Digital Mapping of Surface and Subsurface Soil Organic Carbon and Soil Salinity Variation in a Part of Qazvin Plain (Case Study: Abyek and Nazarabad Regions)

IntroductionKnowledge of the spatial distribution of soil salinity and soil organic carbon (SOC) leads to obtaining valuable information that is effective in decision-making for agricultural activities. More than a third of the world's land is affected by salt, which threatens the growth and production of crops, and prevents the development of sustainable agriculture. The high electrical conductivity (EC) content in soils poses significant challenges in arid and semi-arid regions, greatly impacting agricultural production. Saline and sodic soils often exhibit high levels of sodium which is a key characteristic. The presence of sodium ions leads to the destabilization of soil aggregates and the dispersion of soil particles resulting in the closure of soil pores. Consequently, unfavorable changes occur in the soil physical, chemical, and biological properties increasing its susceptibility to water and wind erosion. Additionally, high sodium levels can lead to the decomposition of soil organic carbon (SOC). SOC is crucial for water retention, cation exchange, and nutrient availability, making its reduction in agricultural soils a significant threat to sustainable soil management. Therefore, the investigation of soils in terms of EC and SOC contents and their spatial distribution is of great importance to support decision-makers in agricultural development planning to reduce challenges related to food security in arid and semi-arid regions.Materials and MethodsThis study was conducted with the aim of investigating the EC and SOC in topsoil (0-30 cm) and subsoil (30-60 cm) layers using four machine learning (ML) algorithms namely, random forest (RF), decision tree (DTr), support vector regression (SVR) and artificial neural network (ANN) performed in Qazvin Plain. The study area includes a part of agricultural lands and natural areas of Alborz and Qazvin provinces, between the Nazarabad and Abyek cities in Iran. This region with an area of 60,000 hectares is located at latitude 35° 54´ to 36° 54´ to the north and 50° 15´ to 50° 39´ to the east. This research was carried out in four stages including (i) soil sampling and measuring the physical and chemical properties of the soil and preparation of environmental covariates from a digital elevation model (DEM) with spatial resolution 12.5 m and Landsat 8 satellite imagery with spatial resolution 30 m by SAGA GIS and ENVI software, (ii) spatial modeling of soil EC and SOC in the topsoil and subsoil layers by the RF, SVR, ANN, and DTr ML algorithms, (iii) evaluating the efficiency of the ML algorithms and determining the relative importance of environmental covariates, and (iv) preparation of spatial prediction maps of EC and SOC in the topsoil (0-30 cm) and subsoil (30-60 cm) layers in the study area.Results and Discussion         The result of the spatial prediction maps of EC showed that the studied area has non-saline to very saline soils up to a depth of 60 cm. It is also possible that the EC equivalent shows a decreasing trend in soil salinity with a depth from 6.05 to 5.55 ds/m from the topsoil to the subsoil layer. The highest amount of SOC was observed in the surface layer equal to 3.3%. Globally SOC content decreased from the surface (average of 0.84%) to depth (average of 0.4%). The high spatial variability of SOC showed that the soils of the study area are affected by management activity. Environmental covariates were extracted as a proxy of topography and remote sensing indices including elevation, diffuse Insolation (Diffuse), Multi-Resolution Index of Valley Bottom Flatness (MrVBF), Normalized Differences Vegetation Index (NDVI), SAGA wetness index (SWI) and wind Effect (WE) were used as representatives of soil formation factors. The topography parameters, including the elevation, diffuse insolation, and Multi-Resolution Index of Valley Bottom Flatness, were most closely related to EC and SOC variations in each topsoil and subsoil layer. Elevation can be justified around 50% and 35% of EC and 28.56% and 29.47% of SOC variations in the topsoil and subsoil layers, respectively, followed by the diffuse variable can succeed to justified 19.7% and 25.1% of EC and 27.28% and 27.67% of SOC spatial variations in the topsoil and subsoil layers, respectively.The results confirmed that the RF was recognized as outperforming the ML model for predicting EC in the topsoil (R2 =0.74, RMSE =0.36, and nRMSE= 0.07), as well as predicting SOC in topsoil and subsoil layers (R2= 90 and R2=0.80), followed by the DTr for predicting EC (R2 0.77, RMSE/0.9, and nRMSE 0.17) in the subsoil layer in comparison other models. Conclusion       The RF (Random Forest) and DTr (Decision Tree) models incorporating topographic parameters demonstrated satisfactory accuracy in predicting the variation of topsoil and subsoil electrical conductivity (EC) and soil organic carbon (SOC) in the study area. Topography plays a crucial role in soil formation, and elevation-based topographic attributes are commonly used as key predictors in digital soil mapping projects. The variability in topography influences water flow and sedimentation processes which, in turn, affects soil development and the spatial distribution of soil properties. The resulting soil maps can be valuable tools for decision-making programs related to soil management in the region

A proposed new approach to identify limiting factors in assessing land suitability for sustainable land management

Making optimal use of scarce resources in developing countries is a major challenge for sustainable crop production. Characterization of factors limiting crop production is an important step in meeting this challenge. We outline and test a new method to identify characteristics of land limiting maize production in 61000 ha of the Qazvin province, northern Iran. Several soil profiles and augers were then investigated on soil samples collected from horizons of 21 representative profiles and analyzed for soil properties, such as particle size distribution, exchangeable sodium percentage, electrical conductivity, soil pH, organic carbon content, calcium carbonate equivalents, gypsum, and cation exchange capacity. Soil mapping units were surveyed and separated in the study area. Climatic, soil, and landscape characteristics were rated according to their likely influence on maize growth. Limiting characteristics included drainage, salinity/alkalinity, pH, coarse fragments, calcium carbonate equivalents, and slope. Our method shows the type and magnitude of limitation and as such is an improvement over existing approaches that only show where limitation is occurring. Our method lends itself to making transparent and quicker decision for the sustainable production of crops

A new robust hybrid model based on support vector machine and firefly meta-heuristic algorithm to predict pistachio yields and select effective soil variables

Pistachio production is an economically important crop that grows in arid environments. To predict yield and sustainably manage the use of natural resources such as soil and water, we modelled the effect of soil properties by classification and regression tree, k-nearest neighbors, support vector machines and developed a new hybrid model of support vector machines and the firefly meta-heuristic algorithm. We sampled soils from 124 pistachio orchards in Iran and analyzed them for a range of parameters. Available phosphorus and potassium, exchangeable sodium percentage, soil salinity, gypsum, calcium carbonate and gravel were selected as predictors in the subsequent model based on correlation coefficients, sensitivity analysis and ANOVA hypothesis testing. For modeling, the optimized values for the Kernel function parameters in the hybrid model of ζ, ε and γ were 8.76, 0.001 and 0.99, respectively, while the ideal numerical combinations for p and k parameters in the k-nearest neighbors model were 0.3 and 5, respectively. We checked the difference between the models using paired t-tests which showed that improvements were significant. According to the results, k-nearest neighbors, classification and regression tree and support vector machines algorithms could explain 83, 84 and 88% of the variation of pistachio yield, respectively, but improved to 94% in the hybrid model because it was more able to efficiently capture non-linear relationships. Soil available phosphorus was the most important determinant of pistachio yield, with soil salinity, exchangeable sodium percentage, potassium, gypsum, calcium carbonate and gravel ranked in order of decreasing importance. These outputs can help planners and farmers to better manage soil properties to increase pistachio yield and sustainable production

Integration of ANP and Fuzzy set techniques for land suitability assessment based on remote sensing and GIS for irrigated maize cultivation

Land suitability assessment can inform decisions on land uses suitable for maximizing crop yield while making best use, but not impairing the ability of natural resources such as soil to support growth. We assessed the suitability of maize to be produce in 12,000 ha land of Dasht-e-Moghan region of Ardabil province, northwest of Iran. Suitability criteria included soil depth, gypsum (%), CaCO₃ (%), pH, electrical conductivity (EC), exchangeable sodium percentage (ESP), slope (%) and climate data. We modified and developed a novel set of techniques to assess suitability: fuzzy set theory, analytic network process (ANP), remote sensing and GIS. A map of suitability was compared a map created using a traditional suitability technique, the square root method. The coefficient of determination between the land suitability index and observed maize yield for square root and ANP-fuzzy methods was 0.747 and 0.919, respectively. Owing to greater flexibility to represent different data sources and derive weightings for meaningful land suitability classes, the ANP-fuzzy method was a superior method to represent land suitability classes than the square root method

Development of a model using matter element, AHP and GIS techniques to assess the suitability of land for agriculture

Land suitability assessment is an essential step for land use planning and development. Over the last decade, many researchers, organizations, research institutes and governments have tried to provide a comprehensive procedure for the optimal use of agricultural land, but have failed to balance competing issues in a systematic way. Matter element is a multiple criteria decision analysis (MCDA) technique which has shown high potential for solving complicated issues. The use of MCDA techniques such as matter element and analytic hierarchy process (AHP) based on remote sensing (RS) and GIS is a flexible and effective framework to assess and map several different criteria for the strategic placement of cropping. We used data from 167 soil profiles covering 12,000 ha of land located in Ardabil province, northwest of Iran to assess criteria using MCDA techniques that may limit barley production under irrigation. After soil sampling and analysis, 24 soil series (all Aridisols) and 66 land units were identified and separated in the study area. Several criteria were limiting, but the most limiting criteria included: soil depth, slope, climate characteristics, pH, electrical conductivity, exchangeable sodium percentage, calcium carbonate equivalent and gypsum content. Combining and analyzing criteria in an AHP-matter element model generated a land suitability map for barley production. The coefficient of determination (R2) between land suitability index and observed barley yield was 0.947 for AHP-matter element hybrid model. Modeled estimates were compared and showed that the hybrid approach of AHP and matter element techniques was more accurate than the storie and square root methods in selecting the most suitable areas for barley production. The AHP-matter element hybrid method can therefore improve planning and decision making regarding land which is suitable for barley cultivation. This approach may also be suitable other crops