Land suitability assessment is essential for increasing production and planning a sustainable agricultural system, but such information is commonly scarce in the semi-arid regions of Iran. Therefore, ...our aim is to assess land suitability for two main crops (i.e., rain-fed wheat and barley) based on the Food and Agriculture Organization (FAO) “land suitability assessment framework” for 65 km2 of agricultural land in Kurdistan province, Iran. Soil samples were collected from genetic layers of 100 soil profiles and the physical-chemical properties of the soil samples were analyzed. Topography and climate data were also recorded. After calculating the land suitability classes for the two crops, they were mapped using machine learning (ML) and traditional approaches. The maps predicted by the two approaches revealed notable differences. For example, in the case of rain-fed wheat, results showed the higher accuracy of ML-based land suitability maps compared to the maps obtained by traditional approach. Furthermore, the findings indicated that the areas with classes of N2 (≈18%↑) and S3 (≈28%↑) were higher and area with the class N1 (≈24%↓) was less predicted in the traditional approach compared to the ML-based approach. The major limitations of the study area were rainfall at the flowering stage, severe slopes, shallow soil depth, high pH, and large gravel content. Therefore, to increase production and create a sustainable agricultural system, land improvement operations are suggested.
Land suitability assessment, as an important process in modern agriculture, involves the evaluation of numerous aspects such as soil properties, climate, relief, hydrology and socio-economic aspects. ...The aim of this study was to evaluate the suitability of soils for wheat cultivation in the Gavshan region, Iran, as the country is facing the task of becoming self-sufficient in wheat. Various methods were used to evaluate the land, such as multi-criteria decision-making (MCDM), which is proving to be important for land use planning. MCDM and machine learning (ML) are useful for decision-making processes because they use complicated spatial data and methods that are widely available. Using a geomorphological map, seventy soil profiles were selected and described, and ten soil properties and wheat yields were determined. Three MCDM approaches, including the technique of preference ordering by similarity to the ideal solution (TOPSIS), gray relational analysis (GRA), and simple additive weighting (SAW), were used and evaluated. The criteria weights were extracted using Shannon’s entropy method. Random forest (RF) model and auxiliary variables (remote sensing data, terrain data, and geomorphological maps) were used to represent the land suitability values. Spatial autocorrelation analysis as a statistical method was applied to analyze the spatial variability of the spatial data. Slope, CEC (cation exchange capacity), and OC (organic carbon) were the most important factors for wheat cultivation. The spatial autocorrelation between the key criteria (slope, CEC, and OC) and wheat yield confirmed these results. These results also showed a significant correlation between the land suitability values of TOPSIS, GRA, and SAW and wheat yield (0.74, 0.72, and 0.57, respectively). The spatial distribution of land suitability values showed that the areas classified as good according to TOPSIS and GRA were larger than those classified as moderate and weak according to the SAW approach. These results were also confirmed by the autocorrelation of the MCDM techniques with wheat yield. In addition, the RF model showed its effectiveness in processing complex spatial data and improved the accuracy of land suitability assessment. In this study, by integrating advanced MCDM techniques and ML, an applicable land evaluation approach for wheat cultivation was proposed, which can improve the accuracy of land suitability and be useful for considering sustainability principles in land management.
Accurate spatiotemporal monitoring and modeling of soil moisture (SM) is of paramount importance for various applications ranging from food production to climate change adaptation. This study deals ...with modeling SM with the random forest (RF) algorithm using datasets comprising multispectral data from Sentinel-2, Landsat-8/9, and hyperspectral data from the CoSpectroCam sensor (CSC, licensed to AgriWatch BV, Enschede, The Netherlands) mounted on an unmanned aerial vehicle (UAV) in Iran. The model included nine bands from Landsat-8/9, 11 bands from Sentinel-2, and 1252 bands from the CSC (covering the wavelength range between 420 and 850 nm). The relative feature importance and band sensitivity to SM variations were analyzed. In addition, four indices, including the perpendicular index (PI), ratio index (RI), difference index (DI), and normalized difference index (NDI) were calculated from the different bands of the datasets, and their sensitivity to SM was evaluated. The results showed that the PI exhibited the highest sensitivity to SM changes in all datasets among the four indices considered. Comparisons of the performance of the datasets in SM estimation emphasized the superior performance of the UAV hyperspectral data (R2 = 0.87), while the Sentinel-2 and Landsat-8/9 data showed lower accuracy (R2 = 0.49 and 0.66, respectively). The robust performance of the CSC data is likely due to its superior spatial and spectral resolution as well as the application of preprocessing techniques such as noise reduction and smoothing filters. The lower accuracy of the multispectral data from Sentinel-2 and Landsat-8/9 can also be attributed to their relatively coarse spatial resolution compared to the CSC, which leads to pixel non-uniformities and impurities. Therefore, employing the CSC on a UAV proves to be a valuable technology, providing an effective link between satellite observations and ground measurements.
Land use change and soil organic carbon stock (SOCS) depletion over time is one of the predominant worldwide environmental problems related to global warming and the need to secure food production ...for an increasing world population. In our research, satellite images from 1988 and 2018 were analyzed for a 177.48 km2 region in Kurdistan Province, Iran. Across the study area. 186 disturbed and undisturbed soil samples were collected at two depths (0–20 cm and 20–50 cm). Bulk density (BD), soil organic carbon (SOC), rock fragments (RockF) and SOCS were measured. Random forest was used to model the spatial variability of SOCS. Land use was mapped with supervised classification and maximum likelihood approaches. The Kappa index and overall accuracy of the supervised classification and maximum likelihood land use maps varied between 83% and 88% and 78% and 85%, respectively. The area of forest and high-quality rangeland covered 5286 ha in 1988 and decreased by almost 30% by 2018. Most of the decrease was due to the establishment of cropland and orchards, and due to overgrazing of high-quality rangeland. As expected, the results of the analysis of variance showed that mean values of SOCS for the high-quality rangeland and forest were significantly higher compared to other land use classes. Thus, transformation of land with natural vegetation like forest and high-quality rangeland led to a loss of 15,494 Mg C in the topsoil, 15,475 Mg C in the subsoil and 15,489 Mg C−1 in total. We concluded that the predominant causes of natural vegetation degradation in the study area were mostly due to the increasing need for food, anthropogenic activities such as cultivation and over grazing, lack of government landuse legislation and the results of this study are useful for land use monitoring, decision making, natural vegetation planning and other areas of research and development in Kurdistan province.
Soil quality assessment based on crop yields and identification of key indicators of it can be used for better management of agricultural production. In the current research, the weighted additive ...soil quality index (SQIw), factor analysis (FA), and multiple linear regression (MLR) are used to assess the soil quality of rainfed winter wheat fields with two soil orders on 53.20 km2 of agricultural land in western Iran. A total of 18 soil quality indicators were determined for 100 soil samples (0–20 cm depth) from two soil orders (Inceptisols and Entisols). The soil properties measured were: pH, soil texture, organic carbon (OC), cation exchange capacity (CEC), electrical conductivity (EC), soil microbial respiration (SMR), carbonate calcium equivalent (CCE), soil porosity (SP), bulk density (BD), exchangeable sodium percentage (ESP), mean weight diameter (MWD), available potassium (AK), total nitrogen (TN), available phosphorus (AP), available Fe (AFe), available Zn (AZn), available Mn (AMn), and available Cu (ACu). Wheat grain yield for all of the 100 sampling sites was also gathered. The SQIw was calculated using two weighting methods (FA and MLR) and maps were created using a digital soil mapping framework. The soil indicators determined for the minimum data set (MDS) were AK, clay, CEC, AP, SMR, and sand. The correlation between the MLR weighting technique (SQIw-M) and the rainfed wheat yield (r = 0.62) was slightly larger than that the correlation of yield with the FA weighted technique (SQIw-F) (r = 0.58). Results showed that the means of both SQIw-M and SQIw-F and rainfed wheat yield for Inceptisols were higher than for Entisols, although these differences were not statistically significant. Both SQIw-M and SQIw-F showed that areas with Entisols had lower proportions of good soil quality grades (Grades I and II), and higher proportions of poor soil quality grades (Grades IV and V) compared to Inceptisols. Based on these results, soil type must be considered for soil quality assessment in future studies to maintain and enhance soil quality and sustainable production. The overall soil quality of the study region was of poor and moderate grades. To improve soil quality, it is therefore recommended that effective practices such as the implementation of scientifically integrated nutrient management involving the combined use of organic and inorganic fertilizers in rainfed wheat fields should be promoted.
•Soil quality of salt-affected agricultural land in Kurdistan Province, Iran, was assessed using three indices; the Additive Soil Quality Index (SQIa), the Weighted Additive Soil Quality Index ...(SQIw), and the Nemoro Soil Quality Index (SQIn).•Three soil quality indices (SQIa, SQIw, and SQIn) were calculated using a Total Data Set (TDS) and a Minimum Data Set (MDS) approach.•Principal Component Analysis (PCA) was used to define MDS from TDS.•The SQIw-MDS index was the best to assess soil quality.•Salt effects degraded severely soil quality.
Soil quality indices (SQIs) were an important tool for evaluating agro-ecosystems. Salinization and alkalization are major environmental problems that have threatened agricultural productivity since ancient times. The aim of this study is to assess soil quality in salt-affected agricultural land in Kurdistan Province, Iran, using three indices; the Additive Soil Quality Index (SQIa), the Weighted Additive Soil Quality Index (SQIw), and the Nemoro Soil Quality Index (SQIn). Each of the soil quality indices were calculated using a Total Data Set (TDS) and a Minimum Data Set (MDS) approach. The TDS consisted of nine soil quality parameters measured on 150 samples (0–30 cm depth): pH, Electrical Conductivity (EC), Organic Carbon (OC), Cation Exchange Capacity (CEC), Carbonate Calcium Equivalent (CCE), Exchangeable Sodium Percentage (ESP), Sodium Adsorption Ratio (SAR), Mean Weight Diameter (MWD), and Bulk Density (BD). Principal components analysis (PCA) was used to determine which indicators were to be included in the MDS. Indicator Kriging (IK) highlighted areas with a high risk of exceeding critical threshold values of EC, ESP, and SAR and having low soil quality. In non-salt-affected areas soil quality and the risk of exceeding critical threshold values and having low soil quality were lower and higher, respectively, compared to salt-affected regions. The MDS method showed a decrease in the area and proportion of grades with high and very high quality (I and II) and an increase in grades with low and very low quality (IV and V) compared to the TDS. The results of linear correlation, match, and kappa statistic analysis showed that soil quality was better estimated using the SQIw compared to the SQIa and the SQIn. In addition there were higher values of agreement (match and kappa statistic) for the TSD than MSD. However, using the SQIw index and MDS method can adequately represent the TDS (R2 = 0.82) and thus reduce the time and cost involved in evaluating soil quality.
The cuurent study was performed to predict spatial distribution of some heavy metals (Ni, Fe, Cu, Mn) in western Iran, using environmental covariates and applying two machine learning methods ...comprised Random forest (RF), and Cubist. In this respect, a combination of different input environmental variables (remote sensing data, topographic attributes, thematic maps and soil properties) were used in modeling under four scenarios (I: remote sensing data (RS); II: RS + topographic attributes resulted from digital elevation model (DEM); III: RS + topographic attributes + thematic maps; IV: RS + topographic attributes + thematic maps +soil properties). The maps of Euclidean distance from mines and roads as well as the geology map have been used as thematic maps. A total of 346 soil samples were taken using stratified random sampling from the surface layers (0–20 cm depth) of the studied area and selected heavy metals (Ni, Fe, Cu, Mn), and soil properties were measured in the laboratory. RF and Cubist models were used to predict soil heavy metals in four scenarios. The results indicated that the best prediction accuracy was achieved for the fourth scenario (IV) when all input variables were combined to predict selected heavy metals. Moreover, two models showed different capability for various metals. According to our results, the random forest model had a high accuracy in predicting Ni (R2 = 0.67) and Cu (R2 = 0.60), In contrast, the Cubist model had a higher accuracy in predicting Mn (R2 = 0.55). For predicting Fe, both models provided a similar accuracy (R2 = 0.73). This study proved the high capability of machine learning methods to use easily available environmental data to predict studied heavy metals in the large scale that are essential for decision making in sustainable management in agricultural and environmental concerns.
•Parent material had a high influence on heavy metals variability in the studied area.•A combination of RS indices, topographic derivatives, thematic data, and soil properties had the best performance.•Random forest (RF) is more capable than the cubist model to predict heavy metals.
•Digital maps of pH, EC, and SAR using hybridized random forests and covariate data.•Three optimization algorithms were compared.•RF + PSO was the most accurate model for predicting pH, EC, and ...SAR.•Mean values of pH, EC and SAR in physiographic units and land uses were different.
Salinization and alkalization are predominant environmental problem world-wide which their accurate assessment is essential for determining appropriate ways to deal with land degradation, for better soil and crop management. In the current research, a combination of random forests and covariate data were used to assess spatial variability of soil salinity and sodicity in 436 km2 agricultural salt-affected land in Kurdistan Province, Iran. Using the conditioned Latin hypercube sampling method, 295 soil samples were sampled across the study area, and then soil reaction (pH), electrical conductivity (EC), and sodium adsorption ratio (SAR) were measured. Covariate data including terrain attributes, remotely-sensed data, groundwater table, and categorical maps were acquired. Random forest (RF) models were used to predict the spatial distribution of pH, EC, and SAR by making a relationship between soil data and covariates. Furthermore, three optimization algorithms (particle swarm optimization-PSO, genetic algorithm-GA, and bat algorithm-BAT) were used to explore if the hybridized RF works better than the standard RF. Results of 10-fold cross-validation with 100 replications indicated that the accuracy of RF + PSO was higher for predicting pH (RMSE = 0.52 and R2 = 0.67), EC (RMSE = 2.32 dSm−1 and R2 = 0.57), and SAR (RMSE = 8.98 and R2 = 0.54, respectively) in comparison to the other implemented models. Furthermore, the results disclosed that the most important covariates to predict pH, EC, and SAR were groundwater table, categorical maps, salinity index, and multi-resolution ridge top flatness. Besides, the results indicated that the mean values for pH, EC, and SAR in lowland and bare land were significantly different from the other physiographic units and land uses, respectively. Importantly, the classified map of salt-affected soils highlighted areas with a high risk of exceeding critical threshold values of pH, EC, and SAR, which is located in the center of the study area, and showed that 6.30%, 3.1%, and 4.6% of the study area are saline-sodic soil, saline soil, and sodic soil, respectively. These up to date spatial soil information on severity of soil salinity and sodicity is crucial for agricultural management of affected areas and the proposed method can be used to the other similar regions.
•Conversion of forest to dryland farming negatively affects soil physical and chemical properties.•Soil organic carbon of all primary particles is higher on the forest land than on the dryland ...farming.•Impact of cultivation is more pronounced on macro-aggregate associated OC than micro-aggregates.•Clearing forests for cultivation adversely affect all soil quality indexes tending to degrade SQ.
Increasing trend of native forests conversion into farmlands can adversely influence the soil quality. Such impacts on soil attribute must be studied in more details in order to protect or improve the present status of soil quality. Consequently, a large-scale study was conducted to investigate the impacts of deforestation on soil quality indicators. For this purpose, two neighboring land uses of dry farming, which have been converted from sparse forest, and a sparse forest were selected. Topsoil samples (0–15 and 15–60 cm) were collected from three sites of each land use and their designated physical and chemical properties were analyzed with standard methods. Analyzing the obtained data revealed that deforestation and, therefore, dry farming significantly increases soil pH, electrical conductivity (EC), bulk density (BD) and soil erodibility factor (K-factor) but tend to reduce SOC, total porosity (TP), saturated hydraulic conductivity (Ks), mean weight aggregate diameter (MWD), and geometric mean aggregate diameter (GMD). Furthermore, aggregates with sizes ≥2 mm were more prevalent in the sparse forest soils, while the dry farming soils consisted of more aggregates with sizes <2 mm. In both studied land uses and among all examined soil properties, only calcium carbonate equivalent (CCE), SOC and Ks at 0–15 cm depth substantially differ from those of 15–60 cm depth. Our results further indicated that SOC content of all primary particles has reduced at depth of 0−60 cm as a result of 60 years dry farming. Among the primary particles, the OC associated with sand including particulate organic carbon (POC) had the largest value in both land uses. Long-term cultivation led to reduce OC in macro-aggregates and a surge in OC in micro-aggregates, demonstrating aggregate hierarchy. Our overall observations show that forest clearance and subsequent cultivation practice, due to land degradation, has a significant negative impact on soil quality index, as such, a drop of 44.5 % of SQI was occurred.
In recent decades, the conversion of forest to agricultural land has been a major worldwide concern and a cause of environmental and soil-quality degradation. In this study, soil-quality indices ...(SQIs) were applied using several soil properties to determine the effects of land use on soil quality in a 206.50 km
2
area in Kurdistan Province, Iran. The Weighted Additive Soil Quality Index (SQI
w
) was calculated using two scoring methods and two soil indicator selection approaches. Nine soil-quality indicators/variables were measured for 124 soil samples (0-30 cm depth). Calculated SQIs were digitally mapped with a random forest (RF) model using auxiliary data. The RF model was the best predictor of the SQI computed using the total dataset (TDS) and linear score function (SQI
w-TDS-linear
). Soil quality was better estimated using non-linear scoring (r
2
= 0.82) than with linear scoring (r
2
= 0.73). The mean values of all SQIs were significantly greater in forestland than cropland. It is clear that soil quality is considerably reduced by deforestation, and that best management practices that maintain soil quality and reduce erosion must be developed for the soils of this region if they are to remain productive.
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