Development of sensors to detect the location and depth of hard pans in real time is a major restriction on the application of Site Specific Crop Management (SSCM). In this study, a soil compaction profile sensor equipped with four horizontal operating penetrometers for on-the-go sensing and mapping of the location and intensity of hard pans artificially formed in a soil bin was developed and tested. The leading edge of a 600 mm long vertical soil cutting blade held four 8 mm diameter, 80 mm long, and 30 degree conic tip stainless steel soil penetrating rods equally spaced at 100 mm vertical intervals. With this arrangement, when the cutting blade was driven into the soil up to a 500 mm depth, the conic tips sensed soil penetration resistances at 100, 200, 300 and 400 mm depths. The penetration resistance force was transmitted by the rod end to the elastic diaphragm of a hydrostatic oil chamber beneath each rod. Each oil chamber was connected to a force magnifying piston and cylinder located off the soil engaging tools. The penetration force was magnified five times before being sensed by a strain gage load cell. Software programs with the capability of discriminating 16 levels of soil compaction intensity were developed for monitoring soil impedances sensed by the soil probes and for converting them to soil compaction maps. For conducting the tests in the soil bin, the sensor mounted on the tool carrier frame was moved along the bin, where artificially formed compacted soil blocks with various densities (1.45, 1.65 and 1.85 Mg/m3) were placed at different locations and depths (up to 500 mm deep at 100 mm increments). While the probe was cutting and advancing through the soil, the corresponding compaction map was simultaneously displayed on a PC monitor, and the soil penetration resistance data of all four sensing tips was displayed and stored in program files.

The growing interest in simulation of water movements in soils is in response to the need for development of solutions for various agricultural and environment problems, such as irrigation. In order to be able to adapt models for simulation, it is important that the capabilities of these models and credibility of their results be tested. In this study results from LEACHM simulation model are compared with the measured soil water data. We carried out an experiment in Khorasan Agriculture Research Center, which included three treatments: 1- crop was irrigated when Maximum Allowable Deficiency (MAD) corresponded to 50%, 2- crop was irrigated when MAD corresponded to 70% and 3- crop was irrigated once, then irrigation was stopped until MAD corresponded to 90%, after that crop was irrigated normally. A sensitivity analysis was performed to identify the input parameters of LEACHM, which most influence the water content variations. Sensitivity analysis showed that bulk density has an important effect on soil water content. Simulated soil water contents were in close agreement with measured values with a mean absolute error (MAE) between measured and predicted soil water contents, ranging from 1.87 to 4.54% in treatment one, 1.90 to 3.11% in treatment 2 and 3.98 to 5.58% in treatment 3. Generally, the model overestimated water contents.

Sewage sludge is widely used as a fertilizer and soil conditioner on agricultural lands. However because of high concentration of soluble salts in sewage sludge, it increases soil salinity. The objective of this study was to evaluate the residual and cumulative effects of sludge application on soil salinity. This study was conducted in Lavark experimental site with four rates (0, 25, 50 and 100 ton.ha-1) of sewage sludge. The plots received one, two, three or four years of sludge applications. Corn (Zea mays) and wheat (Triticum sativum) were planted in the first and second half of each year, respectively. Soil samples were collected at the end of wheat growing season at 20 cm increments to 100 cm depth from each plot. Soil samples were analyzed for EC, OC, Na, K, Mg, Ca, Cl, SO4 and HCO3. Sludge application significantly increased concentration of Na, Ca, K and Mg in the soil depths. Concentration of Cl, SO4 and HCO3 also significantly increased with sludge application. However, the effect of residual sludge application on soil salinity was minimal and because of leaching, soil EC did not reach the critical value that affect most plant growths.

The rate and duration of downward flow during redistribution process determines the effective soil water storage at any time. This property is vitally important, particularly in arid and semi-arid regions where plants must rely for long periods of time on the remained soil water of the root zone. In this study a new approach for scaling of soil moisture redistribution process based on the Green-Ampt redistribution theory was developed. Using the scaled results of numerical solution of the general flow (Richards’ equation), an empirical equation for predicting the soil moisture profile during redistribution process was derived. An important advantage of the empirical equation is adopting the effect of hysteresis in soil retention curve on redistribution process. To validate the proposed empirical equation, its outputs were compared with those of Richards’ solution for 11 soil textural classes (from sand to clay). The comparison showed negligible amount of error for all of the 11 soil textural classes and for a wide range of initial conditions. However, some deviations from results of Richards’ solution were observed under high initial infiltrated water depth and/or high initial soil water content. Therefore, a model which can estimate the soil moisture content at any depth and time during redistribution phase with accuracy of numerical models and simplicity in application of analytical models was obtained.

Background and Objective: Biological soil crusts are a collection of lichens, mosses, fungi, cyanobacteria, etc. that are part of the soil ecosystem. Estimation of density and distribution of biological soil crusts in arid and semi-arid regions of Iran, which is the subject of soil erosion and wastage is very important. Methods based on remote sensing techniques are important in terms of cost and time less efficient methods to achieve this goal. Segzi plain is one of the critical points of wind erosion in Iran and identifying and determining the distribution of biological soil crusts as a soil modifier is an effective step in reducing wind erosion in the region. In this research, BSCI (Biological Soil Crust) index has been used to prepare the distribution map of lichen-dominated biological soil crusts. Materials and Methods: The study area is part of the Sajzi Desert (Central Deserts of Iran) which is located in Isfahan province of Iran. The study area with an area of 199. 5 hectares is spread between the eastern lengths of 51o52'32" to 52o27'41" and the northern widths of 32o33'31" to 32o55'01". The average slope of Segzi plain is 1. 08 percent and its average height is 1680 meters. According to the statistics of East Isfahan Meteorological Station (Shahid Beheshti Station), the average annual rainfall in the region is 106 mm. According to the Dumarten climatic classification, the climate of the region is dry and according to the Amberge classification it is cold. The BSCI index is a combination of the relationships used to estimate vegetation and bare soil surface, and its mathematical relationship is the slope of the soil line. To calculate the soil line in an area, one must first separate the pixels that have bare soil and no vegetation. In order to calculate the soil line equation, in four seasons of a year, images of Landsat OLI 8 satellite related to 2018 were downloaded from the site of the US Geological Survey and 20 to 30 pixels of pure bare soil were extracted by drawing the reflection values of these pixels in the red and infrared band. Red near soil line coefficients were calculated for each season in the Segzi Plain. Based on BSCI index, lichen-dominated biological soil crust are identified using at least VIS-NIR spectral reflection and the slope between the red and green bands compared to bare soil and dry vegetation. Using ENVI software, the distribution shells of biological shells with lichen dominance were prepared in four seasons since 2018 in Segzi plain. Then, the prepared maps were validated based on land points and the total accuracy and kappa index were calculated in all four seasons. The collected lichen samples were identified based on their morphological characteristics and using a stereomicroscope, conventional microscope and common color reagents such as potassium hydroxide (KOH). After applying the BSCI index on the Landsat OLI 8 satellite image, using ENVI software, spectral profiles related to 4 points of Segzi plain in four seasons of the year were prepared and the spectral reflection in four seasons of the year in different points were examined. Results and Discussion: The slope of the soil line is lower in the rainy season, which coincides with the growth of herbaceous and annual plants, compared to the summer season, which has the least amount of rainfall, and the annual plants have dried up and become extinct. In May, the slope of the soil line was minimal (0. 39) and in late summer it has its maximum value (0. 78). In fact, the slope of the soil line has decreased from mid-August to May, and then has increased with the loss of annual vegetation and the increase of bare soil surface. The distribution maps of bio-shells in all four seasons of the year were validated during field visits and the year it was found that the highest accuracy of the map related to the map produced from Landsat 8 image is related to summer with 94% total accuracy and Kappa index equal to 0. 7412. Interpretation of the spectral profiles of the BSCI index shows that the reflections of the spectra related to the zephyr and strain prepared on the lichen dispersion points are very close to each other and also the spectral profiles of the mid-autumn and early spring are quite consistent. Whereas in the faults, which did not cover the biological crust, the amount of reflection was higher and there was a slight difference between the reflection diagrams of autumn and spring. Although the reflectance values of a range of agricultural lands and the distribution points of biological crusts are very close to each other, the spectral diagrams of all four seasons are very different from each other. But in all seasons of the year and in all places, the least reflection has occurred in the beginning of winter and the most reflection has occurred in summer. The climate of Segzi plain is Mediterranean and precipitation occurs in the cold season of the year. Simultaneously with the increase of precipitation from the middle of autumn, annual plants and mosses at the base of shrubs begin to grow and reach their peak in early winter and again at the beginning of spring. Decreases in rainfall have reduced their density. If the winter spectrum has the least reflection in all places. While in late summer, when the annuals and mosses have dried up, it has had the greatest spectral reflection. In Fasaran, which is a barren area and a landfill, it has shown its maximum reflection. Therefore, the BSCI index relative to the percentage of organic matter has a significant error in the detection of biological soil crust and where the organic matter is high may not provide accurate diagnosis of soil bioshells. Of course, since the BSCI index is defined for the detection of throat compounds in lichen tissues. The error rate for organic matter is reduced to a minimum. As it has been observed in the final map, there is no cover of biological soil crusts in Fasaran and only soil biological crusts are observed in the areas around Fasaran in the agricultural areas. In agricultural areas, due to human intervention and cultivation, the amount of annual plants is different from the field of natural resources in different seasons of a year have become. Conclusion: Spectral similarity of the most important soil surface, including vegetation, the involvement of human factors in increasing or decreasing soil organic matter, bare soil, etc. limits the efficiency of the BSCI index and therefore in the time period of satellite images and regional conditions have a great impact on It has the accuracy of BSCI index.

Uncertainty is the measure of reliability associated with a particular set of results. There are lots of parameters affecting the water movement through the unsaturated zone measurement or estimation of which are a difficult task encompassing uncertainties of some kind. In this study, a methodology based on fuzzy set theory is presented to express imprecision of input data, in terms of fuzzy number, to quantify the uncertainty in predictions. Richards’ equation as a certain model was solved numerically. To estimate the uncertainty in the model the input parameters (qs, qr, Ks, a, and n) were introduced as fuzzy parameters. After introducing suitable fuzzy membership functions for input parameters, boundary values were obtained for each parameter for different b-cut levels in input parameters. Using these values and considering different result interval boundaries, the mathematical operation on fuzzy sets are performed resulting in the moisture values in specific times and locations. Corresponding to different b-cuts, fuzzy membership functions were derived for soil moisture at any time and depth. The results showed that uncertainty in simulating soil moisture profile was minimum in saturated phase and maximum in advance phase. This was because of the maximum number of parameters taking part in maximum uncertainty in the later phase. The shape of fuzzy membership function for soil moisture in specific time was varying for different depths corresponding to the different role of the effective initial parameters in any time and depth.

Using mathematical models for irrigation management have great impacts to increase irrigation efficiency and product amount, in fields. In this study, simulation results by SWAP model for moisture, compared with soil profiles moisture values, measured in the field. Moisture data, measured at three wheat farms in the Neyshabur plain, were used to predict moisture. Results show good agreement between simulated and measured moisture values. R2 coefficient values were 0.611 for Farob Roman farm, 0.648 for Haji Abad farm and 0.679 for Soleimani farm, respectively. Model absolute value was between 1.5 to 2.9 percent and root mean square error (RMSE) value was between 1.9 to 4 percent. According to these statistical indices, SWAP model has been able to simulate moisture, in soil profile in different depths and times, accurately. Therefore, SWAP can be used for irrigation management in Neyshabur plain, with relatively sufficient accuracy.

Drip irrigation management requires proper information on the horizontal and vertical distribution of moisture in the soil of the root zone. In this study, soil moisture distribution was simulated up to 72 hours after drip irrigation by coefficients of soil water characteristic curve with three different methods in the Hydrus-2D model. These methods were included estimating the soil water characteristic curve of inverse solution methods with soil water characteristic curve data (IS1), inverse solution with soil moisture data after irrigation (IS2), and Rosetta. To determine the accuracy of the simulated moisture values with the measured values, RMSE and R2 were used. The results showed that RMSE and R2 for estimating the soil water characteristic curve in IS1, IS2, and Rosetta methods were obtained 0.008 and 0.999, 0.153 and 0.958, 0.125 and 0.977, respectively. The accuracy of IS1 methods depended on the information of the soil water characteristic curve, Rosetta depended on some early soil properties, and IS2 depended on soil moisture after irrigation. Average RMSE and R2 in the simulation of horizontal moisture distribution for IS1, IS2, and Rosetta were obtained 4.6 and 0.9941, 2.8 and 0.9951, 5.5, and 0.9905, respectively. Also, the mean of these statistics in simulating the vertical distribution of moisture for the mentioned methods were 4.6 and 0.9830, 4.4 and 0.9928, 5.4 and 0.9932, respectively. Finally, the use of IS1 method was recommended due to the shorter time and cost of simulations and the possibility of performing it before the design of the drip irrigation system.