جستجوی مقالات مرتبط با کلیدواژه « رگرسیون خطی چندگانه » در نشریات گروه « کشاورزی »

In this research, the water quality of Sefidroud River during the years 2013-2018 using Landsat 8 satellite images as well as 10 qualitative chemical and physical parameters including Ca2+, Na+,Mg2+, (Cl-,SO4-2, HCO3-, TDS, EC, TH and pH were studied in three hydrometric stations. Drinking water quality index (WQI) was calculated and its relationship with satellite bands and band ratios (28 parameters) was analyzed using univariate and multivariate regression models. The results of the univariate regression model showed that the WQI index with band 5 and the ratio of band B4/B3 had a linear and power correlation at a significance level of 1% with coefficient of determination (R2) of 0.55 and 0.51, respectively. The implementation of the stepwise linear multivariate regression model of WQI with all the studied bands and ratios showed that the three band 5 variables and band ratios B4/B3 and B6/B5 were correlated with WQI, with an R2 of about 0.80 at a 5% significance level. After preparing the spatial changes map of WQI using multivariate linear regression model, the results indicated that the water quality in the head branches of Sefidroud, that is, the Qezaluzen and Shahroud rivers, was lower compared to the lower areas and Manjil Dam Lake, although the water quality of the Qezeluzen and Shahroud rivers was lower. Shahrood was weak, but after entering Manjil Dam, it became a good water class. However, the WQI of the water coming out of the dam had gradually increased by passing through the agricultural lands, residential and industrial areas along the river until it reached the Caspian Sea, and it had weakened water class. In general, the results of the research showed that the use of Landsat 8 satellite images and multivariable regression model has a high power for water quality monitoring.

Artificial intelligence models as powerful methods in modeling nonlinear complex problems, have a significant ability and this has been proven in numerous articles. Artificial intelligence has been used in various issues, including engineering, medicine, etc. The success of this method in comparison with analytical and numerical methods, their easiness, speed and accuracy caused to open their place among researchers as much as possible. Today, Considering that one of the challenges of human life is the issues related to water resources management, so in this study, an attempt has been made to investigate the performance of artificial intelligence and regression models in the cases of water resources. Various researches have been done in the case of modeling and parametric analysis of water resources. However, in this study, artificial intelligence (Learning Machine) models were used to simulate the qualitative and quantitative parameters of water. The models used in this study are: Self-Adapting Extreme Learning Machine (SAELM), Least Square Support Vector Machine (LSSVR), Adaptive Neuro Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR) model which was used to predict changes in hydrogeological parameters. Today, due to the growing global population, one of the most important challenges is access to safe drinking water. In our country, Iran, due to its location in the semi-arid region and low rainfall, this danger is felt more than ever. One of the serious issues is the salinity leakage into groundwater resources. In this study, an attempt has been made to simulate the leakage of salinity dynamic flow into the freshwater resources of the coastal aquifer, using artificial intelligence and statistical models. At the end, the simulation results and the accuracy of the models are given. The study area in this work, is Mighan Wetland and Mighan aquifer in Markazi province. Annual rainfall occurs in small amounts in this area. According to the statistical results provided by synoptic and rain gauge stations in the region, the maximum and minimum rainfall values range from 461 mm in the northeast to 208 mm in the center of Arak plain. The hydraulic outlet of the aquifer to the Mighan plain is located in the center of the plain. The water entering the Mighan plain and leaves the system due to evaporation from the water table. Observatory wells were used to sampling this lake due to its saline water. The wells were located in an area called Vismeh near the lake.

In this study, qualitative and quantitative parameters: water salinity, total dissolved solids (TDS), chlorine ion (cl), sampling time (t), electrical conductivity (EC), Salinity and groundwater level (GWL) were simulated. In this work, Adaptive Neuro Fuzzy Inference System (ANFIS), Least square support vector machine (LSSVM), Self Adaptive Extreme learning machine (SAELM) and Multiple linear regression (MLR) models were used for simulation. In this study, data from 173 months of sampling were used. 70% of the sample size was used for training and 30% for testing models.

Simulation was performed using artificial intelligence models and regression model. The simulation results showed higher accuracy of artificial intelligence models. After simulation and obtaining the results, then the uncertainty analysis was performed by Wilson Score method without continuity correction. In this method, the prediction error (ei), the mean prediction error (Mean) and the standard deviation of the error is (Se). If the mean error value of a model in predicting the target variable is positive, it means that the performance of the model is Over Estimated. Also, if the average value of the model error is negative, the performance of the model is Under Estimated. Moreover, the results of Uncertainty Analysis with a significance of 5% were obtained. and finally we briefly write the subsequent performance Over Estimated (OS) and Under Estimated (US).

The results showed that different models were successful in predicting water parameters. In order to comprehensively evaluate the accuracy of the models in the simulation, the performance of the models was measured by five approaches. The proposed approaches were: 1) Evaluation of prediction by accuracy chart, 2) Performance evaluation by mathematical indices, 3) Performance evaluation, by Uncertainty Analysis by Wilson Score method without continuity correction, 4) Accuracy evaluation by error distribution charts and 5) Performance evaluation by discrepancy rate (DR) charts. Finally, all the results are given at the end of each section, respectively.Approach 1- According to the prediction accuracy charts, 16 charts were drawn and the most accurate models of which are depicted in Figures 4 to 7. After modeling, the results showed that the most accurate models in simulating groundwater parameters were SAELM model in GWL simulation. According to the results, SAELM model in GWL and EC simulation, LSSVM in TDS simulation and MLR in Salinity simulation were the superior midel, Respectively.Approach 2- According to the performance measurement indices, finally the results showed that SAELM model was the best model in simulating parameters (EC) and (GWL). The LSSVM model was also the most accurate model in modeling (TDS). MLR model was the best model in (Salinity) parameter simulation.Approach 3- Uncertainty analysis was performed based on Wilson score method. The performance of the models in the simulation showed that the performance of the SAELM model was determined as Under estimated and other superior models in simulation had Over estimated performance.Approach 4- Based on the error distribution diagrams, the best accuracy was assigned to SAELM and MLR models.Approach 5- Based on the discrepancy ratio, SAELM and MLR models were estimated to be the most accurate models in the simulation.

Water infiltration to soil play an important role in irrigation management, storage moisture in soil especially in dry and semi-dry area and increasing agronomy yield. Understanding water infiltration to soil is of important in designing and applying water conservation methods, flood and runoff control and soil erosion management. Additionally, accurately measuring water infiltration to soil in different times is more important for predicting water storage in root zone, irrigation designing and planning and agronomy management. In other hand, water infiltration to soil is a base for precision agriculture, therefore, producing accurate maps for water infiltration to soil play an important role in land management and applying precision agriculture. Modeling soil water infiltration at the field scale with ruler of calcareous, saline and sodic conditions is important for a better understanding of infiltration processes in these soils and future of infiltration modeling. The present study aimed for estimating water infiltration to soil at different times using soil spatial prediction functions and spatial estimators.

in present study, 72 soil samples were collected using a random sampling method in Marvdasht plain, Fars Province. In selected points, soil bulk density, sand silt, clay, pH, electrical conductivity, calcium carbonate, solution sodium, solution calcium and magnesium and organic carbon contents. For measuring water infiltration to soil, the double ring method were used. Multiple linear regression (MLR), artificial neural network (ANN) and spatial estimators were used for deriving soil spatial prediction functions models between water infiltration to soil in different times including 5, 10, 20, 45, 90, 150, 210 and 270 min. In this study, the readily available soil properties and auxiliary variables such as remote sensing and topography data were used in soil spatial prediction functions.

The results of evaluating regression and artificial neural networks models based on the mean error (ME), coefficient of determination (R2) and root mean square error (RMSE) criteria in testing phase were showed that the developed artificial neural network models in present study performed better than multiple linear regression models in water infiltration to soil prediction at different times. Moreover, the results showed that the combined estimators (artificial neural network - Kriging) performed better than ordinary kriging model for estimating water infiltration to soil.

In totally, the results of this study showed that the applying soil spatial prediction functions (using auxiliary variables such as remote sensing data and topography data with the readily available soil properties) had a great potential to predict spatial estimation of water infiltration to soil at most considered times.

In order to manage the forests and optimal and sustainable utilization of the forest, it seems necessary to know the information on the volume of the residual stand. In this study, a systematic randomized inventory was carried out in 186 circular 10-acre plots in the educational and research forest of Darabkola, Sari, Golestan, Iran and the volume of each plot was obtained. In the next step, the physiographic layers of the area were prepared using the topographic map and the vegetation characteristics were prepared using the LISS-III image of the IRS-P6 satellite with a resolution of 23.5. After preparing physiographic layers and vegetation characteristics, their value was calculated for all plots. Then, regarding these variables and using two methods of multilayer perceptron neural network and multiple regression model, modeling was done. The results showed that the multiple linear model could model the volume changes in the region with higher accuracy (R2=0.75 and RMSE=0.3). The results of this research can be used in management planning and as one of the effective factors in the design of logging routes and forest roads so that areas with larger volumes are covered more.

Evapotranspiration plays an imperative role in management of regional water resources, climate change and agricultural production. In this study efficiency of some data-driven techniques, including support vector machine (SVM), artificial neural networks (ANN) and its hybrid with wavelet transform (WANN), multi linear regression (MLR) and decision tree (DT) for predicting Evapotranspiration rates at Scottsbluff Station in Nebraska have been monitored. For this purpose, 5 meteorological parameters utilized as inputs for the models. Daily meteorological information, data used in this study, were between 2005 - 2013 years to train and test the models. In order to implement each of the models 8 scenarios were considered according to combination of input parameters. For evaluate performance of the studied techniques, three different statistical indices were used which included root mean square error (RMSE), correlation coefficient (R) and Nash-Sutcliffe coefficient (NSE). In addition, Taylor charts were used to test similarity between observation and prediction data. The results showed that at the Scottsbluff station, WANN8 (is the eighth scenario for the WANN model) according to the root mean square error (RMSE), correlation coefficient (R) and Nash-Sutcliffe equal to 0.097, 0.999 and 0.999 performed better than ANN, SVM, MLR and DT. The SVM and ANN models also showed excellent accuracy, and the DT and MLR models performed worse than the other models despite their acceptable accuracy. As a conclusion, the results of the present study were proved that WANN provides reasonable procedures for modeling Scottsbluff at the Scottsbluff station.

Cation exchange capacity (CEC) is one of the most important soil chemical properties that plays an important role in soil fertility. However, standard laboratory methods for measuring CEC are difficult, costly, and time-consuming. The aim of this study was to use new methods such as 1) Pedotransfer Functions (PTF) based on the basic soil properties using Multiple Linear Regression (MLR), 2) soil spectroscopy (Vis–NIR, 400 – 2500 nm) using Partial Least Squares Regression (PLSR), and Support Vector Regression (SVR), for estimating soil CEC. Also, from the regression coefficient analysis, key wavelengths were introduced. For this purpose, CEC was measured using the sodium acetate method for 72 soil samples collected, and spectral reflection of soil samples was determined using spectroscopy. These methods are made from a calibration set (70% of data) and evaluated with a validation set (30% of data). The results showed that Vis - NIR method performed better than PTF. In this study, wavelength ranges around 566, 854, 1354, 1418, 1906, 2071, 2203, 2319, and 2341 nm were investigated as the key wavelengths for estimation of CEC. Furthermore, the results of prediction models showed that SVR has a better performance than PLSR. This study proved that Vis-NIR is a promising method for soil CEC estimation.

Quantitative and qualitative analysis of water resources has become one of the most widely used topics in water resources research today. In this research, data mining, artificial intelligence, mathematical techniques have been used to simulate water behavior and estimate its parameters changes. The models used to estimate hydrogeological parameters are Self-adaptive Extreme learning machine (SAELM), Least square support vector machine (LSSVM), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple linear regression (MLR) models. Also, to evaluate the performance of these models, the accuracy of the models was assessed in the form of 5 approaches. The results showed that the SAELM model was the best model based on the simulation and correlation diagrams. Based on accuracy evaluation indices, the SAELM model with RMSE, MAPE and, R indices equal to 0.1545, 0.0070, and 0.9979, respectively, had the highest accuracy in hydrogeological parameters prediction. Based on Uncertainty Analysis by the Wilson Score method, the performance of the top model (SAELM) was estimated to be underestimated. Also, based on the error ratio diagrams, the most accurate results were related to the SAELM model. Finally, the SAELM model was assigned the lowest error rate using the error distribution diagrams.

The humus index is a numerical score based on the visual assessment of organic horizons and humus forms identification. This index reflects different characteristics of forests stand and soil. Forest humus is an intermediate between plants and soil, which is affected by various factors, such as topography, climate, vegetation and soil. Nevertheless, by digging the humus profile and determining its index, it is also possible to determine many ecological factors. The aim of this study was to investigate the humus index and its relationship with topographic, soil and forest cover characteristics along an altitudinal gradient in the four-altitude class, including 0, 500, 1000 and 1500 meters above sea level (m a.s.l.), in Vaz forests located in Mazandaran province.

In each altitude class, the characteristics of the trees such as crown canopy, density, diameter and height, as well as topography and soil characteristics such as altitude, slope percentage, soil moisture and temperature were taken in 400 m2 plots, and in each plot, five 30 × 30 cm profiles for identification of humus forms were drilled and then identified and classified based on Humusica classification. In order to evaluate the humus index between different altitude classes was used one-way analysis of variance (ANOVA), as well as to analyze its relationship with the mentioned characteristics, multiple linear regression analysis (MLR) was used.

The results showed that the highest amount of humus index and litter thickness belonged to the altitude class 1500 m a.s.l. and their trend is increasing along the altitudinal gradient. Also, the results of MLR indicate the existence of a linear and significant relationship is between humus index and topographic and soil characteristics with high determination coefficient (R2 = 0.76), especially soil temperature with (P-value = 0.000), while the tree cover characteristics with a low determination coefficient (R2 = 0.31), cannot well be a good predictor for evaluating the humus index. Meanwhile, soil temperature and trees crown canopy are two factors that have shown the highest t-statistics in relation to the humus index, so that the decrease in soil temperature and trees crown canopy has increased the humus index.

In general, it can be concluded that with increasing altitude, the organic part of the forest floor and Humus index increase, and their variability is mostly due to changes in topographic and soil temperature characteristics.

Soil water Infiltration plays an important role in the water cycle of nature. However, since the direct measurement of soil water infiltration is laborious, time-consuming and expensive. Therefore, in this study was conducted to investigate the possibility of estimating the coefficients of water infiltration equations such as Kastiakov, Kastiakov-Lewis, Horton and USA Soil Conservation Service (SCS) using readily available soil properties and to evaluate the validity of these functions outside of their derivation regions. Therefore, soil physical properties and cumulative infiltration were measured in two different regions (T1 and T2). Parametric functions were derived using T1 location measurements and validation was performed using independent T2 measurements. Cumulative infiltration was measured using double rings with three replications at 78 points. Parametric functions were created using multiple linear regression. The highest accuracy of parametric functions in the derivation stage was related to SCS equations with the coefficient of explanation (R2) equal to 0.66 and the lowest accuracy with the value of R2 equal to 0.04 was related to the power of Horton equation. In the validation stage, the accuracy of the functions showed a large decrease. R2 changes range from 0.00 to 0.26, normalized root mean square error (nRMSE) range from 1.76 to 80.93 and geometric mean error ratio (GMER) range from 0.94 to 1.73 Was obtained. Therefore, in this study, the use of parametric functions to estimate the coefficients of infiltration equations outside of their derivation regions was not efficient.

Soil organic carbon is one of the most important indicators of soil quality. The purpose of this study is to study the spectral and non-spectral behaviors of soil in order to estimate the organic carbon of topsoil using factor analysis and multiple regression methods in the semi-steppe rangelands of Asuran, Semnan province. Soil sampling was performed using stratified random sampling method. After creating a map of homogeneous units in the area, in each homogeneous unit according to its area, several sampling points were selected completely randomly. A total of 145 sampling points were collected. At each sampling point, a composite soil sample (a mixture of 9 observations) was taken. Soil organic carbon was measured using Valkyli-Block titration method. Data of 114 samples were used to calibrate the model and data of 31 samples were used to validate it. The results showed that the correlation of spectral variables obtained from Landsat OLI sensor with surface soil organic carbon is higher than non-spectral variables obtained from 1: 25000 topographic maps. Also, the results of factor analysis by principal component analysis with eigenvalues greater than one showed that the total cumulative variance explained by 14 variables was equal to 90.2%, which was explained by three factors. The regression equation generated by the three extracted factors had suitable potential for predicting surface soil organic carbon (R2 = 0.59). The root mean square error (RMSE) of the proposed model was calculated to be 0.3.

Soil temperature (Ts) is a key factor controlling the soil physical, chemical and biological properties and processes and consequently affects agricultural crop productions. The objective of this study was to estimate Ts from meteorological data using different machine learning methods. For this purpose, meteorological data and soil temperature at different depths (5, 10, 20, 30, 50 and 100 cm from the soil surface) for 25 years (1994-2018) were collected from 17 synoptic stations in Khuzestan province, Iran. Air temperature, wind speed, relative humidity, evaporation, precipitation, and vapor pressure were used as inputs to train the models. Multiple Linear Regression (MLR), Multilayer Perceptron Neural Network (MLPNN) and Extreme Learning Machine (ELM) models were used to predict soil temperature from metrological data. The results indicated that all models predicted temperature of the top layer (0-30 cm) better than the ones in sublayers. on the other hand, by increasing soil depth the accuracy of the models diminished; so that, the best and worst Ts predictions were belong to 5 cm and 100 cm depth, respectively. The results revealed that MLR, MLPNN and ELM models provided desirable performance in modeling Ts at all depths, with R2 values ranging 0.700-0.864, 0.967-0.997, and 0.967-99, RMSE values ranging 2.557–2.873, 0.034–0.072, and 0.028–0.078 °C, and MAE values ranging 1.398–1.529, 0.023–0.063, and 0.023–0.065 °C, respectively. Overall, the results showed that MLPNN and ELM models had approximately similar performance and better accuracy than MLR model. However, because of the high computational speed of the ELM model, it is recommended to use MLPNN model for estimation of soil profile Ts.

   Organic carbon is one of the most important soil quality indices, affecting almost all physical, chemical and biological properties of the soil. The purpose of this study was to investigate soil spectral and morphometric factors to estimate the organic carbon of topsoil, using factor analysis and multiple regression methods in semi-steppe rangelands of Lazour. Soil samples were taken with a stratified random method. For this purpose, 157 training sites were selected in homogeneous units. Of these, 127 sites were used to calibrate the model and 30 sites were used to validate the model. In each of the training site in a random manner, a soil sample including nine observations was taken from a depth of 0 to 20 cm of soil surface. Soil Organic Carbon (SOC) was measured using Walkley-Black titration method. The results showed that the variables of Albedo, Clay Index (CI), NDVI, Relative Relief and Tasseled-Cap's Brightness and Greenness indices had a significant correlation with the SOC (p<0.05). Also, the result of factor analysis by Principal Component Analysis (PCA) method with eigen-values greater than one indicated that the total cumulative variance, explained by the six variables, was equal to 81.1%.This variance was explained by two components. Using multiple regression model, an appropriate regression equation was calculated to predict SOC (R2=0.789). The Root Mean Square Error and the Mean Absolute Relative Error of the proposed model were calculated as 0.24 and 0.10, respectively. Due to the direct relationship between the SOC and the factors such as soil fertility and sustainability against erosion, a spatial distribution model of SOC could be an important sub-model to design other complex models such as the terrestrial ecosystems biomass and soil erosion models.

This study aimed to predict population fluctuation of sunn pest in the field using artificial neural network and multiple linear regression was performed. The data on population fluctuation of Sunn pest in years 2015 and 2016 on a farm with an area of one hectare in the city Chadegan was obtained. In this model of the variables sampling date, the average temperature, average relative humidity, wind speed, wind direction, rainfall as the input variables and population changes mother Sunn pest was used as the outcome variable. The network was used of type Multilayer Perceptron with back propagation algorithm and was learning method Levenberg Markvart. Results showed between these two models, artificial neural network with coefficient of determination 0.96 better than regression with coefficient of determination 0.40 population density of mother Sunn pest was predicted. After sensitivity analysis model for easier and factors more effective extraction, four factors: the number of days of the year, temperature, humidity and wind speed were selected. Neural network model was trained again using the four factor model and a model with 11 hidden layer gave the best result. The coefficient of determination testing stepe was 0.97 that was showed high accuracy relative to the multiple linear regression model with the coefficient of determination 0.43.

Statistical analysis and forecast discharge data play an important role in management and development of water systems. The most fundamental issues of statistical analysis and forecast discharge in Iran are lack of data in long term period and lack of stream flow data in gauging stations. Considering the issues mentioned in this study, we tried to estimate the daily data flow (runoff) of Santeh gauging station in Kordestan province using the nearby hydrometric and meteorological stations data. This estimation occurred based on the sixteen different input combinations, including data of daily flow of hydrometric stations Safakhaneh and Polanian and daily runoff in Santeh precipitation gauging station. In this research, the daily flow estimation of the Santeh station in each of the months of the year was evaluated for sixteen different combinations and artificial neural network models and multiple linear regressions. The performance of each model was evaluated with the indicators RMSE, CC, NS and t-student statistic. The results showed good performance of both models but the performance of the artificial neural network model was better than the regression model in estimation of the daily runoff in the most months of the year. Mean error of artificial neural network and multiple linear regression models was respectively estimated as 6.31 and 8.07 m3/s in the months of the year. It should be noted that the artificial neural network, for each sixteen combination used, had better result than the regression model.

Large-scale climatic signals including ocean-atmosphere interactions, are the main factors influencing the earth’s climatic oscillations and are the most important indices in predicting of climate variables. In this research, precipitation in the next month was predicted by applying artificial neural network (ANN), neuro-fuzzy network (NFN), and multiple linear regression (MLR) in Semnan synoptic station. For this purpose, monthly series of precipitation of Semnan synoptic station and signals of large-scale climate signals were used during a period of 45 years (1966–2010). From 540 monthly time series, the first 80% was used for training and the other 20% for testing. Performance of the models was compared by using correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE) criteria. Results of the validation step showed that the obtained correlation coefficients (0.829, 0.793 and 0.767) are related to ANN, ANFIS and MLR models. Based on the results of this study, the next month’s precipitation of Semnan synoptic station could be predicted by ANN, NFN and MLR models, respectively.

Pesticides are considered as one of the most significant environmental pollutants. The transfer of pesticides into the living organism and the maximum residue limit of pesticides is one of the main environmental concerns. Pesticides have got the attention due to their various applications in biochemistry, environment and agriculture. Consequently, the physical and chemical characteristics of pesticides particularly the pesticides toxicity needs to be taken into consideration. The aim of this research is investigation the relationship between Log P of some pesticides with topological descriptors by graph theory and multiple linear regression methods. Quantitative structure-property relationship study was used and results was showed that Platt, Harary, Randic, Szeged indices are suitable for prediction of Log P the pesticides than the other topological indices. The best model in this study indicated that those structural descriptors, play an important role in effect on Log P of pesticides. For the first time, the relationship between Log P of pesticides and some topological indices using SPSS and multiple linear regression model is investigated.

Knowledge of seasonal fluctuations is of great importance for sale management. The aim of this research is to assess the impact of sale season on the log and lumber prices. Hence، after evaluation of sale documents in Azarood forestry plan، the data of 410 timber sale lots at roadside of four seasons from 1992 to 2008 were extracted and incorporated into a dataset. Calculating the average selling price for each species group and products per season and year، four time series of seasonal price for two species group and products (log and lumber) were obtained. Then، all prices were deflated to the base year of 2007. The Augmented Dicky-Fuller test were then employed to evaluate stationarity of time series. Impact of season sale variables (independent dummy variable) on timber price was analyzed using multiple regression analysis، MRA، and stepwise method of SPSS 14. 0 software. The impact of sale season on log and lumber prices was appeared to be significant for first species group while insignificant impact was found for second species group (at 5% level). The results revealed that selling lumbers and logs of first species group in summer and autumn caused a price raise of 13 and 11 percent (i. e.، 120409 and 150798 Rial/m3 at constant prices of 2007) compared to other seasons، respectively. Wood trade and building boom in summer and autumn seems to contribute to timber prices raise in these seasons. Implications of such analysis seem to be of interest both for forest managers and wood industries.

In the present study, the effect of soil factors on above-ground vegetation and soil seed bank diversity under grazing and exclosure conditions was studied. Soil sampling was done in winter 2010 using randomized systematic method along four transects in the grazing and exclosure areas of Vaz watershed. Along each transect, 10 plots of 1m2 were established and soil samples were collected from a depth of 0-10 cm for seed bank and physico-chemical studies. Vegetation cover was measured in each plot in late spring of 2011. Soil factors such as porosity, bulk density, relative humidity, EC, pH, nitrogen, organic matter, and stone percentage were measured. Shannon and Simpson diversity indices were calculated for above-ground cover and soil seed bank and the most important factors influencing on the diversity were specified using multiple linear regression by backward elimination method. Results showed that the porosity, bulk density and organic matter had the greatest effect on the diversity of seed bank in the exclosure area. On the other hand, EC, nitrogen, organic matter, soil bulk density and porosity were the most important factors affecting the seed bank diversity of grazing area. According to the obtained results, organic matter and nitrogen were the main factors affecting the plant species diversity in the exclosure area, while in the grazing area, physical parameters such as bulk density, porosity and stone percentage had the highest impact on species diversity.

Climate variability is the most effective parameter on crop productivity especially rainfed crops.in this study, the anomaly index was calculated for meteorological elements such as maximum temperature(T max), minimum temperature(T min), precipitation(P) and crop yield(Y) for Razavi Khorasan province in Iran, which is located in arid and semi arid region of the world. Precipitation Anomaly Percentage (PAP) is a traditional drought monitoring index, it shows the precipitation as a percentage of the long-term average or normal Thepurpose of this study is finding significant relation between various indexes such as yearly precipitation anomaly (PA) with yearly maximum temperature anomaly(T max A) and yearly minimum temperature anomaly(T min A) for the period (1989-2005).At first, moving average of 5, years old,were examined for 10 stations. In addition, yearly precipitation anomaly, probability and return period of it were calculated using sigmoidfunction. Pearson correlation was examined to find relationship between change percent of yearly precipitation anomaly(PAP), yearly minimum(TminAP), maximum temperature anomaly(TmaxAP) and yearly rainfed crop yield anomaly of wheat (YAP). Multiple linear regression was calculate between YAP (as dependent parameter) and climate variability(indpendent parameter). Sarakhs, Ghuchan and Mashhad had significant correlation (P <0.05) 0.63,0.55 and 0.54 respectively. In other station, no relation found between PAP and YAP. A significant relation between TmaxAP and YAP was obtained in Mashhad (P < 0.05, r= -0.48) and TminAP and YAP was found only in Ghuchan(r=0.56).