Growth under cold conditions in a wide perennial ryegrass panel is under tight physiological control

Combined genomic and transcriptomic analysis reveals the contribution of tandem duplication genes to low-temperature adaptation in perennial ryegrass

Perennial ryegrass (Lolium perenne L.) is an agronomically important cool-season grass species that is widely used as forage for ruminant animal production and cultivated in temperate regions for the establishment of lawns. However, the underlying genetic mechanism of the response of L. perenne to low temperature is still unclear. In the present study, we performed a comprehensive study and identified 3,770 tandem duplication genes (TDGs) in L. perenne, and evolutionary analysis revealed that L. perenne might have undergone a duplication event approximately 7.69 Mya. GO and KEGG pathway functional analyses revealed that these TDGs were mainly enriched in photosynthesis, hormone-mediated signaling pathways and responses to various stresses, suggesting that TDGs contribute to the environmental adaptability of L. perenne. In addition, the expression profile analysis revealed that the expression levels of TDGs were highly conserved and significantly lower than those of all genes in different tissues, while the frequency of differentially expressed genes (DEGs) from TDGs was much higher than that of DEGs from all genes in response to low-temperature stress. Finally, in-depth analysis of the important and expanded gene family indicated that the members of the ELIP subfamily could rapidly respond to low temperature and persistently maintain higher expression levels during all low temperature stress time points, suggesting that ELIPs most likely mediate low temperature responses and help to facilitate adaptation to low temperature in L. perenne. Our results provide evidence for the genetic underpinning of low-temperature adaptation and valuable resources for practical application and genetic improvement for stress resistance in L. perenne.

Water Stress Thresholds and Evaluation of Coefficient Ks for Perennial Ryegrass in Tropical Conditions

Perennial ryegrass (Lolium perenne) is the predominant forage crop in the equatorial highland zones of Colombia due to its high nutritional value and versatility to produce both milk and meat. This study aimed to determine the relationship between the relative depletion of usable soil water and the Ks values of canopy expansion and closure stomatal of perennial ryegrass, as well as to identify the threshold values of water stress. The experiment was carried out in pots under a controlled environment condition. These pots were arranged in a completely randomized manner. The experiment consisted of five treatments—including control treatment—of water deficits in the soil that progressively increased the depletion level as the crop cycle developed. This generated a wide range of conditions in the growth stages. For each treatment, four repetitions were performed Biomass production was significantly affected by water stress. The results show that the upper and lower thresholds of Ks were 0.28 and 1.3 of the depletion level (p) of the total available water (TAW) in the soil for the expansion of the canopy (CE), and 0.25 and 1.1 p of the TAW for stomatal closure (gs). Quadratic functions were fitted for both the CE (R2 = 0.72) and CS (R2 = 0.73); moreover, the Ks function of FAO-AquaCrop with positive shape factor (sf) was as follows: sf = 11, RMSE 0.22 for CE, and sf = 4.3, RMSE 0.19 for gs. Our results indicate that ryegrass is moderately sensitive to water stress. The differences found between the Ks function of FAO and the experimental data call for the need to use modeling with parameters adapted for each case.

Evaluation of Molly model predictions of ruminal fermentation, nutrient digestion, and performance by dairy cows consuming ryegrass-based diets

Several studies have been conducted to improve grazing management and supplementation in pasture-based systems. However, it is necessary to develop tools that integrate the available information linking the representation of biological processes with animal performance for use in decision making. The objective of this study was to evaluate the precision and accuracy of the Molly cow model predictions of ruminal fermentation, nutrient digestion, and animal performance by cows consuming pasture-based diets to identify model strengths and weaknesses, and to derive new digestive parameters when relevant. Model modifications for adipose tissue, protein synthesis in lean body mass and viscera representation were included. Data used for model evaluations were collected from 25 publications containing 115 treatment means sourced from studies conducted with lactating dairy cattle. The inclusion criteria were that diets contained ≥45% perennial ryegrass (Lolium perenne L.), and that dry matter intake, dietary ingredient composition, and nutrient digestion observations were reported. Animal performance and N excretion variables were also included if they were reported. Model performance was assessed before and after model reparameterization of selected digestive parameters, global sensitivity analysis was conducted after reparameterization, and a 5-fold cross evaluation was performed. Although rumen fermentation predictions were not significantly improved, rumen volatile fatty acids absorption rates were recalculated, which improved the concordance correlation coefficient (CCC) for rumen propionate and ammonia concentration predictions but decreased CCC for acetate predictions. Similar degradation rates of crude protein were observed for grass and total mixed ration diets, but rumen-undegradable protein predictions seemed to be affected by the solubility of the protein source as was the intestinal digestibility coefficient. Ruminal fiber degradation was greater after reparameterization, driven primarily by hemicellulose degradation. Predictions of ruminal and fecal outflow of neutral detergent fiber and acid detergent fiber, as well as total fecal output predictions, improved significantly after reparameterization. Blood urea N and urinary N excretion predictions resulted in similar accuracy using both sets of model parameters, whereas fecal N excretion predictions were significantly improved after reparameterization. Body weight and body condition score predictions were greatly improved after model modifications and reparameterization. Before reparameterization, yield predictions for daily milk, milk fat, milk protein, and milk lactose were greatly overestimated (mean bias of 61.0, 58.7, 73.7, and 64.6% of mean squared error, respectively). Although this problem was partially addressed by model modifications and reparameterization (mean bias of 3.2, 1.1, 1.7, and 0.4% of mean squared error, respectively), CCC values were still small. The ability of the model to predict grass digestion and animal performance in dairy cows consuming pasture-based diets was improved, demonstrating the applicability of this model to these productive systems. However, the failure to predict grass digestion based on standard model inputs without reparameterization indicates there are still fundamental challenges in characterizing feeds for this model.

Global Changes in Asexual Epichloë Transcriptomes during the Early Stages, from Seed to Seedling, of Symbiotum Establishment

Asexual Epichloë fungi are strictly seed-transmitted endophytic symbionts of cool-season grasses and spend their entire life cycle within the host plant. Endophyte infection can confer protective benefits to its host through the production of bioprotective compounds. Inversely, plants provide nourishment and shelter to the resident endophyte in return. Current understanding of the changes in global gene expression of asexual Epichloë endophytes during the early stages of host-endophyte symbiotum is limited. A time-course study using a deep RNA-sequencing approach was performed at six stages of germination, using seeds infected with one of three endophyte strains belonging to different representative taxa. Analysis of the most abundantly expressed endophyte genes identified that most were predicted to have a role in stress and defence responses. The number of differentially expressed genes observed at early time points was greater than those detected at later time points, suggesting an active transcriptional reprogramming of endophytes at the onset of seed germination. Gene ontology enrichment analysis revealed dynamic changes in global gene expression consistent with the developmental processes of symbiotic relationships. Expression of pathway genes for biosynthesis of key secondary metabolites was studied comprehensively and fuzzy clustering identified some unique expression patterns. Furthermore, comparisons of the transcriptomes from three endophyte strains in planta identified genes unique to each strain, including genes predicted to be associated with secondary metabolism. Findings from this study highlight the importance of better understanding the unique properties of individual endophyte strains and will serve as an excellent resource for future studies of host-endophyte interactions.

Development and Validation of a Phenotyping Computational Workflow to Predict the Biomass Yield of a Large Perennial Ryegrass Breeding Field Trial

Increasing dry matter yield (DMY) is the most important objective in perennial ryegrass breeding programs. Current yield assessment methods like cutting are time-consuming and destructive, non-destructive measures such as scoring yield on single plants by visual inspection may be subjective. These assessments involve multiple measurements and selection procedures across seasons and years to evaluate biomass yield repeatedly. This contributes to the slow process of new cultivar development and commercialisation. This study developed and validated a computational phenotyping workflow for image acquisition, processing and analysis of spaced planted ryegrass and investigated sensor-based DMY yield estimation of individual plants through normalized difference vegetative index (NDVI) and ultrasonic plant height data extraction. The DMY of 48,000 individual plants representing 50 advanced breeding lines and commercial cultivars was accurately estimated at multiple harvests across the growing season. NDVI, plant height and predicted DMY obtained from aerial and ground-based sensors illustrated the variation within and between cultivars across different seasons. Combining NDVI and plant height of individual plants was a robust method to enable high-throughput phenotyping of biomass yield in ryegrass breeding. Similarly, the plot-level model indicated good to high-coefficients of determination (R ²) between the predicted and measured DMY across three seasons with R ² between 0.19 and 0.81 and root mean square errors (RMSE) values ranging from 0.09 to 0.21 kg/plot. The model was further validated using a combined regression of the three seasons harvests. This study further sets a foundation for the application of sensor technologies combined with genomic studies that lead to greater rates of genetic gain in perennial ryegrass biomass yield.