Finding out Relationships Among Some Morpho-Biochemical Parameters of Christ's Thorn (Ziziphus spina-christi) under Drought and Salinity Stresses¹

A Descoberta de Relações entre Alguns Parâmetros Morfobioquímicos do Espinheiro-de-Cristo (Ziziphus spina-christi) sob os Estresses Hídrico e Salino

ABSTRACT

Taking into consideration that modeling and indicating relationship among traits and variables are among the most useful numerical techniques in the biological and botanical researches, and also undeniable impact of drought and salinity stresses, this research was implemented with the aim to consider change in relationship among the traits under separately drought and salinity stresses related to scarcely distributed medicinal plant called Christ thorn (Ziziphus spina-christi). A glass house experiment was implemented in controlled conditions at the College of Agriculture, Shiraz University, Shiraz, Iran. Five different water stress levels (irrigation at four, six, eight and 10 days intervals, compared to the unstressed control plants with every even days irrigation) and five salinity stress levels (0, 3.2, 6.4, 9.6 and 12.3 dS m^-1) were separately induced on plants. Results of this study indicated that salinity and water stresses in addition to their destructive impact on cell and tissue compartment, can adverse and change the relationship among morphological and biochemical parameters in different ways. Cluster analysis could clearly separate traits into two groups under water stress while three groups under salinity stress. Principal component showed that this technique can accounted for a high variation among data under both water and salinity stresses so that it is possible to consider relationship among the traits using principal component in place of classic methods such as correlation coefficients. As result of principal component, it revealed that change in relationship among traits under different stresses is plausible.

Keywords:
salinity; drought; stepwise regressions; principal component analysis; cluster analysis