Evaluation of endophyte toxin production and its interaction with transgenic perennial ryegrass (Lolium perenne L.) with altered expression of fructosyltransferases.

Alkaloid concentration of perennial ryegrass herbage is affected by endophyte strain and host plant genotype. However, previous studies suggest that associations between host and endophyte also depends on environmental conditions, especially those affecting nutrient reserves and that water-soluble carbohydrate (WSC) concentration of perennial ryegrass plants may influence grass-endophyte associations. In this study a single transgenic event, with altered expression of fructosyltransferase genes to produce high WSC and biomass, has been crossed into a range of cultivar backgrounds with varying Epichloë endophyte strains. The effect of the association between the transgenic trait and alkaloid production was assessed and compared with transgene free control populations. In the vast-majority of comparisons there was no significant difference between alkaloid concentrations of transgenic and non-transgenic plants within the same cultivar and endophyte backgrounds. There was no significant difference between GOI+ (gene of interest positive) and GOI- (gene of interest negative) populations in Janthritrem response. Peramine concentration was not different between GOI+ and GOI- for 10 of the 12 endophytes-cultivar combinations. Cultivar Trojan infected with NEA6 and Alto with SE (standard endophyte) exhibited higher peramine and lolitrem B (only for Alto SE) concentration, in the control GOI- compared with GOI+. Similarly, cultivar Trojan infected with NEA6 and Alto with NEA3 presented higher ergovaline concentration in GOI-. Differences in alkaloid concentration may be attributable to an indirect effect in the modulation of fungal biomass. These results conclude that the presence of this transgenic insertion, does not alter the risk (toxicity) of the endophyte-grass associations. Endophyte-host interactions are complex and further research into associations with high WSC plant should be performed in a case by case basis.

Functional analyses of caffeic acid O-Methyltransferase and Cinnamoyl-CoA-reductase genes from perennial ryegrass (Lolium perenne).

Cinnamoyl CoA-reductase (CCR) and caffeic acid O-methyltransferase (COMT) catalyze key steps in the biosynthesis of monolignols, which serve as building blocks in the formation of plant lignin. We identified candidate genes encoding these two enzymes in perennial ryegrass (Lolium perenne) and show that the spatio-temporal expression patterns of these genes in planta correlate well with the developmental profile of lignin deposition. Downregulation of CCR1 and caffeic acid O-methyltransferase 1 (OMT1) using an RNA interference-mediated silencing strategy caused dramatic changes in lignin level and composition in transgenic perennial ryegrass plants grown under both glasshouse and field conditions. In CCR1-deficient perennial ryegrass plants, metabolic profiling indicates the redirection of intermediates both within and beyond the core phenylpropanoid pathway. The combined results strongly support a key role for the OMT1 gene product in the biosynthesis of both syringyl- and guaiacyl-lignin subunits in perennial ryegrass. Both field-grown OMT1-deficient and CCR1-deficient perennial ryegrass plants showed enhanced digestibility without obvious detrimental effects on either plant fitness or biomass production. This highlights the potential of metabolic engineering not only to enhance the forage quality of grasses but also to produce optimal feedstock plants for biofuel production.

Within and combined season prediction models for perennial ryegrass biomass yield using ground- and air-based sensor data.

Across-season biomass assessment is crucial in the cultivar selection process to accurately evaluate the yield performance of lines under different growing conditions. However, it has been difficult to have an accurate, reliable, and repeated fresh biomass (FM) estimation of large populations of plants in the field without destructive harvesting, which incurs significant labor and operation costs. Sensor-based phenotyping platforms have advanced in the data collection of structural and vegetative information of plants, but the developed prediction models are still limited by low correlations at different growth stages and seasons. In this study, our objective was to develop and validate the global prediction models for across-season harvested fresh biomass (FM) yield based on the ground- and air-based sensor data including ground-based LiDAR, ground-based ultrasonic, and air-based multispectral camera to extract LiDAR plant volume (LV), LiDAR point density (LV_Den), height, and Normalized Difference Vegetative Index (NDVI). The study was conducted in a row-plot field trial with 480 rows (3 rows in a plot per cultivar) throughout the whole 2020 growing season up to the reproductive stage. We evaluated the performance of each plant parameter, their relationship, and the best subset prediction models using statistical stepwise selection at the row and plot levels through the seasonal and combined seasonal datasets. The best performing model: F M ~ L V ∗ L V _ D e n ∗ N D V I had a determination of coefficient R 2 of at least 0.9 in vegetative stages and 0.8 in the reproductive stage. Similar results can be achieved in a simpler model with just two LiDAR variables- F M ~ L V ∗ L V _ D e n . In addition, LV and LV_Den showed a robust correlation with FM on their own over seasons and growth stages, while NDVI only performed well in some seasons. The simpler model based on only LiDAR data can be widely applied over season without the need of additional sensor data and may thus make the in-field across-season biomass assessment more feasible and practical for fast and cost-effective development of higher biomass yield cultivars.

Rapid Screening of Nitrogen Use Efficiency in Perennial Ryegrass (Lolium perenne L.) Using Automated Image-Based Phenotyping.

Perennial ryegrass (Lolium perenne L.) is a dominant species in temperate Australian pastures. Currently, nitrogenous fertilizers are used to support herbage production for pasture and fodder. Increasing the nitrogen use efficiency (NUE) of pasture grasses could decrease the amount of fertilizer application and reduce nitrogen (N) leaching into the environment. NUE, defined as units of dry matter production per unit of supplied nitrogen, is a complex trait in which genomic selection may provide a promising strategy in breeding. Our objective was to develop a rapid, high-throughput screening method to enable genomic selection for y -60NUE in perennial ryegrass. NUE of 76 genotypes of perennial ryegrass from a breeding population were screened in a greenhouse using an automated image-based phenomics platform under low (0.5 mM) and moderate (5 mM) N levels over 3 consecutive harvests. Significant (p < 0.05) genotype, treatment, and genotype by treatment interactions for dry matter yield and NUE were observed. NUE under low and moderate N treatments was significantly correlated. Of the seven plant architecture features directly extracted from image analysis and four secondarily derived measures, mean projected plant area (MPPA) from the two side view images had the highest correlation with dry matter yield (r = 0.94). Automated digital image-based phenotyping enables temporal plant growth responses to N to be measured efficiently and non-destructively. The method developed in this study would be suitable for screening large populations of perennial ryegrass growth in response to N for genomic selection purposes.

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.

Using Sensors and Unmanned Aircraft Systems for High-Throughput Phenotyping of Biomass in Perennial Ryegrass Breeding Trials.

Increasing herbage biomass is the predominant objective for pasture plant breeding programs. Three types of field trials are commonly involved during forage plant breeding, i.e., individually spaced plants, row plot, and sward trials. Assessments of biomass production at individual plant, row plot, and sward plot levels are through visual scoring and/or cutting of biomass manually or mechanically. Both visual scoring and cutting of plants are laborious, time consuming, and costly. The development of sensor technology such as multispectral sensors and unmanned aircraft systems (UAS) provide the opportunity to accelerate the process of biomass evaluation and to increase throughput, improve resolution, and reduce time and cost. We tested either the handheld Trimble GreenSeeker® or Parrot Sequoia multispectral sensors attached to a 3DR Solo Quadcopter to assess biomass in perennial ryegrass field trials sown as spaced individual plants, row plots, and simulated sward plots. Significant correlations were observed between visual score and normalized difference vegetation index (NDVI) in a spaced plant field trial and between biomass yield and NDVI in row plot and sward trials (r = 0.12 ~ 0.93). NDVI obtained from multispectral sensors and UAS can replace visual scoring in spaced plant trials. It was also a valuable proxy for yield estimation in row plot and sward trials. These technologies will assist in transition for the forage grass breeding from pen and notepad to digital and data era.

Regulated expression of a cytokinin biosynthesis gene IPT delays leaf senescence and improves yield under rainfed and irrigated conditions in canola (Brassica napus L.).

Delay of leaf senescence through genetic modification can potentially improve crop yield, through maintenance of photosynthetically active leaves for a longer period. Plant growth hormones such as cytokinin regulate and delay leaf senescence. Here, the structural gene (IPT) encoding the cytokinin biosynthetic enzyme isopentenyltransferase was fused to a functionally active fragment of the AtMYB32 promoter and was transformed into canola plants. Expression of the AtMYB32xs::IPT gene cassette delayed the leaf senescence in transgenic plants grown under controlled environment conditions and field experiments conducted for a single season at two geographic locations. The transgenic canola plants retained higher chlorophyll levels for an extended period and produced significantly higher seed yield with similar growth and phenology compared to wild type and null control plants under rainfed and irrigated treatments. The yield increase in transgenic plants was in the range of 16% to 23% and 7% to 16% under rainfed and irrigated conditions, respectively, compared to control plants. Most of the seed quality parameters in transgenic plants were similar, and with elevated oleic acid content in all transgenic lines and higher oil content and lower glucosinolate content in one specific transgenic line as compared to control plants. The results suggest that by delaying leaf senescence using the AtMYB32xs::IPT technology, productivity in crop plants can be improved under water stress and well-watered conditions.