In crop breeding, the interest of predicting the performance of candidate cultivars in the field has increased due to recent advances in molecular breeding technologies. However, the complexity of the wheat genome presents some challenges for applying new technologies in molecular marker identification with next-generation sequencing. We applied genotyping-by-sequencing, a recently developed method to identify single-nucleotide polymorphisms, in the genomes of 384 wheat (Triticum aestivum) genotypes that were field tested under three different water regimes in Mediterranean climatic conditions: rain-fed only, mild water stress, and fully irrigated. We identified 102,324 single-nucleotide polymorphisms in these genotypes, and the phenotypic data were used to train and test genomic selection models intended to predict yield, thousand-kernel weight, number of kernels per spike, and heading date. Phenotypic data showed marked spatial variation. Therefore, different models were tested to correct the trends observed in the field. A mixed-model using moving-means as a covariate was found to best fit the data. When we applied the genomic selection models, the accuracy of predicted traits increased with spatial adjustment. Multiple genomic selection models were tested, and a Gaussian kernel model was determined to give the highest accuracy. The best predictions between environments were obtained when data from different years were used to train the model. Our results confirm that genotyping-by-sequencing is an effective tool to obtain genome-wide information for crops with complex genomes, that these data are efficient for predicting traits, and that correction of spatial variation is a crucial ingredient to increase prediction accuracy in genomic selection models.

Changing climates and associated increased variability pose risks to alfalfa (Medicago sativa L.) cultivation, with the requirement to establish, survive, and maintain production under water stress. Crop wild relatives (CWR) of alfalfa include populations that have evolved to survive in a number of different, extreme environments, but until recently have had limited use in breeding programs. Here we report on the phenotypic diversity of alfalfa crop wild relatives that were selected to represent extremes in drought tolerance (by sourcing germplasm from environments with extremes in low rainfall, high temperature, shallow soils, and winter freezing) with the aim of providing germplasm with drought tolerance and improved forage yield traits for breeding programs in both warm and cool dry temperate environments. Newly formed hybrids created between M. sativa, M. arborea L. (a woody shrub), and M. truncatula Gaertn. (an annual species from the Mediterranean region) were developed or acquired to introduce new genetic diversity from the tertiary genepool. Preliminary characterization and evaluation was used for taxonomic classification, and to identify wild accessions and pre‐bred (hybrid) lines that offer new diversity for growth habit, seed size, fall dormancy, and forage yield. The accessions and pre‐breeding lines described have been donated to the Australian Pastures Genebank for conservation and distribution.

Herbaceous species can modify leaf structure during the growing season in response to drought stress and water loss. Evolution can select combinations of traits in plants for efficient water use in restricted environments. We investigated plant traits that mediate adaptation and acclimation to water stress in two herbaceous drought-tolerant species. Anatomical, morphological and physiological traits related to stems and leaves were examined under optimal watering (OW) and a long period of restricted watering (RW) in 11 accessions from three Solanaceae species (Solanum chilense, S. peruvianum and S. lycopersicum). The relationships between these traits were tested using linear regression and PCA. There were significant differences in anatomical traits between the species under both OW and RW, where leaf area correlated with stem diameter. Proline and total carbohydrates accumulated highly in S. chilense and S. peruvianum, respectively, and these osmolytes were strongly correlated with increased osmotic potential. Stomatal density varied between species but not between acclimation treatments, while stomatal rate was significantly higher in wild tomatoes. There was a strong positive relationship between stem growth rate and a group of traits together expressed as total stomatal number. Total stomata is described by integration of leaf area, stomatal density, height and internode length. It is proposed that constitutive adaptations and modifications through acclimation that mediate RW play an important role in tolerance to drought stress in herbaceous plants. The capacity for growth under drought stress was not associated with any single combination of traits in wild tomatoes, since the two species differed in relative levels of expression of various phenotypic traits.

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Physiological traits and productivity of the recombinant chromosome substitution lines (RCSLs) of barley, developed through the cross of Hordeum vulgare ssp. vulgare cv. Harrington and the wild ancestor Hordeum vulgare ssp. spontaneum, were measured in plants growing in microplots (with and without irrigation) and in field conditions in two Mediterranean-type environments, Cauquenes (rainfed) and Santa Rosa (irrigated). The objectives were to assess the degree of phenotypic variability in response to terminal drought stress and to test whether the introgression of the wild ancestor into cv. Harrington can increase the terminal drought tolerance of RCSLs of barley. Days from emergence to anthesis and from anthesis to maturity of the 80 RCSLs were reduced in only 2-4 days under water stress, in microplots. Specific leaf area (SLA) and stomatal conductance (gs) of 80 RCSLs and cv. Harrington decreased greatly under water stress in plants growing in microplots and field conditions (in 2004/05 growing season). No G × E interaction was detected except for SLA in the microplot experiment. The principal component analysis provided a clear distinction between RCSLs. Along the first principal component, it was possible to identify 24 RCSLs which represent the whole range of grain yield (GY), gs and SLA observed in the 80 RCSLs. The selected 24 RCSLs were evaluated in field conditions at Cauquenes and Santa Rosa, during two growing seasons (2007/08 and 2008/09). The gs and carbon isotope discrimination in grains ( 13 C) were significantly (P < 0.001) lower in the rainfed condition (Cauquenes), but the water-soluble carbohydrates (WSC) in stems at anthesis and maturity was significantly (P < 0.001) higher than in well-irrigated condition (Santa Rosa). Grain yield was reduced by 63% under drought conditions. Differences between RCSLs in gs, WSC and GY were significant (P < 0.001) in 2007/08. The stress tolerance index (STI) was highly (P < 0.01) correlated with GY in all environments (rainfed and irrigated conditions and the two growing seasons). The relationship between STI and 13 C under rainfed condition allowed identifying drought tolerant and susceptible RCSLs; the former were high yielding lines under rainfed and irrigated conditions (and higher STI values), but with similar GY to cv. Harrington, but presented higher grain 13 C values than cv. Harrington. The drought susceptible lines presented lower GY, STI and 13 C values than cv. Harrington. These results suggest that H. spontaneum has contributed alleles that increase terminal drought tolerance to some of the RCSLs.

Plant breeders are demanding high‐throughput phenotyping methodologies to complement the abundant genomic information currently available. Remote‐sensing technologies offer new tools for high‐throughput phenotyping in field conditions, and many remote sensors have shown high capacity for describing plant physiological behavior. The objective of this study was to evaluate the genotypic relationship between high‐throughput phenotyping based on image analysis and canopy reflectance estimated traits and dry matter (DM) production, the most important trait in forage species. An experiment of a white clover (Trifolium reens L.) association‐mapping population was established in three locations. Plant DM production was evaluated during two growing seasons. The plant area (PA), normalized difference vegetation index (NDVI), and plant growth were estimated from multispectral aerial images collected with an unmanned aerial vehicle. Additionally, canopy reflectance was evaluated with a spectroradiometer (350–1075 nm) and 10 spectral reflectance indices (SRIs) were calculated, including NDVI. The image‐derived PA trait showed the highest genetic correlation with DM production (rg = 0.88, < 0.001) with a broad‐sense heritability (H2) value of 0.56. All the SRIs showed highly significant genetic correlation with DM production with rg absolute values between 0.54 and 0.72 ( < 0.001). However, the popular NDVI index showed one of the lowest DM correlations using both systems. The results indicate that aerial‐image‐derived traits and SRIs could be used together as a high‐throughput proxy to estimate genotypic variation of white clover DM production. Use of these variables could contribute to alleviating phenotypic bottleneck in discovering genes or predicting yield using genomic data.

Core Ideas Association analyses of cold‐tolerance traits of white clover revealed 17 quantitative loci Genomic tetraploid parameterization allowed description of population Genomic tetraploid parameterization improved QTL detection in this polyploidy species WSCdr found to be important physiological mechanism conferring cold tolerance Four candidate genes discovered for WSCdr Significant phenotypic variation explained by markers found associated with traits MAS for some tolerance traits now seems possible White clover (Trifolium repens L.) is the most important grazing perennial forage legume in temperate climates. However, its limited capacity to survive and restore growth after low temperatures during winter constrains the productivity and wide adoption of the crop. Despite the importance of cold tolerance for white clover cultivar development, the genetic basis of this trait remains largely unknown. Hence, in this study, we performed the first genome‐wide association study (GWAS) analyses in white clover to identify quantitative trait loci (QTL) for cold‐tolerance‐related traits. Seeds from 192 divergent genotypes from six populations in the Patagonia region of South America were collected and seed‐derived plants were further clonally propagated. Clonal trials were established in three locations representing temperature gradient associated with elevation. Given the allotetraploid nature of the white clover genome, distinct genetic models (diploid and tetraploid) were tested. Only the tetraploid parameterization was able to detect the 53 loci associated with cold‐tolerance traits. Out of the 53 single nucleotide polymorphism (SNP) trait associations, 17 controlled more than one trait or were stable across multiple sites. This work represents the first report of QTL for cold‐tolerance‐related traits, providing insights into its genetic basis and candidate genomic regions for further functional validation studies.

Nine naturalized white clover populations and two cultivars (Huia and Will) were evaluated at two soil phosphorus ( P ) levels (6 and 20 P mg kg−1) to characterize them for DM production and P efficiency, and to facilitate the selection of suitable genotypes to produce cultivars for P‐deficient marginal soils. The study was carried out in Chile during 2007–2010 under field grazing conditions using a randomized complete factorial block design with three replicates. The clover was grown with perennial ryegrass. Botanical composition and herbage DM production were recorded, phosphorus concentrations in soil and in clover and ryegrass herbage were determined, and P absorption (PAE) and P utilization (PUE) efficiency were calculated. Population 8‐1‐X yielded more DM under the low than under the high soil‐ P level and was the highest yielding clover in the low soil‐ P treatment. It also had the highest PAE (0·6 kg P ha−1 per mg kg−1 Olsen P) under low P availability. Dry‐matter yield of 9‐2‐X was statistically similar to 8‐1‐X and Huia at low P level; however, because of its lower P absorption, it had the highest PUE (455 kg DM ha−1 per kg ha−1 absorbed P). Furthermore, the highest yield of ryegrass occurred when grown with 8‐1‐X, and this combination gave the maximum total yield. These two populations have potential for inclusion in breeding programmes.