سلمان میرزایی

The aim of this study was to evaluate the nutritional status of dryland wheat using diagnosis, and recommendation integrated system (DRIS) method. To determine the DRIS indices, and norms in dryland wheat, 54 samples were collected from shoots, and concentrations of nutrients nitrogen (N), phosphorus (P), potassium (K), iron (Fe), manganese (Mn), copper (Cu), and zinc (Zn) were determined. To determine the norms, the farms were divided into two groups of high and low-yielding farms based on yield. Optimum concentrations of nutrients for macronutrients N, P, and K were 0.87- 1.35, 0.0360 - 0.104, 2.18 - 1.02 (%), and for micronutrients including Fe, Mn, Cu, and Zn were 170-430, 29-51, 3.6 – 8.4, 13 – 27 (mg kg-1), respectively, as well as optimum nutrient concentrations for N, P, and K were 1.11, 0.07 and 1.6 (%), and for Fe, Mn, Cu, and Zn 300, 40, 6, and 20 (mg kg-1) respectively, were determined. In most low-yielding farms, the nutritional balance index (NBI) was much higher than zero, indicating more nutrient imbalance in these farms. The overall priority for macro and micronutrients is P > K > Fe > N > Mn > Zn > Cu, which should be paid attention to P, K, Fe, and N in the nutritional program.

Citrus is an important fruit crop in Iran. One of the main reasons for decreasing yield of these plants, in addition to moisture stress, is their unbalanced nutrition, so nutrient balance is an important factor in increasing quantity and quality of crop production. In proper plant nutrition, each nutrient must not only be sufficiently available to the plant but also create a state of equilibrium, and observance of the ratio between the nutrients is of particular importance. Diagnosis and recommendation integrated system (DRIS), and deviation from optimum percentage (DOP) can be used as efficient methods to interpret the results of plant analysis and the nutritional diagnosis in crops and fruit trees. In the north of Khuzestan, due to the dense cultivation of trees and soil depletion of the plant's nutrients, it requires widespread use of fertilizers containing macro and micronutrients. The objective of this study was to determine the optimum level of the nutrients and evaluate the nutritional status of Lisbon lemon (Citrus lemon) and Perl tangerine (Citrus tangerina) in Dezful area using DRIS and DOP methods. 30 Lisbon lemon and 30 Perl tangerine gardens were randomly selected from citrus gardens in Dezful region in the south of Iran. Leaf samples as composite were collected from non-fruiting branches in late September 2015, and N, P, K, Ca, Mg, Fe, Mn, Zn, Cu, and B concentrations were determined. DRIS norms were achieved from the gardens with high-yielding. Then, DRIS and DOP indices for nutrients in the gardens with low-yielding, considering average yield, were calculated to evaluate nutrients balances and order of nutrient requirements. Sufficiency ranges of macro and micronutrients were derived by the DRIS method. The mean concentrations of nutrients in the high-yielding population were used as reference values to calculate DOP indices. Finally, nutritional balance index (NBI) was calculated for all nutrients. The results showed that the optimum level of the nutrients in Lisbon lemon leaves were 2.97, 0.11, 1.85, 3.88 and 0.17% for N, P, K, Ca, Mg, and 200.5, 24.9, 23.9, 68.8, 32.9 mg kg-1 for Fe, Zn, Mn, Cu, and B, respectively. Also, the optimum level of these nutrients in Perl tangerine leaves were 2.97, 0.09, 1.57, 3.44, and 0.34% for N, P, K, Ca, Mg, and 167.2, 32.7, 26.1, 28.0, 48.4 mg kg-1 for Fe, Zn, Mn, Cu, and B, respectively. DRIS-derived sufficiency ranges in Lisbon lemon (Citrus lemon) were 1.7-4.2, 0.08-0.14, 1.2-2.5, 3.2-4.5, 0.13- 0.2% for N, P, K, Ca, Mg, and 126.5-274.6, 20.7-29.2, 25.1-112.5, 16.4-31.5, 9.8-56.1 mg/kg for Fe, Mn, Zn, Cu, B, respectively, and also in Perl tangerine (Citrus tangerina) were 1.7-4.2, 0.07-0.12, 1.1-2.1, 2.6-4.2, 4.2-5.0, and 0.6-0.9% for N, P, K, Ca, Mg, and 117.1-217.5, 18.1-47.4, 5.2-50.1, 19.6-32.5, and 22.1-74.7.1 mg/kg for Fe, Mn, Zn, Cu, B, respectively. A comparison of the DRIS method with the DOP showed that Iron for Lisbon lemon and Boron for Perl tangerine gardens had the most negative index values. According to the DRIS index, priorities on the macro and micronutrients were determined for Lisbon lemon as Fe > N > B > K >Mn> Ca > Mg = P > Cu > Zn and for Perl tangerine as B > Fe > K > Cu > N > Ca > Mg >Mn> Zn > P. Based on DOP index, priorities on the macro and micronutrients were determined for Lisbon lemon as Fe > K > B > Cu > Mn > Ca > N > P > Mg > Zn and Perl tangerine as B > Zn > Fe > N > Mn > P > K > Mg > Ca > Cu. However, priorities on the nutrients were different in DRIS and DOP methods for both gardens except for the first priority. The efficiency of DRIS and DOP methods of this study compared to previous research on the priority of certain nutrients can be related to plant nutrition management, plant type, and climatic conditions of the study area. In general, nitrogen and potassium were the most deficient for both gardens, and among the micronutrients Iron and boron had the highest deficiency for both gardens. Overall, nitrogen, potassium, iron, boron, and priority nutrients should be given special attention to their nutrition.

The issue of global warming, climate change and drought are among the major challenges facing the world today, which can cause widespread fluctuations in the Earth's climate. Areas with Mediterranean and semi-arid climates are highly dependent on temperature and precipitation and as a result are affected by climate change. Iran is one of the countries in the semi-arid and arid regions of the world that is more sensitive to climate change. Global warming due to increasing greenhouse gas concentrations and land use change has caused obvious changes in Iran's climatic parameters, including increasing temperature, decreasing rainfall and increasing the incidence of destructive atmospheric-climatic phenomena in the country. This research has been carried out with the aim of detecting future changes in temperature and precipitation in Liqvan watershed. For this purpose, statistical Downscaling of SDSM model and CanESM2 model under RCP2.6, RCP4.5 and RCP8.5 climate change scenarios on long-term data of Tabriz Synoptic Station (1951-2020) has been used. The results show that in four periods of 20 years (2020-2100) and based on the three existing scenarios, the temperature will increase and the precipitation will decrease. This temperature increase for the minimum temperature will sometimes be up to 14.35 ° C (January with RCP8.5 scenario and time period (2100-2081). Examination of rainfall in four periods and three scenarios shows that the amount of rainfall in October, November, December and April will increase and in the remaining months will decrease.In the present study, climatic parameters of temperature and precipitation were simulated using multiple linear model of SDSM and general circulation models of barley using data of Tabriz city for Liqvan watershed. In this research, the output of canESM2 model under scenarios RCP8.5, RCP4.5, RCP2.6 has been used for future periods.The results showed that temperature data correlated better with observational data (compared to rainfall data), this is because temperature variability is less than rainfall and temperature is a parameter with a normal possible distribution. One of the reasons for the decrease in rainfall correlation is that different factors affect rainfall and on the other hand, rainfall is a discrete variable. These results are consistent with the results of Sajjad Khan et al. (2006), Sarvar et al. (2010) and Nouri and Alam (2014). Therefore, solving the correlation problem in the development of future climate change models should be considered.Also, the results of this research are in line with the results of most researchers such as trainee et al. (2009), Sajjad Khan et al. (2006), Sarvar et al. (2010), Mino et al. (2012), Chima et al. (2013), Dehghanipour et al. (2011). And Shakeri et al. (2021) agree that the SDSM model has a good ability to small-scale temperature and precipitation data.Climate change can cause temporal and spatial changes in climate variables. The characteristics of these variables can have detrimental effects on ecosystem components. According to the results, it was found that during the 21st century, temperature is increasing and precipitation is decreasing.In Tabriz station, in general, precipitation will decrease in the three scenarios studied and in only one scenario and for the period 2100-2080, there will be an increase in precipitation. Also, rainfall will generally increase in winter and the rest of the seasons will decrease. These results are consistent with the results of Golmohammadi and Masah Bavani (2011) which introduced the period 2069-2040 as a period with increased rainfall in Qarasu basin but with the results of Rajabi and Shabanloo (2012) using the SDSM model in Kermanshah region in Western Iran has considered the period 2070-2070 to be drier, there is a difference, so that according to the results of the present study at Tabriz station, from May to September, we will have a decrease in rainfall for all periods and scenarios. Figures 8 and 9 show the changes in monthly and annual rainfall in the RCP scenarios and the four periods compared to the base period, which show a decrease in precipitation in most months except October, November and December compared to the base period.Changes in the average minimum temperature of Tabriz station in all months except October, November and December will increase in future periods. Figures 11 and 12 show the monthly and annual minimum temperature changes in the RCP scenarios and the four periods compared to the base period, which show an increase in the minimum temperature compared to the observation period, which is more than the other scenarios in RCP 8.5. .

Soil fertility represents the soil ability to provide optimal plant growth conditions. Information on soil fertility status and preparation of related maps, plays an essential role in sustainable agricultural planning, maximizing economic benefits, maintaining soil quality, and preventing environmental degradation.In order to evaluate soil fertility status in some drylands in south of western Azerbaijan province and north of Kurdistan province, 101 samples were randomly collected and different soil properties such as pH, electrical conductivity (EC), calcium carbonate equivalent (CCE), and total nitrogen (N), phosphorus (P), potassium (K), iron (Fe), manganese (Mn), zinc (Zn), and copper (Cu) were measured.Nutrient Index Value (NIV) was used to interpret the results and map soil fertility.The results showed that 100% of the soil EC and pH of areas were classified in the low and high classes, respectively.Organic carbon (OM) and CCE were in the medium class in 72% and 61% of the areas, respectively.Total Nitrogen in 94% of areas in medium class, available P in 94% of areas in low class, K in almost 100% in high class, available Fe in 88% of areas in low class, Zn in 33% in low class, 39% in adequate class, and 28% in high class, Cu and Mn available in 100% of areas are in low class.It can be concluded that NIV method is useful to evaluate soil fertility status and its predictions conform to the reported critical level for rainfed wheat.

Surface gravel cover is an important factor for contoroling of soil erosion in dry area specially in area that plant can not grow because of excessive dryness and salinity. The objective of this study was investigation effect of different surface gravel cover on runoff and sediment yeild. For this porpose, 36 field plots with 20 meter length and 0.5 meter width at 3 percent slope were constructed in research field of agriculturev faculty, Shahrekord university. Treatments were including four level gravel cover (0, 10, 20 and 30 percent) and three surface flow rate (2.5, 5 and 7.5 L min-1) at three replications that expriment was done in a factorial with randomized complete block design. The results showed that as increasing surface gravel cover decresed the runoff rate and sediment yield significanty (p<0.001) in comparision with control treatment as linear and exponential, respectively. Aso, statisticaly comparision of the effect different gravel cover on runoff and sediment indicated that the effective gravel cover on runoff and sediment was different with increasing surface flow rate, as suitable gravel cover for surface flow 2.5 and 5 L min-1 was 20 percent and for surface flow 7.5 L min-1 was 30 percent gravel cover.

In cereal crops, nitrogen is the most important element for maintaining growth status and enhancing grain yield. Nitrogen is an important constituent of the chlorophyll molecule and the carbon-fixing enzyme ribulose-1, 5-bis-phosphate carboxylase/oxygenase. Therefore, providing enough nitrogen to achieve optimal yield is essential. Common chemical analyzes are used to determine the nutrient elements of plants using laboratory methods. Conventional laboratory techniques are expensive, laborious, and time-consuming. Determination of plant biochemical content by remote sensing could be used as an alternative method  which reduce the problems of laboratory analyses. Expensive and time-consuming direct determination of the nutritional status of the plant play an important role in the quantitative and qualitative yield of the product. However, exposure to rainfed wheat nutrient stresses (in particular, nitrogen) compared to irrigated wheat resulting in attempts to evaluate these features with acceptable accuracy without the direct measurement. In this regard, remote sensing data and satellite images are of the basic dryland management and optimal wheat production methods. As such, it collects massive information periodically from the surface of the planet, and it is easy to use this timely information to identify the stresses and apply appropriate agronomic methods in order to counteract them or reduce their negative impact on the production of this strategic product. Therefore, the goal of this study was to determine the nitrogen concentration of dryland wheat in the laboratory and its fitting with ETM+ images, evaluate the accuracy of remote sensing in determining the total nitrogen content of the plant and establish a regression relationship to estimate the amount of canopy nitrogen in the plant.

This research was undertaken in parts of the south of the West Azerbaijan Province in Iran. The sampling was done from 45 dryland wheat fields using a stratified random method in May 2016. The wheat canopy nitrogen was determined using the Kjeldahl method. Satellite images of the ETM+ were downloaded on the USGS website. Then the required pre-processing was performed on images to reduce systematic and non-systematic errors. Statistical analyses were performed by excel and SPSS. Descriptive statistics and correlations were obtained between reflectance data obtained from various satellite bands and nitrogen measured in the laboratory. Correlated variables among the reflectance data of different bands were analyzed by principal component to reduce repeat calculations. The regression relationship between the plant canopy nitrogen and the first principal component has been evaluated using the stepwise regression method. To draw the plant canopy nitrogen, map, the equation was obtained and the ETM+ image has been used for land uses. Finally, the map of canopy N distribution at the studied area was drawn.

The results showed that nitrogen content varied from 1.6% to 0.79%, with an average of 1.11%. The normality data was verified by the Shapiro Wilk test. The results of the Pearson correlation showed that the wheat canopy nitrogen has a high correlation with digital number values of all bands of satellite images except band 4, so that it has the highest and the least correlation with band 2 and band 4, respectively. The correlation between remote sensing data in different bands was also evaluated using bi-plot statistics, which results showed a high correlation between all bands except band 4 with the first one of the principal component (PC1). Therefore, only PC1 data has been used to study the regression relationships between wheat canopy nitrogen and remote sensing data. A regression equation  between wheat canopy nitrogen and ZPC1 (R2= 0.71) was developed. ZPC1 is obtained according to the following formula:  where ZPC1 is the standardized Z parameter, is the average of PC1 and the ????pc1 is the standard deviation of PC1. Finally, the map of canopy N distribution was drawn to the studied area. According to the results of this study, the application of remote sensing data such as Landsat ETM+ data is a very important variable for improving and managing the prediction of wheat canopy nitrogen.

Overall, the results indicated that the remote sensing data provide more accurate and timely information from the drylands of Iran to manage farm fertilization and prevent the decline in yields at critical points. However, proper management to avoid the fertilizer loss by precise and timely application of N-fertilizer is needed.

Resistance to surface flow such as Manning's and Darcy-Weisbach rouphness coefficients were an important input for estimating soil erosion by soil erosion process models. Also, it is very important for designing and implementing soil and water conservation practices. The objective of present research was predicting Manning's and Darcy-Weisbach rouphness coefficients in surface of a loess soil under different rock fragment covers. For this porpose, a flume was used with 6 m length, 0.5 m width and 3% slope. The treatments included rock fragment cover (0, 10, 20 and 30%) and three levels of flow discharges (3, 6 and 9 lit. min-1). The results showed that Manning's and Darcy-Weisbach rouphness coefficients increased as exponential with increasing rock fragment cover. Darcy-Weisbach coefficient increased 84.3, 83.8 and 85.7% with an increase rock fragment cover from 0 to 30% at 3, 6 and 9 lit min-1 flow discharges, respectively, and Manning's rouphness coefficient increased 96.9, 96.7 and 97.4% at mentioned flow discharges. Rouphness coefficients decreased as exponential (R2=0.99) with increasing flow velocity at a rock fragment cover. Also, (8/f)0.5 increased as logarithmic with increasing relative submergence. Generally, results of this study showed that rouphness coefficients not only were dependent on size and shape rock fragment cover but also, were influenced by factors such as flow rate, depth and velocity and also, rock fragment cover percentage.

Citrus is an important fruit crop cultivated in tropical regions of the world with immense nutritional value and advised on daily basis in diet. In Iran, it is cultivated in high reaches of northern and southern regions. The low productivity has been ascribed mainly to the nutritional health of the plantations which is the most concern among farmers. To plan fertilization efficiently, it is necessary to know the desirable concentration of macro and micro nutrient in tissues that are representative of the plant’s nutritional status. Traditionally, to determine the optimum fertilizer doses the most appropriate method was to apply fertilizer on the basis of soil test and crop response studies (Regar and Singh, 2014) which defied the synergistic and antagonistic effects in relative availability of different essential nutrients from soil. The foliar nutrient concentration is considered most pertinent and reliable method to judge the well-being of a tree as it represents the in situ condition in a holistic way and is a very powerful tool for nutritional diagnosis to assess deficiency symptoms and make fertilizer recommendations (Filho, 2004). Because of the dynamic nature of the leaf tissue composition, strongly influenced by leaf age, maturation stage, and the interactions involving nutrient absorption and translocation, the tissue diagnosis may be a practice of difficult understanding and utilization (Walworth and Sumner, 1987). The Diagnosis and Recommendation Integrated System (DRIS) developed by Beaufils (1973), expresses the result of foliar analysis through indices, which represent in a continuous numeric scale, the effect of each nutrient in the nutritional balance of plant. DRIS is advantageous as it presents continuous scale and easy interpretation; allows nutrient classification (from the most deficient up to the most excessive); can detect cases of yield limiting due to nutrient imbalance, even when none of the nutrient is below the critical level; and finally, allows to diagnose the plant nutritional balance through an imbalance index (Baldock and Schulte, 1996). Nutritional balance is an important factor in increasing the yield and improving the quality of horticultural products especially Citrus. Hence, the objective of this study was to determining the optimum level of the macro and micro nutrient elements and evaluating the nutritional status of Lisbon lemon and Perl tangerine in Dezful.
For this purpose, 30 Lisbon lemon and 30 Perl tangerine gardens were selected randomly from citrus gardens in Dezful. Leaf samples were collected from middle of terminal shoots of current year growth in the periphery of tree from in late September. Leaf samples were washed in detergent followed by tap water and distilled water. Leaves dried under shade and then dried in hot air oven at 70ºC for 48 hours. The dried leaves were grounded to fine powder by using mixer and stored in air tight butter paper bags for nutrient analysis. Kjeldahl method was followed to measure total nitrogen, and phosphorus was measured by vanado-molybdophosphoric yellow colour method using spectronic, while potassium was measured by flame photometric method. Other elements content was determined by atomic absorption system. The gardens were divided into two groups of low and high yielding. All forms expression and their variance into two groups and variance ratio the group of low to high yielding in tow type gardens were calculated. Then using DRIS calibration formula, DRIS index for nutrient elements with low yielding were determined and nutrient balance index (NBI) were calculated.
The results showed that the optimum level in Lisbon lemon leaves were 2.97, 0.11, 1.85, 3.88 and 0.17% for N, P, K, Ca, Mg and 200.5, 24.9, 23.9, 68.8, 32.9 mg.kg-1 for Fe, Zn, Mn, Cu and B, respectively. In addition, the optimum level in Perl tangerine leaves were 2.97, 0.09, 1.57, 3.44 and 0.34% for N, P, K, Ca, Mg and 167.2, 32.7, 26.1, 28.0, 48.4 mg.kg-1 for Fe, Zn,Mn, Cu and B, respectively.
In general, based on DRIS indices priority on the macro and micro nutrients as Fe > N > B > K >Mn> Ca > Mg = P > Cu > Zn for Lisbon lemon and B > Fe > K > Cu > N > Ca > Mg >Mn> Zn > P for Perl tangerine were determined. The NBI of all gardens with low yielding was more than zero, indicating an imbalance nutritional in low yielding gardens.

Rock fragments on soil surfaces can also have several contrasting effects on the hydraulics of overland flow and soil erosion processes. Many investigators have found that a cover of rock fragments on a soil surface can decrease its erosion potential compared to bare soil surface (1, 12 and 18). This has mainly been attributed to the protection of the soil surface by rock fragments against the beating action of rain. This leads to a decrease in the intensity of surface sealing, an increase in the infiltration rate, a decrease in the runoff volume and rate, and, hence, a decrease in sediment generation and production for soils covered by rock fragments. Parameters that have been reported to be important for explaining the degree of runoff or soil loss from soils containing rock fragments include the position and size (15), geometry (18), and percentage cover (11 and 12) of rock fragments and the structure of fine earth (16). Surface rock fragment cover is a more important factor for hydroulic properties of surface flows such as flow depth, flow velocity, Manning’s roughness coefficient (n parameter) and flow shear stress and geometrics properties of formed rill such as time, location, number, length, width and depth of rill. Surface rock fragment cover is directly affected soil erosion processes in dry area specially in areas that plant can not grow because of sever dryness and salinity. Also, Surface rock fragment prevent the contact of rain drops to aggregates, decreasing physical degradation by decreasing flow velocity. The objective of this study was to investigate the effect of different surface rock fragment cover on hydraulic properties of surface flows and geometrics properties of formed rill.
For this purpose, 36 field plots of 20 meter length and 0.5 meter width with 3% slope were established in research field of agricultural faculty, Shahrekord University. Before each erosion event, topsoil was tilled and smoothed with hand tools to remove soil irregularities and soil sealing, update aggregates which come from deeper soil. Then, for beginning the experiment, surface rock fragment cover is scattered randomly on plot surface. Experiment equipment such as collecting the runoff systems installed at the end of plots. In each experiment after setting the surface flow, surface runoff inter to soil surface and testing continued for 60 minutes after starting runoff. Flow velocity was measured using a dye-tracing technique (potassium permanganate) and depth, width and length of rill were measured using a ruler. Treatments were including four level rock fragment cover (0, 10, 20 and 30%) and three rate runoff (2.5, 5 and 7.5 L min-1) with three replications that experiments were done in a factorial with randomized complete block design. Surface runoff samples were oven-dried and weighed to determine sediment loads. Sediment concentration was determined as the ratio of dry sediment mass to runoff volume, while the erosion rate was calculated as the sediment yield per unit area per period of time.
The results of this study showed that surface rock fragment cover plays an important role in water distribution. Based on the results, the positive effects of rock fragment cover on Manning’s n and the negative effect on flow velocity. Increasing surface rock fragment cover increased hydroulic properties such as flow depth, Manning’s n and flow shear stress significantly (p

Cation Exchange Capacity (CEC) is an important characteristic of soil in absorption and desorption of nutrient and potential of heavy metals hazard and some of organic contamination. Knowing spatial patterns of CEC for sustainable management of ecosystems, has special importance. hence, the main objectives of this research was to evaluate, recognize and introduce the best interpolation methods for prediction of CEC in some of Guilan province soils. 153 points of surface soil in depth 0-15 cm were sampled and were measured clay, Organic carbon and CEC. Interpolation methods including kriging, cokriging, fuzzy kriging and regression kriging have been done using GIS. Results showed that the hybrid methods including regression kriging and fuzzy kriging, respectively with RMSE values of 1.02 and 1.2, significantly reduced the error of prediction compared with the other methods. In addition, between these two mentioned methods, the regression kriging had more efficiency in prediction of CEC. The results also revealed that increasing the number of data by means of the best pedotransfer functions (created by ANFIS) will enhance the accuracy of prediction. In general, combination of the best pedotransfer functions with the best interpolation method increased the accuracy of CEC prediction in the study area.

Topographic factor is an important factor of computer models of USLE family due to the high sensitivity of model results to its variation and complicated effects of topography on soil erosion. Moore and Wilson (1992) have introduced a well-known method to compute LS factor of RUSLE model using digital elevation model (DEM).The objective of this study was to determine the proper cell size of DEM for computing LS factor in a 7116 ha area, in Rashakan region at the southern part of Urmia. DEM models with cell sizes of 30, 50, 75,100, 200 and 400 m were prepared in ArcGIS 10 environment and the raster based maps of LS were derived from each DEM model based on Moore and Wilson method. Spatial dependence of the LS factor was analyzed and the proper cell size of DEM model was selected based on the degree of spatial dependence. Results showed that the spherical model explain the spatial pattern of LS factor and the DEM model with 75 m cell size has the strongest spatial dependence (0.523) and the highest coefficient of variation (0.983). As a result, in this region DEM with 75 m cell size is suggested as a proper cell size to compute LS factor based on Moore and Wilson method.

Different resolutions of digital elevation models (DEMs) can generate varied topographic and hydrological features. The objective of this study was determination of suitable cell size of DEMs and its effects on prediction of some soil properties. For this purpose, two study areas were selected with different topographic properties in Selin plain, East-Azerbaijan Province. A total of 31 and 37 points were selected randomly from study area (1) and (2), respectively, and then, elevation, slope, clay and organic matter contents were measured by GPS, manual, hydrometer and Walkly-Black methods. The results showed that the number of cells with sink was more in smaller cell size than bigger cells, causing error in determination of hydrological characteristics. Therefore, they must be removed. Appropriate cell size of DEMs depends on the properties of the area topography, which for the study area (1) and (2) with flat and severely undulating topography, the cell size was 50 and 40 meters, respectively. Geostatistical analysis showed that, in both study areas, spatial correlation linearly decreased with increases in cell size upto 75 m; while in study area (2), it decreased with more intensity, reflecting the loss of large volumes of topographic information. Difference between R2 values for the estimation of soil organic matter and clay from DEMs with different cell sizes was less in area (1) than area (2). Generally, the results of this study showed that lower cell size (

Accuracy of spatial estimation of soil properties such as clay, organic matter and calcium carbonate equivalent is very important in plant nutrition and environmental planning. The most important step before statistical analysis and using geostatistical estimators is data check. In addition to investigation of outlier data and data distribution, another important action in geostatistical studies is trend surface analysis. Analyzing trend surface can evaluate the role of factors such as stone kin, climate, topography and generally regional anomalies. The objective of this study was trend-surface analysis and its effects on variogram modeling and mapping of clay, organic matter and calcium carbonate equivalent.
For this purpose, 100 surface soil samples in 0-15 cm depth were selected randomly based on different classes area slope from 41353 ha area in Selin plain farmland located in Kaleibar region, East-Azerbaijan. Soil properties such as clay, calcium carbonate equivalent and organic matter were measured by hydrometer, return titration and wet oxidation method, respectively. For analyzing trend surface used multiple regression models which its independent variables was geographical coordinate and dependent variable was a soil properties. For zoning clay, organic matter and calcium carbonate equivalent and residuals of removing trend used ordinary kriging. The effect of removing trend surface in variogram modeling and kriging estimating were evaluated by cross-validation method with indexes mean error (ME), root mean square error (RMSE) and determination coefficient (R2).
Trend surface analysis showed that the best regression models for trend determination of clay, calcium carbonate equivalent and organic matter were first order, first order and quadratic, respectively. Removing the detected trend led to decrease in sill but the nugget effect did not changed. However, no significant difference was observed between accuracy of kriging estimator in presence and remove of trend. This can be attributed to the fact that both abnormal manner of environment and activations of human. So that, the regression models of the trends were 35, 18 and 21% of clay, organic matter and calcium carbonate equivalent variations, respectively. However, removing the detected trend led to increase 9.1, 2.7 and 6.6% of R2 for clay, organic matter and calcium carbonate equivalent, respectively.
generally, investigation of trend surface recommended in soil studies that is more deals to spatial data. Because the trend depends on the location and conditions of the study area as well, the source of the creating trend trend trend trend trend.

Understanding the spatial distribution and variability of erodibility and soil properties is essential for planning of water conservation methods, controlling of flood and runoff and managing of soil erosion or watershed. Selecting and using appropriate interpolation techniques for soil properties and erodibility mapping by erosion models such as WEPP is essential. The objective of this study was regionalization of interrill erodibility and effective factors like clay, organic matter and lime using kriging and cokriging and remote sensing data (Landsat 7). For this purpose, 100 soil samples were selected randomly from 0-15 cm depth of Selin watershed in Kaleibar region of East Azerbaijan. Interrill erodibility of WEPP model and some soil properties as clay, organic matter and lime were measured. Correlation analysis between soil properties and digital number (DN) ETM image showed that clay, organic matter, lime and interrill erodibility had the highest correlation with DN of Band 7, 1, 1 and 3 ETM image (−0.406, -0.431, 0.291 and 0.299), respectively. Therefore, the DN of these bands used as auxiliary data for cokriging estimator. The spherical model was fitted the best model to calculate variogram of interrill erodibility, clay, organic matter and lime. No significant difference were noted between kriging and cokriging despite using remote sensing data as auxiliary data. This can be attributed no strong correlation between interrill erodibility, clay, organic matter and lime and remote sensing data.

Soil erosion on agricultural land is a main problem and constitutes a threat to soil quality and agricultural production. Preparation of a risk map is needed as a prerequisite for control of soil erosion and planning of environmental projects. This study was conducted to assess the risk of soil erosion by mapping and zonation of soil erosion risk in farmland of Tehran province and determine their extents. The map of soil erosion risk was prepared with four classes (low، moderate، high، and very high); by integration of slope gradients، land use، and erosivity map using Geographical Information System. Results showed that the area of Tehran province farmland is 320887 ha (17. 05% of total area). 4478 ha of farmlands are located in areas with slopes more than permitted slope for cultivation، that 3121 ha of them have rainfalls with high and very high erosivity. In general، 98. 1، 0. 5، 0. 4، and 0. 97% of farmlands have the low، moderate، high، and very high erosion risk، respectively. Soil erosion and land degradation are occurred due to crop production activities in the areas with high and very high erosion risks (4478 ha of farmland). Therefore، in these areas، special soil conservation and land management should be conducted، otherwise، the land use of these areas are necessary to changed to grassland، garden، and artificial forests.

Field plots are widely used in studies related to the measurements of soil loss and modeling of erosion processes. Research efforts are needed to investigate factors affecting the data quality of plots. Spatial scale or size of plots is one of these factors which directly affects measuring runoff and soil loss by means of field plots. The effect of plot size on measured runoff or soil loss from natural plots is known as plot scale effect. On the other hand, variability of runoff and sediment yield from replicated filed plots is a main source of uncertainty in measurement of erosion from plots which should be considered in plot data interpretation processes. Therefore, there is a demand for knowledge of soil erosion processes occurring in plots of different sizes and of factors that determine natural variability, as a basis for obtaining soil loss data of good quality. This study was carried out to investigate the combined effects of these two factors by measurement of runoff and soil loss from replicated plots with different sizes. In order to evaluate the variability of runoff and soil loss data seven plots, differing in width and length, were constructed in a uniform slope of 9% at three replicates at Koohin Research Station in Qazvin province. The plots were ploughed up to down slope in September 2011. Each plot was isolated using soil beds with a height of 30 cm, to direct generated surface runoff to the lower part of the plots. Runoff collecting systems composed of gutters, pipes and tankswere installed at the end of each plot. During the two-year study period of 2011-2012, plots were maintained in bare conditions and runoff and soil loss were measured for each single event. Precipitation amounts and characteristics were directly measured by an automatic recording tipping-bucket rain gauge located about 200 m from the experimental plots. The entire runoff volume including eroded sediment was measured on storm basis using the collection tanks. The collected runoff from each plot was then mixed thoroughly and a sample was taken for determining sediment concentration by weight. The per-storm soil loss was then obtained. A wide range of rainfall characteristics were observed during the study period.The results indicated that the maximum amount of coefficients of variation (CVs) for runoff and soil loss from replicated plots were 60 and 80 percent, respectively, which were considerably higher than the variability of soil characteristics from these plots. CV of runoff and soil loss data among the replicates decreased as a power function of mean runoff (R2= 0.661, P.

The objective of this study was to determine the efficiency of the evaluation criteria for comparison some of soil water retention curve models. For this purpose، 70 soil samples were taken from Karaj، Urmia and Shahrekord plains. The soil Water retention curves were obtained using the result of the pressure plate device. Then، the mean of coefficient of determination (R2)، the adjusted coefficient of determination (R2adj) and root mean square error (RMSE) were calculated for each model and the efficiency of these criteria at the evaluation of mentioned models tested with statistical methods. Then، Akaike’s information criterion and Mallows criteria were used for comparison of reference model with other models for each soil. Difference the mean of R2، R2adj weren’t significant for all soils and textural classes and for RMSE weren’t significant in six classes from eight textural classes (clay and silty clay) at among of the five models. So، R2، R2adj and RMSE were not suitable criteria for the selection of optimum model (P≤ 0. 05). According to Akaike’s information criterion tests Gardner، Pereira and Fredlund، Khlosi and Van Genuchten- Mualem models for 27. 1، 10، 45. 7 and 68. 6% soils and on the basis of Mallows statistic mention models for 34. 3، 10، 48. 6 and 71. 4% soils were better than the Van Genuchten model، respectively. Additionally، Van Genuchten، Van Genuchten- Mualem and Pereira and Fredlund models had the exact prediction from saturation moisture، but Gardner and Khlosi models had larger errors.

The infiltration process is one of the most important components of the hydrologic cycle. Quantifying the infiltration water into soil is of great importance in watershed management. Prediction of flooding, erosion and pollutant transport all depends on the rate of runoff which is directly affected by the rate of infiltration. Quantification of infiltration water into soil is also necessary to determine the availability of water for crop growth and to estimate the amount of additional water needed for irrigation. Thus, an accurate model is required to estimate infiltration of water into soil. The ability of physical and empirical models in simulation of soil processes is commonly measured through comparisons of simulated and observedvalues. For these reasons, a large variety of indices have been proposed and used over the years in comparison of infiltration water into soil models. Among the proposed indices, some are absolute criteria such as the widely used root mean square error (RMSE), while others are relative criteria (i.e. normalized) such as the Nash and Sutcliffe 1970) efficiency criterion (NSE). Selecting and using appropriate statistical criteria to evaluate and interpretation of the results for infiltration water into soil models is essential because each of the used criteria focus on specific types of errors. Also, descriptions of various goodness of fit indices or indicators including their advantages and shortcomings, and rigorous discussions on the suitability of each index are very important. The objective of this study is to compare the goodness of different statistical criteria to evaluate infiltration of water into soil models. Comparison techniques were considered to define the best models: coefficient of determination (R2), root mean square error (RMSE), efficiency criteria (NSEI) and modified forms (such as NSEjI, NSESQRTI, NSElnI and NSEiI). Comparatively little work has been carried out on the meaning and interpretation of efficiency criteria (NSEI) and its modified forms used to evaluate the models. The collection data of 145 point-data of measured infiltration of water into soil were used. The infiltration data were obtained by the Double Rings method in different soils of Iran having a wide range of soil characteristics. The study areas were located in Zanjan, Fars, Ardebil, Bushehr and Isfahan provinces. The soils of these regions are classified as Mollisols, Aridisols, Inceptisols and Entisols soil taxonomy orders. The land use of the study area consisted of wheat, barley, pasture and fallow land.The parameters of the models (i.e. Philip (18), Green and Ampt (3), SCS (23), Kostiakov (6), Horton (5), and Kostiakov and Lewis (11) models) were determined, using the least square optimization method. All models were fitted to experimental infiltration data using an iterative nonlinear regression procedure, which finds the values of the fitting parameters that give the best fit between the model and the data. The fitting process was performed using the MatLab 7.7.0 (R2008b) Software Package. Then, the ability of infiltration of water into soil models with the mean of coefficient of determination (R2), root mean square error (RMSE), efficiency criteria(NSEI) and modified forms (such as NSEjI, NSESQRTI,NSElnI and NSEiI) were determined and goodness of criteria was compared for the selection of the best model. The results showed the mean of RMSE for all soils cannot always be a suitable index for the evaluation of infiltration of water into soil models. A more valid comparison withNSEI, NSEjI, NSESQRTI, NSElnI indices indicated that these indices also cannot apparently distinguish among the infiltration models for the estimation of cumulative infiltration. These indices are sensitive to the large amount of data. The NSEiI index with giving more weight to infiltration data in shorter times was selected as the most appropriate index for comparing models. According to the NSEiI index, Kostiakov and Lewis, Kostiakov, SCS, Philip, Horton, and Green and Ampt models were the best models in approximately 72.42, 4. , 26.9, 53.11, 11.73 and 1.0 percent of soils, respectively. The results of this study indicated that the ability of modified forms of NSE indices in evaluation of infiltration of water into soil models depend on the influence of models from infiltration data values in different time series. This encourages us to be cautious on the application and interpretation of statistical criteria when evaluating the models.

Preparation of the topographic factor (LS) map in RUSLE model is a very tedious step because of the complicated effects of topography on soil loss. The objective of this study was to determine the suitable cell size of Digital Elevation Model (DEM) for computing LS factor by the method of Moore and Wilson (1992) in a 5326 ha area، northwest of Tehran Province. Towards this end، cell sizes of 30، 50، 100، 200 and 400 m were derived from the 10 m cell size in ArcGIS 9. 3. The appropriate cell size was selected making use of the criteria of spatial dependence and the coefficient of determination (R2). Results revealed that in the preparation of flow accumulation map، the sinks created in digital elevation models must be resolved. Also، analysis of variograms showed that the calculated LS factor obtained from DEM of 50 m cell size is of the most spatial dependence (0. 613) with the highest coefficient of determination (R2=0. 983). As a result، a DEM of 50 m cell size was found out to lead to the production of a more accurate LS factor map.

Soil surface conditions such as roughness, structure, vegetation and rock fragment cover have very important effects on infiltration, run-off and soil erosion. Rock fragments at the soil surface directly affects soil erosion processes, particularly in arid and semiarid environments where vegetation cover is poor. The objective of the present research was to study the influence of rock fragment cover on the rate of soil loss and the hydraulic properties of flow such as roughness coefficient and shear stress in a loess soil sample from Golestan province. The investigation was conducted using a flume with 6 m length, 0.5 m width, and 3% gradient. The treatments included rock fragment covers (0, 10, 20 and 30%), and flow discharges of 0.5, 1.0 and 1.5 (10-4 m3 s-1). With increasing of rock fragment cover from 0 to 30% in flow discharges of 0.5, 1.0 and 1.5 (10-4 m3 s-1), relative velocity of water flow decreased 68.9, 67.7 and 70.9%, respectively. The value of relative roughness coefficient and shear stress increased linearly (R2=0.99) with increasing of rock fragment covers. In addition, with increasing of rock fragment cover from 0 to 30%, the rate of relative soil loss in flow discharges of 0.5, 1.0 and 1.5 (10-4 m3 s-1) decreased 85.0, 83.7 and 73.6%, respectively. Decreasing soil loss rate was related to Rock fragment cover by an exponential function (R2=0.98).

Erosion Plots are widely used to evaluate the main factors affecting soil erosion. Therefore, understanding the effects of different variables such as spatial scale on their performance is needed. This study was carried out to assess the effect of plot scale on measured runoff and sediment yield at event scale. To represent different spatial scales, plots with seven sizes differing in length (2, 5, 10, 15, 22.1, 25, and 30 m) and width (1, 1, 1.2, 1.6, 1.8, 2, and 2.4 m, respectively) were constructed in Poldasht region, west Azarbayjan province, northwest Iran. For each size of plots, specific runoff and sediment yield were separately measured for 11 runoff producing natural storms during the study period from September, 2010 to September, 2011. The results indicated that the unit area runoff and sediment yield decreases with power form relation as plot area or length increases. Statistical analysis of runoff and sediment yield data showed that, in most cases, there is a significant difference between the results of 10 m or shorter in length, plots and longer plots (p<0.05). However, there was no significant difference between the results of 15 m and longer plots. Based on these results, plots with 10 to 15 m in length were able to produce comparable results with large plots.

Rock fragments cover is a key factor for soil erosion control, particularly in arid and semiarid environments where vegetation cover is low. The objective of the present research was to study the influence of rock fragment cover on soil erosion and the hydraulic properties of surface flow. The investigation was conducted using a flume with 6 m length, 0.5 m width, and 3% gradient. The treatments included rock fragment cover (0%, 10%, 20%, and 30%), and three levels of flow discharges (3, 6, and 9 lit. min-1). Velocity of water flow decreased 69.2%, the Manning roughness coefficient increased from 0.012 to 0.115, and the Froude number decreased from 2.24 to 0.28 with an increase in rock fragment cover. The Reinhold’s number showed a small variation among different rock fragment covers, but increased with increasing flow discharge. In addition, the rate of soil loss averagely decreased 80.1% in different flow rates with a rise in rock fragment cover from 0 to 30%. Decreasing soil loss rate was related to rock fragment cover by an exponential function.