mirnaser navidi

One of the consequences of the climate change in Khuzestan Province is thedrying up of a large part of the wetlands of this province, including Miangaran and Hoor al-Azim, which has caused dust storms in recent years. In this regard, this research aims to predictclimate changes in the area of Miangaran and Hoor al-Azim wetlands by using the SDSMstatistical microscale model based on HadCM3 B2 and A2 climate scenarios. Considering thespecific conditions of the region and the fact that few studies have been done regardingtemperature change in these areas, knowing the state of temperature change can help bettermanagement of resources, especially water resources management.

These parameters include the average sea level pressure, thegeopotential height of the surface of 850 hectopascals and the average temperature at a heightof two meters. For this purpose, by using the daily data of average temperature, minimum andmaximum temperature in the synoptic stations of Izeh and Bostan as the closest stations to Hooral-Azim and Miangaran wetlands in the periods of 2010-2039, 2040-2069 and 2070-2099,predictions were made and then a comparison was made with the base period (1961-1990). Theselected predictors in climate parameters were chosen using NCEP observational large-scaleparameters and SDSM software. These parameters included average sea level pressure, surfacegeopotential height of 850 hectopascals, and average temperature of two meters above thesurface. Also, with scenarios A2 and B2 until the year 2099, the prediction of the return periodof extreme climatic events in the HadCM3 model was done.

The results of the simulation of the HadCM3 model along withobservational data from the Izeh station, modeled annual average temperature data was 18.47°Cand for the Bostan station, modeled annual data average temperature was 19.10°C. Both stationshad a higher average temperature in the base period, and the maximum temperature in theMiangaran wetland was much higher than Hoor al-Azim wetland in the base period. The resultsshowed that in both stations in scenario A2, the average temperature had significant cycles withreturn periods of 1.2 years and the lowest significant cycles for the two stations were in returnperiods of 2.3 and 1.3 years, respectively. In scenario B2, the average temperature in twostations has significant cycles with return periods of 7.5 years and the lowest significant cyclewith a return period of 1.2 years. The results of examining the cycles in the studied areasindicate that in the A2 scenario, certain climatic conditions in the area have short-term returnperiods. One year was obtained in both scenarios, which indicates a wider range of the returnperiod and a higher probability of extreme temperature events in the B2 scenario. In thecomparison of the two stations, it can be seen that in scenario A2, the average temperature ofthe Miangaran wetland is 1.02 °C and the Hoor al-Azim wetland is 1.08 °C.

The results of the data analysis in the future observation and simulation periodswith scenarios B2 and A2 showed an increase in the mean, minimum, and maximum averagetemperature in the future simulation periods compared to the base period in Izeh and Bostanstations. In Bostan station, the average minimum and maximum annual temperature alsoincreased in the third period compared to the base period. In both scenarios, due to the increasein temperature, the drying process of both wetlands will continue.

The soil units in the irrigated plains across the country were identified based on previous soil studies, satellite images, land use maps, and agricultural-climatic zones. The purpose was to determine the land potential for irrigated cultivation of industrial crops such as sugar beet, cotton, safflower, and sugarcane. Then, the FAO framework and the square root parametric method were used to evaluate the land suitability for irrigated cultivation of these crops. The survey results revealed the land suitability classes for four crops under irrigated cultivation: sugar beet, cotton, safflower, and sugarcane. For sugar beet, out of 4 million hectares (Mha) of surveyed land, 1.23 Mha were in class S1 (highly suitable), 1.5 Mha in S2 (moderately suitable), 740 thousand hectares (Tha) in S3 (marginally suitable), 390 Tha in N1 (currently suitable), and 420 Tha in class N2 (permanently suitable). For cotton, out of 2.4 Mha of surveyed land, 75 Tha were in class S1, 671 Tha in S2, 895 Tha in S3, 420 Tha in N1, and 340 Tha in N2. For safflower, out of 2.2 Mha of surveyed land, 38 Tha were in class S1, 56 Tha in S2, 1.3 Mha in S3, 363 Tha in N1, and 398 Tha in N2. For sugarcane, out of 615 Tha of surveyed land, 6 Tha were in class S1, 18 Tha in class S2, 193 Tha in class S3, 98 Tha in class N1, and 300 Tha in class N2. The data was statisticaly analysed and the results indicated that the the land suitability classes were classified with resonable accuracy. The following factors were the main limiting characteristics that limited cultivation of the four crops under irrigation: For sugar beet: soil pH, salinity, alkalinity, and drainage; for cotton: climate, slope, salinity, alkalinity, lime and gypsum; for safflower: pH, drainage, salinity, alkalinity and climate; and for sugarcane: salinity, alkalinity, drainage and slope. Due attention to the prepared land suitability maps of the aforementioned crops would facilitate cultivation management for farmers and planners.

Al land use changes pose a most serious challenge to agricultural production, resulting in the loss of fertile soil and food security. The present study was conducted to investigate the challenges associated with fertile agricultural lands in Iran as an attempt to examine and identify the different aspects of the threats agricultural lands are faced with. As a second step, the laws and regulations governing land use change in different countries and in Iran were reviewed and the general policies related to the protection of agricultural lands were examined.  The review of land use laws in Iran showed that, despite their abundance, they have suffered from poor law enforcement as a result of both social and historical reasons as well as a public negligence of and non-adherence to the laws. Evaluations of temporal changes in the growth and development of metropolises as provincial centers revealed that they had greatly expanded beyond their original limits to annex fertile agricultural lands in the suburbs to these large cities. The five-year statistics of the requests submitted for land use change in each province also revealed landlords’ increasing demands submitted for land use change motivated by more profits out of their property, which result in loss of fertile soil, decreased agricultural production, and endangered food security.

This study expresses the steps of achievement to the indigenous-executive framework of integrated soil and water management at farm level through a mega project with 19 sub-projects. The projects were carried out during 2013-2020 with active participation of international, national and regional experts as well as local farmers in Honam Sub-basin, Lorestan Province, Iran. To understand the soil and water management problems for agriculture in the same homogeneous units (HSWMUs), farmlands were classified regarding physical, chemical and biological characteristics of soil, making it possible to convert soil maps into thematic maps. In this research, the fertility capability classification (FCC) method that integrates surface and sub-surface soil textures with modifieres was used. In addition, several modifieres were revised for soils of semi-arid regions and new modifieres were introduced for Irrigation Capability Classification (ICC). The required information was collected from 119 soil profiles and 101 surface soil samples in detailed scale of 1:25000 and the corresponding measurements were made. Accordingly, different data layers including detailed soil map were produced via geopedological method icluding land limitation, land suitability, and land production potential (LPP) of major crops, land degradation, location of water withdrawal, land use and agricultural cadastre of 2395 farms were prepared. To evaluate the current soil and water behavior of farmers within HSWMUs, the multidisciplinary team recorded the issues through field visits and open semi-structured questionnaire of 84 selected farmers in 24 villages who were interviewed during 2018-2019. The results showed that 23 and 30 HSWMUs that covered, respectively, 93% and 90% of the irrigated and rainfed areas were delineated on the map. Investigations showed that the sub-basin suffers from problems such as plow pan, phosphorus and zinc deficiencies, poor irrigation scheduling, and three types of farmers. Finally, recommended packages were prepared and validated for each HSWMU, which will be discussed in the part 2.

Considering the phenomenon of climate change and subsequent changes in plant water requirements, it is crucial to recognize and estimate climate change in the coming decades with the aim of proper environmental planning to adapt to future climate conditions and reduce its effects. The main factor in the consumption of water resources in arid and semi-arid areas is agriculture and, accordingly, evaporation-transpiration, therefore, awareness of the process of changes and its prediction plays an effective role in planning, development, and water resources management. Since evaporation-transpiration accounts for an important part of the water balance of arid and semi-arid regions, its correct estimation is very important in the optimal preservation of water resources. On the other hand, knowledge of evaporation-transpiration process is necessary to estimate plant water consumption and design irrigation systems. According to the conducted research, estimation of evapotranspiration in the present and future periods is one of the basic needs of development managers, therefore, this research aims to evaluate the evapotranspiration potential in the base period (1991-2005), and predict temperature changes, using 3 scenarios (RCP2.6, RCP4.5, and RCP8.5) and evapotranspiration based on CMIP5 models (1.1 BCC-CSM and CCSM4) in the 75 year period (2025-2100) was carried out in Tabriz city.

For this purpose, the Penman-Monteith method was used as a standard algorithm to estimate potential evaporation and transpiration in the basic period (1991-2005). Then, potential evaporation and transpiration in the period of 2025 to 2100 were estimated using 2.6 RCP, 4.5 RCP, and 8.5 RCP scenarios and the LARS-WG6 model. Finally, the predicted values of evaporation and transpiration in future periods were predicted using the statistical indicators and the calculated evaporation and transpiration values for the base period (1991-2005). The ROC skill score curve also was used in order to general assessment of the quality of the estimations.

The results showed an increase in the predicted potential evaporation and transpiration under the RCP scenarios. In all three scenarios, the highest amount of evaporation-transpiration was obtained in July and the lowest amount was obtained in December. Moreover, an increase in the predicted transpiration evaporation was observed in July, August, January and February, compared to the base period. In addition, a decrease in the predicted transpiration evaporation was obtained in November and December, compared to the base period. In the RCP 8.5 scenario, the difference was much higher than in the base period. The evaluation of the model performance showed that for the hot months of the year, the model had a better ability to estimate the amount of potential evaporation-transpiration compared to the cold months, and the lowest RMSE error was in the hot months, so that in January with a value of 0.15 mm, the lowest value Dara showed the error. A comparison between the months indicated the two months of February and August as the best estimation for the estimation of annual evapotranspiration values. Likewise, the results showed that in all future periods and under all scenarios, the average reference evaporation and transpiration in annual scales will increase significantly at the level of 0.01 compared to the base period. The verification results also showed an acceptable ability for the predictions of the potential transpiration evaporation model.

This research amed at investigation of the amount of reference evapotranspiration changes based on RCP radiative forcing scenarios and climate models from 2025 to 2100 in Tabriz. The obtained results indicate the increase of reference evapotranspiration in all RCP scenarios for each of the future periods. In addition, the highest percentage of the reference evapotranspiration changes in the 8.5 RCP scenario is more than other scenarios because this scenario predicts the worst climate conditions and obvious changes in evapotranspiration will occur. The results of this research can be beneficial to solve the challenges of managers and relevant officials in the future periods. Considering this, the water, environment and health sector planners should adopt the necessary solutions for adaptation and reducing the consequences. Reasonable use of water resources is inevitable under the conditions of warming weather in Tabriz. Increasing evaporation and creating important changes in the quality and quantity of water resources, consequently changes in the quantity and quality of agricultural products. This situation determines the necessity of planning changes in the exploitation of water resources and agriculture. The future plans should be such that the upcoming changes have less harmful effects on the water and agriculture sector in this region. It is necessary to consider measures to improve the irrigation system, reduce evaporation, reuse wastewater and improve the cultivation pattern.

Maize has special importance among cereals across the world, because it has many industrial applications in addition to food consumption. However, the production of this crop requires high amounts of water and fertilizers. The number of inputs will be increased in case of the limited lands are available for cultivation of this plant. Accordingly, land suitability for maize should be determined in order to find the best suitability classes for selection of lands with lower land limitations. The objective of this study was to investigate the effects of soil properties on grain yield of maize in order to determine different soil rating for suitability classes of Maize.

To achieve the objectives of this study, a total of 101 grain maize farms in Khuzestan, Kerman, Fars and Qazvin provinces were selected. A questionnaire form was completed for each farm. A soil pedon was also studied and some soil samples were taken to accomplish the related physicochemical analyses. After that, multivariate regression was performed simultaneously between yield as a dependent variable and different soil properties as independent variables. Subsequently, in order to modify an earlier version of soil and landscape requirement table, the simple regression analysis were performed to find the relationships between soil properties and maize yield for modyfing the rating classes of different land suitability classes. The available soil and landscape table for maize is based on a review of literature and experts opinion. Therefore, it was modified based on the collected data as well as the actual maize yield of farms in different regions of Iran. The verification of the proposed table was performed using data from 14 maize farms (about 14% of data) which was not used in the preparing the table. In the random selection of these 14 points, two samples from Fars, three samples from Qazvin, five samples from Kerman and four samples from Khuzestan were included.

Results indicated that the range of yield variation, lime, sand, clay, silt, exchangeable sodium percentage and soil salinity are high, but the percentage of organic carbon, soil pH and gravel percentage are distributed in a small range. In multivariate regression, the variables of salinity, exchangeable sodium percentage, sand percentage, organic carbon, soil pH, lime and gypsum contents were included, respectively. The coefficient of determination of the proposed relationship was about 0.83. Therefore, the variables entered in the model were able to explain about 83% of variance of the changes related to dependent variable. By using this equation, some simple soil properties can be used to estimate the maize yield. Using simple regression equations, the effect of each soil characteristic on yield was determined and with their help, the soil and landscape requirement table of maize was modified. The value of coefficient of determination between yield and soil index for the verification data was about 0.86, which indicates the reliability of the table proposed in this study. Comparison of this table with the proposed soil and landscape requirements table for maize presented by Sys et al. (1993) indicated that there are many differences in the boundaries of proportion classes for lime and gypsum variables. There is also a difference in the salinity and sodicity variability of the soil at the boundaries of classes S2, S3 and N.

Overall results of this study indicate that the main land limiting characteristics for maize production in the studied regions are soil salinity and sodicity. After these two variables, organic matter and soil pH are the main and effective factors influencing grain maize yield in selected farms across the country. In some areas, such as Kerman and Qazvin provinces, the physical properties of soil are limiting for maize cultivation and thus adversely affect reduction of the yield. As a consequence, it is clear that before planting any crop, the land suitability evaluation for any crop must first be assessed. By using the crop requirements table of each crop and land suitability evaluation one can recognize the land imitations of each region for cultivating that crop. Then, according to the type of limitationt, special management can be assigned for each region.

Over the last three decades, there has been a general tendency to change methods in research on soil resource management from conventional and mainly qualitative methods to Quantitative ones based on spatial correlation models which are called digital soil mapping (DSM). The present study was carried out in Shabankareh plain with an area of ​​15,000 hectares with different physiographic units that are mainly used as agricultural farms in Bushehr province, Southern Iran. Target sites (172 points) were selected for soil sampling at depths of 0-30 and 30-60 based on hypothetical networking on satellite images and visual differences observed in the study area. Digital soil texture maps were drawn for both old soil texture triangle (include sand, silt and clay particles) and the new one (include Geometric mean particle diameter and geometric standard deviation of soil particle diameter). Soil texture is considered as one of the most important characteristics in determining the type and density of agricultural activities and types of land use. Two geostatistical programs include GS+ and ArcGIS and various methods of data estimators such as inverse distance weighting and ordinary Kriging method were used in this project. The results showed the strongest spatial structure class was observed in geometric standard deviation of the soil particle diameter (0.48) and the weakest in silt (0.73). The highest and lowest effective range among soil texture parameters were related to soil clay particles and geometric standard deviation of soil particle diameter with 684 and 336 meters, respectively. Number of drilled profiles (11 ones) was based on digital uniformity map. The generated digital maps can provide spatial information of important soil properties such as permeability and drainage, water holding capacity, fertility, soil erosion and salinity which increases the accuracy in the optimal management of agricultural lands.

In recent years, the increased of greenhouse gases due to agricultural activities has been significant. The purpose of this study is to investigate the extent of carbon dioxide and methane emissions in agricultural lands of Semnan province and Bastam region, including: apricots, grapes, wheat, barley, corn, forage and pastures.

For this purpose, soil samples were taken from a depth of 0-30 cm. Soil properties including salinity, pH, percentage of sand, silt and clay, organic matter, total nitrogen, phosphorus, potassium and calcium carbonate were measured. Closed static chambers were used for gas sampling and in each cultivated and used product, 3 to 5 points and total of 31 points were selected. Finally, the amount of greenhouse gas, global warming potential and environmental costs were calculated.

Based on the results, the highest carbon dioxide emissions were observed for fodder corn in autumn with an average of 3 g / m2 / day and the lowest emissions for pastures in autumn with an average of 0.25 g / m2 / day. For methane gas, the results showed that forage corn in the two autumn seasons had the highest gas emissions with an average of 3 g / m2 / day and the lowest emissions for pastures in autumn with an average of 1.03 g / m2 / day.

Land suitability classification is a useful management method to ensure using the planned and sustainable agricultural land according to their potential. Therefore, the suitability of irrigated plains in Iran was evaluated for wheat, barley, grain maize, rice and triticale in the years 2017 to 2022. The results showed that 1.4 million ha of the studied lands for wheat are in highly suitable class (S1), 2.8 million ha in moderately suitable class (S2), 1.39 million ha in marginally suitable class (S3), lands with an area of 524,531 ha in currently unsuitable class (N1) and 199,503 ha in permanently unsuitable class (N2). For barley, about 1.3 million ha are in S1 class, 2.2 million ha in S2 class, S3 class lands with an area of 1.3 million ha, about 501 thousand ha in N1 class and 489 thousand ha in N2 class. About 39 thousand ha for grain maize are in S1 class, 1.1 million ha in S2 class, S3 class lands with an area of 1.6 million ha, 853 thousand ha in N1 class and 999 thousand ha in N2 class. For rice cultivation, there are 310 thousand ha of land in S2 class, S3 class lands with 1.5 million ha, 877 thousand ha in N1 class and 949 thousand ha in unsuitable N2 class. There are 127,000 ha of land for Triticale in S1 class, about 549,000 ha in S2 class, 323,000 ha of land in S3 class, about 97,000 ha in N1 class and 83,000 ha in N2 class. Statistical analysis of the data showed that the separation of land suitability classes for all crops has acceptable accuracy. The main limiting characteristics were; texture, salinity/alkalinity, slope, drainage and in some areas climatic characteristics. With regard to the distinction of suitable and unsuitable lands, the application of the obtained results can be effective into correct programming for sustainable exploitation, production and cultivation pattern development.

Land use priority assessment is the principal basis of land use planning and management. It is a kind of decision-making with several criteria that sometimes contradict each other. This is why multi-criteria decision making (MCDM) methods have in recent years found wide applications in solving land suitability assessment problems. The objective of this study was to introduce the Best-Worst Method (BWM) and its application in determining the suitability of land for agricultural crop production. Furthermore, the results obtained were compared with those reported in previous studies that had employed the analytical hierarchy process (AHP) as the most widely used multi-criteria decision-making method. For this purpose, soil profile samples were taken from 12 orchards under date cultivation in Jahrom region, Fars Province and subjected to the necessary physicochemical analyses to identify the soil properties affecting crop yield. Once the soil and yield data matrices had been prepared, the BWM and AHP methods were used to determine the weight of each criterion. Land suitability index was subsequently obtained for each garden as the sum of the products of the standardized value of each soil property by its weight. The indices thus obtained were compared with the corresponding actual crop yield to determine the performance of the models employed. Although the results indicated almost identical index values from both methods, the evaluation criteria of minimum violation, total deviation, and conformity revealed more consistent BWM judgments. It was finally concluded that BWM could be recommended as a more reliable and efficient method for solving multi-criteria decision-making problems as required in land use management owing to its varied advantages such as the need for fewer pairwise comparisons compared to other matrix-based MCDM methods like AHP, more consistent and compatible weights ultimately derived in the decision-making problem, the possibility for its application simultaneously with other methods, the use of integers in comparisons, and ease of application.

Land suitability studies of Iran have been scattered and few of them have investigated specifically land suitability for agriculture. Indeed, most of the studies have been conducted locally and, so far, comprehensive studies have not been done at the country level. Determining the suitability of Iran’s agricultural lands for production of crops is one of the most important tools for the optimal use of soil resources. For this purpose, by consolidating, preparing and digitizing soil survey and classification studies in the country and according to satellite images and land use maps and considering agro-climatic zones, soil units were identified to determine the potential of land for irrigated wheat in the irrigated plains of the country. Then, by entering the climate, soil and topographic data in the prepared software based on the FAO approach and parametric square root method, the suitability of all soil units was assessed for irrigated wheat. The results show that, in the irrigated plains, 1.4 Mha of land is in S1 class, 2.08 M ha are in S2 class, 1.4 Mha in S3 class, and 524 thousand ha have currently unsuitable class (N1), with 503 thousand ha being permanently unsuitable (N2). Statistical analysis of the data showed that the separation of land suitability classes has acceptable accuracy. The main limiting characteristics of lands are the soil physical properties including texture, salinity and alkalinity, slope, drainage, and in some areas, climatic characteristics. Due to the comprehensive map of land suitability and differentiation of suitable lands from the unsuitable ones, application of the results obtained with ideal management and usage of irrigated lands in the country will lead to the conservation of land resources, especially soil and sustainable production. Notably, the above results are the basic information layer that along with other aspects including economic issues are needed for developing  a suitable cropping pattern.

Meeting the demand for rice in the country as a strategic product is of special importance. The objective of this study was to investigate the effect of soil properties on rice yield and their rating to prepare a table of soil and landscape requirements of this crop for use in land suitability assessment studies. For this purpose, 99 rice fields in Mazandaran, Guilan, and Khuzestan provinces were selected. In each farm, a soil profile was studied and a land use questionnaire was completed. After the necessary physical and chemical analyses, a simultaneous multivariate regression between yield and different soil properties was investigated. Then, in order to prepare a table of soil and landscape requirements for rice, simple regression relationships between effective soil characteristics and yield were investigated and rated for different land suitability classes. Verification of the proposed table was performed using data from 18 rice fields (about 18% of the whole data). The results showed that the range of yield, lime, sand, clay and silt in selected fields was extensive, but the percentage of exchangeable sodium and soil salinity were in a more limited range. In multivariate regression, the variables of salinity, ESP, lime, soil reaction, percentage of clay and sand, respectively, were efficient. The coefficient of determination of the proposed equation was about 0.38. Also, the value of the coefficient of determination between yield and soil index for verification data was about 0.71, which indicates the reliability of the proposed table in this study.

The present study was conducted to investigate changes in soil physical and chemical properties and organic carbon storage at different stages of sugarcane growth (Plant, Ratoon 1, Ratoon 2, Ratoon 3, and Ratoon 4) in some sugarcane plantations in southern parts of Khuzestan Province, Iran. For this purpose, a number of soil profiles were dug in the sugarcane fields and 30 sites were selected and studied in a completely randomized design with five treatments (i.e. sugarcane growth stages) and six replications. The results of this study showed that long-term cultivation of sugarcane and different stages of growth in soils of Sugarcane Cultivation and Industry Company caused changes in physicochemical properties and soil carbon storage. Indeed, abundant irrigation and leaching and cultivation management of sugarcane reduced the salinity and sodium in soil solution. Also, different growth stages changed the physical and chemical properties of the soil. Different stages of sugarcane growth reduced electrical conductivity (EC), dissolved sodium in the soil, the available potassium (K), and increased the amount of bulk density (Bd) in the soil. There was no significant difference in the amount of organic matter (OM) and soil acidity (pH) of sugarcane fields at different stages of growth. Finally, in order to prevent possible negative consequences and depletion of soil nutrients, especially potassium, it is necessary to periodically study the complete properties of the soils and evaluate these changes, appropriate management methods can be performed to maintain soil quality. The results showed that difference in soil carbon sequestration was significant (p<5%) in the treatments, such that the highest carbon sequestration was in Raton 3 (28.84 tons/ha) and the lowest in Raton 4 (15.50 tons/ha). The reason for the higher amount of carbon storage can be attributed to more vegetation and, therefore, more plant debris, which reduce evaporation from the soil surface, a positive effect on vegetation, especially in arid and semi-arid regions. In general, optimal management of sugarcane fields plays an important role in improving atmospheric carbon sequestration capacity.

The objective of this study was to investigate the effect of soil and land characteristics on saffron yield, their rating for preparation of a soil requirements table of this plant for land suitability studies by FAO method. First, 124 saffron farms in Khorasan Razavi, South Khorasan, Fars, Golestan, Markazi and Kerman provinces were selected. In each field, suitable soil samples were taken and a land use questionnaire was completed during three years from 2017 to 2019. Soil samples were tested to determine their physicochemical properties. After preparing the data, they were statistically analyzed. Multivariate regression between yield as a dependent variable and salinity, percentage of exchangeable sodium, soil reaction, gypsum, lime, clay, sand, silt, gravel and available potassium and phosphorus of soil were studied as independent variables by stepwise method. By examining the relationships of simple regression between important and effective land characteristics and yield, land characteristics rating was performed. Then, the saffron soil requirements table prepared. The proposed table was verified with the data of 21 new farms. The results showed that available potassium, sand, soil salinity, percentage of exchangeable sodium and lime had the highest and the reaction of soil and organic carbon had the lowest range of changes. Regression results showed that the independent variables of soil salinity, ESP, lime, gypsum, gravel and available potassium and phosphorus, respectively, affect yield. Coefficient of determination of multivariate regression showed that the variables entered in the model were able to determine 95% of the variance related to the dependent variable. The largest contribution to the reduction of saffron yield is related to soil salinity, gravel, exchangeable sodium percentage and the amount of lime. Validation results showed that the yield correlation coefficient with the soil index of the proposed table for saffron is about 0.92, which indicates the acceptable accuracy of the proposed table.

The strategic importance of cotton in providing the fiber needed by humans, animal feed and job creation raises high attention to cotton production. This study was aimed to investigate the effect of soil characteristics on cotton yield and their rating for applying in land suitability assessment studies. Consequently, 123 farms in Qom, Khorasan Razavi, Golestan and Fars provinces were selected. In each farm, a soil profile was studied and a land use questionnaire completed.After handling the necessary physical and chemical analyzes, multivariate regression was performed by the backward method between yield (dependent variable) and different soil characteristics (independent variables). By creating simple regression relationships between effective soil characteristics on yield, the characteristics rating was determined for different land suitability classes. Results showed that the amount of variance of salinity, ESP, potassium, lime and soil texture components was high and insignificant for soil pH and organic carbon. Therefore, most of the data were very extensive. In multivariate regression, the variables of salinity, lime, organic carbon content, silt, ESP, soil pH and gypsum percentage, which had the highest correlation with yield, remained in the equation, respectively. The coefficient of determinationof the proposed relationship was about 0.9. The validation of the proposed soil requirements table was performed using data from 24 cotton fields (about 20% of the data). The value of the coefficient of determination between yield and soil index was 0.78, which indicates the reliability of land characteristics rating and the table prepared in this study.

Identifying land potential and suitability based on soil characteristics is one of the appropriate management tools to increase sustainable land use. The objective of this study was to investigate the effect of soil characteristics on fig yield, their rating and the preparation of vegetative requirements table of this plant for land suitability studies by FAO method. Therefore, 94 fig orchards were selected in Fars, Lorestan, Kermanshah and Kerman provinces. In each orchard, a soil profile was studied, a land use questionnaire completed and physical and chemical tests performed on soil samples. The values of soil characteristics were calculated by applying weighting factors with depth in the profiles and multivariate regression between yield and soil characteristics was investigated by stepwise method. The high coefficient of determination of the multivariate model (R2= 0.89, P<0.01) showed that the variables entered in the model were able to determine 89% of the variance related. The results showed that among the measured soil characteristics, the effect of salinity, alkalinity, amount of lime, gypsum, gravel, available phosphorus and potassium was significant on fig yield. Finally, according to the results, soil salinity, alkalinity, amount of gypsum, lime and gravel had a decreasing effect and organic carbon, available phosphorus and potassium had an increasing effect on fig yield.

Understanding the factors that limit prediction of regional performance of sugarcane crop and improve management practices is essential. The purpose of this study was to select a model that could estimate the potential of sugarcane production by considering the climatic, plant, and soil and terrain characteristics of the region. The study areas in the south of Khuzestan Province included the agro-industries of Amir Kabir, Miza Kuchak Khan, Dabal Khazaei, Salman Farsi, and Farabi. For this purpose, 100 sites in farms of different areas under sugarcane cultivation were studied based on diversity in soil characteristics. The method of this research was done in two hierarchies. In the first stage, estimation of sugarcane production potential using FAO growth model and, in the second stage, estimation of land production potential for sugarcane according to the effect of limiting factors in soil (as calculated using soil index with parametric method) that reduced yield in the first stage. To analyze the data, regression by standard methods, stepwise, and estimation curve were used. In standard and stepwise regression, soil properties were considered as an independent variable and the observed yield was considered as a dependent variable. In the linear, second and third degree estimation curve methods, the observed yield of sugarcane was considered as an independent variable and predicted yield was selected as dependent variable. The results of the first phase showed that the production potential of the FAO growth model was 95.8 tons/ha while the results of the second phase showed that the production potential of sugarcane in the region at different management levels was estimated from 18 to 69.3 tons/ha. Factors of yield reduction included limiting factors such as lime content, heavy and very heavy soil texture, drainage, salinity and sodicity, and lack of proper management. The results of standard and stepwise regression methods showed that the coefficient of determination was 0.52 and 0.49 and the standard error (ME) was 10.13 and 9.77 tons/ha, respectively. Soil properties could predict yield by standard method up to 52% and stepwise method up to 49%. In the estimation curve method for linear model, second degree, and third degree, the coefficients of determination were 0.74, 0.85, and 0.87, respectively, and standard errors were 7.8, 5.8 and 5.3 tons/ha. Therefore, the third degree estimation curve method, which uses the FAO growth model to predict crop yield, has higher accuracy and less error than the standard and stepwise regression model, which use only the effect of land characteristics on the observed yields.

Accurate assessment of land suitability for agricultural production plays an important role in reducing the effects of land degradation. The aim of this study was to apply fuzzy, AHP and GIS techniques to determine the degree of land suitability for wheat production in lands of about 61,000 ha located in Qazvin plain. By Investigating the land use maps, geology, topography, slope and satellite image of the region, the delineation of primary soil units was separated, then in the separated units, profiles were excavated and described, and soil horizons were sampled. Based on the characteristics of representative profiles in each map unit, land suitability assessment for wheat cultivation was performed using fuzzy method with AHP and square root method and land suitability map was prepared in GIS environment. According to the results of AHP-fuzzy method 48169.1 ha (78.2%) of the land are moderately suitable (S2), 9086 ha (14.7%) marginally suitable (S3) and 4346.3 ha (7.1%) currently unsuitable (N1). The most important limiting characteristics were slope, pH, ESP, drainage and soil salinity. The results showed that the fuzzy method integrated with AHP was able to evaluate accurately with high value of determination coefficient between land index and yieldcompared to the parametric method of the square root and the modified square root due to determining the appropriate limits of critical points, suitable weights for characteristics, use of fuzzy logic relations and principles and suitable membership functions. Therefore, the mentioned integrative method can be an accurate and effective method for better planning and management of land use for agricultural production.   Identification of Land Potential and Characterizing Limitations for Irrigated Wheat Cultivation in a Part of Qazvin Plain Land Using Fuzzy and AHP Techniques

Iran is one of the major date-producing countries and not only has a long history in this field, but also it currently has the second grade of Date production in the world. Therefore, the cultivation and production of Dates in Iran is of particular importance both nationally and for the inhabitants of the producing provinces. In this regard, planning for optimal utilization of production resources such as soil and lands for development of the yield in the country has a special place. Consequently, evaluation of land suitability and utilization of lands in proportion to their potential and capacity for specific land use seems an important solution. However, one of the necessities of land suitability assessment is to determine the crop requirements of the plants such as the status of the soil as a production bed. The objective of this study was to investigate the effect of soil characteristics on Date yield and their rating for use in land suitability evaluation studies.

First, 91 palm Date orchards were selected with diversity of soil and yield in Kerman, Fars, Khuzestan, Hormozgan and Bushehr provinces, in each garden, a soil profile was studied and a land use questionnaire completed. Physicochemical and fertility analyses were carried out on the collected soil samples. Multivariate regression was investigated between yield as dependent variable and independent variables including salinity, percentage of exchangeable sodium, pH, gypsum, calcium carbonate equivalent, clay, sand, silt, gravel, available potassium and phosphorus by stepwise method. Then, by checking the relations of simple regression between the important and effective characteristics with yield, related equations and diagrams were drawn and the rating of land characteristics set. The proposed crop requirement table was evaluated and validated using soil and yield data of 20 orchards.

Results showed that potassium, sand percentage, soil salinity, ESP and CaCO3 had the highest and soil organic carbon content and pH had the least variation. Maximum amount of lime and gypsum was 74 and 17% and soil texture varied from sandy to clay, respectively. Regression results showed that independent variables including soil salinity, ESP, CaCO3, gypsum, gravel, available potassium and phosphorus were effective on yield, respectively. The determination coefficient of multivariate regression indicated that the variables entered into the model can determine 79% of the variance related to the dependent variable. In simple regression equations, soil salinity, ESP, gypsum, CaCO3 and gravel had a decreasing effect and organic carbon, available phosphorus and potassium had increasing effect on yield. The highest contribution to the decline in Date yields was related to soil salinity, gravel, ESP and calcium carbonate.

The coefficient of determination between yield and soil index obtained from the proposed soil and landscape requirements table for the Date was about 0.79 which showed acceptable accuracy of the prepared table. Soil salinity, ESP, gypsum and calcium carbonate limit for Date palm were determined 8 dS/m, 12, 8 and 38 percent, respectively.

The purpose of this study was to investigate the effect of soil characteristics on sugarcane yield and to revise the “Table of Soil and Landscape Requirements for Sugarcane”. For this purpose, 120 points in different areas under sugarcane cultivation in Khuzestan and Mazandaran provinces were studied based on diversity in soil characteristics. Parametric methods were used in this study to determine the land index. For data analysis, multivariate regression by stepwise method and simple regression were used. Sugarcane yield was considered as a dependent variable and factors including salinity, percentage of exchangeable sodium, soil acidity, gypsum, lime, clay, sand and silt, as independent variables. Then, by examining the simple regression between important and effective land characteristics with sugarcane yield, the relevant equations and diagrams were drawn and soil properties were rated by FAO method. The results showed that exchangeable sodium, clay, lime, acidity, and salinity, in decreasing order, had a significant correlation with yield of sugarcane, while and the variables of sand and silt had no significant correlation. Multivariate regression coefficient analysis showed that the variables entered in the model were able to explain 67% of the variance related to the dependent variable. In simple regression equations, soil salinity, exchangeable sodium content, and lime had a decreasing effect on yield. The variables of exchangeable sodium percentage and soil salinity had the greatest effect on sugarcane yield. The proposed table on sugarcane soil and landscape requirements was evaluated and validated by using soil data and yields of 30 sugarcane fields that had not been used to extract this table. The coefficient of determination between the sugarcane yield and the land index was about 0.81, indicating the acceptable accuracy of the table.

Pistachio is one of the most important and valuable orchard products in Iran, and its compatibility with unfavorable growth conditions, especially drought and salinity, causes its high production in the country. The study aim was to investigate the effect of soil characteristics on pistachio yield and their rating for applying in land suitability assessment studies. So, 124 pistachio orchards were selected in Kerman, Fars, Khorasan-Razavi, Isfahan and East-Azarbaijan provinces. In each orchard, a soil profile was studied and a land use questionnaire completed. After the necessary physical and chemical analyzes, multivariate regression was performed by the stepwise method between yield (dependent variable) and different soil characteristics (independent variables). By creating simple regression relations between effective soil characteristics on yield, the characteristics rating was determined for different land suitability classes. The results showed available potassium, salinity, ESP, sand and CaCO3 had the highest variation, and pH, organic carbon and available phosphorus had the lowest one. The results of stepwise regression showed the input of salinity, ESP, gypsum, CaCO3, gravel, available phosphorus and potassium to the equation were significant (P<0.01). The variables entered into the regression model with NRMSE=0.280 determined 75% of the variance related to the dependent variable (yield). In simple regression relations, salinity, ESP, gypsum, CaCO3 and gravel had negative influence and organic carbon, available phosphorus and potassium had positive influence on yield. The validation of soil variables ratings with R2=0.82 and NRMSE=0.224 distinguished acceptable accuracy of the proposed table which can be confidently applied for use in land suitability assessment.

Introduction:

 In order to land classification for agricultural and natural resources, the most important criterion and factor is the production or yield of the lands. The best way to evaluate a method is to interact between the yield and potential of the product with the land specification involved in the production. One of the new methods in the land evaluation for different uses is the modeling or simulation of the intended use. Artificial neural networks are one of the new tools that are used today to simulate yield and determine the effective factors in the production of agricultural crops. New land evaluation methods include different modeling techniques, which these new methods, including simulations, are used in land valuation methods to test the ability of models for determining the relationship mentioned. An artificial neural network is one of the techniques that can do nonlinear analysis. It is important to recognize the most important input parameters to the predictive models of olive yield, which are also considered to be effective variables in production. Through the process of sensitivity analysis, valuable information about the sensitivity of the model to its input variables can be provided to the designer and model architect. In north of Iran, in the southern areas of Gilan Province, the most important olive gardens of the country are located in the southern part of the country. Different soil and water, topographical and climatic factors affect the yield of olive orchards.

Materials and Methods:

 Climatic data, soil and water characteristics, topographic characteristics and leaf nutrition elements as input variables and olive yield were used as output models. Twenty-eight factors were selected as the most important factors or variables affecting the yield of olive orchards. These input variables included soil properties: EC, (TNV), organic carbon percentage, available phosphorus, available potassium, clay percentage and silt percentage; irrigation water characteristics including: EC and content of irrigation water; topographic characteristics including: altitude and the slope; the concentration of nutrients in leaves included: nitrogen, phosphorus, potassium, iron and zinc; climatic factors including: sunny hours, evaporation rate, average temperature and olive yield were considered as output of artificial neural network model.

Results and Discussion:

 Using the MATLAB software by artificial neural network, the best structure of this network was obtained for the component of the yield of olive. The trained structure had 28 input nodes in 5 groups and one output node. The number of hidden nodes, 38 nodes and the most appropriate number of repetitive learning based on the test and error, 20 were determined for olive product yield. After determining the best structure of the neural network with a R-test of 80%, using Hill's sensitivity analysis, the model's response to each of the input variables was studied and the most important factors influencing the yield of olive oil were obtained. Based on the results of sensitivity analysis, the most important parameters affecting the yield of olive are content of leaf nitrogen, soil phosphorus, winter evaporation, summer evaporation, average autumn temperature, summer sunshine, leaf potassium, salinity, salinity, and slope, respectively. In general, this study showed that the amount of nitrogen and phosphorous is the most important factor for the production of olives, and then variables that are related to the amount of water in the soil, (summer evaporation, average autumn temperature, summer sunshine), were identified as important. The concentration of potassium, iron and zinc in the leaf as a nutrient element and water and soil salinity from stressors for plants and slope are important topographical factors that affected the amount of soil depth and soil water content as the most important factors in olive crop production.

Conclusion:

 In brief, the role of micro and macro nutrients and the factors affecting the maintenance of water in the soil and providing moisture for the plant. In addition to nutrients, gardeners should consider soil and water salinity, which is a stressor for plants, as well as slope, which is important topography factor that effective on soil depth and water available and nutrients for plants as the most important factors in olive production.