Biplot Analysis: Proceed with caution

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Source: Soil Science Society of America

Selection of a crop cultivar is one of the most important management decisions a farmer makes. However, choosing the “right” cultivar for a particular environment (location or year) has been an immense challenge because of the unpredictable performance of cultivars across environments, which is known as the genotype-by-environment interaction (GE). Despite continued efforts to breed for cultivars with wide adaptability, GE is still a major impediment to reliable identification of superior cultivars for optimal production.

Over the past several decades, numerous statistical methods have been developed to facilitate the analysis and understanding of complex GE variability in regional cultivar trials, thereby enhancing our ability to correctly identify superior cultivars. However, Rong-Cai Yang, a scientist with Alberta Agriculture and Rural Development and the University of Alberta, says some of these methods have been overutilized or abused to a point that dubious results and conclusions may have been drawn.

In the September–October 2009 issue of Crop Science, Yang and colleagues Jose Crossa and Juan Burgueño from the International Maize and Wheat Improvement Center (CIMMYT) and Paul Cornelius from the University of Kentucky provide a critical evaluation of one of these methods—the biplot analysis.

Biplot, a scatter plot that simultaneously displays points or scores for genotypes and environments, has been extensively used for studying GE or any two-way data table. Its descriptive and visualization capabilities along with the availability of user-friendly software have enabled plant scientists to examine any two-way data with the click of a mouse. However, according to Yang, the problem is the utility and interpretations of such biplots beyond their functionality and capability.

“A biplot is simply a descriptive, graphical tool for a quick view of GE data, but it cannot be used for hypothesis testing because there is no uncertainty measure,” Yang says. Yang and his co-authors identified and discussed six critical issues arising from the use of biplot analysis with GE analysis. These authors stressed that mere subjective judgment calls from visualization of biplots would not be sufficient. They recommended the use of confidence regions for individual genotype and environment scores in biplots, thereby selecting and recommending cultivars on sound statistical and scientific bases.

 In particular, they proposed the use of a bootstrap re-sampling strategy for constructing such confidence regions. Research is ongoing to add statistical inference capability to the biplot analysis for sound decision on cultivar selection and recommendation.

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