The effects of drought on growth, protein content, lipid peroxidation, superoxide dismutase (SOD), peroxidase (POX), catalase (CAT) and polyphenol oxidase (PPO) were studied in leaves and roots of Sesamum indicum L. cvs. Darab 14 and Yekta. Four weeks after sowing, plants were grown under soil moisture corresponding to 100, 75, 50 and 25 % field capacity for next four weeks. Fresh and dry masses, and total protein content in leaves and roots decreased obviously under drought. However, several new proteins appeared and content of some proteins was affected. Measurement of malondialdehyde content in leaves and roots showed that lipid peroxidation was lower in Yekta than in Darab 14. Severe stress increased SOD, POX, CAT and PPO activities in leaves and roots, especially in Yekta. According to the present study Yekta is more resistant to drought than Darab 14.Additional key words: catalase, malondialdehyde, peroxidase, polyphenol oxidase, Sesamum indicum, superoxide dismutase.

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This paper describes the modeling of the color yield (F k) of 100% cotton fabric dyed with six selected direct dyes (two from each groups of A, B and C) using Taguchi and factorial experimental designs as well as a response surface regression method. The factors chosen were dye concentration, electrolyte (sodium chloride) concentration, temperature and time of dying. To conduct the tests using the Taguchi approach, two levels were chosen for each factor. After obtaining the data (F k), the significant factors were determined by an analysis of variance (ANOVA). Then, the level of significant factors was increased from two to three and the supplementary tests were carried out using full factorial design. ANOVA was applied again and, finally, the initial response surface regression model was produced considering the significant factors. After verifying the validity of the initial models, the BOX-COX transformation was implemented until the models achieved validity.

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Problem statement: Always because of weather change, determine of optimum sowing date in each zone is difficult. Dynamic models can help us for solving this problem. In order to evaluation of soybean simulation by using of CROPGRO-Soybean model at four sowing date in field research of Azad university of Karaj branch a field experiment conducted in form of split plot in based on randomize complete block design with four replication in 2009s. Approach: At this experiment simulation of some traits such Leaf Area Index (LAI), Leaf Dry Weight (LDW), Stem Dry Weight (SDW) and Biomass (B) evaluated for cv. Williams using of CROPGRO-Soybean. According to results, model was successful in the traits simulation, because of high Wilmot coefficient produced (0.6), 20 days after planting to the end of the growth duration. Results: Model explained well stem dry weight, as correlation coefficient in each sowing date was significant (p<0.01). Simulation precision for biomass was suitable, as coefficient differentiation was significant (p<0.01) for first to fourth sowing date (S1-S4) 0.889, 0.986, 0.909 and 0. 796, respectively. These statistic parameters designated high ability of model for simulation of some traits measured in soybean for four sowing date management. Conclusion: We can use by model for sowing date management of soybean in Karaj climate condition, of course after repetitions of experiment and doing of model calibration. We proposed that soil and weather data measured in each place of experience and also plant morphology parameter measured precisely because this help to us for obtaining of objects