Epichloë fungal endophyte interactions in perennial ryegrass (Lolium perenne L.) modified to accumulate foliar lipids for increased energy density

BACKGROUND

Commercial cultivars of perennial ryegrass infected with selected Epichloë fungal endophytes are highly desirable in certain pastures as the resulting mutualistic association has the capacity to confer agronomic benefits (such as invertebrate pest deterrence) largely due to fungal produced secondary metabolites (e.g., alkaloids). In this study, we investigated T2 segregating populations derived from two independent transformation events expressing diacylglycerol acyltransferase (DGAT) and cysteine oleosin (CO) genes designed to increase foliar lipid and biomass accumulation. These populations were either infected with Epichloë festucae var. lolii strain AR1 or Epichloë sp. LpTG-3 strain AR37 to examine relationships between the introduced trait and the endophytic association. Here we report on experiments designed to investigate if expression of the DGAT + CO trait in foliar tissues of perennial ryegrass could negatively impact the grass-endophyte association and vice versa. Both endophyte and plant characters were measured under controlled environment and field conditions.

RESULTS

Expected relative increases in total fatty acids of 17-58% accrued as a result of DGAT + CO expression with no significant difference between the endophyte-infected and non-infected progeny. Hyphal growth in association with DGAT + CO expression appeared normal when compared to control plants in a growth chamber. There was no significant difference in mycelial biomass for both strains AR1 and AR37, however, Epichloë-derived alkaloid concentrations were significantly lower on some occasions in the DGAT + CO plants compared to the corresponding null-segregant progenies, although these remained within the reported range for bioactivity.

CONCLUSIONS

These results suggest that the mutualistic association formed between perennial ryegrass and selected Epichloë strains does not influence expression of the host DGAT + CO technology, but that endophyte performance may be reduced under some circumstances. Further investigation will now be required to determine the preferred genetic backgrounds for introgression of the DGAT + CO trait in combination with selected endophyte strains, as grass host genetics is a major determinant to the success of the grass-endophyte association in this species.

Above-Ground Biomass Estimation in Oats Using UAV Remote Sensing and Machine Learning

Current strategies for phenotyping above-ground biomass in field breeding nurseries demand significant investment in both time and labor. Unmanned aerial vehicles (UAV) can be used to derive vegetation indices (VIs) with high throughput and could provide an efficient way to predict forage yield with high accuracy. The main objective of the study is to investigate the potential of UAV-based multispectral data and machine learning approaches in the estimation of oat biomass. UAV equipped with a multispectral sensor was flown over three experimental oat fields in Volga, South Shore, and Beresford, South Dakota, USA, throughout the pre- and post-heading growth phases of oats in 2019. A variety of vegetation indices (VIs) derived from UAV-based multispectral imagery were employed to build oat biomass estimation models using four machine-learning algorithms: partial least squares (PLS), support vector machine (SVM), Artificial neural network (ANN), and random forest (RF). The results showed that several VIs derived from the UAV collected images were significantly positively correlated with dry biomass for Volga and Beresford (r = 0.2-0.65), however, in South Shore, VIs were either not significantly or weakly correlated with biomass. For Beresford, approximately 70% of the variance was explained by PLS, RF, and SVM validation models using data collected during the post-heading phase. Likewise for Volga, validation models had lower coefficient of determination (R2 = 0.20-0.25) and higher error (RMSE = 700-800 kg/ha) than training models (R2 = 0.50-0.60; RMSE = 500-690 kg/ha). In South Shore, validation models were only able to explain approx. 15-20% of the variation in biomass, which is possibly due to the insignificant correlation values between VIs and biomass. Overall, this study indicates that airborne remote sensing with machine learning has potential for above-ground biomass estimation in oat breeding nurseries. The main limitation was inconsistent accuracy in model prediction across locations. Multiple-year spectral data, along with the inclusion of textural features like crop surface model (CSM) derived height and volumetric indicators, should be considered in future studies while estimating biophysical parameters like biomass.

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.

Transcriptome analysis reveals major transcriptional changes during regrowth after mowing of red clover (Trifolium pratense)

BACKGROUND

Red clover (Trifolium pratense) is globally used as a fodder plant due its high nutritional value and soil improving qualities. In response to mowing, red clover exhibits specific morphological traits to compensate the loss of biomass. The morphological reaction is well described, but the underlying molecular mechanisms and its role for plants grown in the field are unclear.

RESULTS

Here, we characterize the global transcriptional response to mowing of red clover by comparing plants grown under greenhouse conditions with plants growing on agriculturally used fields. Unexpectedly, we found that biotic and abiotic stress related changes of plants grown in the field overlay their regrowth related transcriptional changes and characterized transcription related protein families involved in these processes. Further, we can show that gibberellins, among other phytohormones, also contribute to the developmental processes related to regrowth after biomass-loss.

CONCLUSIONS

Our findings show that massive biomass loss triggers less transcriptional changes in field grown plants than their struggle with biotic and abiotic stresses and that gibberellins also play a role in the developmental program related to regrowth after mowing in red clover. Our results provide first insights into the physiological and developmental processes of mowing on red clover and may serve as a base for red clover yield improvement.

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.

Ultrasonic Proximal Sensing of Pasture Biomass

The optimization of pasture food value, known as ‘biomass’, is crucial in the management of the farming of grazing animals and in improving food production for the future. Optical sensing methods, particularly from satellite platforms, provide relatively inexpensive and frequently updated wide-area coverage for monitoring biomass and other forage properties. However, there are also benefits from direct or proximal sensing methods for higher accuracy, more immediate results, and for continuous updates when cloud cover precludes satellite measurements. Direct measurement, by cutting and weighing the pasture, is destructive, and may not give results representative of a larger area of pasture. Proximal sensing methods may also suffer from sampling small areas, and can be generally inaccurate. A new proximal methodology is described here, in which low-frequency ultrasound is used as a sonar to obtain a measure of the vertical variation of the pasture density between the top of the pasture and the ground and to relate this to biomass. The instrument is designed to operate from a farm vehicle moving at up to 20 km h−1, thus allowing a farmer to obtain wide coverage in the normal course of farm operations. This is the only method providing detailed biomass profile information from throughout the entire pasture canopy. An essential feature is the identification of features from the ultrasonic reflectance, which can be related sensibly to biomass, thereby generating a physically-based regression model. The result is significantly improved estimation of pasture biomass, in comparison with other proximal methods. Comparing remotely sensed biomass to the biomass measured via cutting and weighing gives coefficients of determination, R2, in the range of 0.7 to 0.8 for a range of pastures and when operating the farm vehicle at speeds of up to 20 km h−1.

Applications of the Soil, Plant and Rumen Microbiomes in Pastoral Agriculture

The production of dairy, meat, and fiber by ruminant animals relies on the biological processes occurring in soils, forage plants, and the animals' rumens. Each of these components has an associated microbiome, and these have traditionally been viewed as distinct ecosystems. However, these microbiomes operate under similar ecological principles and are connected via water, energy flows, and the carbon and nitrogen nutrient cycles. Here, we summarize the microbiome research that has been done in each of these three environments (soils, forage plants, animals' rumen) and investigate what additional benefits may be possible through understanding the interactions between the various microbiomes. The challenge for future research is to enhance microbiome function by appropriate matching of plant and animal genotypes with the environment to improve the output and environmental sustainability of pastoral agriculture.

Understanding the Complexity of Cold Tolerance in White Clover using Temperature Gradient Locations and a GWAS Approach

White clover ( 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.

Harnessing the Potential of Forage Legumes, Alfalfa, Soybean, and Cowpea for Sustainable Agriculture and Global Food Security

Substantial improvements in access to food and increased purchasing power are driving many people toward consuming nutrition-rich foods causing an unprecedented demand for protein food worldwide, which is expected to rise further. Forage legumes form an important source of feed for livestock and have potential to provide a sustainable solution for food and protein security. Currently, alfalfa is a commercially grown source of forage and feed in many countries. However, soybean and cowpea also have the potential to provide quality forage and fodder for animal use. The cultivation of forage legumes is under threat from changing climatic conditions, indicating the need for breeding cultivars that can sustain and acclimatize to the negative effects of climate change. Recent progress in genetic and genomic tools have facilitated the identification of quantitative trait loci and genes/alleles that can aid in developing forage cultivars through genomics-assisted breeding. Furthermore, transgenic technology can be utilized to manipulate the genetic makeup of plants to improve forage digestibility for better animal performance. In this article, we assess the genetic potential of three important legume crops, alfalfa, soybean, and cowpea in supplying quality fodder and feed for livestock. In addition, we examine the impact of climate change on forage quality and discuss efforts made in enhancing the adaptation of the plant to the abiotic stress conditions. Subsequently, we suggest the application of integrative approaches to achieve adequate forage production amid the unpredictable climatic conditions.

Grassland futures in Great Britain - Productivity assessment and scenarios for land use change opportunities

To optimise trade-offs provided by future changes in grassland use intensity, spatially and temporally explicit estimates of respective grassland productivities are required at the systems level. Here, we benchmark the potential national availability of grassland biomass, identify optimal strategies for its management, and investigate the relative importance of intensification over reversion (prioritising productivity versus environmental ecosystem services). Process-conservative meta-models for different grasslands were used to calculate the baseline dry matter yields (DMY; 1961-1990) at 1km² resolution for the whole UK. The effects of climate change, rising atmospheric [CO₂] and technological progress on baseline DMYs were used to estimate future grassland productivities (up to 2050) for low and medium CO₂ emission scenarios of UKCP09. UK benchmark productivities of 12.5, 8.7 and 2.8t/ha on temporary, permanent and rough-grazing grassland, respectively, accounted for productivity gains by 2010. By 2050, productivities under medium emission scenario are predicted to increase to 15.5 and 9.8t/ha on temporary and permanent grassland, respectively, but not on rough grassland. Based on surveyed grassland distributions for Great Britain in 2010 the annual availability of grassland biomass is likely to rise from 64 to 72milliontonnes by 2050. Assuming optimal N application could close existing productivity gaps of ca. 40% a range of management options could deliver additional 21∗10⁶tonnes of biomass available for bioenergy. Scenarios of changes in grassland use intensity demonstrated considerable scope for maintaining or further increasing grassland production and sparing some grassland for the provision of environmental ecosystem services.

Challenges of feeding dairy cows in Australia and New Zealand

There is a continuing evolution of feeding systems in both Australian and New Zealand dairy industries and this presents challenges for the future. Since the turn of the century, the two countries have diverged in industry growth characteristics, with Australian dairying having contracted, with 10% less milk being produced because of 20% fewer cows producing 15% more per cow, whereas New Zealand dairying has expanded, producing 83% more milk driven by a 54% increase in cow numbers and a 31% increase in milk production per cow. Solutions to optimise feed efficiency included the common themes of (1) growing more forage on farm, (2) increasing its utilisation and (3) more efficient use of supplements resulting in increases in DM intake, and they remain relevant. In New Zealand, many of the recent research activities have aimed at improving feed supply while limiting environmental impacts driven by increasing societal concern surrounding the environmental footprint of a growing and intensifying agricultural sector. In Australia, many of the recent research activities have aimed at improving feed efficiency, with a focus on understanding situations where partial mixed ration feeding systems (Australian Farm Systems 3 and 4) are sustainable. Simply growing more feed on farm can no longer be a sole objective; farms must be operated with a view to reduce the environmental footprint, with New Zealand dairy farmers increasingly needing to farm within nitrogen limits. The present review revisits and reinforces many of the concepts developed in previous reviews, but also examines the evolution of feeding systems in both countries and opportunities to improve feed efficiency and profit, while satisfying public expectations around environmental stewardship. We also identify some of the gaps in the current knowledge that warrant further research.