, Volume 201, Issue 2, pp 253–264 | Cite as

Benefit of spatial analysis for furrow irrigated cotton breeding trials

  • S. M. Liu
  • G. A. Constable
  • B. R. Cullis
  • W. N. Stiller
  • P. E. Reid


Appropriate analysis of plant breeding trials is critical for the accurate assessment of test lines and selection decisions. The objectives of this study were two-fold: firstly, to examine the performance of two-dimensional spatial models based on the first order separable autoregressive process in comparison with randomised complete block (RCB) and randomisation based (RB) models in analysis of cotton breeding trials; secondly, to understand the presence and forms of spatial variations and their association with field layout. The different models were first used to analyse a lint yield dataset from the CSIRO cotton breeding program, which consisted of 96 trials under furrow-irrigated conditions from 1995 to 2002 and Residual Maximum Likelihood ratio test and the Akaike Information Criterion were used to identify adequate model (i.e. dataset-preferred model) for individual datasets. The spatial models fitted 62 trials adequately and outperformed the RB model (31) with the worse being RCB model (3). Spatial variations in various forms were commonly present in trials in which spatial models were adequate, and was dominant in planting row direction. Layouts with more plots in dimensional directions tended to have a higher level of spatial variation. Spatial models offered about 176 % mean relative efficiency over RCB, which was comparable with that achieved by the dataset-preferred models but about 20 % higher than the RB model. Therefore, a routine use of spatial analysis in conjunction with efficient trial designs would mitigate the impact of spatial variations on the yield estimate of cotton breeding trials and improve the accuracy of selection.


Cotton Breeding trial Spatial analysis Randomisation based analysis Experimental design Residual maximum likelihood 



The authors acknowledge the assistance of all the co-operators to conduct cotton ALTs on their farms and technical support of our past and present CSIRO team members: Lindsay Heal, Chris Tyson, Kellie Cooper, Dave Shann, Chris Allen, Max Barnes, Kay Smith, Sandra Magann, and Megan Smith; as well as Gavin Mann of QDEEDI. The authors are thankful to Dr Alison Smith, The University of Wollongong for her advice and help in data analysis. The authors also thank Mr. Alec Zwart, CSIRO Computational Informatics and two anonymous reviewers for their comments improved the manuscript. The Cotton Research and Development Corporation provided funding support for these experiments.


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • S. M. Liu
    • 1
  • G. A. Constable
    • 1
  • B. R. Cullis
    • 2
    • 3
  • W. N. Stiller
    • 1
  • P. E. Reid
    • 1
  1. 1.CSIRO Agriculture FlagshipNarrabriAustralia
  2. 2.National Institute for Applied Statistics Research AustraliaThe University of WollongongWollongongAustralia
  3. 3.CSIRO Computational InformaticsCanberraAustralia

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