago sativa L.) that maintains high biomass yield is a key breeding goal to enhance productivity in water-limited areas. In this study, 424 alfalfa breeding families were analyzed to identify molecular markers associated with biomass yield under drought stress and to predict high-merit plants. Biomass yield was measured from 18 harvests from 2020 to 2023 in a field trial with deficit irrigation. A total of 131 significant markers were associated with biomass yield, with 80 markers specifically linked to yield under drought stress; among these, 19 markers were associated with multiple harvests. Finally, genomic best linear unbiased prediction (GBLUP) was employed to obtain predictive accuracies (PAs) and genomic estimated breeding values (GEBVs). Removing low-informative SNPs [SNPs with p-values > 0.05 from the additive Genome-Wide Association (GWAS) model] for GBLUP increased PA by 47.3%. The high number of markers associated with yield under drought stress and the highest PA (0.9) represent a significant achievement in improving yield under drought stress in alfalfa.

It has been hypothesized that the genetic control of forage traits, especially biomass, for grass plants growing as spaced-plants versus swards is different. Likewise, the genetic control of compatibility in grass–legume polyculture mixtures is assumed to be different than for forage production in a grass monoculture. However, these hypotheses are largely unvalidated, especially at the DNA level. This study used an intermediate wheatgrass mapping population to examine the effect of three competition environments (spaced-plants, polyculture, and monoculture) on classical quantitative genetic parameters and quantitative trait loci (QTL) identification for biomass, morphology, and forage nutritive value. Moderate to high heritable variation was observed for biomass, morphological traits, and nutritive value within all three environments (H ranged from 0.50 to 0.87). Genetic correlations (r_G) among environments for morphology and nutritive value were predominantly high, however, were moderately-low (0.30 to 0.48) for biomass. Six biomass QTL were identified, including three on linkage groups (LG) 1, 6, and 15 that were only expressed in the monoculture environment. Moreover, three biomass QTL on LG 10, 14, and 15 exhibited significant QTL by environment interactions. This study verified that the genetic control of grass biomass in a monoculture versus a grass–legume mixture is only partially the same, with additional genes expressed in monoculture, and that biomass in widely spaced-plants versus swards is predominantly under different genetic control. These results indicate that selection for improved grass biomass will be most successful when conducted within the targeted monoculture or polyculture sward environment per se.

High nitrogen (N) fertilizer costs and increased environmental stewardship have renewed interest in grass–legume pastures. This research compared herbage mass, nutritive value, steer growth performance and economics of binary mixtures of tall fescue (Schendonorus arundinaceus [Schreb.] Dumort, nom. Cons.) (TF) with alfalfa (ALF) (TF + ALF, Medicago sativa L.) and birdsfoot trefoil (BFT) (TF + BFT, Lotus corniculatus L.), to tall fescue with (TF + N) and without N fertilizer (TF–N). Pastures were established in Lewiston, Utah, USA, and rotationally stocked by Angus beef steers for 112 days in 2012 and 2013. Herbage samples were collected prior to each 7‐day stocking period and analyzed for mass and nutritive value. Steers were weighed each 28‐day rotation cycle and cumulative average daily gain (ADG) calculated. Steer performance differed (p = .05) among most treatments with ADG greatest for TF + BFT (0.73 kg/day) and TF + ALF (0.67 kg/day), followed by TF + N (0.61 kg/day) and the least for TF–N (0.40 kg/day). Multivariate analysis indicated that small inherent differences in herbage energy, fiber and digestibility were highly correlated with steer growth. Other variables associated with ADG included legume proportion and herbage mass. Fescue–legume mixtures had 29 to 33% legume proportion, and more (p = .05) favorable energy and fiber concentrations than grass monocultures. Whereas herbage mass was greatest (p = .05) for TF + N (2,956 kg/ha), followed closely by TF + BFT (2,741 kg/ha) and TF + ALF (2,623 kg/ha). Overall, TF + BFT and TF + ALF pastures had less herbage, but better nutritive value and increased steer growth performance compared to TF + N, without the added cost of fertilizer.