seyed amirhossein mousavi

This study aimed to determine the structural model of cognitive depression based on early maladaptive schemas with the mediation of cognitive processing of personality traits in students. The research method was correlational and based on structural equation modeling. A sample of 300 students from Tehran universities was selected using a multistage cluster sampling method. Participants completed the Beck Depression Inventory (BDI, 1961), Young’s Early Maladaptive Schemas Questionnaire (1998), Cognitive Processing Questionnaire, and the NEO Five-Factor Inventory (NEO-FFI). Data were analyzed using descriptive statistics and structural equation modeling in SPSS version 25 and AMOS version 22. The results showed that the proposed model did not exhibit an adequate fit. Direct effect analysis indicated that early maladaptive schemas had a significant positive impact on cognitive depression and personality traits. However, the examination of indirect effects revealed that early maladaptive schemas did not mediate cognitive depression through the cognitive processing of personality traits. Overall, the findings suggested that early maladaptive schema components can directly influence students' cognitive depression, but this effect is not significant through the mediation of cognitive processing of personality traits.

It is so vital to note the cultivation of rice because of its high irrigation water consumption. This crop is the most important food source in the Iran, hence, it is necessary to know how much irrigation water must be used to grow different rice cultivars under various cultivation types. On the other hand, applying farm tests to achieve the goal need high costs and waste much time. Regarding that, it is necessary to use crop modeling. In this study, AquaCrop was used to simulate yield, biomass and water use efficiency of rice. The research was conducted at Khuzistan Agricultrual Research Station. In this study, three types of cultivation (D1: transplanting, D2: current directs seeding consorted seeding, and D3: dry bed seeding) and rice cultivars (V1: Red-Anbori, V2: Champa, V3: Danial) were considered to simulate abovementioned parameters. MBE, RMSE and NRMSE values for seed yield were 0.25 ton.ha-1, 1.0 ton.ha-1 and 0.1, respectively. Those values for biomass were 0.3 ton.ha-1, 1.15 ton.ha-1 and 0.05, respectively, and for water use efficiency were 0.07 kg.m-3, 0.24 kg.m-3 and 0.03, respectively. EF values for seed yield, biomass and water use efficiency were 0.87, 0.56 and 0.65, respectively, and d values for all parameters were equal to 0.99. Regarding the results, AquaCrop had good accuracy for simulation of rice yield, biomass and water use efficiency.

Simulation of rice yield and its water productivity studied using AquaCrop, WOFOST and CropSyst models, in an experiment at Khuzestan Agricultural Research Station. In this study, three types of planting methods (D1: transplanting, D2: direct seeding, and D3: dry bed seeding) and three rice cultivars (V1: Red-Anbori, V2: Champa, V3: Danial) were considered. Results of MBE (0.36 t.ha-1), RMSE (0.1.07 t.ha-1) and NRMSE (0.14 t.ha-1). MBE, RMSE and NRMSE values for water productivity calculated by using AquaCrop model were -0.11 kg.m-3, 0.40 kg.m-3 and 0.15, respectively. The values for yield simulation using WOFSOT model were 0.06 ton.ha-1, 1.14 t.ha-1 and -0.01, respectively, and aforementioned values for water productivity simulated by WOFOST were 0.15 kg.m-3, 0.40 kg.m-3 and -0.13, respectively. The mentioned values for CropSyst simulated as 0.11 t.ha-1, 0.80 t.ha-1 and -0.24 for yield and 0.15 kg.m-3, 0.40 kg.m-3 and -0.14 for water productivity, respectively. According to the results, accuracy for all models were accepted to simulate rice yield and water productivity. However, WOFOST accuracy was better than the other models in most treatments. Thus, it is recommended to use WOFOST for simulation of rice yield and water productivity at different rice cultivars.