Applying statistics to a wheat cultivar development program

Authors: Bettge, Arthur; Morris, Craig

Submitted to: Proceedings of the International Cereal Chemists Conference
Publication Type: Proceedings
Publication Acceptance Date: 9/26/2005
Publication Date: 10/1/2005
Citation: Bettge, A.D., Morris, C.F. 2005. Applying statistics to a wheat cultivar development program. Proceedings of the international Cereal Chemists Conference. Gosford, NSW, Australia. pp 74-78.

Interpretive Summary: The goal of cultivar development programs is to advance, through breeding, varieties suitable in quality for release to growers. Wheat breeders make many crosses to get desirable traits. Making interpretation difficult is the influence of environment, which can cause large variation in quality attributes across different locations and growing years. Statistical approaches can be used to make sense of the large volume of information, place the information in context and provide perspective and guidance to wheat breeders as to which varieties to advance in breeding programs and which to discard.

Technical Abstract: The goal of cultivar development programs is to advance, through breeding, varieties suitable in quality for release to growers. Wheat breeders make many crosses to introgress desirable traits. Due to genetic segregation and recombination, the breeding process quickly becomes an exercise in dealing with large numbers of wheat breeding lines and the copious data associated with each line. Making interpretation difficult, overlaid on the genetic, heritable aspects of end-use quality is the influence of environment, which can cause large variation in quality attributes across different locations and growing years. Statistical approaches can be used to make sense of the large volume of information, place the information in context and provide perspective and guidance to wheat breeders as to which varieties to advance in breeding programs and which to discard. In all locations where paired samples exist (the standard check variety, and the experimental variety), analysis using Analysis of Variance (ANOVA) procedures, or a general linear models test to determine the Least Standard Difference (LSD) for the mean values is undertaken for the numeric results of end-use tests. A balanced LSD test is exactly equivalent to a statistical paired t-test analysis. Not only does the LSD test examine whether the two wheats are statistically the same or different for any given parameter, but it also provides a measure of the magnitude of the difference. The LSD statistical testing procedure also affords the ability to continually add data to improve the resolution of the paired comparisons. All that is required is to grow the same pair of wheat varieties together in the same environment, run the same analytical tests and then regenerate the statistical output with the same ANOVA model. Multiple comparisons among varieties are possible, but are generally confounded due to missing pairs of data points. The LSD test, or paired t-test comparison, provides statistically valid separations of means that can be used to guide breeding programs. This method provides the means to determine whether the variety will perform satisfactorily or not. Producing wheat that is of uniform, excellent end-use quality is the goal of both the wheat breeding programs. The statistical management tools discussed here provide a means to reach that goal.