Crop Science Journal of Natural Resources and Life Sciences Education
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Published online 24 February 2006
Published in Crop Sci 46:799-806 (2006)
© 2006 Crop Science Society of America
677 S. Segoe Rd., Madison, WI 53711 USA
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PLANT GENETIC RESOURCES

Aegilops Species from Semiarid Areas of Lebanon: Variation in Quantitative Attributes under Water Stress

R. Baalbaki*, N. Hajj-Hassan and R. Zurayk

Faculty of Agricultural and Food Sciences, American Univ. of Beirut, P.O. Box 11-02361, Beirut, Lebanon

* Corresponding author (riadbaal{at}aub.edu.lb)

Aegilops species in semiarid areas constitute potentially important gene sources for abiotic stress resistance of domesticated Triticum species. Drought tolerance of several Aegilops species was evaluated, and the structure and extent of variation in quantitative attributes of those species when subjected to different degrees of water stress was evaluated. Twenty-one populations belonging to six species were collected: Ae. biuncialis Vis., Ae. cylindrica Host, Ae. geniculata Roth, Ae. markgrafii (Greuter) Hammer, Ae. triuncialis L., and Ae. vavilovii (Zhuk.) Chennav. Quantitative attributes, namely above-ground dry weight, plant height, tillers per plant, days to maturity, productive tillering, spike length, kernels per spike, seed number, seed weight, and yield, were evaluated at three levels of moisture stress. On the basis of changes in measured quantitative attributes under different levels of drought stress, Ae. geniculata and Ae. markgrafii were found to be the most drought tolerant species. Attributes accounting for most of the variation under different levels of water stress were total seed number, seed weight per spike, total number of tillers, and productive tillering capacity, although the extent of each attribute's variation depended on water stress level. Under severe water stress, the ability of plants to produce many fertile tillers with few large seeds seems to be an important adaptive mechanism which should be considered in evaluating plants for drought tolerance.

Abbreviations: PCA, principal component analysis







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