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.

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.

Sensor-based phenotyping technologies may offer a non-destructive, high-throughput and efficient assessment of herbage yield (HY) to replace current inefficient phenotyping methods. This paper assesses the feasibility of combining normalised difference vegetative index (NDVI) from multispectral imaging and ultrasonic sonar estimates of plant height to estimate HY of single plants in a large perennial ryegrass breeding program. For sensor calibration, fresh HY (FHY) and dry HY (DHY) were acquired destructively, and plant height was measured at four dates each in 2017 and 2018 from a selected subset of 480 plants. Global multiple linear regression models based on K-fold and random split cross-validation methods were used to evaluate the relationship between observed vs. predicted HY. The coefficient of determination (R 2 ) = 0.67-0.68 and a root mean square error (RMSE) between 5.43-7.60 g was obtained for the validation of predicted vs. observed DHY. The mean absolute error (MAE) and mean percentage error (MPE) ranged between 3.59-5.44 g and 22-28%, respectively. For the FHY, R 2 values ranged from 0.63 to 0.70, with an RMSE between 23.50 and 33 g, MAE between 15.11 and 24.34 g and MPE between~22% and 31%. Combining NDVI and plant height is a robust method to enable high-throughput phenotyping of herbage yield in perennial ryegrass breeding programs. current methods on large numbers plants or plots is slow and expensive, making it difficult to include large numbers of individual plants or plots [5]. This makes it challenging to capture an accurate representation of the population and estimate the correct values for individual genotypes. Therefore, HY estimation requires rapid, non-destructive phenotyping methods that can also facilitate genomic tools (e.g., genomic selection, GS) to shorten the breeding time and accelerate genetic gain. However, the number of plants for GS is likely higher than the number of plants traditionally used by breeders to perform selection breeding (e.g., The DairyBio initiative has 48,000 individual plants for genomic sub-selection breeding) where phenotyping of individual plants require accurate evaluation [5]. In recent years, high-throughput phenotyping (HTP) technologies have brought new insights to evaluate phenotypic traits efficiently in large breeding programs [6][7][8][9].Previous studies have used sensor-based data sources from aerial and ground-based platforms to estimate biophysical characteristics of various vegetations, including herbage yield of forage crops [5,10,11]. The aerial-based phenotyping platforms are suitable for lightweight red-green-blue (RGB), multispectral, and hyperspectral imaging systems and have used vegetative indices to build models for herbage yield [10,12] and biomass [13-15] estimation of pasture and cereal crops respectively. Normalised difference vegetative index (NDVI) is a widely used vegetative index for estimates of biomass [16][17][18] with limitations at high-biomass and density of crop cover [19]. Small, low-cost unmanned aerial systems (U...

The Lr56/Yr38 translocation consists primarily of alien-derived chromatin with only the 6AL telomeric region being of wheat origin. To improve its utility in wheat breeding, an attempt was made to exchange excess Ae. sharonensis chromatin for wheat chromatin through homoeologous crossover in the absence of Ph1. Translocation heterozygotes that lacked Ph1 were test-crossed with Chinese Spring nullisomic 6A tetrasomic 6B and nullisomic 6A-tetrasomic 6D plants and the resistant (hemizygous 6A) progeny were analyzed with four microsatellite markers. Genetic mapping suggested general homoeology between wheat chromosome 6A and the translocation chromosomes, and showed that Lr56 was located near the long arm telomere. Thirty of the 53 recombinants had breakpoints between Lr56 and the most distal marker Xgwm427. These were characterized with additional markers. The data suggested that recombinants #39, 157 and 175 were wheat chromosomes 6A with small intercalary inserts of foreign chromatin containing Lr56 and Yr38, located distally on the long arms. These three recombinants are being incorporated into adapted germplasm. Attempts to identify the single shortest translocation and to develop appropriate markers are being continued.

Increasing the yield of perennial forage crops remains a crucial factor underpinning the profitability of grazing industries, and therefore is a priority for breeding programs. Breeding for high dry matter yield (DMY) in forage crops is likely to be enhanced with the development of genomic selection (GS) strategies. However, realising the full potential of GS will require an increase in the amount of phenotypic data and the rate at which it is collected. Therefore, phenotyping remains a critical bottleneck in the implementation of GS in forage species. Assessments of DMY in forage crop breeding include visual scores, sample clipping and mowing of plots, which are often costly and time-consuming. New ground- and aerial-based platforms equipped with advanced sensors offer opportunities for fast, nondestructive and low-cost, high-throughput phenotyping (HTP) of plant growth, development and yield in a field environment. The workflow of image acquisition, processing and analysis are reviewed. The “big data” challenges, proposed storage and management techniques, development of advanced statistical tools and methods for incorporating the HTP into forage breeding systems are also reviewed. Initial results where these techniques have been applied to forages have been promising but further research and development is required to adapt them to forage breeding situations, particularly with respect to the management of large data sets and the integration of information from spaced plants to sward plots. However, realizing the potential of sensor technologies combined with GS leads to greater rates of genetic gain in forages.