Principal Variance Component Analysis of Crop Composition
Data: A Case Study on Herbicide-Tolerant Cotton
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Abstract
Compositional
studies on genetically modified (GM) and non-GM crops
have consistently demonstrated that their respective levels of key
nutrients and antinutrients are remarkably similar and that other
factors such as germplasm and environment contribute more to compositional
variability than transgenic breeding. We propose that graphical and
statistical approaches that can provide meaningful evaluations of
the relative impact of different factors to compositional variability
may offer advantages over traditional frequentist testing. A case
study on the novel application of principal variance component analysis
(PVCA) in a compositional assessment of herbicide-tolerant GM cotton
is presented. Results of the traditional analysis of variance approach
confirmed the compositional equivalence of the GM and non-GM cotton.
The multivariate approach of PVCA provided further information on
the impact of location and germplasm on compositional variability
relative to GM