Search

Perennial ryegrass (Lolium perenne L.) is a commonly grown pasture species in temperate agriculture, mainly serving as a primary energy source for dairy cows. However, its limited persistence often leads to missed production potential and early resowing, especially in countries that experience summer drought, e.g., Australia and New Zealand. Therefore, understanding the factors influencing perennial ryegrass pasture persistence is crucial for sustainable land management and climate resilience in pasture-based animal production systems. Significant gaps in knowledge exist regarding the factors influencing pasture persistence, as the number of conducted studies in this area remains limited. This study aimed to investigate the factors influencing the expression of persistence in perennial ryegrass populations using airborne and ground-based sensors. A field experiment was conducted in the southwest region of Victoria, Australia, involving ten commercial perennial ryegrass cultivar–endophyte combinations in two different populations. Persistence was evaluated using sensor-based and conventional pasture measurements over two consecutive autumns. The results revealed significant fixed effects of cultivar, endophyte, and environment and their interactions on persistence traits of perennial ryegrass. Cultivars Alto, Samson, and One50 exhibited high levels of persistence when infected with novel endophyte strains. Furthermore, prolonged environmental stresses were found to drive directional selection within pasture populations. The findings emphasise the importance of selecting appropriate cultivar–endophyte combinations and early detection of signs of poor persistence to optimise sward longevity and financial returns from pasture-based animal production systems. This study fills a knowledge gap regarding the factors influencing pasture persistence and provides valuable insights for sustainable pasture management strategies. [ABSTRACT FROM AUTHOR]

Genotype-by-environment interaction (G×E) is commonly observed in perennial ryegrass species and significantly impacts dry matter yield (DMY) performance across environments. Variation in DMY of perennial ryegrass cultivars and their distinct responses to different environments directly influence the profitability of the Australian dairy industry. In this study, we implemented a separate two-way factor analytic strategy to fit multi-environment-multi-harvest data collected from 18 trials spanning 14 years to account for the G×E effects across complex pasture environments in south-eastern Australia to achieve accurate DMY predictions. Three mega-environments were identified, and the seasonal and environmental DMYs of 126 cultivars or breeding lines (defined by a combination of varieties and endophytes) were predicted. Statistical differences in DMY performance and crossover G×E effects in cultivar rankings were observed among the three mega-environments. Several high-yielding cultivars specific to each mega-environment were also identified. The analysis demonstrated the importance of accounting for G×E effects when predicting the DMY of perennial ryegrass and highlighted the potential for identifying high-yielding cultivars specific to mega-environments. The research rationalizes implementing an appropriate independent pasture trialling system to generate necessary data for the dairy industry and could further facilitate dairy farmers to select suitable cultivars based on their specific environments and perennial ryegrass breeders when accounting for the genetic relationship of cultivars. • G×E impacts perennial ryegrass cultivar rankings and breeding strategies. • Factor analytic model is a robust tool for analysing G×E. • Analysing G×E provides accurate DMY prediction and facilitates identifying high-yielding cultivars. • Identified 3 mega-environments and predicted DMYs for 126 cultivars. • Appling an independent trialling system is necessary to identify environmental-specific cultivars. [ABSTRACT FROM AUTHOR]

• Accurate seasonal predictions of ryegrass cultivar DM yields using LMMs. • Base AR 37 and Bealy NEA2 were the best performing cultivars based on annual ranking. • Seasonal variability was found to be larger than genotypic variability. • An antedependence of order 3 was the best residual covariance structure for harvests. With over 60 commercial perennial ryegrass cultivars available on the market in Australia, selecting cultivars with high dry matter (DM) yield and economic profitability requires accurate estimates of their DM yield across target environments. This study, using data from multi-environment multi-harvest (MEMH) trials conducted in south-eastern Australia, derived accurate seasonal predictions of DM yield of these cultivars using linear mixed models (LMM). Base AR37 and Bealy NEA2 were found to be the best performing cultivars in most of the seasons in the target south-eastern Australian environments. Seasonal variability was found to be larger than genotypic variability as usually is the case in multi-environment trials. We have provided details of the LMM methodology used, along with ASReml-R code, to enable others to apply it in similar studies with appropriate changes as required for dataset used. The statistical theory underlying this methodology has also been briefly described in an Appendix for interested readers. [ABSTRACT FROM AUTHOR]