Statistical methods for analysis of multi-harvest data from perennial pasture variety selection trials
Joanne De Faveri A F , Arūnas P. Verbyla B , Wayne S. Pitchford C , Shoba Venkatanagappa D and Brian R. Cullis EA Department of Agriculture and Fisheries, PO Box 1054, Mareeba, QLD, 4880, Australia.
B Data Analytics, CSIRO Digital Productivity Flagship and School of Agriculture, Food and Wine, The University of Adelaide,Atherton, QLD, 4883, Australia.
C School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy Campus, SA, 5371, Australia.
D NSW Department of Primary Industries, Tamworth, NSW, 2340, Australia. Current address: Enza Zaden Australia, Narromine, NSW, 2821, Australia.
E National Institute for Applied Statistics Research Australia (NIASRA), School of Mathematics and Applied Statistics, University of Wollongong, NSW, 2522, Australia.
F Corresponding author. Email: Joanne.DeFaveri@daf.qld.gov.au
Crop and Pasture Science 66(9) 947-962 https://doi.org/10.1071/CP14312
Submitted: 23 November 2014 Accepted: 28 April 2015 Published: 4 September 2015
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.
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