Investigation variation of the forage yield and related agro-morphological traits of Bromus inermis Leyss. populations

Maleki, Zahra; Nouraein, Mojtaba; Mohammadi, Reza

doi:10.22092/ijrfpbgr.2021.353865.1378

Investigation variation of the forage yield and related agro-morphological traits of Bromus inermis Leyss. populations

Document Type : Research Paper

Authors

¹ M.Sc graduated, Dept. Plant Genetics and Production, Faculty of Agriculture, University of Maragheh, Maragheh, Iran

² Assoc. Prof. Dept. Plant Genetics and Production, Faculty of Agriculture, University of Maragheh, Maragheh, Iran

³ Branch for Northwest &amp; West region, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education and Extension Organization (AREEO), Tabriz, Iran

10.22092/ijrfpbgr.2021.353865.1378

Abstract

The smooth brome grass (Bromus inermis Leyss.) is one of the most important forage and rangeland plants distributed in the rangelands of Iran. In order to investigate the genetic diversity of yield and morphological traits and identify the high yielding populations, 22 populations of B. inermis were evaluated using in a randomized complete block design with three replications in the research farm of the Agricultural Biotechnology Research Institute of the northwest and west of Iran in the 2018-2019 cropping year. Result showed significant differences (P≤0.05) among the populations in terms of the studied traits, which indicated presence of high genetic variability. The populations of B.in-15, B.in-9, B.in-8, and B.in-1 with higher value of than 540 g/plant had annual higher forage production and B.in-1 and B.in-19 with 100 and 60 g/plant seed yield, respectively had higher seed production than other populations. The simple correlation among the studied traits showed that there was a positive and significant relationship between the annual forage yield and the number of stems, crown diameter, forage yield in the first and second cuts, seed yield, and canopy diameter. Based on the principal component analysis (PCA), four components were introduced with respect to eigenvalues greater than one, which explained total of 82% of variation among the populations. According to the cluster analysis, the populations were arranged into three groups. Therefore, the studied population provided a suitable genetic background for selecting superior populations from different clusters, which can be used for breeding improved varieties through polycross and hybridizations.

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