Subterranean clover (Trifolium subterraneum L.) is the most widely sown annual pasture legume species in southern Australia, valued in the livestock and grains industries as a source of high-quality forage and for its ability to fix atmospheric nitrogen. From its initial accidental introduction into Australia in the 19th Century and subsequent commercialisation in the early 1900s, 45 cultivars have been registered in Australia. These consist of 32 cultivars of ssp. subterraneum, eight of ssp. yanninicum, and five of ssp. brachycalycinum and range in flowering time from 77 to 163 days from sowing, enabling the species to be grown in a diversity of rainfall environments, soil types, and farming systems. Eleven of these cultivars are introductions from the Mediterranean region, 15 are naturalised strains collected in Australia, 18 are the products of crossbreeding, and one is derived from mutagenesis. Cultivars developed in Italy have been commercialised for the local market, whereas other cultivars developed in Spain, Portugal, and France have not had commercial seed production. Important traits exploited include: (i) selection for low levels of the oestrogenic isoflavone formononetin, which causes reduced ewe fertility; (ii) increased levels of dormancy imposed by seed-coat impermeability (hard seeds) for cultivars aimed at crop rotations or unreliable rainfall environments; (iii) strong burr-burial ability to maximise seed production; (iv) resistance to important disease pathogens for cultivars aimed at medium- and high-rainfall environments, particularly to Kabatiella caulivora and root rot pathogens; (v) resistance to pests, particularly redlegged earth mites; and (vi) selection for unique leaf markings and other morphological traits (where possible) to aid cultivar identification. Cultivar development has been aided by a large genetic resource of ~10 000 accessions, assembled from its centre of origin in the Mediterranean Basin, West Asia, and the Atlantic coast of Western Europe, in addition to naturalised strains collected in Australia. The development of a core collection of 97 accessions, representing almost 80% of the genetic diversity of the species, and a genetic map, provides a platform for development of future cultivars with new traits to benefit the livestock and grains industries. New traits being examined include increased phosphorous-use efficiency and reduced methane emissions from grazing ruminant livestock. Economic analyses indicate that future trait development should focus on traits contributing to increased persistence and autumn–winter productivity, while other potential traits include increased nutritive value (particularly of senesced material), increased N2 fixation ability, and tolerance to cheap herbicides. Beneficial compounds for animal and human health may also be present within the species for exploitation.

BackgroundGenomic selection based on genotyping-by-sequencing (GBS) data could accelerate alfalfa yield gains, if it displayed moderate ability to predict parent breeding values. Its interest would be enhanced by predicting ability also for germplasm/reference populations other than those for which it was defined. Predicting accuracy may be influenced by statistical models, SNP calling procedures and missing data imputation strategies.ResultsLandrace and variety material from two genetically-contrasting reference populations, i.e., 124 elite genotypes adapted to the Po Valley (sub-continental climate; PV population) and 154 genotypes adapted to Mediterranean-climate environments (Me population), were genotyped by GBS and phenotyped in separate environments for dry matter yield of their dense-planted half-sib progenies. Both populations showed no sub-population genetic structure. Predictive accuracy was higher by joint rather than separate SNP calling for the two data sets, and using random forest imputation of missing data. Highest accuracy was obtained using Support Vector Regression (SVR) for PV, and Ridge Regression BLUP and SVR for Me germplasm. Bayesian methods (Bayes A, Bayes B and Bayesian Lasso) tended to be less accurate. Random Forest Regression was the least accurate model. Accuracy attained about 0.35 for Me in the range of 0.30-0.50 missing data, and 0.32 for PV at 0.50 missing data, using at least 10,000 SNP markers. Cross-population predictions based on a smaller subset of common SNPs implied a relative loss of accuracy of about 25 % for Me and 30 % for PV. Genome-wide association analyses based on large subsets of M. truncatula-aligned markers revealed many SNPs with modest association with yield, and some genome areas hosting putative QTLs. A comparison of genomic vs. conventional selection for parent breeding value assuming 1-year vs. 5-year selection cycles, respectively, indicated over three-fold greater predicted yield gain per unit time for genomic selection.ConclusionsGenomic selection for alfalfa yield is promising, based on its moderate prediction accuracy, moderate value of cross-population predictions, and lack of sub-population structure. There is limited scope for searching individual QTLs with overwhelming effect on yield. Some of our results can contribute to better design of genomic selection experiments for alfalfa and other crops with similar mating systems.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2212-y) contains supplementary material, which is available to authorized users.

Genetic progress for forage quality has been poor in alfalfa (Medicago sativa L.), the most-grown forage legume worldwide. This study aimed at exploring opportunities for marker-assisted selection (MAS) and genomic selection of forage quality traits based on breeding values of parent plants. Some 154 genotypes from a broadly-based reference population were genotyped by genotyping-by-sequencing (GBS), and phenotyped for leaf-to-stem ratio, leaf and stem contents of protein, neutral detergent fiber (NDF) and acid detergent lignin (ADL), and leaf and stem NDF digestibility after 24 hours (NDFD), of their dense-planted half-sib progenies in three growing conditions (summer harvest, full irrigation; summer harvest, suspended irrigation; autumn harvest). Trait-marker analyses were performed on progeny values averaged over conditions, owing to modest germplasm × condition interaction. Genomic selection exploited 11,450 polymorphic SNP markers, whereas a subset of 8,494 M. truncatula-aligned markers were used for a genome-wide association study (GWAS). GWAS confirmed the polygenic control of quality traits and, in agreement with phenotypic correlations, indicated substantially different genetic control of a given trait in stems and leaves. It detected several SNPs in different annotated genes that were highly linked to stem protein content. Also, it identified a small genomic region on chromosome 8 with high concentration of annotated genes associated with leaf ADL, including one gene probably involved in the lignin pathway. Three genomic selection models, i.e., Ridge-regression BLUP, Bayes B and Bayesian Lasso, displayed similar prediction accuracy, whereas SVR-lin was less accurate. Accuracy values were moderate (0.3–0.4) for stem NDFD and leaf protein content, modest for leaf ADL and NDFD, and low to very low for the other traits. Along with previous results for the same germplasm set, this study indicates that GBS data can be exploited to improve both quality traits (by genomic selection or MAS) and forage yield.

The reinvestigation of saponin composition from Medicago arabica from Italy allowed the detection of nineteen (1-19) saponins. All of them were purified by reverse-phase chromatography and their structures elucidated by spectroscopic and spectrometric (1D and 2D NMR; ESI-MS/MS) and chemical methods. Fourteen were known saponins, previously found in other plants including other Medicago species. They have been identified as glycosides of oleanolic acid, 2beta,3beta-dihydroxyolean-12-en-28-oic acid, hederagenin, bayogenin and soyasapogenol B. Five saponins, identified as 3-O-[-alpha-L-arabinopyranosyl(1-->2)-beta-D-glucuronopyranosyl]-30-O-beta-D-glucopyranosyl 2beta,3beta,30-trihydroxyolean-12-en-28-oic acid (1), 3-O-[alpha-L-arabinopyranosyl(1-->2)-beta-D-glucuronopyranosyl]-30-O-[beta-D-glucopyranosyl] 3beta,30-dihydroxyolean-12-en-28-oic acid (2), 3-O-[beta-D-glucuronopyranosyl]-30-O-[alpha-L-arabinopyranosyl(1-->2)-beta-d-glucopyranosyl] 2beta,3beta,30-trihydroxyolean-12-en-28-oic acid (3), 3-O-[beta-D-glucuronopyranosyl]-30-O-[alpha-L-arabinopyranosyl(1-->2)-beta-D-glucopyranosyl] 3beta,30-dihydroxyolean-12-en-28-oic acid (4) and 3-O-[beta-D-glucuronopyranosyl]-30-O-[beta-D-glucopyranosyl] 2beta,3beta,30-trihydroxyolean-12-en-28-oic acid (5), are reported here as new natural compounds. These new saponins, possessing a hydroxy group at the 30-methyl position of the triterpenic skeleton, have never been previously found in the genus Medicago.

Long-term persistence and, hence, agronomic success as a pasture of the annual species subterranean clover depend primarily on seed yield and seed survival over seasons . In natural populations, plant characteristics influencing seed setting and formation of seed reserves in the soil are expected to be `adjusted' to the prevailing environmental conditions of the sites of origin . Knowledge on plant/environment relationships may provide information on adaptive strategies of persistence, and guidelines for selecting adapted varieties to specific conditions. On pure lines from a number of populations such relationships were assessed for flowering time, seed yield, burr fertility, individual seed weight, initial hardseededness, and rate of hardseededness breakdown over summer . Flowering time decreased on decreasing annual rainfall, i .e ., on shortening the growing season, as adaptive response to the need of producing adequate seed before the onset of the dry season . Individual seed weight decreased on decreasing rainfall, and increasing temperatures . Hard-seed maintenance over summer was higher in populations from hot and dry environments, where the marked effect of temperature on hardseededness breakdown exerts a strong selective pressure . Within-population variation, assessed on flowering time, was particularly wide, with early genotypes occurring even in populations from long-season environments. The adaptive relevance of maintaining high levels of within-population polymorphism to cope with unpredictable climatic fluctuations is discussed. Number of constituent lines as a measure of the population structure, and intra-population variation were both influenced by altitude and rainfall, tending to decrease as the climatic selective pressure becomes severe, under both low-rainfall, hot conditions and high-elevation, cold-prone environments .

Adaptation to severe drought and to irrigated cropping can both contribute to increased water use efficiency of lucerne, but knowledge on the relevant adaptive traits is limited. Five cultivars featuring contrasting adaptive responses for 3‐year forage yield across 10 agricultural environments of the western Mediterranean basin were currently studied, to identify physiological and morphological traits associated with specific and wide‐adaptation responses. The landraces Mamuntanas, Demnat 203 and Erfoud 1, and the varieties SARDI 10 and Prosementi, were grown in replicated metal containers (55 cm long × 12 cm wide × 75 cm deep; 21 plants per container) under irrigation (weekly restoring soil field capacity) and under moderate and severe drought stress (implying decreased irrigation for 30 days followed by withheld irrigation for 33 and 58 days, respectively). Cultivar post‐stress survival reflected the known cultivar adaptation to drought‐prone agricultural environments. Demnat 203, specifically adapted to irrigated, frequently mown environments, displayed higher amounts of starch, soluble proteins and total nitrogen in the crown and the root under irrigation. This was due to outstanding organ size and, for starch, higher concentrations. Mamuntanas, specifically adapted to drought‐prone environments, exhibited high water‐soluble carbohydrate concentration in storage organs under severe stress, along with a water‐conservation strategy implying less water used in initial drought‐stress phases due to limited early root development that resulted in more water available under severe stress. Drought‐tolerant germplasm also displayed lower wilting under early stress, more plants with green tissues under severe stress, and more stems per plant in stress or favourable conditions. Multivariate patterns of cultivar variation for physiological and morphological traits were strictly associated with cultivar variation for adaptation pattern. Our results highlighted the difficulty to combine some traits of environment‐specific adaptive value into a unique widely adapted variety, supporting the selection of varieties specifically adapted to irrigated or severely drought‐prone environments.

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Background A thorough verification of the ability of genomic selection (GS) to predict estimated breeding values for pea ( Pisum sativum L.) grain yield is pending. Prediction for different environments (inter-environment prediction) has key importance when breeding for target environments featuring high genotype × environment interaction (GEI). The interest of GS would increase if it could display acceptable prediction accuracies in different environments also for germplasm that was not used in model training (inter-population prediction). Results Some 306 genotypes belonging to three connected RIL populations derived from paired crosses between elite cultivars were genotyped through genotyping-by-sequencing and phenotyped for grain yield, onset of flowering, lodging susceptibility, seed weight and winter plant survival in three autumn-sown environments of northern or central Italy. The large GEI for grain yield and its pattern (implying larger variation across years than sites mainly due to year-to-year variability for low winter temperatures) encouraged the breeding for wide adaptation. Wider within-population than between-population variation was observed for nearly all traits, supporting GS application to many lines of relatively few elite RIL populations. Bayesian Lasso without structure imputation and 1% maximum genotype missing rate (including 6058 polymorphic SNP markers) was selected for GS modelling after assessing different GS models and data configurations. On average, inter-environment predictive ability using intra-population predictions reached 0.30 for yield, 0.65 for onset of flowering, 0.64 for seed weight, and 0.28 for lodging susceptibility. Using inter-population instead of intra-population predictions reduced the inter-environment predictive ability to 0.19 for grain yield, 0.40 for onset of flowering, 0.28 for seed weight, and 0.22 for lodging susceptibility. A comparison of GS vs phenotypic selection (PS) based on predicted genetic gains per unit time for same selection costs suggested greater efficiency of GS for all traits under various selection scenarios. For yield, the advantage in predicted efficiency of GS over PS was at least 80% using intra-population predictions and 20% using inter-population predictions. A genome-wide association study confirmed the highly polygenic control of most traits. Conclusions Genome-enabled predictions can increase the efficiency of pea line selection for wide adaptation to Italian environments relative to phenotypic selection. Electronic supplementary material The online version of this article (10.1186/s12864-019-5920-x) contains supplementary material, which is available to authorized users.