In order to explore the inner associated rules of maize ear characters’statistical data under differentyears, eleven ear traits data of two maize breeds and seven maize combinations were surveyed in southern Shanxi from 2011 to 2013, which was for comparing quantity variance in maize hybridized combination. In order to complete data analysis, we extracted 3-4 principal components through principal component analysis. The result showed ear traits vectors reflected by principal components were not alike in different years. The principal component scoring presented a similar trend in different years which was inferred from principal component evaluation function calculation. PB6, PB7 had higher principal component scoring values and better comprehensive ear traits performance in contrast with the control group‘( Zhengdan958’,‘Xianyu335 ’), it also mean higher yield potentialities. Therefore, principal component analysis (PCA) could serve as an auxiliary data analysis method in maize conventional breeding and improve breeding efficiency.
Key words
Principal Component Analysis; Ear Traits; Evaluation Function
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