Abstract
The efficiency of various trialling systems for wheat variety evaluation in New South Wales (NSW) is considered. This involved the estimation of the variance components due to genotype, genotype-by-year, genotype-by-location and genotype-by-year-by-location. It is shown that there is a significant reduction in the magnitude of these variance components by the inclusion of the interaction of genotype maturity, winter habit and aluminium tolerance with environment.
Similar content being viewed by others
References
Basford KE (1982) The use of multidimensional scaling in analysing multi-attribute genotype response across environments. Aust J Agric Res 33:473–480
Becker RA, Chambers JM, Wilks AR (1988) The new S language. Wadsworth and Brooks/Cole, London
Brennan JP (1988) An economic investigation of wheat breeding programmes. Agricultural Economics Bulletin No. 35. Department of Agricultural Economics and Business Management, University of New England, Australia
Brennan PS, Byth DE (1979) Genotype x environment interactions in wheat yields and selection for widely adapted wheat genotypes. J Agric Sci 30:221–232
Brennan PS, Shepherd JA (1985) Retrospective assessment of environments in the determination of an objective strategy for the evaluation of the relative yield of wheat cultivars. Euphytica 34:397–408
Brennan PS, Byth DE, Drake DW, De Lacy IH, Butler DG (1981) Determination of the location and number of test environments for a wheat cultivar evaluation programme. Euphytica 32:183–201
Cullis BR, Thomson FM, Fisher JA, Gilmour AR, Thompson R (1995) The analysis of the NSW wheat variety database. I. Modelling trial error variance. Theor Appl Genet
Gauch HG (1988) Model selection and validation for yield trials with interaction. Biometrics 44:705–715
Gilmour AR, Thompson R, Cullis BR (1996) AI, an efficient algorithm for REML estimation in linear mixed models. Biometrics (in press)
Gollob HF (1968) A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrica 33:73–115
Kempton RA (1984) The use of biplots in interpreting variety by environment interactions. J Agric Sci 103:123–135
Krooneberg PM, Basford KE (1989) An investigation of multiattribute genotype response across environments using threemode principal component analysis. Euphytica 44:109–123
Mungomery VE, Shorter R, Byth DE (1974) Genotype x environment interactions and environmental adaptation. I. Pattern analysis — application to soya bean populations. Aust J Agric Res 25:59–72
Patterson HD, Thompson R (1971) Recovery of interblock information when block sizes are unequal. Biometrika 31:100–109
Patterson HD, Silvey V, Talbot M, Weatherup STC (1977) Variability of yields of cereal varieties in UK trials. J Agric Sci 89:238–245
Pederson DG, Rathjen AJ (1981) Choosing trial sites to maximize selection response for grain yield in spring wheat. Aust J Agric Res 32:411–424
Pugsley AT (1973) Control of development patterns in wheat through breeding. In: Sears ER, Sears LMS (eds) Proc 4th Int Wheat Genet Symp. University of Missouri, Columbia, Mo., pp 857–879
Scott BJ, Fisher JA (1989) Selection of genotypes tolerant of aluminium and manganese. In: Robson AD (ed), Soil acidity and plant growth. Academic Press, Sydney, pp 167–204
Talbot M (1984) Yield variability of crop varieties in the UK. J Agric Sci 102:315–321
Thomson NJ, Cunningham RB (1979) Genotype x environment interactions and evaluation of cotton cultivars. Aust J Agric Res 30:105–112
Williams ER, Luckett DJ, Reid AE, Thomson NJ (1992) Comparison of locations used in cotton breeding trials. Aust J Exp Agric 32:739–746
Zadoks JC, Chang TT, Konzak CF (1974) A decimal code for the growth stages of cereals. Weeds Res 14:415–421
Zobel RW, Wright MJ, Gauch HG Jr (1988) Statistical analysis of a yield trial. Agron J 80:388–393
Author information
Authors and Affiliations
Additional information
Communicated by G. Wenzel
Rights and permissions
About this article
Cite this article
Cullis, B.R., Thomson, F.M., Fisher, J.A. et al. The analysis of the NSW wheat variety database. II. Variance component estimation. Theoret. Appl. Genetics 92, 28–39 (1996). https://doi.org/10.1007/BF00222948
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF00222948