Modelling and prediction of dry matter yield of perennial ryegrass cultivars sown in multi-environment multi-harvest trials in south-eastern Australia
Introduction
Perennial ryegrass (Lolium perenne L.) forms the basis of “home grown” forage supply on dairy farms in south-eastern Australia. With over 60 cultivars commercially available on the market, selecting the most productive (in terms of dry matter (DM) yield) and economically profitable cultivars requires accurate estimates of DM yield performance of these cultivars based on data from multi-environment multi-harvest (MEMH) trials. These estimates are also used to develop seasonal forage value indices (FVI) that combine DM yield performance of cultivars with their relative economic values. These indices assist farmers to select better performing cultivars with the best possible balance of attributes for their production system and environment in different seasons of the year. Seasonal FVIs have been derived for perennial ryegrass in Ireland (McEvoy et al., 2011), New Zealand (Chapman et al., 2017) and Australia (Leddin et al., 2018).
To enable reliable FVI-based selection of better performing cultivars, this study aimed to derive accurate seasonal predictions of DM yield performance of commercial perennial ryegrass cultivars in the target population of south-eastern Australian environments using data from MEMH trials. The predictions were derived using a linear mixed model (LMM) methodology as appropriate for the analysis of data from MEMH trials in perennial crops. This paper provides a more detailed version of this methodology, along with ASReml-R code, to enable others to implement it in similar studies. While the statistical methods used are well established, this is the first report on an application of these statistical methods for perennial ryegrass cultivar evaluation using data from MEMH trials. The statistical theory underlying this methodology has been briefly described in the Appendix.
Section snippets
Trials, cultivars and design
Nine perennial ryegrass cultivar evaluation trials (Table 1), managed by the Pasture Variety Trial Network (PVTN) and commercial seed companies across south-east Australia (Victoria, New South Wales and Tasmania), were used in this study. These nine trials were treated as nine environments. Where a location had more than one trial starting in different years, they were treated as separate environments. The trials generally started in different calendar years, ran for three to four years and had
Exploratory analyses
As a cubic spline fixed effect for row and column could not be fitted for all harvests, linear effects of row and column were therefore fitted as fixed effects. These linear fixed effects were significant (P <0.05) across most of the harvests and trials. The random effects for row and column were mostly negligible. The residual covariance indicated a common spatial covariance structure of AR(1) in row and column directions in all 188 harvests.
Combined analyses
The results presented below pertain to the final
Discussion
Due to difficulties in obtaining direct and comparative estimates of forage DM yield and also the subsequent milk production from different perennial ryegrass cultivars (Hendriks et al. (2017)), estimates of cultivar performance from randomised plot trials conducted in multiple environments remain the primary source of information to assess the relative DM yield performance of perennial ryegrass cultivars. The BLUPs for perennial ryegrass DM yield derived and reported in this work would enable
Conclusion
The adoption of the LMM methodology of Smith et al. (2007), comprising exploratory and combined analyses, enabled the use of data from nine highly imbalanced multi-environment trials in deriving best possible accurate predictions of the seasonal DM yield performance of ryegrass cultivars in the target population of south east Australian environments. Base AR37 and Bealy NEA2 were found to be the best performing cultivars in the target population. Seasonal variability was found to be larger than
Acknowledgements
This work was undertaken as part of the ‘Forage Value Index’ project jointly funded by the State of Victoria through the Department of Jobs, Precincts and Regions and Dairy Australia. The authors would like to acknowledge the advisory committee involved in the FVI development which included members from the Australian Seed Federation and its member companies, Meat and Livestock Australia who provided data from the Pasture Variety Trial Network and the seed companies who have provided data which
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