l. parviz

In this study, the effect of empirical mode decomposition (EMD) and maximal overlap discrete wavelet transform (MODWT) approaches in the processing of average temperature, wind speed and precipitation annual, monthly and single month time scales were evaluated. The selected study stations were Ramsar, Kermanshah, Hamadan, Semnan, Mashhad, Shiraz, Ardabil,Urmia, Zanjan, Yazd, Tabriz, Maragheh and Mianeh. In the time series decomposition using EMD, the trend of combined sub-series and in MODWT, the trend of sub-series was consistent with the Mann-Kendall test values of observed data time series, although, it can also be affected with the value and sign of of Mann-Kendall statistic. The significance level of The Mann-Whitney test in comparison of main annual observed time series with subseries of both approaches was more than 0.05, which indicates the acceptable performance of EMD and MODWT. In general, the comparisons of the sub-series of two approach with the main time series in each station, showed that the corresponding significance level for MODWT approach is higher than EMD. Also, the average of significance level for temperature series greater than those of precipitation and wind speed series. The LSD test at the 95% confidence level indicates good agreement of the observed data time series and combined sub-series.

Accurate precipitation forecasts are much attractive due to their complexity. This study aimed to use the hybrid Seasonal Autoregressive Integrated Moving Average (SARIMA) model and machine learning techniques such as Artificial Neural Networks (ANN) and Support Vector Machines (SVM) to improve precipitation forecasts. Time variation analysis and time series decomposition were the two concepts applied to construct the hybrid models. The performance of the two concepts was evaluated with monthly precipitation time series of two stations in northern Iran. Time variation analysis of time series was conducted with the clustering analysis, which increased the accuracy of forecasting with 20.99% decrease in the geometric mean error ratio for the two stations. SVM model decreased the forecasted error compared to ANN in the internal process of time variation analysis. Average of Mean Relative Error (MRE) were MRESVM= 0.72, MREANN= 0.89, and Mean Absolute Error (MAE) in the two stations were MAESVM= 18.02 and MAEANN= 23.88. Therefore, SVM outperformed the ANN model. Comparison of the two hybrid models indicated that more accurate results belonged to the concept of time series decomposition (the decrease in root mean square error from time variation to time series decomposition concepts was 13.35%). Extracting the pattern of data with SARIMA-based hybrid model with time series decomposition improved the precipitation forecasting. Configurations related to nonlinear components of time series with time steps of residual had good performance (the average of agreement index was 0.9). The results suggest that the hybrid model can be a valuable and effective tool for decision processes, and time series decomposition to linear and nonlinear components has a better performance.

  Oilseed canola plant (Brassica napus L.) is an important agricultural crop grown primarily for its edible oil. It is well known that abiotic stresses especially drought stress are very restrictive factors for agricultural production around the world. Drought stress affects a vast range of morphological, physiological and biochemical characteristics in plants. However, exogenous application of materials such as salicylic acid (SA) has been regarded as a good alternative to counter the adverse effects of various environmental stresses on plant functions. Salicylic acid is a naturally occurring plant hormone that controls plant growth and induces water deficit tolerance in plants.
Material and The experiment was carried out as a split split plot layout with three replications in East Azarbaijan Research Center for Agriculture and Natural Resources. Treatments included three level of drought stress (well-watered, drought stress at flowering and podding stages) (as main plot), two level of SA spraying (0 and 150 ppm) (as sub plot) and four cultivars of spring canola (Zafar, Zarfam, Dalgan and RGS003) (as sub-sub plot). The studied traits were included number of pods, number of seeds per pod, 1000 seed weight, grain yield, chlorophyll a, b and total, carotenoid, H2O2, MDA, proline, total sugar contents and seed oil percentage. The collected data were analyzed using SAS 9.1 software and means were compared with Duncan test at the 5% and 1% level of probability using MSTATC software. Results indicated that withholding irrigation from flowering stage had a more negative effect on yield components and physiological traits compared to withholding irrigation from podding stage. Withholding irrigation in both stages reduced the content of chlorophyll a, b and total and increased the content of H2O2 and MDA. Also, Zarfam and RGS003 cultivars had the highest content of chlorophyll and the lowest content of H2O2 and MDA. Salicylic acid (SA) spraying increased 31.79%, 41.1% and 7.33% of proline content, leaf soluble carbohydrate and seed oil, and decreased the content of MDA and H2O2 by 16.1% and 18.67% respectively. The results also showed that SA spraying led to 31.96% increase in grain yield under drought stress from podding stage. SA may compensate the negative impacts of drought stress on plant yield and other parameters. In general, in this experiment, the use of salicylic acid not only improved the morphological and physiological traits of the plant under drought stress, probably through the effect on the antioxidant system but also increased the yield and quality of the cultivars