Effect of proline, soluble carbohydrates and water potential on resistance to salinity of three Salsola species (S.rigida, S. dendroides, S.richteri)

Abstract Various resistances to salinity are due to plants genetically variations, and selection on the basis of one factor will not be a suitable criterion in measurement of resistance to salinity, therefore in choosing resistant varieties, it is necessary to notice to a set of resistant indexes to salinity, creating changes in physiology, anatomy and morphology of plant species. This research was conducted using a factorial experiment based on CRD design with 4 replication at research institute of forests and rangelands. Experimental treatments included a combination of three species (S.rigida, S.dendroides and S.richteri) along with 7 salinity levels of 0, 100, 200, 300, 400, 500, and 600 mM concentrations. The purpose of this research was to investigate metabolites, accumulation in terms of compatibility or incompatibility value for salinity stress in three species. The results of physiology features measurement demonstrated that S.dendroides water potential is more than two others. Comparing of the measured characteristics, averages demonstrated that S.rigida and S.dendroides have reposed in one statistical group in terms of proline concentration and significant different have with S.richteri. Also, increasing salinity levels leads to the highest proline concentration in S.richteri in salinity treatment of 400 mM. In all three species, it is seen that salinity increase reduces plants soluble carbohydrates concentration rate and it reveals that salinity have a negative effect on these materials

Optimal operation of reservoir systems with the symbiotic organisms search (SOS) algorithm

This work introduces the symbiotic organisms search (SOS) evolutionary algorithm to the optimization of reservoir operation. Unlike the genetic algorithm (GA) and the water cycle algorithm (WCA) the SOS does not require specification of algorithmic parameters. The solution effectiveness of the GA, SOS, and WCA was assessed with a single-reservoir and a multi-reservoir optimization problem. The SOS proved superior to the GA and the WCA in optimizing the objective functions of the two reservoir systems. In the single reservoir problem, with global optimum value of 1.213, the SOS, GA, and WCA determined 1.240, 1.535, and 1.262 as the optimal solutions, respectively. The superiority of SOS was also verified in a hypothetical four-reservoir optimization problem. In this case, the GA, WCA, and SOS in their best performance among 10 solution runs converged to 97.46%, 99.56%, and 99.86% of the global optimal solution. Besides its better performance in approximating optima, the SOS avoided premature convergence and produced lower standard deviation about optima