|Al-Maskri, A - SULTAN QABOOS UNIV.|
|Shahid, M - INTL CTR BIOSALINE AGRIC|
Submitted to: International Journal of Food, Agriculture, and the Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 18, 2006
Publication Date: September 19, 2006
Citation: Al-Maskri, A.Y., Shahid, M., Jaradat, A.A. 2006. Multivariate phenotypic structures in the Batini barley landrace from Oman. International Journal of Food, Agriculture, and the Environment. 4(2):208-212. Interpretive Summary: The Batini barley landrace from Oman exhibits large levels of variation in time to heading, time to maturity and filling period; we used these three characteristics to develop a procedure to identify phenological classes and to select genotypes with high grain yield, forage yield or both. Biological yield, time to heading, time to maturity, grain yield, plant height and number of tillers per plant, in decreasing order, have the most discriminating power among phenological classes in this barley landrace. Plant breeders and farmers will benefit from the knowledge of the magnitude of variation in these traits. This knowledge will aid in selecting genotypes for grain or forage production or for both in a participatory plant breeding scheme.
Technical Abstract: We identified 14 phenological classes in three (grain, forage, and dual-purpose) end-use types and formulated discriminant functions to help select elite germplasm for breeding purposes. Biological yield, pre- and post anthesis thermal time, grain yield, plant height and tillers per plant, in decreasing order, were most influential in discriminating among phenological classes. Extensive divergence in the phenotypic covariance matrices among phenological classes suggests that directional selection, especially in the phenological traits, resulted in large, idiosyncratic changes in the principal components’ structure, and that some of the changes are attributed to shifts in the mean phenotype. Knowledge of genetic co-variation of these traits will be useful for plant breeders by targeting traits that have a disproportionately large influence on differences in the mean covariance.