Modelling and prediction of dry matter yield of perennial ryegrass cultivars sown in multi-environment multi-harvest trials in south-eastern Australia
| Autor: | Kevin F. Smith, Subhash Chandra, Khageswor Giri, Kohleth Chia, Joe L. Jacobs, Clare M. Leddin, C Ho |
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| Rok vydání: | 2019 |
| Předmět: |
0106 biological sciences
Perennial plant Soil Science 04 agricultural and veterinary sciences 01 natural sciences Generalized linear mixed model Agronomy Yield (wine) 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Dry matter Cultivar Agronomy and Crop Science South eastern 010606 plant biology & botany Mathematics |
| Zdroj: | Field Crops Research. 243:107614 |
| ISSN: | 0378-4290 |
| DOI: | 10.1016/j.fcr.2019.107614 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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