جستجوی مقالات مرتبط با کلیدواژه « Winter » در نشریات گروه « آب و خاک »

In this research, the simulation and phase transfer of heavy metal solution in the river has been done by developing a one-dimensional qualitative model. Environmental parameters (T), temperature, dissolved oxygen, (DO) (pH) and salinity (EC) are considered as influencing parameters on the change in the concentration of the solution phase of heavy metals lead, cadmium and zinc in springs and wells of the displacement-diffusion equation. Became to determine the above hydrodynamic and environmental parameters for use in the developed model, Hec-Ras hydrodynamic model and Qual2kw qualitative model have been used. In decoupling the spring and well terms of the displacement-diffusion equation of the qualitative model developed using the measured data from the Sarouk River, various relationships were considered to relate the kinetic coefficient of the first-order reaction of the dissolved phase of heavy metals to the above environmental parameters. And to find the constant coefficients of these relations, the method of minimization of simulation errors and Vba coding was used, and the linear relation including all the above parameters is considered as the final relation. The kinetic coefficient of the reaction is different in different parts of the river, and to simulate and determine the concentration of the solution phase of heavy metals at each point, the kinetic coefficient of the reaction corresponding to the same point should be used. Also, there is a significant increase in the accuracy of the developed model if the above environmental parameters are used in the spring and well term of displacement-diffusion equations and the effect of other qualitative parameters is insignificant, which indicates its proper efficiency in simulating the dissolved phase of heavy metals in the river.

Irrigation scheduling under water and salinity stress, is much more difficult than full irrigation scheduling. To investigate the effect of plant water stress, there are many indicators. One of them is crop water stress index (Idso method). To evaluate the ability of this index for irrigation scheduling of summer Maize (SC704 variety) and winter Maize (Mobin variety)- in conditions of using saline water, a research was designed at the research station of Shahid Chamran University in 2013-2014 . This research consisted of five quality treatments of irrigation water, including S0: Water Caron, S1: EC= 3.5dS / m, S2: EC=4.5dS / m, S3: EC=5.5dS / m and S4: EC=6.5 dS / m. In the both of the growing seasons, result showed, The upper and lower base lines were affected by salinity stress. The amount of upper and lower base lines for summer Maize were higher than the winter Maize. The lower and upper baseline's equation for summer Maize were (Tc-Ta)l.l=1.641-0.178VPD and h=2.52 0c and for winter Maize were (Tc-Ta)l.l=2.161-0.221VPD and h=3.69 0c. CWSI, which is the base of irrigation scheduling, was calculated 0.23 for summer Maize and 0.17 for winter Maize. By increasing water salinity from 2.2 to 6.5 ds/m, the temperature difference between canopy cover and air was 30C for summer maize and 20C for winter Maize. At this research CWSI was calculated on the days, before irrigation for all treatments and result showed that in the both of seasons, CWSI was increased about three times when water salinity increased from 2.2 to 6.5 dS/m. The effect of salinity stress was calculated separately according to the days, after irrigation (at These days, the effect of salinity is sensible). results showed that CWSI increased about 3.5 times for summer Maize and around 3 times for winter Maize when water salinity increased from 2.2 to 6.5 dS/m. Comparing the results of the two seasons showed that CWSI is affected by irrigation water salinity and it's trend was nearly the same in both of the growing seasons.

In order to evaluate effect of planting date and plant density on grain yield, yield components and some quality and morphological traits of chickpea (Cicer arietinum L.), an experiment was conducted at the research center of agricultural and natural resources of west Azerbaijan in Urmia rain-fed research farm during 2005-2006 growing season in a split plot arrangement, based on a completely randomized block design with four replications. The cultivar of chickpea (Qazvin local mass) was planted in three sowing dates (mid November, mid March and mid April) in main plots, and four plant densities (intra row space: 7.5, 10, 15 and 20 cm) representing (45, 34, 23 and 17 plant/m2) in sub plots. Yield, number of pod per plant, seed protein content, plant height, number of secondary branches and 100 seed weight were evaluated. Results indicated that date of sowing had a significant effect on the yield, number of pod per plant and seed protein content but was not significant for plant height, secondary branches and 100 seed weight. Highest number of pod per plant was obtained in mid November sowing (37.69) and high protein content (22.63) in mid March. Different plant density effects were also not significant on yield, plant height, secondary branches and 100 seed weight but the effect of density on the number of pod per plant and seed protein content was significant. Highest number of pod per plant was obtained in the third density (31.5), and highest protein content in the first density (22.31). The results of study indicated that between mid March planting in the density (45 plant/m2) had highest yield (1042.08kg/ha) and protein content compared with the other planting dates.

In Iran, about 75% of national rice production is supplied in Gilan and Mazandaran proviences which have the highest amount of precipitation. Seasonal prediction of rainfall induces significant improvement on yield production and on preventing climate hazardz over these feritle areas. Canonical correlation analysis (CCA) model was carried out evaluates the possibility of the prediction of winter rainfall according to the states of ENSO events. The time series of (southern oscilation index (SOI) and SST (sea surface temperature) over Nino's area (Nino's SST) are used as the predictors, and precipitation in Bandar Anzali and Noushahr are used as the predictands. Emperical orthogonal functions (EOF) were applied for reducing the number of original predictors variables to fewer presumably essential orthogonal variables. Four modes of variations (EOF1, EOF2, EOF3, EOF4) which account for about 92% of total variance in predictors field were retained and the others were considered as noise. Based on the retained EOFs and precipitation time series, the canonical correlation analysis (CCA) was carried out to predict winter precipitation in Noushahr and Bandar Anzali. The results indicated that the predictors considered account for about 45% of total variance in the rainfall time series. The correlation coefficents between the simulated and observed time series were significant at 5% significant level. For 70% of events the anomalies of observed and simulated values have the same sign indicating the ability of the model for reasonable prediction of above or below normal values of precipitation. For rainfall prediction, the role of Nino's SST (Nino4 in particular) was found to be around 10% more influential than SOI. .