, Volume 157, Issue 1–2, pp 253–266 | Cite as

Varietal selection for perennial crops where data relate to multiple harvests from a series of field trials

  • Alison Barbara Smith
  • Joanne K. Stringer
  • Xianming Wei
  • Brian R. Cullis


Varietal selection for yield from a series of multi-environment trials can be regarded as a multi-trait selection problem in which the yields in different environments are synonymous with traits. As such an analysis of the data combined across environments should be conducted in order to form an index for selection. Analytical methods that include appropriate models for both the genetic variance structure (that is, the variances and covariances of genotype effects from different environments) and the residual variance structure (which typically comprises spatial covariance models for each trial) have been published previously. In the case of perennial crops, yields are often obtained from multiple harvests which implies that the data comprise short sequences of repeated measurements. Varietal performance in individual harvests is important for selection so that a combined analysis across both trials and harvests is required. The repeated measures nature of the data provides additional modelling challenges. In this paper we propose an approach for the analysis of multi-environment, multi-harvest data that accommodates the major sources of variation and correlation (including temporal). The approach is illustrated using two examples from sugarcane breeding programmes. The proposed models were found to provide a superior fit to the data and thence more accurate selection decisions than the common practice of conducting separate analyses of individual trials and harvests.


Multi-environment trials Varietal selection Perennial crops Mixed model 



We would like to thank BSES Limited staff Mark Hetherington, Dion Appo, Ross McIntyre, Michael Porta and Phil Lethbridge for technical assistance and the contribution of numerous field staff in the Mackay and Northern regions in collecting the data. We acknowledge the financial contribution of SRDC. We thank the referees for comments that have improved the manuscript. Finally we thank Prof. Robin Thompson who always inspires us to “try harder”.


  1. Becker RA, Chambers JM, Wilks AR (1988) The new S language. Wadsworth and Brooks/ColeGoogle Scholar
  2. Butler DG, Cullis BR, Gilmour AR, Gogel BJ (2003) samm reference manual. Training Series, No QE02001. QLD Department of Primary Industries and Fisheries, Brisbane, QLDGoogle Scholar
  3. Cullis BR, Gleeson AC (1989) Efficiency of neighbour analysis for replicated field trials in Australia. J Agric Sci Cambridge 113:233–239CrossRefGoogle Scholar
  4. Cullis BR, Gleeson AC (1991) Spatial analysis of field experiments - an extension to two dimensions. Biometrics 47:1449–1460CrossRefGoogle Scholar
  5. Cullis BR, Smith AB, Coombes NE (2006) On the design of early generation variety trials with correlated data. J Agric Biol Environ Stat 11:381–393CrossRefGoogle Scholar
  6. Gilmour AR, Thompson R, Cullis BR (1995) AI, an efficient algorithm for REML estimation in linear mixed models. Biometrics 51:1440–1450CrossRefGoogle Scholar
  7. Gilmour AR, Cullis BR, Verbyla AP (1997) Accounting for natural and extraneous variation in the analysis of field experiments. J Agric Biol Environ Stat 2:269–273CrossRefGoogle Scholar
  8. Gilmour AR, Cullis BR, Gogel BJ, Welham SJ, Thompson R (2002) ASReml user guide. Release 1.0. VSN International Ltd, 5 The Waterhouse St, Hemel Hempstead, HP1 1ES, UKGoogle Scholar
  9. Gleeson AC, Cullis BR (1987) Residual maximum likelihood (REML) estimation of a neighbour model for field experiments. Biometrics 43:277–288CrossRefGoogle Scholar
  10. Kelly A, Smith AB, Eccleston J, Cullis BR (2007) The accuracy of varietal selection using Factor Analytic models for multi-environment plant breeding trials. Crop Sci (In press)Google Scholar
  11. Kempton RA (1984) The use of biplots in interpreting variety by environment interactions. J Agric Sci Cambridge 103:123–135Google Scholar
  12. Oakey H, Verbyla A, Pitchford W, Cullis B, Kuchel H (2006) Joint modelling of additive and non-additive genetic line effects in single field trials. Theor Appl Genet 113:809–819PubMedCrossRefGoogle Scholar
  13. Patterson HD, Thompson R (1971) Recovery of interblock information when block sizes are unequal. Biometrika 31:100–109Google Scholar
  14. Piepho H-P (1998) Empirical best linear unbiased prediction in cultivar trials using factor-analytic variance-covariance structures. Theor Appl Genet 97:195–201CrossRefGoogle Scholar
  15. Smith AB, Cullis BR, Thompson R (2001) Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend. Biometrics 57:1138–1147PubMedCrossRefGoogle Scholar
  16. Smith AB, Cullis BR, Thompson R (2005) The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches. J Agric Sci Cambridge 143:449–462CrossRefGoogle Scholar
  17. Stram DO, Lee JW (1994) Variance components testing in the longitudinal mixed effects setting. Biometrics 50:1171–1177PubMedCrossRefGoogle Scholar
  18. Stringer JK, Cullis BR (2002) Aplication of spatial analysis techniques to adjust for fertility trends and identify interplot competition in early stage sugarcane selection trials. Aust J Agric Res 53:911–918CrossRefGoogle Scholar
  19. Thompson R, Cullis BR, Smith AB, Gilmour AR (2003) A sparse implementation of the Average Information algorithm for factor analytic and reduced rank variance models. Aust NZ J Stat 45:445–460CrossRefGoogle Scholar
  20. Verbyla AP, Cullis BR, Kenward MG, Welham SJ (1999) The analysis of designed experiments and longitudinal data using smoothing splines (with discussion). J R Stat Soc, Ser C 48:269–312CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  • Alison Barbara Smith
    • 1
  • Joanne K. Stringer
    • 2
  • Xianming Wei
    • 3
  • Brian R. Cullis
    • 1
  1. 1.Wagga Wagga Agricultural InstituteNSWDPIWagga WaggaAustralia
  2. 2.BSES LimitedQLDAustralia
  3. 3.BSES LimitedQLDAustralia

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