Multivariate statistical analysis of some traits of bread wheat for breeding under rainfed conditions

  • Janmohammadi M
  • Movahedi Z
  • Sabaghnia N
N/ACitations
Citations of this article
20Readers
Mendeley users who have this article in their library.

Abstract

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.U cilju procene nekoliko agro-morfoloskih osobina kod 56 genotipova psenice, sproveden je ogled zasnovan na potpuno slucajnom blok sistemu sa tri ponavljanja. Analiza glavnih komponenti (PCA) i faktorska analiza su koriscene za razumevanje strukture podataka i odnosa osobina. PCA je pokazala da su pet komponenti objasnile 69% ukupne varijacije koja postoji medju osobinama. Prva glavna komponenta je cinila 28%, a druga glavna komponenta je cinila 13% ukupne varijacije medju osobinama. Prva glavna komponenta se vise odnosila na broj zrna, broj cvetica, broj izdanaka, precnik stabljike, sirinu lista i broj klasica. Prema tome, selekcija moze biti izvrsena prema prvoj komponenti i to je bilo od pomoci za dobar program oplemenjivanja za razvoj visokoprinosnih sorti. Koeficijent korelacije izmedju bilo koje dve osobine se priblizno izracunava kosinusom ugla izmedju njihovih vektora u polju prve dve PCA. Najistaknutiji odnosi su bili izmedju precnika zrna i prinosa zrna, kao i izmedju duzine zrna i mase 1.000 semena. Faktorska analiza je podelila osamnaest osobina u pet faktora i prvi faktor je ukljucivao precnik stabljike, sirinu lista, broj izdanaka, duzinu klasa, broj cvetica, broj klasica, broj zrna i prinos zrna. Drugi faktor se sastojao od odredjenih morfoloskih osobina i ukazivao je na vaznost precnika zrna, duzinu zrna, masu 1.000 semena i prinos zrna. Utvrdjeno je da su metode PCA i faktorske analize dale komplementarne informacije i takva saznanja bi mogla pomoci oplemenjivacima biljaka prilikom selekcije. Drugim recima, ovo smanjenje podataka ce omoguciti oplemenjivacima biljaka smanjenje troskova poljskih ogleda potrebnih za dobijanje procene geneticih parametara neophodnih za indekse selekcije.

Cite

CITATION STYLE

APA

Janmohammadi, M., Movahedi, Z., & Sabaghnia, N. (2014). Multivariate statistical analysis of some traits of bread wheat for breeding under rainfed conditions. Journal of Agricultural Sciences, Belgrade, 59(1), 1–14. https://doi.org/10.2298/jas1401001j

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free