SummaryOrchardgrass (Dactylis glomerata L.) is an important forage grass for cultivating livestock worldwide. Here, we report an ~1.84‐Gb chromosome‐scale diploid genome assembly of orchardgrass, with a contig N50 of 0.93 Mb, a scaffold N50 of 6.08 Mb and a super‐scaffold N50 of 252.52 Mb, which is the first chromosome‐scale assembled genome of a cool‐season forage grass. The genome includes 40 088 protein‐coding genes, and 69% of the assembled sequences are transposable elements, with long terminal repeats (LTRs) being the most abundant. The LTRretrotransposons may have been activated and expanded in the grass genome in response to environmental changes during the Pleistocene between 0 and 1 million years ago. Phylogenetic analysis reveals that orchardgrass diverged after rice but before three Triticeae species, and evolutionarily conserved chromosomes were detected by analysing ancient chromosome rearrangements in these grass species. We also resequenced the whole genome of 76 orchardgrass accessions and found that germplasm from Northern Europe and East Asia clustered together, likely due to the exchange of plants along the ‘Silk Road’ or other ancient trade routes connecting the East and West. Last, a combined transcriptome, quantitative genetic and bulk segregant analysis provided insights into the genetic network regulating flowering time in orchardgrass and revealed four main candidate genes controlling this trait. This chromosome‐scale genome and the online database of orchardgrass developed here will facilitate the discovery of genes controlling agronomically important traits, stimulate genetic improvement of and functional genetic research on orchardgrass and provide comparative genetic resources for other forage grasses.

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Drought is a major environmental stress that limits growth and development of cool-season annual grasses. Drought transcriptional profiles of resistant and susceptible lines were studied to understand the molecular mechanisms of drought tolerance in annual ryegrass (Lolium multiflorum L.). A total of 4718 genes exhibited significantly differential expression in two L. multiflorum lines. Additionally, up-regulated genes associated with drought response in the resistant lines were compared with susceptible lines. Gene ontology enrichment and pathway analyses revealed that genes partially encoding drought-responsive proteins as key regulators were significantly involved in carbon metabolism, lipid metabolism, and signal transduction. Comparable gene expression was used to identify the genes that contribute to the high drought tolerance in resistant lines of annual ryegrass. Moreover, we proposed the hypothesis that short-term drought have a beneficial effect on oxidation stress, which may be ascribed to a direct effect on the drought tolerance of annual ryegrass. Evidence suggests that some of the genes encoding antioxidants (HPTs, GGT, AP, 6-PGD, and G6PDH) function as antioxidant in lipid metabolism and signal transduction pathways, which have indispensable and promoting roles in drought resistance. This study provides the first transcriptome data on the induction of drought-related gene expression in annual ryegrass, especially via modulation of metabolic homeostasis, signal transduction, and antioxidant defenses to improve drought tolerance response to short-term drought stress.

Italian ryegrass (Lolium multiflorum) is an important cool-season, annual forage crop for the grassland rotation system in Southern China. The primary aim of breeding programs is always to seek to improve forage quality in the animal productivity system; however, it is time- and labor-consuming when analyzed excessive large number of samples. The main objectives of this study were to construct near-infrared reflectance spectroscopy (NIRS) models to predict the forage chemistry quality of Italian ryegrass including the concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and water soluble carbohydrate (WSC). The results showed that a broader range of CP, NDF, ADF and WSC contents (%DM) were obtained (4.45–30.60, 21.29–60.47, 11.66–36.17 and 3.95–51.52, respectively) from the samples selected for developing NIRS models. In addition, the critical wavelengths identified in this study to construct optimal NIRS models were located in 4,247–6,102 and 4,247–5,450 cm-1 for CP and NDF content, and both wavelengths 5,446–6,102 and 4,247–4,602 cm-1 could for ADF and WSC. Finally, the optimal models were developed based on the laboratory data and the spectral information by partial least squares (PLS) regression, with relatively high coefficients of determination (R2CV, CP = 0.99, NDF = 0.94, ADF = 0.92, WSC = 0.88), ratio of prediction to devitation (RPD, CP = 8.58, NDF = 4.25, ADF = 3.64, WSC = 3.10). The further statistics of prediction errors relative to laboratory (PRL) and the range error ratio (RER) give excellent assessments of the models with the PRL ratios lower than 2 and the RER values greater than 10. The NIRS models were validated using a completely independent set of samples and have coefficients of determination (R2V, CP = 0.99, NDF = 0.91, ADF = 0.95, WSC = 0.91) and ratio of prediction to deviation (RPD, CP = 9.37, NDF = 3.44, ADF = 4.40, WSC = 3.39). The result suggested that routine screening for forage quality parameters with large numbers of samples is available with the NIRS model in Italian ryegrass breeding programs, as well as facilitating graziers to monitor the forage development stage for improving grazing efficiency.

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