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Statistical methods for analysis of multi-harvest data from perennial pasture variety selection trials

Journal Article


Abstract


  • Variety selection in perennial pasture crops involves identifying best varieties from data collected from multiple harvest times in field trials. For accurate selection, the statistical methods for analysing such data need to account for the spatial and temporal correlation typically present. This paper provides an approach for analysing multi-harvest data from variety selection trials in which there may be a large number of harvest times. Methods are presented for modelling the variety by harvest effects while accounting for the spatial and temporal correlation between observations. These methods provide an improvement in model fit compared to separate analyses for each harvest, and provide insight into variety by harvest interactions. The approach is illustrated using two traits from a lucerne variety selection trial. The proposed method provides variety predictions allowing for the natural sources of variation and correlation in multi-harvest data.

Authors


  •   De Faveri, Joanne (external author)
  •   Verbyla, Ari P. (external author)
  •   Pitchford, Wayne S.. (external author)
  •   Venkatanagappa, Shoba (external author)
  •   Cullis, Brian R.

Publication Date


  • 2015

Citation


  • De Faveri, J., Verbyla, A. P., Pitchford, W. S., Venkatanagappa, S. & Cullis, B. R. (2015). Statistical methods for analysis of multi-harvest data from perennial pasture variety selection trials. Crop and Pasture Science, 66 (9), 947-962.

Scopus Eid


  • 2-s2.0-84940903815

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5296

Has Global Citation Frequency


Number Of Pages


  • 15

Start Page


  • 947

End Page


  • 962

Volume


  • 66

Issue


  • 9

Place Of Publication


  • Australia

Abstract


  • Variety selection in perennial pasture crops involves identifying best varieties from data collected from multiple harvest times in field trials. For accurate selection, the statistical methods for analysing such data need to account for the spatial and temporal correlation typically present. This paper provides an approach for analysing multi-harvest data from variety selection trials in which there may be a large number of harvest times. Methods are presented for modelling the variety by harvest effects while accounting for the spatial and temporal correlation between observations. These methods provide an improvement in model fit compared to separate analyses for each harvest, and provide insight into variety by harvest interactions. The approach is illustrated using two traits from a lucerne variety selection trial. The proposed method provides variety predictions allowing for the natural sources of variation and correlation in multi-harvest data.

Authors


  •   De Faveri, Joanne (external author)
  •   Verbyla, Ari P. (external author)
  •   Pitchford, Wayne S.. (external author)
  •   Venkatanagappa, Shoba (external author)
  •   Cullis, Brian R.

Publication Date


  • 2015

Citation


  • De Faveri, J., Verbyla, A. P., Pitchford, W. S., Venkatanagappa, S. & Cullis, B. R. (2015). Statistical methods for analysis of multi-harvest data from perennial pasture variety selection trials. Crop and Pasture Science, 66 (9), 947-962.

Scopus Eid


  • 2-s2.0-84940903815

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5296

Has Global Citation Frequency


Number Of Pages


  • 15

Start Page


  • 947

End Page


  • 962

Volume


  • 66

Issue


  • 9

Place Of Publication


  • Australia