مهرداد اسفندیاری

In recent years, the irreversible phenomenon of land subsidence has led to environmental hazards in various plains of Iran including Gorgan. In general, the most important activities causing this phenomenon are inappropriate groundwater withdrawal and geological factors. The research method consists of two sections: identifying subsidence areas and examining the effective factors and parameters and evaluating the impact of each. In identification section, radar interferometry technique was used to compare the phase taken from two radar sets from the same region at two different times and measurement of land surface changes over time can be achieved through interferogram, and in the effective factors analyzing section, the determination and analysis of effective parameters such as water level drop, texture and thickness of soil layers, especially fine-grained layers were investigated. The results of the satellite data analysis indicate that the region is steadily subsiding. The mean velocity map along the satellite line of sight obtained from time series analysis showed a subsidence rate of 14 mm / month (169 mm / year). The identified subsidence range is approximately eastern-western which is consistent with trends in structures such as the Caspian. Figures of water level and precipitation in this area during 2007 to 2009 show a decreasing trend despite of seasonal fluctuations, and analysis of effective parameters shows that the subsidence is due to the same drop in water level or the difference of same thickness of the fine-grained layer at different depths

The rise in water demand and reduction of water quality and soil in irrigating areas, especially in dry and semi-arid areas of the world, have turned into one of the most crucial challenges for water and soil engineering in recent years. This issue leads us toward optimal quantitative and qualitative management of these valuable resources aimed at achieving economic performance and water productivity. The periodic evaporation and transpiration of the plant in the conditions of simultaneous water and salinity stress are known as one of the most important factors in the qualitative and quantitative growth of the plant yield. Applying mathematical models that simulate the relationship between field variables and yield can be seen as a useful tool in water and soil management issues in such a situation, which has the potential to ensure optimal use of the water and soil resources of any country by providing the plant's water needs and preventing its further loss.

A factorial experiment was performed in 2019 based on completely randomized blocks design with three replications in plots with an area of 9 square meters at the agricultural and animal husbandry farm of Aliabad Fashafuyeh, located in Qom province to examine the simultaneous effect of different levels of water stress and salinity on the periodic evaporation-transpiration and fresh yield of the single cross 704 forage corn cultivar. The applied treatments included the irrigation water salinity at three electrical conductivity levels of 1.8 (S0), 5.2 (S1), and 8.6 (S2) deci Siemens/meter (dS/m), which were prepared by mixing saline well water of the region with fresh (drinking) water and three water stress levels of 100% (W0), 75% (W1), and 50% (W2) of the plant's water requirement. The depth of soil moisture in the corn plant root zone was measured by the TDR device at five depths of 7.5, 12, 20, 40, and 60 cm during different growth stages of the plant using pairs of 7.5, 12, and 20 cm stainless steel electrodes.

The simultaneous water and salinity stresses, which led to the reduced amount of periodic evaporation-transpiration of the yield compared to ideal conditions (without stress), were simulated by additive and multiplicative models. The results suggested a decrease in the evaporation and transpiration with the increased simultaneous water and salinity stresses so that the amount of total evaporation-transpiration in different treatments was measured to be between 692.7 and 344.9 mm and the fresh yield was estimated between 50.4 and 3.2 tons per hectare. Also, the highest amount of periodic evaporation and transpiration in all treatments was found to occur in the development and intermediate stages, and the relative fresh yield in the W0S0 to W2S2 treatments was calculated between 66% and 100%. The results of modeling the relative yield of the crop based on the amounts of relative evaporation and transpiration of corn in different growth stages and under the different treatments of water stress and salinity, indicated that Singh's additive model and Rao's multiplicative model were appropriate, while the Minhas model was recognized to be inappropriate in this estimation.

The research results suggested the significant impact of water stress and salinity at least at the 95% level on evaporation and transpiration and the corn yield. Moreover, the effect of the sensitivity of different growth stages of the plant on the reduction of evaporation and transpiration of corn varies so that in the three treatment groups W0, W1, and W2, the highest average decrease in slope was related to the final stage (13.6%) followed by the middle stage with an average decrease of 8.4% compared to the control treatment. Therefore, the highest decrease rate in evaporation-transpiration slope has been observed in these two growth stages due to the beginning of flowering, fruit formation, and physiological ripening of seeds. These results come from the lack of sufficient water storage and increased salinity of irrigation water in the soil. Water stresses and salinity will reduce water absorption and evaporation-transpiration, and ultimately, reduce crop production due to the decreased amount and potential of water in the soil. Another finding to be mentioned is the priority of water stress compared to salinity stress in reducing evaporation and transpiration and production yield. Also, by managing water and salinity stresses in the critical stages of plant growth (especially the middle stage), which is the time of flowering and the beginning and completion of the maize production process, a significant reduction in the crop can be somewhat prevented.

 Wind erosion has occurred in a large part of Iran, which has caused land degradation and reduced fertility along with environmental effects. Identifying erosion-sensitive areas can help natural resource and environmental managers in soil conservation planning.

This study is a step to estimate the erodible component of soil against the wind (EF) from soil accessibility characteristics in Allahabad plain located in the east of Qazvin province. For this purpose, the soil erodibility component, which is closely related to soil erosion versus wind, using multivariate regression (MLR), artificial neural network (ANN), and artificial neural network with genetic algorithm for weight optimization (GA-ANN) were estimated using accessible characteristics. Regarding soil map, soil differences, and environmental characteristics of Allahabad plain, 103 soil samples were collected according to a stratified random pattern of 10 cm of soil surface. In soil samples, some soil properties were measured as inputs of models for estimating erodible soil components against the wind. The inputs of each model included pH, ECe, CCE, SAR, bulk density, sand particles, silt and clay, coarse soil particles with a diameter of more than 2 mm, and organic matter. Accuracy and reliability of the results of the created models were compared with each other according to the criteria of coefficient of determination, square of error, Morgan-Granger-Newbold and Akaike information criterion.

Based on data, the highest correlation between soil erodible fraction to wind erosion (EF) was observed with soil clay content (r = -0.789). Also, soil erodible components showed a correlation with other soil properties including pH, electrical conductivity, SAR, organic matter, and the should be omitted density. This correlation was significant with three properties of SAR, organic matter, and clay at a should be added 1% level. The models created by the three methods were much more capable of predicting EF in the test data series than the training series data. The results also showed that the neural network model had a should be omitted more accuracy and less estimation error compared to hybrid and regression models. The results of sensitivity analysis of the models also showed that the highest sensitivity of the model to input variables in the ANN model, related to organic matter and SAR, respectively, and in the model GA-ANN was related to soil clay content variable.

According to the results, R2 in the regression model of training data was more than 50% in estimating EF, but this value (R2 = 0.56) is not reliable. According to the test data, all three models, including regression, artificial neural network, and its combination with genetic algorithm had not been efficient enough in estimating EF, so that can be omitted the highest R2 in the neural network model in the test data (R2 = 0.43) had an accuracy of less than 50% in estimating the EF, which cannot be an appropriate accuracy in predicting EF.

The optimum and sustainable use of soil is only possible with a correct and complete understanding of its properties. This study was conducted to investigate of should be omitted some physical, chemical, and micromorphology properties of soil formed on the should be added landscapes in the should be added semi-arid region of Lorestan province.

For this study 5 profiles, a long, a should be omitted transect in the direction of slop (plateau, gravelly colluvial fan and pediment plain) were identified in the area, and divided into different physiographic unit based on the type of parent material. At each, physiographic units a profil was excavated, dissected sampled. Physicochemical together with micromorphological observations performed and Soil Taxonomy and WRB were used for soil classification.

The results showed that should be omitted low amounts of organic carbon in the surface and depths of the soil due to the high rate of decomposition of these substances in these areas. The equivalent calcium carbonate in all profiles showed an increasing trend with depth. Thin section investigation showed that there was not much difference between the horizons in terms of the type of voids and the relative distribution of large particles (C/F). Planar, vugh and cannel voids were the most dominant voids of the profiles. Accumulation of calcite was seen in various forms such as coating, infilling, needle- shaped calcite, and nodules calcite which indicates that secondary calcite, and caused a should be omitted crystallite calcite of kind b-fabric. Soil structure in most the horizons of kind angular blocky and sub angular blocky. Also, lenticular plates gypsum was observed on the Bky horizon on the of the gypsum physiographic units plateau. According According to the existence of pedogenic gypsum in the soils of the region, and the important of the gypsum horizon in the soil classification system and its important effect on plant growth. New great groups, and subgroups of Xerepts (Gypsic Calcixerepts) are suggested to be included in soil taxonomy.

Results of the study showed raise in topography and parent material on the variability of soil properties in the area.

Erosion is one of the main factors restricting the soil fertility and dust production, in several parts of the world, including Iran, has effects on climate agriculture, and human health. Controlling wind erosion would be more effective once sufficient information concerning the effective factors is available. Soil Erodible Fraction (EF) is one of the soil properties that shows the sensitivity of soil particles to wind erosion. The current research aimed to utilize ANN methods and integrating it with GA in order to estimate the soil erodible fraction to wind erosion. Allahabad plain in the southwest of Abiek city in Qazvin province is considered as one of the areas sensitive to wind erosion with strong wind direction from southwest to northeast. The drying up of Allahabad wetland will intensify wind erosion in the region and turn it into a crisis. Determining the extent of land erodibility and identifying its factors affecting can be the basis of a comprehensive plan for soil protection and land sustainability and prioritizing its implementation steps. The present study was conducted to use artificial neural network methods and combine it with genetic algorithm to estimate the soil erodible factor.

In the study area, which was part of the Allahabad plain in Qazvin province, between the coordinates of 50°15 ́- 50°57 ́ east longitude and 35°53 ́- 35°57 ́ north latitude, 95 samples were taken from 10 cm of soil surface. In the samples, the percentage of aggregates with a diameter of less than 0.84 mm as an indicator of EF and percentage of clay, sand and silt, soil saturation capacity, pH, EC, SAR, equivalent calcium carbonate (CCE) and organic matter were measured as input to the models. In this paper, to model the EF using early available characteristics, two methods of artificial neural network (ANN) and its integration with genetic algorithm (GA-ANN) were employed in order to optimize the weights. In this regard, the data were primarily divided into three categories as follows: 60% of the data series was allocated to training, 20% to validation, and 20% to network testing. In this study, MLP networks were used to model the artificial neural network in estimating the values ​​of soil erodible Fracion. In this structure, each artificial neural network includes inputs and hidden and output layers. During the learning process, the degree of network learning by the objective functions was regularly evaluated and networks with the lowest error rate were accepted. To determine the optimal network with the highest level of performance of all stimulus functions defined in the software (axon hyperbolic tangent, axon sigmoid, axon linear hyperbolic tangent, axon linear sigmoid, axon bias, linear axon and axon) by trial and error The most results were used. Levenberg-Marquardt training functions were used to teach defined networks. In this study, genetic algorithm was used to find the optimal point of complex nonlinear functions in combination with artificial neural network (GA-ANN). The genetic algorithm optimizes the weights of the artificial neural network. In fact, the objective function of the genetic algorithm is a function of the statistical results of the artificial neural network.

The results showed that the erodible fraction of soil with five soil properties including pH, electrical conductivity, SAR, clay and organic matter, had a significant correlation at the level of one percent. The models used did not have an appropriate accuracy in estimating EF in both training and testing stages, so that the highest R2 was obtained in the artificial neural network model (0.49) with test series data. Both models were slightly overestimated and the GMER values ​​for the ANN and GA-ANN models were 1.15 and 1.08, respectively, but according to the AIC index, both models had similar predictive power. Sensitivity analysis of the data showed that the greatest effect on EF in the ANN model was related to organic matter (4.07) and in the GA-ANN model was related to clay (8.14).

In the current research, the relationship between soil chemical characteristics and EF might be attributed to their previous effects on vegetation in the region. Additionally, regional evidence indicates the same finding. The highest correlation was observed between EF and soil organic matter. Based on the sensitivity analysis, in the neural network model, the greatest effect on erodible fraction was related to organic matter, pH, and EC, respectively. The effect of pH and salinity on EF could be interpreted due to their effects on vegetation and consequently, the effect of vegetation on aggregates.  An important issue in the research was that the proposed models, which were ANN and its integration with GA for estimating the soil erodible fraction, were not efficient enough for obtaining the highest coefficient of determination (R2) in the model in the neural network in the test phase (R2 = 0.49), which has an accuracy of less than 50% for estimating EF.

Saffron (Crocus sativus L.) is one of the most expensive crop which is grown in restricted areas of the world. Due to its economic values, some farmers, based on similarities of climatic conditions have cultivated it in some regions of country regardless of land capability and suitability, which sometimes the result was not satisfactory. Saffron yield prediction based on soil properties enables us to assess the land suitably for cultivation of this valuable plant. For this purpose, 100 soil samples were collected from Vamenan Saffron fields in Golestan province and the soil chemical and physical properties including the percentage of constituents of the mineral part of soil texture (Sand, Silt, Clay), Phosphorus, potassium, Nitrogen, pH, Electrical Conductivity (EC), Organic matter and Calcium Carbonate Equivalent were measured. In addition, the weight of Saffron wet flower (kg.Ha-1) was measured. In the present study, various combinations of soil properties as input were applied and nine models were developed using artificial neural networks and multiple linear regression models for predicting the saffron yield. Performance of the models was validated using Root Mean Square Error (RMSE), Correlation Coefficient (R) and Geometric Mean of Error Ratio (GMER) methods. The results of the correlation analyses showed phosphorus and organic matter are most effective factors in the production of Saffron. Results showed that performance of the models is much different where R2 value varies from 0.45 to 0.89. Comparing the performance of Saffron yield estimation models indicated the optimal model was obtained from the combination of phosphorous, organic matter, potassium and calcium carbonate equivalent as input and values of R2 and RMSE equal to 0.874 and 0.996 kg.ha-1, respectively.Evaluation of model results indicated that the coefficient varied was obtained from 0.45 to 0.89. The best model in saffron yield estimation was obtained when phosphorous, organic matter, potassium and electrical conductivity were as the input, so that values of R2 and root mean square error (RMSE) were obtained 0.891 and 0.89 kg.ha-1, respectively.

This study was conducted in order to digital mapping soil classes according to WRB system was conducted by boosted regression tree (BRT) method on about 60.000 hectares of Hirmand county lands. 108 soil profiles were dug and soil profiles were sampled and described based on WRB system. Twenty environmental covariates were used as estimators for soil mapping including terrain attributes and remote sensing covariates. Results showed that the young soil covered the study area and mostly influenced by flood sediment which classified as Fluvisol and Cambisol groups and Solonchak group in salt -affected area. The variable importance showed that the environmental attributes such as Multi-resolution Valley Bottom Flatness Index (MrVBF), Valley Depth, Convergence Index, Catchment Area and Salinity Index (NDSI) had the highest importance among all covariates for two levels of WRB prediction. The validation results showed that the BRT model could predict WRB1 and WRB2 levels with overall accuracy of 47 and 25%, respectively, and also from WRB1 to WRB2 levels the accuracy decreased. In low relief area and young soil that the low soils variability, digital soil mapping approach could be useful, efficient, and fast technique to produce and predict soil classes map.

Recently, the Lake Urmia has become one of the key dust sources in Iran due to a sharp drop in its water level. One of the essential information to decide how to deal with this problem is to know the distribution of the particles size carried at different heights above the soil surface. In order to trap the dust particles of the Lake Urmia area soils erosion, one of dust centers that was located in the southeastern of the Lake Urmia in 30 kilometers distance from the Tabriz-Azarshahr road was selected and the BSNE samplers (Big Spring Number Eight) were used. 14 pole, each of which had 4 samplers at 0.15, 0.5, 1 and 2 m heights above the soil surface were installed in a circle pattern. The samplers were evaluated in 12 interval periods (March 2016-February 2017) and the particle size distribution and the vertical profile of gathered particles were determined. Results showed that the amount of fine particles (0.15-0.053 mm) was greater than the coarse particles (1-0.25 mm). The highest amount of the fine particles was obtained at 100 and 200 cm and of the coarse particles was obtained at 15 cm height above the soil surface. When the height above the soil surface increased, the weight of gathered particles decreased. The power function was the best to explain changes between the weight of gathered particles and the height above the soil surface. There was a negative correlation between monthly rainfall and the weight of gathered particles.

Soil and plant resource management has been introduced in recent years as one of the most important ways to reduce management costs using modern methods, including artificial intelligence techniques. The present study attempted to use the neural network method to predict the nutritional status of the two copper and zinc elements in the Echinops plant using the data of the chemical forms of the copper and zinc elements, as well as the stability indicators of the aggregates. For this purpose, the chemical forms of the two elements of copper and zinc, as well as the average of weight diameter and geometric mean diameter of aggregates in 34 soil samples, and zinc and copper concentrations in the Echinops plant were analyzed for each soil sample and eventually Multilayer perceptron modeling was performed using Levenberg-Marquardt algorithm with four separate inputs in each section. The results showed that the sum of carbonate and exchangeable forms as the input data had the highest coefficient of variation and the lowest standard error in predicting Zn concentration of the plant. On the other hand, the total exchange and carbonate forms with the stability of aggregates as inputs had the best prediction of the copper status of the plant. Unlike zinc, soil physical data (aggregate stability indicators) could have a significant effect on predicting the state of copper in the plant.

In this research, a primary and basic soil production model was utilized. This model has considered soil formation in a landscape conditioned by a digital elevation model. The model demonstrated the application in quantifying pedogenesis. The model stated the changes in soil thickness with respect to time duration; it depends on physical weathering rate of rock, the loss due to chemical weathering and transport of the soil through erosion. The rate of physical weathering or lowering of the bedrock surface is represented by an exponential decline with soil thickness. The movement of materials in the landscape was characterized by diffusive transport, leaching and dissolution. Dissolvation, authigenesis and hydration of soil were considered as losses by chemical weathering. The model was solved numerically using finite difference approach and applied to a digital elevation model. The obtained results showed that the soil thickness is highly related to the profile curvature. The effect of climate, rock type and land management were presented by different combinations of weathering rate and erosive diffusivity. The model also exhibits the characteristics of a nonlinear dynamic system. Simulation of soil development in Qazvin plain for different time scales was illustrated. Finally, results showed promising progress in evaluation of quantitative pedogenesis.

Various parameters, such as rainfall, region height, evaporation rate, temperature, climate factors, drainage, topography and geology of the basin effect runoff in watersheds. Due to the interrelation of some of the mentioned parameters, their quality and effect on runoff may be different for each region. This paper presents a statistic assessment of the parameters that are effective on runoff and spatial zoning of surface water resources in central Zagross, west of Iran. The results showed a relationship between logarithmic distribution of surface runoff, and temperature and height variables with a 0.795 - 0.851 R2 coefficient of determination, applying statistical analysis and multi-variable regression method for the parameters. Considering 80 selected stations of the studying area with a correlation of 0.923, the runoff distribution in the form of discharge logarithm related to rain logarithm and height variable with confidence level of 95% showed meaningful and acceptable relation .The zoning plan was prepared through ArcGIS software on the basis of weighting effect index of each variable. The analysis of factors which affect runoff formation, and also analysis of the effect of the mentioned variables on preparing zoning plan showed tremendous movement of potentially appropriate water resources regions from south towards north and east of the studying area. Furthermore, there was approximate correspondence between hydrological parameters and determination of suitable water resources location, and statistic multi-variable regression analysis, logistic and weighting index determination of variables methods.

Due to the large surface area and high cation exchange capacity zeolite as an amendment of physicochemical soil characteristics can be secondary effects on the ability to use nutrients such as potassium and changing different forms of the element and thus the cycle. However, the addition of this compound to Vertisols mainly with high amounts in clay mineral with high capacity for k fixation such as smectite can affect the distribution of potassium. To investigate this, the effects of adding different amounts of the zeolite and wetting and drying treatment were studied on 6 Vertisols that were collected from different regions of Fars province.
Based on previous soil maps, aerial photos and topographic maps of Fars province`s Vertisols were selected and some soil profiles were dug in different climatic conditions. After description, six pedons were chosen as representative that almost all Vertisols studied in xeric moisture regimes in the northern and central parts of the province and all of them are in great groups Haploxererts and Calcixererts. Soils were located in the piedmont plain, plateau and lowland physiography. Experiment was a completely randomized 3×2×4×6 factorial arrangement on surface horizons after treatment of soils with zeolite (0, 1, 2.5 and 5%) and wetting and drying treatment. After 8 days, different forms of potassium consisting of soluble, exchangeable and non-exchangeable forms) were determined in the samples. Statistical analysis was done by using the software SPSS, Excel, SAS and comparison of means was carried out using the Duncan’s test.
The studied soils comprise ustic and xeric moisture regimes and mesic, thermic and hyperthemic temperature regimes. Although the mineralogical composition of the various soils were more or less the same, due to the different amounts of various clay minerals in different soils the amounts of different kinds of K was very different in soils. The results showed that zeolite application can increase the amounts of soluble and exchangeable potassium while it has no effect on non exchangeable potassium, which could be due to the high amount of potassium in zeolite and great tendency of zeolite to absorb potassium. Zeolite application with and without wetting and drying treatment has increased the amounts of soluble potassium in different soils. Maximum increase was observed in soil which has the lower amounts of smectites. Wetting and drying treatment caused significant reduction of soluble potassium in all samples. Exchangeable K has significantly increased by the addition of zeolite. Highest concentrations of exchangeable potassium were observed in soils with less amounts of smectite. Zeolite application has reduced non-exchangeable potassium in soils but had different effect on soil no. 4 and has increased the non-exchangeable potassium. This indicates potassium fixation in the soils which could be due to the high amounts of minerals capable to fix potassium. Wetting and drying treatment caused the reduction of soluble and non-exchangeable K and increase of exchangeable K.
The increase of soluble K as a result of zeolite application can enhance possibility of potassium leaching but on the other hand it is also able to prevent the leaching and fixation of K by retention of potassium in exchangeable sites. Considering the very low amount of available potassium in highly calcareous soils of Fars, which is the results of intensive agriculture and little or no K fertilization, zeolite application, may be able to improve the status of potassium in these soils.

Sirjan plain is one of main and potential areas of pistachio cultivation in Kerman province, Iran. A east to west transect perpendicular to Sirjan plain development was selected, and 15 profiles were described and sampled in the present physiographics under pistachio cultivation. Physical and chemical property of soil samples were determined and classified up to family level. The Ocric horizon was separated in surface layer and Salic, Gyosic and Cambic horizons was in lower layers. During transect, profiles from east to west classified of Entisols and Aridisols and at least development shown in East and greatest change was observed in center and west of transect. Physiography of the gravelly alluvial and colluvial fans (East and South- East) has been less affected by soil formation processes than in flood plains and low lands (West). The main parameters affecting pistachio production are soil texture and gravel in East of transect (profiles 1 to 4) and salinity, sodium adsorption ratio, gypsum, high groundwater levels and physical properties in the Center and West of transect (4 to 12). For efficient land use under cultivation exact study and monitoring information, correct salinity and alkalinity, management of gypsic soils and improvement of soil fertility and organic matter is recommended.

In recent years, the interaction of land use change with soil erosion has become a major environmental concern and has a dramatic impact on soil erosion and sediment yield. In this research the impact of land use and vegetation on erosion forms and sediment production of the basin vers in Qazvin province was investigated using MPSIAC erosion model by Geographical Information System (GIS). The different forms of erosion (Surface, Rill, gully, bank, Stream) can be seen as moderate to severe in vers watershed. Amount of sediment for the whole of watershed was 7.1 ton per hectare per year and the amount erosion was 7.04 tons per hectare per year. Dryland farming with 2.14 (metric tons per hectare per year) has the greatest impact on erosion and sediment yield. In pasture (type 3) and dry farming have very high sediment yield and indicated the improper use of land. Sediment produced on pasture (Type 1) Less than a garden land use that reflects the positive impact of proper pasture vegetation in reducing the amount of erosion and deposition.

In order to investigate the effect of phosphorus fertilizer on yield ,oil and canola protein Hyola 401 cultivar in dry farming in different groups of soil phosphorus fertility sixteen experiments in four areas and in each area four experiments in four groups of phosphorus usable for the soil (less than 3 ppm, 3-6 ppm, 6-10 ppm and more than 10 ppm) was conducted in the form of randomized complete blocks design in 4 phosphorus fertilizer treatments (0, 25, 50, 75, kgs of P2O5 in each hectare) from the triple super phosphorus source with four repetitions in the city of Behbahan in cropping years(2008-2010). The obtained results showed that phosphorus fertilizer treatments in all soil fertility groups had no significant effect on grain yield, number of capsules, number of grain in the capsule, 1000 -grain weight, percentage of oil and percentage of protein. Also, interaction (phosphorus fertilizer × fertility groups) in all soil phosphorus fertility groups had no significant effect on the measured factors.

Proper application of N fertilizer and its optimization for increasing the economic yield of rice is definitely important. In order to determine the best N application method and amount according to growth stages of hybrid rice, an experiment was carried out at experimental farm of RRII in a factorial experiment based on a randomized complete block design with 3 replications. The treatments included 6 application methods as follow: total nitrogen at transplanting stage, 50% at transplanting stage +50% at early tillering, 50% at transplanting stage +50% at panicle initiation, 50% at transplanting stage +25% at maximum tillering +25% at booting, 34% at transplanting stage + 33% at early tillering + 33% at booting and 70% at transplanting + 30% at panicle initiation. 3 levels of nitrogen (90,120 and 150 kg/ha) from urea source were also used. Recorded traits were grain yield and yield component. Results showed the significant interactions between split methods and N rates on yield, flag leaf area, filled and unfilled grain number per panicle and percentage fertility (p)

This study was conducted to assess soil degradation status in the Abhar-Khorramdarreh Plain. A geopedologic map (soil map) was produced by integration of three information layers including the lithologic, geomorphic, and pedologic characteristics of the study area in the ILWIS environment. Information from the produced geopedologic map, together with the data extracted from the existing soil maps, was used as input to GLASOD approach to assess the susceptibility of soils to degradation. In addition to the above data layers, several other layers including the salinity map, alkalinity map, and land irrigabillity map were also produced to fulfill the requirements of the GLASOD approach. The result revealed that from a total 45,500 ha land areas studied, only 14% showed little or no evidence of degradation while the rest had varied degrees of susceptibility to degradation. Moderately and highly susceptible lands covered, respectively, 6,000 ha and about 17,000 ha. Based on the results, it is recommended to take the necessary measures to combat soil degradation in moderately and highly sensitive areas to ensure sustainable land management in the studied plain.

Rill erosion map is one of the basic maps in erosion and sediment studies and also watershed management programs. Some methodologies for preparing rill erosion map (1:250000) by using RS and GIS were compared in a research carried out in Kan- Sologhan and Jajrood sub-basins in North-west and North-east Tehran, Iran. At the first phase, four working unit's maps were prepared by integration of a) plant cover, geology and slope b) land use, geology and slope c) land use, rocks sensitivity to erosion and slope and d) land use, rocks sensitivity to erosion and land units’ layers in Kan- Sologhan basin. For comparing the efficiency of four working units’ maps, 53 ground control points were investigated. Results showed that map "d" was better than "a", "b" and "c" in providing rill erosion regarding economic and executive considerations. At the second stage, map "d" was compared with three maps of e) land units f) rocks sensitivity to erosion and g) satellite image photomorphic units (ETM+ images) in a broader basin of Jajroud. Rill erosion intensities in 314 ground control points were controlled and ground truth rill erosion map was prepared. Rill erosion map was crossed with different working unit maps where results showed that the highest accuracy is related to the images interpretation (81.0% accuracy). Accuracy was 78.4, 66.8 and 5. percent for methods of "d", "e" and "f", respectively. Root Mean Squared Error was the highest for map "f" and the least for integrated layers method (map "d"). The greatest precision is related to image interpretation and the images interpretation method is better than other methods in preparation of rill erosion map.