Multivariate statistical analysis of some traits of bread wheat for breeding under rainfed conditions
2014
Janmohammadi, Mohsen (Faculty of Agriculture, Maragheh (Iran Islamic Republic). Department of Agronomy and Plant Breeding) | Movahedi, Zahra (Faculty of Agriculture, Malayer (Iran Islamic Republic). Department of Agronomy and Plant Breeding) | Sabaghnia, Naser (Faculty of Agriculture, Maragheh (Iran Islamic Republic). Department of Agronomy and Plant Breeding)
In order to evaluate several agro-morphological traits in 56 wheat genotypes, an experiment based on randomized complete block design with three replications was carried out. Principal component analysis (PCA) and factor analysis were used for understanding the data structure and trait relations. The PCA showed that five components explained 69% of the total variation among traits. The first PCA was assigned 28% and the second PCA was assigned 13% of total variation among traits. The first PCA was more related to grain number, floret number, tiller number, stem diameter, leaf width and spikelet number. Therefore, the selection may be done according to the first component and it was helpful for a good breeding program for development of high yielding cultivars. The correlation coefficient between any two traits is approximated by the cosine of the angle between their vectors in the plot of the first two PCAs and the most prominent relations were between grain diameter and grain yield; and between grain length and 1,000 seed weight. The factor analysis divided the eighteen traits into five factors and the first factor included stem diameter, leaf width, tiller number, spike length, floret number, spikelet number, grain number and grain yield. The second factor was composed of some morphological traits and indicated the importance of the grain diameter, grain length, 1,000 seed weight and grain yield. The two PCA and factor analysis methods were found to give complementary information, and therefore such knowledge would assist the plant breeders in making their selection. In other words, this data reduction would let the plant breeder reduce field costs required to obtain the genetic parameter estimates necessary to construct selection indices.
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