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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]

Improved knowledge of the effects of climate change on farming systems can help increase long-term production, economic performance, and mitigate risk. Here we assessed the effects of stocking rate and seasonal calving times on pasture and milk production under short and long term climate changes on dairy farms in Tasmania, Australia. Annual and monthly perennial ryegrass pasture production was simulated from 1971 to 2065 to examine trends in pasture productivity, forage requirements, and impacts on milk production in 2015 and 2050. The projected future climate for the study region indicates a mean warming of up to 1.4 °C, with moderate declines in rainfall of up to 5%. Despite lower rainfall, climate change will likely have a positive impact on pasture yields, with annual production increasing by 13–16%, even though summer growth is reduced and inter-annual variability is increased. We found that greater pasture production is conducive to greater forage conservation, and together these factors allow intensification of stocking rates. These effects increase milk yields by 3–16% per annum and reduce reliance on purchased feeds, which together implies greater profitability of cool-temperate Australian dairy systems under future climates. We also found that total milk production in a spring calving system allows greater stocking rates and pasture utilisation compared with an autumn calving system, and whilst milk produced per animal declines under higher stocking rates, milk yield per hectare increases. Overall, our results suggest that global warming and climate change will positively affect pasture growth rates in Tasmania, and that greater production and conservation of home-grown feeds will be conducive to increased farm milk yields, provided that stocking rates are intensified sustainably. [ABSTRACT FROM AUTHOR]

Perennial ryegrass (PRG) is an important forage grown on dairy farms in temperate regions globally, including south-eastern Australia. A forage value index (FVI) providing information on the seasonal production of commercially available PRG cultivars is currently available. Despite the importance of the nutritive value of pasture in dairy farm systems, the nutritive characteristics of PRG cultivars are not currently included in the FVI as they are not routinely measured in cultivar evaluation trials. This study investigated differences between cultivar functional groups (diploid and tetraploid). It also examined differences between individual cultivars within seasons at four locations in south-eastern Australia and examined how trial location affects cultivar ranking. Samples were collected from existing cultivar evaluation trials over a 3-year period and analysed for nutritive characteristics. There were differences (p < 0.05) between diploids and tetraploids for metabolisable energy (ME) and neutral detergent fibre (NDF) in each season at each location with a few exceptions in summer and autumn. Crude protein (CP) differed between functional groups in some seasons at some sites. Spearman rank correlations within season were strong for ME between trial locations (r = 0.78–0.96), moderate to high for NDF (0.51–0.86) and variable for CP (−0.69–0.56). These findings provide guidance on methods for implementing nutritive value testing in cultivar evaluation trials and support the imminent inclusion of ME in the Australian FVI. The ranking of cultivars for ME was more consistent across trial sites compared to NDF and CP, suggesting the latter two traits, in particular CP, are more sensitive to environmental influences. Based on these results, we do not recommend the inclusion of CP as an individual trait in the Australian FVI. A significantly larger dataset and further research on the genotype by environment interactions would be needed to reconsider this. The addition of ME in the Australian FVI will lead to better cultivar choices by farmers and could lead to more targeted perennial ryegrass breeding programs. [ABSTRACT FROM AUTHOR]

Crude protein estimation is an important parameter for perennial ryegrass (Lolium perenne) management. This study aims to establish an effective and affordable approach for a non-destructive, near-real-time crude protein retrieval based solely on top-of-canopy reflectance. The study contrasts different spectral ranges while selecting a minimal number of bands and analyzing achievable accuracies for crude protein expressed as a dry matter fraction or on a weight-per-area basis. In addition, the model's prediction performance in known and new locations is compared. This data collection comprised 266 full-range (350–2500 nm) proximal spectral measurements and corresponding ground truth observations in Australia and the Netherlands from May to November 2018. An exhaustive-search (based on a genetic algorithm) successfully selected band subsets within different regions and across the full spectral range, minimizing both the number of bands and an error metric. For field conditions, our results indicate that the best approach for crude protein estimation relies on the use of the visible to near-infrared range (400–1100 nm). Within this range, eleven sparse broad bands (of 10 nm bandwidth) provide performance better than or equivalent to those of previous studies that used a higher number of bands and narrower bandwidths. Additionally, when using top-of-canopy reflectance, our results demonstrate that the highest accuracy is achievable when estimating crude protein on its weight-per-area basis (RMSEP 80 kg.ha − 1 ). These models can be employed in new unseen locations, resulting in a minor decrease in accuracy (RMSEP 85.5 kg.ha − 1 ). Crude protein as a dry matter fraction presents a bottom-line accuracy (RMSEP) ranging from 2.5–3.0 percent dry matter in optimal models (requiring ten bands). However, these models display a low explanatory ability for the observed variability (R2 > 0.5), rendering them only suitable for qualitative grading. [ABSTRACT FROM AUTHOR]