1 2251-7812 University of Hormozgan 451 An Investigation of Land-Use Effect on Dust Concentration and Soil Loss in Desert Areas: A Case of Ein Khosh-Dehloran, Ilam Mirhasani Marzieh b Rostami Noredin c Bazgir Masoud d Tavakoli Mohsen e b Ilam University c Ilam University d Ilam University e Ilam University 1 5 2018 8 1 1 20 12 06 2018 21 10 2018 Extended abstract 1- Introduction The occurrence of dust storms caused by wind erosion is a process that causes the destruction of land and can also be considered as a desertification indicator (Xu, 2006). Generally, the formation of a dust storm depends on three factors: the presence of strong winds, a sensitive surface to wind erosion and unstable weather conditions (Xia and Yang, 1996). Humans play an important role in the formation of dust storms through changing land use in sensitive areas to sand storms. In the arid and semi-arid areas covered with tiny and unstable materials, land degradation may occur as a result of land use change caused by over-grazing and farming. As a result, wind erosion increases, leading to more sand storms (Xu, 2006). 2- Methodology For this research, first, using satellite imagery and land use map, the land uses in the study area that included the Ziziphus Nummularia natural forest, planted Prosopis Juliflora forests, agricultural land, sand dunes and rangelands were determined. After observing and accurately identifying the area, one week after the first rainfall, 15 undisturbed soil samples and 15 disturbed soil samples were collected from the area. After transferring the samples to the laboratory, the samples were exposed to dry air and then some physical and chemical properties of the soil were measured. Wind tunnel was used to determine the soil loss in different land uses, also the dust density determined using a Microdust pro device, which was installed in the outlet of the wind tunnel. This device measures dust concentrations in milligrams per cubic meter. So soil samples were simulated in 4 wind speeds including 2, 9, 16 m/s and wind erosion threshold velocity for 5 minutes. To determine the wind erosion threshold velocity, soil samples were placed in the wind tunnel. Then, by adjusting the wind speed that was possible by the inverter and using the accelerometer, the wind speed erosion threshold was measured in different land uses. In this way, the velocity has slowly increased, and the first particle that began to move was considered as the wind erosion threshold. In this study, the erosion rate was calculated from the ratio of the weight or volume of eroded soil to the sample surface. So, to determine the amount of soil loss, at the end of each experiment, the amount of sediment accumulated in the sediment trap was collected and weighed, and the soil loss was calculated based on the amount of soil erosion in grams per cm2 per minute. 3- Results Based on the results, there was no significant relationship between the dust concentration in the undisturbed and disturbed samples, but there was a significant relationship between soil loss in the undisturbed and disturbed samples. Comparison of the mean of suspended particles and the amount of soil loss in the both sample groups showed that the lowest and highest amount of suspended particles and soil losses was related to the Ziziphus Nummularia natural forest and sand dunes, respectively. According to the correlation results, there was a positive and significant correlation between wind erosion and SAR parameter (P <0.01), but there was a negative and significant correlation between the erosion and OC, Silt, SP and CS (P <0.01). Also, erosion had a negative correlation with EC, Mg, P and had a positive correlation with sand (P<0.05). The Principal Components Analysis (PCA) showed that three main components of wind erosion controller were Pc1, Pc2 and Pc3, whose quota were about 48.7%, 21% and 9.7%, respectively. 4- Discussion & Conclusions The results showed that by increasing the wind speed from 2 to 16 m/s, the intensity of wind erosion and dust concentration increased, but the amount of these parameters was various in different land uses. As in both sampling methods, these parameters had decreased from sandy hill, pasture land, planted Prosopis Juliflora forests, agriculture and Ziziphus Nummularia natural forest, respectively. In general, it can be said that in different land uses, the amount of soil loss and dust concentration in disturbed samples was more than undisturbed samples. Actually, since the soil structure is broken up during the sampling, the stability between the soil particles is lost and the soil is easily exposed to wind erosion. Also, due to the corrosion of the soil, the bulk density varies. As the bulk density increases, the soil quality will decrease (Harris et al, 1996). Finally, it was found that Pc1 components had more control over wind erosion. The components of Pc1 include EC, organic matter, Mg, lime, silt, saturation moisture content, porosity and compressive strength. These parameters have an effect on wind erosion, and cause erosion to be further reduced.
450 An Iranian Model of Desertification Potential Assessment for Sustainable Regional Development zehtabian gholamreza f khosravi hassan g eskandari damaneh hamed h abolhasani azam i f university of Tehran g university of Tehran h university of Tehran i university of Tehran 1 5 2018 8 1 21 38 11 06 2018 21 10 2018 Extended abstract 1-Introduction Desertification means land degradation in the arid, semi-arid and dry sub-humid regions because of natural or anthropogenic factors. Desertification is accounted as the third most important global challenge after the crisis of water shortage and drought in the 21st century. Desertification is a problem for many countries, especially the developing countries. Identifying the regions exposed to desertification is so important for combating desertification and leads to better planning. Also the awareness of inappropriate management practices can reduce the intensity of this phenomenon, and prevent its development. There are many models for preparing desertification maps in the world that the most common of which includes UNEP-FAO, ASSOD, GLASSOD, LADA, and MEDALUS. Also in Iran ICD, MICD and recently, IMDPA models have been presented. IMDPA model includes some criteria affecting desertification, and some indicators for the quantitative assessment of the criteria. In this research IMDPA model was used for the assessment of Iranian desertification condition. 2-Methodology IMDPA model have 8 criteria which are called desertification criteria and their indicators are used for quantifying them. At first 130 indicators were selected for all 9 criteria but on the one hand it wasn’t possible to prepare information of all of them for the whole of country and on the other hand it was costly and time consuming. So for each criterion, up to 4 key indicators were identified. Indicators related to each criterion are as follows: Climate: drought, aridity index, rainfall amount. Geomorphology: land use, rock sensitivity and physiography. Water: negative balance of water, groundwater depletion, EC and SAR. Soil: EC, texture, depth, gravel percentage. Vegetation cover: coverage status, coverage utilization and vegetation cover rehabilitation. Erosion: water erosion (vegetation cover density, land use and water erosion density and type) and wind erosion (days with dust storm index, vegetation cover, non-living cover density and erosion faces appearance). Socio-economic: socio-cultural factors, organization and participation, awareness, experience and native knowledge.   Urban or technology development: mine and road density, converting forests and rangelands to urban and industrial areas, improper agriculture, converting garden lands to residential-industrial regions and amount of green space per person. In this study each index received a weight (1 to 4) according to expert opinions and each criterion was obtained based on its indicators’ geometric mean according to the formula below:                                                                    Index X: each criterion Layer: indicators related to each criterion N: number of indicators related to each criterion Weighted averages of indicators related to each criterion were determined and finally desertification intensity was gained based on the geometric mean of all criteria according to the formula below:                  Then, final map of desertification intensity was determined using different layers and obtained maps related to each criterion and combination of layers and maps. So, the map of each criterion status was obtained from its own indicators. These maps can be used for the study of each index quality and its effect on desertification.  3- Results 3-1-Map of water and irrigation criterion The result of the quantitative value of this criterion that was determined using its indicators is shown in Figure (2). The zoning map of Iran plains based on Q3 is shown in Figure (3). Also this calculation result is shown in Table (2).  3-2-Map of vegetation cover criterion The map of Iran’s desertification condition based on vegetation cover or Q5 is shown in Figure (4). Table (3) shows all of its information. Also the zoning map of Iran’s plains based on Q5 is shown in Figure (5). 3-3-Status quo of Iran’s desertification Finally, the intensity map of Iran’s desertification was prepared and the calculation was done in order to determine the classes of desertification (Table 4).   4-Discussion and conclusion In this research IMDPA model was used in order to prepare Atlas of Iran’s desertification. In this model, there are 8 criteria which are regarded as desertification criteria and their indicators are used for quantifying them. At first, 130 indicators were selected for all the 9 criteria but on the one hand it wasn’t possible to prepare the information of all of them for the entire country, and on the other hand, it was costly and time consuming. So for each criterion, up to 4 key indicators were identified. The desertification intensity map of Iran was determined using these criteria and indices, and also, they were quantified in the arid, semi-arid and sub-humid regions.  The results showed that 88.73% of the country surface was affected by desertification which is equal to 143365238.6 hectares. The surface of more than 49425703.3 hectares equal to 30.59% of the total surface of the country was in the low desertification class, the surface of more than 935677913.6 hectares equal to 57.91% was in class II or medium, and a surface about 371621.7 hectares equal to 0.23% was in class III or intense. Class IV of the desertification or very intense was omitted regarding to IMDPA model and the 8 criteria, and the natural desert areas whose surface was equal to 15624274.3 hectares or 9.67% were beyond this class. 448 Advanced machine learning methods for wind erosion monitoring in southern Iran Rezaei Mahrooz j Sameni Abdolmajid k Fallah Shamsi Seyed Rashid l j Shiraz University k Shiraz University l Shiraz University 1 5 2018 8 1 39 58 08 06 2018 07 10 2018 Extended abstract Introduction Wind erosion is one the most important factors of land degradation in the arid and semi-arid areas and it is one the most serious environmental problems in the world. In Fars province, 17 cities are prone to wind erosion and are considered as critical zones of wind erosion. One of the most important factors in soil wind erosion is land use/cover change. Therefore, accurate mapping of land use/cover and wind erosion evidences in arid and semiarid regions is the utmost importance. Moreover, for discrimination of land covers resulting from wind erosion such as sand sheets and Nebka, we need accurate remote sensing methods. In this study, capability of the advanced machine learning techniques on Landsat 7 and 8 imageries in mapping land use/cover related to wind erosion is evaluated.   2- Methodology The study area is located in the Fars province, in the southern part of Iran, (from 28°07′15″ to 28°13′07″N and 52°07′36″ to 52°23′55″E, covering an area of 17,230 ha), which is considered as the most critical wind erosion area of the province. Landsat 7 (2006) and Landsat 8)2013) images were corrected radiometrically using Dark Object Subtraction method. Although images from USGS website are corrected geometrically, we checked the images using stream and road maps. According to the variations in land use/cover spectral behavior across the study area, it was difficult to define training samples representing thematic classes in a supervised classification procedure. Then different image enhancement techniques were applied. Classification stopped using Support Vector Machine with four different types of kernels including linear, polynomial, Radial Basis Function, sigmoid and Kohonen’s Self-Organizing Map neural network. Results were compared with Maximum Likelihood method. Using separability analysis, the best input band combination for classification was selected. The Overall Accuracy and Cohen’s Kappa coefficient, derived from the error of matrix which were used for the accuracy assessment of the final maps.  3- Results Results from accuracy assessment showed that the best map of the land use/cover in the relation to wind erosion was produced using a combination of original and processed bands and RBF vector machine (overall accuracy of 88 and 90.87 percent for L7 and L8, respectively). According to the separability metrics, the near infrared (NIR) and short infrared band (SWIR1), the WDVI, SAVI, LI indices, and processed bands by edge analysis in the aspect of E were finally selected as the best input band combination. The difference between accuracy of this method with linear, polynomial, SOM, sigmoid and ML methods were 1.5, 2.9, 8.3, 12.4, and 16.4 percent for L7 and 2.16, 4.16, 6.19, 13.89, and 14.67 percent for L8, respectively. In addition, results indicated that there was a significant change in wind erosion potential and land use/cover in relation to wind erosion in the study area in a short period of time. Rangelands were decreased by 73 percent and 10 percent of these areas are covered by sand sheets. More than half of rangelands were converted to agricultural lands. Insusceptible areas with surface crust or rocks were decreased by 59 and 2.39 percent, respectively. 4- Discussion & Conclusions The accuracy of classification increased using a combination of processed and original bands in comparison with using original bands alone. This indicates the fact that processing image classification without paying attention to the quality of input bands, will not results in accurate classification map. One of the advantages of active learning algorithm is its less training samples requirement. This is very important for areas which are difficult to have access to them. Although there were not distinct and large sand dunes in the study area like what can be seen in desert areas of Iran, but discrimination of these small sand dune and nebkas were done accurately using the combination of original and processed bands of Landsat imageries and support vector machine methods. Goodarzimehr et al., (2012) also indicated that support vector machine was a better algorithm for discriminating lithology units comparing to maximum likelihood and neural network methods. Sandification was also recognized using remote sensing methods in this study which is one the indices of land degradation and wind erosion. Sand sheets showed and expansion mostly to the southeastern parts. The results indicated the change of rangelands into agricultural lands which will increase wind erosion potential. Low-efficiency irrigation systems combined with an increase in soil loss from arable lands leads to reduction in productivity. This is in line with findings by Minwer Alkharabsheh et al. (2013) who reported the progressive decrease of the agricultural areas and mixed rain-fed areas as the main reason of declining in soil erosion in Jordan. Generally, this study showed the capability of Landsat imageries and support vector machine learning in study of wind erosion potential in arid areas.  456 Effect of Simulated Dust Storm on some Bio-chemical features of Persian Oak (Quercus brantii Lindl.) Roushani Nia Farshad m Naji HamidReza n Bazgir Masoud o Naderi Mostafa p m Ilam University n Ilam University o Ilam University p Ilam University 1 5 2018 8 1 59 73 18 06 2018 21 10 2018 Extended abstract 1- Introduction Dust storm is a hazardous natural event affecting all creatures. (1). Due to the global warming drought, reduction in precipitation, and mismanagement in the water by humankind, the negative effects of this calamity were more observed (2). With increasing the wind speed, and based on the size of dust particles, land topography, soil humidity, vegetation coverage and some more parameters, the storm is generated (Ataei and Heidari, 2017). Most parts of Iran in South and West are attacked by dust storm coming from some neighboring countries (Liu et al, 2003). About 90% of forests in Ilam, are covered by a valuable tree species, Persian oak (Quercus brantii). The dust storm has become as a big challenge in this area for two last decades (Sayehmiri et al, 2018). The dust reduces the potential water storage (Rasooli et al, 2010), photosynthesis rate, the amount of chlorophyll pigments, carbohydrates reserves and finally leads to tree mortality (Salehi et al, 2018). Therefore, because of the significant effect of dust on the trees and also having no fundamental information on the effects of this event on endemic trees in the national forest, we aimed to evaluate the effects of dust in a controllable condition on the Persian oak. 2- Methodology This study was done in forest laboratory, Ilam University. About 40 two-year seedlings of Q. brantii were provided from governmental nursery in Eyvan. To adapt with the new conditions, the seedlings were put in the open area for two months. The dust for the study was collected from the closest desert of Iraqi desert, Dehloran, which is very similar to the dust originated from natural sources. In the laboratory, the dust was ground to reach the size of about 40 µm. To treat the seedlings with the dust, a chamber with dimensions of 2*2*2 m was made and three barbeque fans were placed to suspend the dust. The process of dusting was done in three periods: 1) 220 g of dust at six 1.5 hrs from 9 am up to 6 pm that at each series about 36 g of dust was re-added to the chamber. Likewise, a similar condition was prepared for control seedlings. 2) The 2nd and 3rd periods of dusting was also the similar to the first one, but with some changes in the concentrations and the time. The time interval between periods was 12 days and dust induced at the second and third periods was 330 and 440 g. One week after the last period, some leaves specimens were collected from seedlings. The leaves were stored in the freezer -80 °C for further analyses. The measured biochemical features were chlorophyll pigments, carbohydrates, and proline. The data was analyzed by ANOVA to determine the effect of dust, periods, and their interactions. 3- Results According to the results, the effect of dust on chlorophyll a, b, total, carotenoid and carbohydrates were significant, but had no significant effect on proline. The interaction effect of dusting on proline was significant, with no significant effect on the other factors. The results of t-test at first the period showed insignificant differences between treated and control seedlings for all features. At second and third periods, significant differences were observed between all features except proline. The highest increase was seen in carbohydrates. The results of mean comparison showed significant differences between chlorophyll a, b, total, carotenoid and carbohydrates. The highest variation was observed in highest concentration in dust at the third period. At the third period compared to the first one, the chlorophyll a, b, total, and carotenoid showed a reduction of 31%, 31%, 31%, and 30%. The carbohydrate in third period was 40% higher than the first period. The proline showed no significant difference. 4- Discussion & Conclusions Chlorophyll pigments are of most important biological factors for the plants that usually are reduced by environmental stresses (Saravana Kumar and Sarala Thambavani, 2012). Linear correlation between photosynthesis rate and stomatal conductance shows the significant of stomata for net photosynthesis productivity (Ashenden and Williams, 1980). Therefore, the dust stress reduces the Co2 in the stomata (Sayyahi et al, 2015). The high activity of chlorophylase enzyme leads to breaking up the chlorophyll that decreases the chlorophyll pigments (Loggini et al, 1999). Furthermore, the shade from dust on the leaves clog the stomata and also increases the leaf temperature resulting into producing that enzyme (Moradi et al, 2017). The leaf alkaline condition makes a reduction in chlorophyll pigments. The reduction in light intensity and also nutritive ions decrease the photosynthesis rate in the pigments (Brandt and Rhoades, 1972). Due to the shading from the dust, the amount of carotenoid is reduced. The first role of this pigment is to conserve the chlorophyll (Allen et al, 1998). Concerning to the carbohydrates, the increasing amount of this feature is due to increasing starch decomposition and other polysaccharides. On the other hand, the increase in carbohydrates could be a reason for reduction of tree growth no consuming of the nutriatives (Ehdaie et al, 2006). Related to the proline, it is a significant osmolite for moderation of osmotic pressure in the cells affecting from stresses (Mohammadkhani and Heidari, 2008). No significant variation in this study might be due to short time of dusting. In general, dust had significant effects on the most physiological features of the Persian oak seedlings as high amount of dust and less amount of chlorophyll pigments. To sum up, in spite of the continuum drought and out-break of pests and diseases, the dust with the origination of neighboring countries are another influencing factor for oak decline in Iran. 458 Analysis of events of dust using satellite monitoring and synoptic analysis in southwest Iran Raispour Koohzad univirsity of zanjan 1 5 2018 8 1 74 93 19 06 2018 04 11 2018 Extended abstract 1- Introduction Dust storms are a kind of severe natural disaster indust source regions, which have a negative impact on human health, industrial products and activities. Iran is a dry  and low water country, the coincidence of this situation and its position in the global rebound belt has brought about very bad conditions. Repeaters in recent years have been affected by the severity and frequency of major events in Iran and, in terms of environmental issues, studying and managing the reduction of its effects is a priority. In recent years, these events have been the main hazards in areas of South-west Iran. Several complex dust storms have recently occurred in the southwestern part of Iran. Detecting the spatial distribution of dust storms in the deposition regions is an essential step for managing this natural and human-induced crisis. Land measurement and remote sensing techniques are currently two of the most important methods for monitoring dust storms. Traditional reference land measurement methods have little spatial and temporal resolutions, so they can not properly monitor and anticipate dust storms. Due to rapid changes in the nature and location of dust storms, there are limitations in monitoring and relating measurements. Meteorological numerical models can not detect dust storms alone. Today, remote sensing technology is known for providing multiple global and regional images with time, spatial and spectral scales as a useful tool for monitoring , measuring and harvesting dust properties. Also, remote sensing can monitor the range and scope of dust storms, their degree of intensity and their route of movement. In this research, by using MODIS images and applying methods of applying the dust algorithm, dust was monitored. This study aimed to investigate the performance of the Normalized Differences Dust Index (NDDI) applied to MODIS data (01/11/2017) for detection of dust storms in the Southwest of  Iran.  2- Methodology Monitoring disasters properly is a necessary requirement. In this study, the dust event that took place in November 2017, based on Terra / MODIS remote-sensing indices, has been monitored by ECMWF database and synoptic analyzes from the NCEP / NCAR database. Therefore, according to the characteristics of reflection and absorption of the aforementioned dust event, the resulting dust range and its intensity are extracted accurately using the NDDI index and their degree of intensity is estimated. The NDDI equation is as follows: Here b3 and b7 reflect the band of three and the band of seven of the MODIS bands. 3- Results Based on the results, the dust pollution eventually increased the air pollution in some areas of Khuzestan, Ilam, Kermanshah and Kurdistan provineces, and greatly reduced the horizontal visibility. The results also showed that the dust storm began from the day in which it started from deserts in the northeast of Saudi Arabia, and after spreading to the vast deserts of southern Syria, northern Arabia and west of Iraq, moving eastward to southwest Iran has moved. The maximum spatial distribution of dust is in Khuzestan province.  After the Khuzestan province, western parts of Ilam, Kermanshah, Kurdistan and Zanjan provinces are located in the next rows. 4- Discussion & Conclusions The results of the satellite monitoring show a great deal of agreement with monitoring the meteorological conditions at the time of the occurrence of dust.  The results also showed that the atmospheric conditions affecting the Atmosphere borderland in creating turbulence and transferring dust from deserts in northern Arabia, east and south of Syria, west and south of Iraq to southwest Iran have an important and undeniable nature.  So, in the dusty event studied, unstable airborne conditions, such as the presence of very deep wrecks, a strong wheel with a significant positive tau in the wake axis, along with instability and climbing the air, made the region an unrestrained and turbulent situation over adjacent Arabian desert areas Provided with the West of Iran and provided very favorable conditions for harvesting, transportation and transportation to the western and southeastern parts of Iran. 465 A Comparative Study of Land-Use Change and its Impact on Erosion Rate Using Object-Oriented Classification Method in Simineh Rood Basin of Boukan asghari saraskanrood Sayyad Palizban Delnya piroozi elnaz Faculty Literature of Humanities, Mohaghegh Ardabili University Faculty Literature of Humanities, Mohaghegh Ardabili University Faculty Literature of Humanities, Mohaghegh Ardabili University 1 5 2018 8 1 94 109 30 07 2018 21 10 2018 Extended abstract 1- Introduction Human beings have always sought to assess changes and discover changes. Soil erosion is one of the most important factors in soil degradation and reduction of fertility. Today, erosion of the soil due to non-expert human intervention has been removed from its natural process and has led to irreparable consequences. Considering the importance of studying changes in land use and its role in soil erosion over time, land use changes in Simineh Rood of the Boukan County in West Azarbaijan province and its role in soil erosion (between 2000 and 2017) were studied. 2- Methodology The current research was conducted based on the integration of data analysis and remote sensing techniques as well as the geographic information system. In the present study, the layers of distance from the waterway and the distance from the road and the slope were delineated using Boukan topographic map. Also, the soil map of the area was prepared, using the soil map of the province. Moreover, the geology map of the area was sketched according to the geology map of the province. The basin rainfall map was set out using Boukan meteorological stations data as well as the adjacent stations, obtaining gradient equation of precipitation. To identify the area and to prepare a map of the city, the map of the county lands and the images of the google earth and the terra images of the Terrestrial Sensor, pertaining to the years 2000 and 2017, were utilized. ENVI 5.3, Arc GIS 10.5, Idrisi selvi and Excel were employed for the processing of the images and for the analyses of the data. The land use map of the basin was prepared using an object-oriented method. The WLC method as a multi-criteria analysis technique was applied to prepare the erosion zonation map. 3- Results The map of the studied basin was prepared in 9 classes (aquaculture, rainforest, orchards, residential areas, communication lines, water, pasture, rocky lands with scattered vegetation and Bayer lands) through an object-oriented method. According to the obtained map, the results showed that the highest area in 2000 was related to the use of rangeland with a total area of 541.979 square miles. In 2017, the highest amount of land covered with the rocky lands with a dispersed land cover was 591.70. Also, the lowest usage rates in both years included the use of communication lines with 5.358 and 8.192 km2, the residential areas with 9.141 and 15.639 sq. Km, and the water with 22.320 and 18.480. The coefficients of evaluation (Kappa coefficient) extracted in 2000 and 2017 were 0/89 and  0/92, respectively. According to the erosion zoning maps in the area, in 2000 the area of the high risk class was 147.924 sq. Km, which increased to 185.971 sq. Km in 2017. In contrast with the high risk area of 470,511 sq km in 2000, it increased to 571 .081 sq km in 2017. 4- Discussion & Conclusions According to the results of the study, during the study period, pastures, drought and water decreased, and in contrast with the use of aquaculture, dry land, rocky land and residential areas, there was an increasing trend. According to the results of erosion hazard zonation, the area of high-risk and high risk classes increased from 8.79% and 28.2% in 2000 to 11.04% and 34.08%, respectively, in 2017. This can be attributed to an increase of 180.408, 129.245, 96.875, and 6.498 km2 from the area of arable land, bayer lands, rocks and residential areas, and a decrease of 359.806 square kilometers of rangelands. Therefore, according to the erosion zoning maps in the study area, in 2000 and 2017, the areas with high-risk categories are either unused or with agronomic uses, and the areas with low risk are very little in the pastures. It can be concluded that the results of the study are as follows: the studies of Esfandiari et al. (2014), Moradi (2016), Faizizadeh (2017), Asghari et al. (2017), Martinsmurilo et al. (2011) and Dasilova et al. (2016) are consistent with the fact that crops are the most common and the pastures have the least potential for erosion.