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Spatial prediction is essential for obtaining the spatial distribution of geographic variables and selecting appropriate covariates for this process can be challenging, especially for non-expert users. For easing the burden of selecting the appropriate covariates, two case-based reasoning strategies, namely the most-similar-case and covariate-classification strategies, have been proposed for automated covariate selection. The former may suggest nonessential covariates due to its case-level reasoning way. And the latter with covariate-level reasoning may overlook related covariates and recommend fewer covariates than the case-level reasoning. In this study, we propose a new strategy of integrating case-level and covariate-level reasoning to effectively leverage the strengths of both previous strategies while also addressing their limitations. The proposed strategy is validated through a case study of automatically selecting covariates for digital soil mapping under reasoning with a case base containing 189 cases. The leave-one-out evaluation demonstrated that our proposed strategy outperformed the previous two strategies. [ABSTRACT FROM AUTHOR]

This research focuses on understanding the spatial variation of Soil Organic Matter (SOM) and pH levels in the North of Morocco. The study employs a comprehensive approach to enhance predictive modelling, incorporating the Boruta algorithm for effective environmental covariates selection and optimizing model parameters through hyperparameter optimization. Utilizing a Random Forest (RF) model with remote sensing indices and topographic features, the research predicts SOM and pH to identify key contributors to their spatial variability. SOM prediction saw significant success, with a notable correlation to remote sensing indices such as the RVI, NDVI, and TNDVI. These indices, indicative of vegetation health and productivity, emerged as primary influencers of SOM. In comparison, the influence of topographic features like elevation, slope, and aspect was found to be less significant. Conversely, predicting pH was challenging due to the minimal spatial variability within the dataset. Addressing this limitation could involve dataset expansion or alternative models for low-correlated data handling. Despite the RF model's limited efficacy in pH prediction, an observable correlation between SOM and pH was identified, consistent with prior research. Areas with higher SOM exhibited lower pH values, indicating relative soil acidification from organic matter decomposition. The study's RF model demonstrated potential in SOM prediction using remote sensing indices, but enhancing pH prediction is essential. Future research may explore dataset expansion, diverse sampling, or testing alternative predictive models for better performance with low-correlated datasets. The study offers valuable insights for advanced predictive model development and enriches understanding of soil management practices. [ABSTRACT FROM AUTHOR]

Soil inorganic carbon (SIC) is important carbon reservoirs in desert ecosystems. However, little attention was paid to estimate carbon stock in these regions. In the present study, the distribution of SIC stock was investigated using digital soil mapping in Bikaner district, Rajasthan, India. A total of 187 soil profiles were used for SIC estimation by Quantile regression forest model. Landsat data, terrain attributes and bioclimatic variables were used as environmental variables. Ten-fold cross-validation was used to evaluate model. Equal-area quadratic splines were fitted to soil profile datasets to estimate SIC at six standard soil depths (0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm). The SIC in the study area ranged from 0.27 to 27.85 g kg−1 in 0–5 cm and 0.31 to 27.84 g kg−1 in 5–15 cm, respectively. The model could capture reasonable variability (R2 = 11–21%) while predicting SIC for different depths. The Lin's concordance correlation coefficient values ranged from 0.20 to 0.32, indicating poor relationship between the predicted and observed values. The values of prediction interval coverage probability (PICP) recorded 86.4–91.1% for SIC at different depths. Annual precipitation and precipitation seasonality were the most important covariates in soil below the 30 cm depth. The predicted SIC stocks were 10.3 ± 0.01, 81.6 ± 0.07 and 186.7 ± 0.13 Mg ha−1 at 0–15, 0–100 and 0–200 cm, depth, respectively. The uncertainty analysis suggests that there is room to improve the current spatial predictions of SIC. It is anticipated that this digital mapping of SIC will be useful for assessment of carbon cycle in arid regions. [ABSTRACT FROM AUTHOR]

The increase in population and decrease in agricultural land per capita requires intelligent management to increase production per unit area and higher productivity of land use to ensure food security. In this regard, evaluation of soil fertility and productivity is a prerequisite for sustainable agricultural development. The present study sought to investigate the soil fertility status of GIS-based Fuzzy-AHP(FAHP) techniques to assess the soil fertility status of wheat crops in Shadkam plain of Fars province. For this purpose 210 study samples from topsoil were collected and the most important chemical parameters affecting soil fertility were analyzed. According to the results, the most important limiting characteristics were OC, available phosphorus and soil pH. The application of FAHP method determined 4130.01 ha, 10.82% of land very high fertility class, 7615.30 ha, 19.95% high fertility class, 23942.30 ha, 62.77% moderately fertility class, and 2452.10 ha, 6.42% low fertility class. Thus, the fuzzy-AHP model is found to be of high accuracy for predicting soil fertility in the study area. Overall, it can be concluded that the preparation of a soil fertility map separately can be an effective tool and first step in soil fertility studies and the optimal use of fertilizers. [ABSTRACT FROM AUTHOR]

Although regional soil mapping is commended, site-specific studies are required for mapping and quantifying soil organic carbon (SOC) stocks at a landscape scale for management and rehabilitation purposes. Site-specific studies are especially important in remote mountainous areas where soil data are largely absent. The aim of this study was to quantify and map the SOCs of an alpine region in the Maloti-Drakensberg. The samples collected in-field along with digital soil mapping techniques were used to map the SOCs. The models were SoLIM’s rule based (RB) and sample based (SB), random forest (RF), least absolute shrinkage and selection operator (LASSO), regression kriging with cubist (RK-CB) and universal kriging. From the results, the mean SOC for the validation dataset of the study area was 12.44 kg organic carbon (OC) m−2. The best model was RK-CB (mean = 12.43 kg OC m−2), with R 2 and root mean square error (RMSE) of 0.61 and 4.01 kg OC m−2, respectively. The underperforming model was SoLIM-RB (mean = 13.27 kg OC m−2), with R 2 = 0.27 and RMSE = 5.75 kg OC m−2. The RK-CB model from this study significantly outperformed a region-scaled model, proving that site-specific studies in small catchments should be preferred to, especially if there are no soil data available for that area. [ABSTRACT FROM AUTHOR]

Our purpose is a preliminary assessment of the effects of war in Ukraine on its soils. Realistic diagnosis demands details at the scale at which war operates. Lacking a soil map at this scale, and a new survey being out of the question, we modelled a predictive soil map of the whole country at scale 1:10,000. This enables accurate assessment of the areas of damage and the ecosystem services foregone. Mimicking personalised medicine and the precision-agriculture principle, farm by soil type, we can now set about healing by soil type – drawing on the particular resilience of particular soils and make allowance for their weaknesses too. For instance, chernozem have extraordinary resilience and self-healing capacity; a calcareous clay can fix radionuclides but peat soils have no such capacity. [ABSTRACT FROM AUTHOR]

Sampling design can significantly reduce the uncertainty in geospatial predictions. In this paper, we developed an adaptive uncertainty-guided stepwise sampling (AUGSS) method to select sampling locations to supplement existing legacy sample points whose representation should be improved. The proposed method selects supplemental samples in a stepwise manner as guided by an objective function with two weighted sub-objectives. One reduces the area with high prediction uncertainty, and the other minimizes the overall prediction uncertainty for the entire area. The method takes an adaptive approach to adjust weights for the two sub-objectives and to tune an uncertainty threshold controlling whether a location can be reliably predicted during the sampling procedure. A case study on soil property prediction shows that AUGSS outperforms the stratified random sampling (SRS) and the non-adaptive uncertainty guided sampling method (UGSS) in terms of RMSE and Lin's concordance correlation coefficient with different sample sizes. This study shows that the AUGSS method offers a potential for effectively adding supplemental samples to existing samples which are insufficient for spatial prediction. The adaptive strategy guided by predicted uncertainty provides an efficient support to improve the spatial pattern of samples, which plays a key role in the result accuracy of geospatial predictive mapping. [ABSTRACT FROM AUTHOR]

In this study some soil phosphorous sorption parameters (PSPs) by using different machine learning models (Cubist (Cu), random forest (RF), support vector machines (SVM) and Gaussian process regression (GPR)) were predicted. The results showed that using the topographic attributes as the sole auxiliary variables was not adequate for predicting the PSPs. However, remote sensing data and its combination with soil properties were reliably used to predict PSPs (R2 =0.41 for MBC by RF model, R2=0.49 for PBC by Cu model, R2=0.37 for SPR by Cu model, and R2=0.38 for SBC by RF model). The lowest RMSE values were obtained for MBC by RF model, PBC by SVM model, SPR by Cubist model and SBC by RF model. The results also showed that remote sensing data as the easily available datasets could reliably predict PSPs in the given study area. The outcomes of variable importance analysis revealed that among the soil properties cation exchange capacity (CEC) and clay content, and among the remote sensing indices B5/B7, Midindex, Coloration index, Saturation index, and OSAVI were the most imperative factors for predicting PSPs. Further studies are recommended to use other proximally sensed data to improve PSPs prediction to precise decision-making throughout the landscape. [ABSTRACT FROM AUTHOR]

Mapping soil organic matter (SOM) content has become an important application of digital soil mapping. In this study, we processed all Sentinel-2 images covering the bare-soil period (March to June) in Northeast China from 2019 to 2022 and integrated the observation results into synthetic materials with four defined time intervals (10, 15, 20, and 30 d). Then, we used synthetic images corresponding to different time periods to conduct SOM mapping and determine the optimal time interval and time period before finally assessing the impacts of adding environmental covariates. The results showed the following: (1) in SOM mapping, the highest accuracy was obtained using day-of-year (DOY) 120 to 140 synthetic images with 20 d time intervals, as well as with different time intervals, ranked as follows: 20 d > 30 d > 15 d > 10 d; (2) when using synthetic images at different time intervals to predict SOM, the best time period for predicting SOM was always within May; and (3) adding environmental covariates effectively improved the SOM mapping performance, and the multiyear average temperature was the most important factor. In general, our results demonstrated the valuable potential of SOM mapping using multiyear synthetic imagery, thereby allowing detailed mapping of large areas of cultivated soil. [ABSTRACT FROM AUTHOR]

Spatial information on land and soil resources are critical towards addressing land degradation for ensuring sustainable soil and crop management. To address these needs, digital soil mapping techniques have emerged as an efficient and low-cost solution. Although digital soil mapping has typically leveraged geospatial environmental variables (e.g. remote sensing), the application and integration of spectroscopic data with those environmental variables remain limited. Hence, this study combines visible and nearinfrared (Vis-NIR) spectroscopy, remote sensing, and topographic data and applies random forests, hybridized with particle swarm optimization algorithm (RF + PSO), to predict the spatial variability of soil clay content, electrical conductivity (EC), and calcium carbonate equivalent (CCE) for 370km² of agricultural land in western Iran. Using a conditioned Latin hypercube approach, 220 soil samples at the 0–20 cm depth increment were acquired throughout the study area. Three sets of environmental covariates were tested: Scenario A (Vis-NIR spectroscopy data), Scenario B (environmental data), and Scenario C (Vis-NIR spectroscopy þ environmental data). According to the 10-fold crossvalidation procedure with 100 replications, the RF + PSO model showed an acceptable level accuracy for all scenarios, although the accuracy of the RF + PSO model using the Scenario C data was higher than all other scenarios: the Lin’s Concordance Correlation Coefficient values were 0.77, 0.83, and 0.74 for the clay contents, EC, and CCE, respectively. The results demonstrated that the combination of Vis-NIR spectroscopic data and commonly available environmental covariates provided the best input data for the hybridized model and enhanced its performance. [ABSTRACT FROM AUTHOR]

Soil apparent electrical conductivity (ECa) is related to various soil attributes and processes. This research aimed to understand the relationship between ECa and paedogenesis, lithology and attributes. The study area is located in S~ao Paulo State, Brazil. Soil samples were collected for physical-chemical analysis from 79 locations (0–20 cm layer). A geophysical field-portable equipment (Geonics EM38-MK2 conductivity meter) was used to measure soil ECa. For that, four toposequences were selected on landscape. Sixteen soil profiles were allocated in the landscape for soil characterization and classification. The ECa values were measured across toposequences, soil types and lithology variations, at the same 79 locations used for physico-chemical characterization. The statistical analysis and Kruskal–Wallis rank sum test and Cluster analysis by k-means and Wilcoxon–Mann–Whitney test was performed to verify differences in ECa values over lithology, relief and soil types. The values of ECa measurements were related to soil attributes variation. The results showed that lithology strongly affects ECa values, mainly in diabase-derived soils, followed by metamorphosed siltstone. In fluvial sediments, the ECa exhibits different behaviours. In less evolved soils, the lithology contributed more to ECa than paedogenesis, as the opposite occurs in more evolved ones. The ECa decreases downslope and correlates with lithology alteration and soil types. This study demonstrates that soil ECa can be a tool to differentiate between various soil types and lithological transitions across the landscape, supporting the soil survey and mapping at finer scales. [ABSTRACT FROM AUTHOR]

Soil Particle Size Distribution (PSD) is a fundamental physical property that can affect soil hydraulic properties, soil structure characterization, and available water. Many models have been applied to define the PSD curve, but predicting the spatial distribution information of PSD has been rarely investigated. Therefore, the main objective of the current study was to predict soil texture fractions using the most accurate PSD models. First, the performance of 16 mathematical PSD models was evaluated. Then, Random Forest (RF) was used to determine the relationship between covariates (i.e., remote sensing and the digital elevation model) and georeferenced measurements of the best PSD models’ parameters. Results indicated that a PSD model may be acceptable for some particle diameters or even whole particles, but not necessarily be suitable for other particles. For example, in the estimation of sand content, the best model was Simple Lognormal, while the Fred-4p was the best model in the estimation of the clay fraction. Importantly, the Jaky model with only one parameter of P did a great job in predicting soil particle fractions. Further, the spatial distribution of clay, silt, and sand contents was accurately derived from the predicted map of P (R² for Sand = 0.86). Consequently, the current research indicated that the combination of PSD models and digital soil mapping techniques can be used to quantify the spatial distribution of the PSD curve in other similar agroclimatological regions. [ABSTRACT FROM AUTHOR]

The exchangeable sodium percentage (ESP) is the parameter that causes poorly developed soil structure and weak drainage over time, subsequently increasing the dispersion of soil particles, especially silt and clay, which cause soil erosion in Loess-derived soils. The number of 474 soil samples was collected within 0 to 30 cm of soil depth. Five non-linear models named Ridge regression (RidgeR), random forest (RF), support vector regression (SVR), cubist, and generalized boosted regression model (GBM), were considered to link several parameters with ESP in the region of approximately 2,730 ha. The mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R² ), were considered to assess the models. The RF and RidgeR algorithms were used to prioritize the variables impacting the ESP. The prediction model for mapping soil ESP had very high accuracy from RidgeR (RMSE = 0.039, R² = 0.99), RF (RMSE = 0.257, R²= 0.97), SVR (RMSE = 0.81, R² = 0.544), cubist (RMSE = 0.006, R² = 0.99), and GBM (RMSE = 0.426, R² = 0.87). The sodium adsorption ratio (SAR) and slope steepness were the most important factors affecting ESP, and slope steepness was recognized as the most effective factor. We also identified that the applied models could simply assess ESP map. In addition, an action plan in different soil horizons, considering erosion and sediment processes, is efficient in controlling runoff at low ESP. [ABSTRACT FROM AUTHOR]

In this work, we tested different combinations of sampling strategies, random sampling and conditioned Latin Hypercube sampling (cLHS)] and sample ratios (10% = 147 and 25% = 368) to predict soil phosphorus and potassium contents, previously estimated using standard laboratory protocols. Other environmental covariates, used as input data for prediction, were obtained from different sources (multispectral Landsat-OLI 8 image, WorldClim database, ISRIC soil database, and ASTER-GDEM). Our findings showed that random sampling was suitable for predicting phosphorus, while the conditioned Latin Hypercube sampling was suitable for predicting potassium. Furthermore, we observed that when the sample ratio increased from 10 to 25%, model accuracy improved in random sampling and cLHS for phosphorus and potassium prediction. However, before generalizing these findings, we recommend that further studies be conducted under different conditions (climate, soil types and parent materials) and testing other sample ratios to determine the best sampling strategy with the optimum ratio to predict soil nutrients better. [ABSTRACT FROM AUTHOR]

Evaluation of spatial variability and mapping of soil properties is critical for sustainable agricultural production in arid lands. The main objectives of the present study were to spatialize soil organic carbon (SOC), soil particle size distribution(clay, sand, and silt contents), and calcium carbonate equivalent (CCE) by integrating multisource environmental covariates, including digital elevation model (DEM) and remote sensing data by machine learning (Cubist, Cu and random forest, RF) in an arid region of Iran. Additionally, Synthetic Soil Images (SySI) were achieved from multi-temporal images of bare soil pixels based on Landsats 4, 5, 7, 8, and a DEM. Three hundred topsoil samples (0-30cm depth) were collected based on the conditioned Latin hypercube sampling (cLHS) approach in Afzar district, Fars province, southern Iran. The models were calibrated and validated by the 10-fold cross-validation approach, and the performance was evaluated using root mean square error (RMSE), the ratio of the performance to interquartile distance (RPIQ), and coefficient of determination (R²). Also, the prediction accuracy was assessed by the relative RMSE (rRMSE). The performance of the best models based on the RPIQ index showed that the model for predicting clay (1.89) had a good prediction, for sand (1.64), SOC (1.55), and CCE (1.59) had a fair prediction, while the model for silt (1.13) performed poorly. We found that the Cu and RF models had the highest and lowest prediction accuracies for CCE (rRMSE = 14.31%) and SOC (rRMSE = 43.93%), respectively. We discovered that a combination of high-quality RS data (SySI) and variables derived from DEM were reasonably able to predict soil properties. We revealed a strong promise to enhance the accuracy of digital soil mapping, especially in regions with limited soil data. Moreover, the application of RS data can reduce the soil sampling cost and, accordingly, soil mapping. [ABSTRACT FROM AUTHOR]

Classification of fields into management units based on soil variability and fertility is important for spatial crop planning. The present study was conducted in Chukanagallu subwatershed (97 km2), Koppal district of Northern Karnataka Plateau, India to map the soil fertility management units and to analyse the suitability of soil for different crops. Random forest regression and classification algorithms were used to map the differentiating characteristics of soil series (soil depth, coarse fragments and soil colour), physicochemical properties (pH, EC and OC) and fertility parameters (P2O5, K2O, S, Fe, Mn, Zn, Cu, B). Random forest model performed well for the prediction of fertility parameters (R2 = 44–73%) and physicochemical properties (R2 = 39–83%) compared to soil depth and coarse fragments (R2 = 17–18%). Predicted soil fertility parameters and physicochemical properties were used for the delineation of different homogenous fertility management units. Soil series characteristics and fertility parameters were also evaluated using a multi-criteria approach for suitability of soil for cotton, groundnut and rice cultivation and the results showed that major area of subwatershed is moderately suitable for the cultivation of cotton, rice and groundnut. The management units derived from DSM approach were symmetrical in production potential and requires similar management aspects which are useful for appropriate planning of management strategies such as crop selections and nutrient management to achieve sustainable production. [ABSTRACT FROM AUTHOR]

The soil organic carbon (SOC) pool is larger than the biotic and atmospheric carbon pools, and understanding cultivated land SOC is critical for evaluating soil fertility and local agricultural management. Digital soil mapping (DSM) efficiently predicts soil properties by utilizing spatially distributed information. To date, many machine learning techniques have been applied in DSM, but model ensemble methods have rarely been used. In this study, two model ensemble methods, including the Granger-Ramanathan averaging and Bayesian model averaging, and three individual mathematical algorithms, such as the random forest, quantile regression forests, and multivariate adaptive regression splines models, were used to estimate SOC at a local level with limited soil samples. The prediction performances of the five methods were compared using an independent validation dataset composed of field observations of soil samples. The results showed that the prediction accuracy of the ensemble methods was higher than that of the individual methods (ΔR2 = 0.02 to 0.21). Compared to the other methods, the Granger-Ramanathan method had a lower root mean square error (RMSE) and mean absolute error and a higher prediction uncertainty. Therefore, the results of this study show the effectiveness of using ensemble methods in mapping SOC with limited soil samples. [ABSTRACT FROM AUTHOR]

Accurate mapping of soil organic carbon (SOC) and inorganic carbon (SIC) contents at regional scales can be very important for sustainable agriculture and soil management. Low variation in terrain attributes (classically used for digital soil mapping) at low relief areas calls for additional spatial data to explain soil variability. The main objective of this study was to evaluate the predictive capability of Sentinel-1 (radar) and Sentinel-2 (optical) time series and statistics, summarized as multi-temporal features (MTF) to improve the spatial predictions of SOC and SIC in Ghorveh plain, located in Kurdistan Province, Western Iran. A systematic grid sampling was then employed to collect 150 soil surface samples (0–30 cm) for SOC and SIC measurements. We applied boosted regression trees (BRT) and random forest (RF) to predict SOC and SIC contents by using covariate sets compiled from radar and optical time series and topographic attributes. Model performance, evaluated by 10-fold cross-validation, showed that RF using the covariate set containing time series of Sentinel-1, Sentinel-2 and topographic attributes performed the best in predicting SOC (RMSE = 0.23, ME = 0.005, R2 = 0.29). On the other hand, for SIC, the covariate set containing MTF of Sentinel-1, Sentinel-2 and topographic attributes ranked the best with BRT (RMSE = 0.77, ME= −0.001, R2 = 0.48). The study indicates that using the time series and MTF from multiple dates of remote sensing data with topographic attributes results in improved predictions. However, model performance for SIC and SOC was moderate to poor, respectively. Therefore more substantial studies would be required to verify if the computational effort is likely justified by an increase in accuracy in general. [ABSTRACT FROM AUTHOR]

The spatial distribution of soil pH, electrical conductivity, available nitrogen, phosphorus and potassium concentrations at Anaimalai block of Coimbatore district, India was interpolated by geostatistical analysis to create digital maps of soil NPK that renders numerous information for agro based policy making. 1627 soil samples in 30,700 acres were gathered by composite soil sampling method and rendered interpolation analysis to predict the soil parameters at unsampled locations. Based on geostatistical, deterministic and interpolation with barriers methods 13 interpolation models were generated and compared to validate the precision. Interpolation reveals the deficiency of soil available nitrogen and phosphorus whereas available potassium was in sufficient range. Radial basis function, inverse distance weighting, probability kriging, empirical Bayesian kriging and local polynomial interpolations were validated by linear correlation method that reveals the precisions of models with R2 values of 0.74, 0.82, 0.98, 0.89 and 0.79 (soil pH, EC, nitrogen, phosphorus and potassium respectively). [ABSTRACT FROM AUTHOR]

Recent increase in the frequency and spatial extent of wildfires motivates the quick recognition of the affected soil properties over large areas. Digital Soil Mapping is a valuable approach to map soil attributes based on remote sensing and field observations. We predicted the spatial distribution of post-fire litter composition in a 40,600 ha basin burned on the 2017 wildfire of Chile. Remotely sensed data of topography, vegetation structure and spectral indices (SI) were used as predictors of random forest (RF) models. Litter sampled in 60 hillslopes after the fire provided training and validation data. Predictors selected by the Variable Selection Using Random Forests (VSURF) algorithm resulted in models for litter composition with acceptable accuracy (coefficient of determination, R2 = 0.51–0.64, Normalized Root Mean Square Error, NRMSE = 16.9–22.1, percentage bias, pbias = −0.35%-0.5%). Modelled litter parameters decrease in concentration respect to the degree of burn severity, and the pre-fire biomass. Because pre-fire vegetation was conditioned by land cover and by a previous (2 years old) wildfire event, our results highlight the cumulative effect of severe wildfires in the depletion of litter composition. [ABSTRACT FROM AUTHOR]

Prediction and accurate digital soil mapping (DSM) of soil organic carbon (SOC) at a local scale is a key factor for any agro-ecological modelling. This study aims to use remote sensing and terrain derivatives to provide a reliable method for SOC prediction. An advanced geostatistical-based empirical Bayesian Kriging regression (EBKR) method was used and performance was compared with the artificial neural network (ANN) and hybrid ANN, i.e. ANN-OK (ordinary kriging) and ANN-CK (cokriging). The result showed that the hybrid ANN model performs better than ANN, whereas the EBKR method outperforms all other methods with the highest R2 of 0.936. The DSM map shows that the highest SOC concentration was found in easternmost part of the study area with grass and agricultural land. This work shows the robustness of the EBKR prediction method over other techniques. The study will also aid the policymakers in adopting sustainable land use management. [ABSTRACT FROM AUTHOR]

Existing GIS software mainly target at expert users and do not sufficiently integrate resources for efficient computing. They are difficult for non-experts to use and are often slow in completing the complicated geographic analysis. To address these problems, future generation of GIS software must be 'easy'. By 'easy' we mean 'easy to use' and 'easy to compute'. 'Easy to use' means that software system should be goal-oriented, rather than the currently procedure-oriented doctrine. The goal-oriented will relieve users, particularly novice users, the burden of knowing the exact commands and their sequences to perform for achieving the goal they want to achieve. 'Easy to compute' means that implementation of GIS analytical functionality should be able to utilize the high-performance computing infrastructures for complicated geographic analysis. Two case studies, one in digital soil mapping and the other in digital terrain analysis, are presented to illustrate the meaning of 'easy'. We believe that the future generations of GIS platforms should be goal-driven, intelligent, high-performance computing enabled, easily accessible, and participatory. It allows anyone to participate in geo-computation at anywhere and anytime. [ABSTRACT FROM AUTHOR]

The present study was conducted to evaluate the effectiveness of combining proximal, and remote sensing with environmental variables for predicting USDA (United States Department of Agriculture) soil great groups (the third hierarchical level of USDA soil classification system) in a semi-arid region located at Jouneqan district in Chaharmahal & Bakhtiary province, Iran. Accordingly, two predictive models, including support vector machine (SVM), and multinomial logistic regression (MLR) using different covariates, including lithology, geomorphology, remote sensing-derived vegetation indices, DEM (digital elevation model)-derived attributes, diffuse reflectance spectroscopy-derived soil colour qualifiers, and magnetic susceptibility were examined. A total of 102 soil profiles were excavated, described, and classified up to the great group level, and soil samples were collected from various genetic horizons. The cross validation leave-one-out (LOOCV) was used as validation approach, and the performance of the models was assessed using the kappa coefficient (κ), and overall accuracy. Results showed that considering the κ values (κ = 0.6–0.8), the classification performance identified as substantial for both MLR, and SVM when geospatial data, soil colour qualifiers, and magnetic susceptibility were used together as predictors. However, the MLR classifier outperformed SVM (κ: 0.78, and 0.66, respectively). Chroma, normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), geomorphology map, and some terrain attributes, including slope were among the most important predictors. Our findings confirmed that combining geospatial data, and proximal sensing information in multivariate classification algorithms could improve soil classification accuracy in a quick, and cost-efficient way. [ABSTRACT FROM AUTHOR]

This study aimed to predict soil properties using visible–near infrared (VIS-NIR) spectroscopy combined with partial least square regression (PLSR) modeling. Special emphasis was given to evaluating effect of pre-processing methods on prediction accuracy and important wavelengths. A total of 114 samples were collected and involved in chemical and spectral analyzes. PLSR model of each soil property was calibrated for all pre-processing methods using all samples, and leave-one-out cross-validation was used to make comparisons between them. Then, PLSR model of each best pre-processing method was calibrated using a 75% of all samples and correspondingly validated with the remaining a 25%. Model accuracy was evaluated based on coefficient of determination (R2), root mean-squared errors (RMSE), and residual prediction deviations (RPD). The high correlation coefficients were found between the tested soil properties and reflectance spectra. The pre-processing methods considerably improved prediction accuracy and filtering methods outperformed linearization methods, and the latter outperformed normalization methods. The performance of cross-validation, calibration and independent validation was similar. An excellent prediction (RPD>2.5) model was obtained for soil organic carbon (SOC) and calcium-carbonate (CaCO3), good quantitative (2.0< RPD<2.5) prediction for sand, silt, and clay, fair prediction (1.4< RPD<1.8) for pH, and poor prediction (1.0< RPD<1.4) for hygroscopic water content (WC). Important wavelengths varied depending on soil property, but some wavelengths were common. This study can be a precursor to building a pioneering soil spectral database, calibrating satellite data, and hyperspectral image mapping of soils as well as digital soil mapping, environmental, and erosion modeling in the Caucasus Mountains. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is an important indicator to evaluate agricultural soil quality. Precise mapping SOC can help to facilitate soil and environmental management decisions. This study applied multiple stepwise regression (MSR), boosted regression trees (BRT) model, and boosted regression trees hybrid residuals kriging (BRTRK) to map SOC of agricultural lands in Wafangdian City, northeastern China. A 10-fold cross-validation procedure was used to evaluate the performance of the three models. The BRTRK method exhibited the best predictive performance and explained 78% of the total SOC variability. The distribution of SOC was mainly explained by elevation, followed by soil-adjusted vegetation index (SAVI), and topographic wetness index (TWI). We conclude that the BRTRK was the most accurate method in predicting spatial distribution of SOC. In addition, our study indicated that topographic variables as key factors to affect SOC should be considered in future SOC mapping. [ABSTRACT FROM AUTHOR]

Accurate fertilization management is a present-day challenge and can conciliate profitability and sustainability in agriculture production. This study presents topsoil concentrations of P2O5 and K2O in apple orchards and vineyards in South Tyrol, Italy. Sixteen thousand georeferenced soil samples were collected and spatialized using ordinary local kriging. Measured average and maximum concentrations of P2O5 were 260 and 1500 mg/kg, respectively, in apple orchards, and 280 and 880 mg/kg, respectively, in vineyards. Similarly, measured average and maximum concentrations of K2O were 210 and 1040 mg/kg, respectively, in apple orchards, and 250 and 820 mg/kg, respectively, in vineyards. Overall, K2O concentration was mostly within the recommended thresholds, while P2O5 concentration was frequently higher than the target level for optimal production. The resulting maps (1:25,000 scale) of P2O5 and K2O showed modest accuracy with RMSE of 115.7 and 78.3 mg/kg, respectively. These maps can support evidence-based decision making by multiple stakeholders. [ABSTRACT FROM AUTHOR]

Soil texture is a key controlling factor of soil properties and its functions include water and nutrient holding capacity, retention of pollutants, root development, soil biodiversity, and biogeochemical cycling. From the geotechnical standpoint, it is interesting to analyze the soil texture in regions due to its relation with the infiltration and runoff processes and, consequently, the effect on erosion processes. The purpose of this study is to present a methodology that provides the soil texture spatial variation by using Fuzzy logic theory and geostatistical technique in Geographic Information System (GIS) platform. A total of 140 soil samples were taken from topsoil (0–30 cm) in the study area located in the north of Guilan Province, the southern coast of Caspian Sea, Northern Iran. The soil textural classes were converted to numerical values (fuzzy values) using the fuzzy logic concept. The fuzzy values were spatially interpolated by ordinary kriging method such that the fitted model on experimental semi-variogram was exponential with moderate structure. The results showed the accuracy of soil texture predictive map was acceptable according to the values of normalized root-mean-square error for train data set (0.182) and test data set (0.179). The knowledge of the spatial variability of soil properties such as the soil texture can be an important tool for land-use planning in order to reduce the potential soil losses during rainy seasons. The results indicated that the integration of fuzzy logic, geostatistics, and GIS can improve the interpolation process. [ABSTRACT FROM AUTHOR]

Identifying changes in soil organic carbon stocks (SOCS) is essential for determining appropriate ways to deal with land degradation, for understanding soil and crop management and for gathering useful information for a range of environmental studies. The aim of this study was to predict SOCS and compare under current and potential future land uses. Soil organic carbon (SOC) and bulk density were measured at 137 locations across the Marivan, Kurdistan Province, Iran, and soil SOCS was computed. Auxiliary data including, terrain attributes and Landsat 8 ETM+ data were acquired. Random forest (RF) models were used to relate the SOCS to the auxiliary data. Results suggested that the mean SOCS in the topsoil and subsurface in croplands were lower than in forestland and wetland, although not significantly so. In the area, approximately 18.48% of forestland and 17.39% of wetland has been brought into cultivation. The authors estimate that this has led to a loss of SOCS from forestland topsoil of 22,860 Mg C, and from subsurface of 15,685 Mg C. The SOCS loss from wetland topsoil and subsurface were not as great, at 4193 and 2680 Mg C, respectively, but this was due to the area not being as large. [ABSTRACT FROM AUTHOR]

The aim of this study was to generate models to predict the salinity of the soils in the geothermal zone which is located in Michoacán, México, over space and time. Systematic sampling was performed at locations 400 m apart with a sampling period every 48 days. At each site, two soil samples, from depths of 0-30 cm and 30-60 cm, were collected and analysed in laboratory. Regression-Kriging was used to make spatial predictions of the electrical conductivity of soil (ECs). Spherical and Gaussian theoretical models fit the experimental semivariograms. The results showed that the soil samples ranged from slightly saline (2.1-4 dS m−1) to strongly saline (8.1-16 dS m−1). In addition, the ECs was greater at a depth of 30-60 cm than it was at a depth of 0-30 cm. The ECs decreased from more than 8.0 dS m−1 to less than 4.0 dS m−1 after the first sampling period. [ABSTRACT FROM AUTHOR]

The objective was to delimitate homogeneous soil zones that correspond to differentiated areas of growth and yield of a vineyard, using soil electrical resistivity. The soil electrical resistivity (ρ) at three depths was measured with an automatic on-the-go recording resistivity meter, the data was georeferenced, and zones of homogeneous soil characteristics were drawn. A grid of equally spaced sampling points was superimposed to the area where vegetative growth and yield data of the grapevines were recorded.ρshowed a marked spatial distribution and two homogeneous areas were delineated.ρranged from 48.6 to 54.3 ohm-metre for cluster A and from 90.2 to 208.5 ohm-metre for cluster B. Negative significant correlations were found betweenρand plant growth and yield variables. Vegetative growth and crop yield were significantly different between homogeneous soilρareas. Plants growing on cluster A had an average shoot length of 1.4 m and yielded 3.1 kg per plant; on cluster B, shoot length was 0.9 m and yield was 0.9 kg per plant. The procedure of measuring the soil electrical resistivity and georeferencing methodology proved fast and reliable, albeit expensive, and might be a useful tool in precision viticulture for delineating homogeneous soil zones. [ABSTRACT FROM PUBLISHER]

The main purpose of this study, is to evaluate an advanced feature selection technique, artificial bee colony (ABC) algorithm; to reduce the number of auxiliary variables derived from a digital elevation model (DEM) and remotely sensed data (e.g. Landsat images). A combination of depth functions (e.g. power, logarithmic and spline) and data miner methods (artificial neural network: ANN and support vector regression: SVR) were applied for three-dimensional mapping of soil organic matter (SOM) in Big Sioux River watershed, South Dakota, USA. Unsurprisingly, the ABC feature selection algorithm indicated that remote sensing data (e.g. NDVI) are powerful predictors at soil surface, however, with the increasing soil depth, the terrain parameters (e.g. wetness index) became more relevant. Our findings from this study demonstrated that both the spatial models generally performed well. The mean R2 values calculated by 10-fold cross validation suggested that SVR and ANN models could explain approximately 50 and 57% of total SOM variability, respectively. However, predictive power of both models increased when ABC feature selection algorithm applied, particularly when it combined with the ANN model. Results showed that DSM approaches are very important and powerful tool to explain the 3D spatial distribution of SOM across the study watershed. [ABSTRACT FROM AUTHOR]

A digital soil map of Quintana Roo was compiled at a 50 m pixel resolution using a geomorphopedological approach to produce a map that reflects a synoptic view of the geomorphology, environmental conditions and associated soils. Initially, it was developed using a geopedological approach and then converted to a digital map. The map was derived from soil-forming factors using mathematical methods to infer information in places where data were not available. Its compilation included three stages; the first two follow the geopedological approach that consists of a synthesis of data from the characterization of the geomorphological landscapes (vertical dissection, karst geomorphometrics, failures, geology) and soils, and the third stage incorporating environmental components (climate and vegetation) and related variables through various methods of statistical analysis (cluster, principal components and classification analysis) to obtain the pattern of soil distribution and to develop a model for the digital soil map of the study area. [ABSTRACT FROM PUBLISHER]

We present a 1:100,000 scale soil texture map of Paragominas county (Pará, Brazil), covering 19,330 km2. The method allows rapid production of a soil texture map of a large area where the strength of a duricrust controls the relief. It is based on an easily accessible explanatory variable, topography, which is represented using a Digital Elevation Model. The method makes it possible to map the spatial distribution of the texture of the topsoil layer. Modelling was complemented by field observations to identify the laws governing the spatial organisation of soil textures. The spatial variability of the elevation above sea-level of the duricrust was obtained by Kriging. The error rate of the resulting map is 26%, and the observations of the four soil texture units were respectively 78%, 90%, 41% and 60% accurately located. [ABSTRACT FROM PUBLISHER]

Spatial information about physical soil properties is in great demand, being basic input data in numerous applications. Soil texture can be characterized by different approaches, such as particle size distribution, plasticity index or soil texture classification. In accordance with the increasing demands for spatial soil texture information, our aim was to compile a topsoil texture class map for Hungary with an appropriate spatial resolution, using the United States Department of Agriculture soil texture classes. The ‘Classification and Regression Trees’ method was applied because it is widely used in Digital Soil Mapping, and has numerous advantages. Primary soil data were provided by the Hungarian Soil Information and Monitoring System. A digital elevation model and its derived components, geological and land cover map, and appropriate remotely sensed products together with the soil map featuring overall physical properties provided by the Digital Kreybig Soil Information System were used as auxiliary environmental co-variables. The resulting map can be used as direct input data in meteorological and hydrological modelling as well as in spatial planning. [ABSTRACT FROM PUBLISHER]

Sampling efforts are constrained by limited availability of resources. Therefore, methods to reduce the number of samples, while still achieving reasonable accuracy are needed. Land-surface segmentation (LSS) has proven a powerful technique to partition digital elevation models (DEMs) and their derivatives into relatively homogeneous areas, which can be further employed as support in soil sampling. Though topography is one of the main soil forming factors, a robust assessment of the potential of this technique to digital soil mapping (DSM) is still missing. In this study, we aimed at evaluating the potential of LSS in stratifying a landscape into relatively homogeneous areas, which can be used as strata for guiding the selection of sampling points in DSM. The experiments were carried out in two study areas where soil samples were available. Land-surface derivatives were derived from DEMs and segmented with a tool based on the multiresolution segmentation algorithm, into objects considered as homogeneous soil-landscape divisions. Thus, one sample was randomly selected within each segment from the existing sample data, based on which predictions of soil classes/sub-orders and properties, i.e. soil texture and A-horizon thickness, were made. Results were compared with predictions based on simple random sampling (SRS) and conditioned Latin hypercube (cLHS). The segmentation-based sampling (SBS) scheme performed better than SRS and cLHS schemes in predicting the A-horizon thickness, soil texture fractions and soil classes, showing a high potential of LSS in stratifying a landscape for the purposes of DSM. The novelty of this study is in the way strata are constructed, rather than in the sampling design itself. Further research is needed to demonstrate the value of a SBS design for practical use. The analyses presented here further highlight the importance of considering locally adaptive techniques in optimization of sampling schemes and predictions of soil properties. [ABSTRACT FROM PUBLISHER]

Large dams can play an important role in rejuvenating economic and social development but are often associated with environmental degradation. The Mzimvubu Water Project will involve the building of two large multi-purpose dams, namely Ntabelanga and Laleni. A key anticipated benefit of the former is the expansion of irrigated agriculture. This article reveals that large areas surrounding the dam are, however, severely affected by soil erosion and that irrigation on these areas is likely to be unsustainable. Focus Group Discussions (FGDs) and interviews were held with members of two communities that will be affected by the dam to gauge community sentiments and anticipations. The FGDs revealed that despite respondents lived experiences of soil erosion, local narratives about it appear irreconcilable with geophysical facts. The one crucial anticipated dam dividend appeared to be support for large-scale irrigated agriculture. Rural anticipations that irrigated agriculture can counteract soil degradation caused by erosion are not supported by scientific evidence, and without appropriate community awareness programmes by the dam developers, the divergence between science facts and local views may in the future engender conflict between the community and other stakeholders. [ABSTRACT FROM PUBLISHER]

Spatial information on soil salinity is increasingly needed for decision making and management practices in arid environments. In this article, we attempted to investigate soil salinity variation via a digital soil mapping approach and genetic programming in an arid region, Chah-Afzal, located in central Iran. A grid sampling strategy with 2-km distance was used. In total, 180 soil surface samples were collected and then analyzed. A symbolic regression was then adopted to correlate electrical conductivity (ECe) with a suite of auxiliary data including predicted maps of apparent electrical conductivity (vertical: ECavand horizontal: ECah), Landsat spectral data and terrain attributes derived from a digital elevation model. The accuracy of the genetic programming model was evaluated using root mean square error (RMSE), mean error (ME), and coefficient of determination (R2) based on an independent validation data set (20% of database or thirty soil samples). In general, results showed that ECahhad the strongest influence on the prediction of soil salinity followed by salinity index wetness index, Landsat Band 3, multi-resolution valley bottom flatness index, elevation, and normalized difference vegetation index. Furthermore, results indicated that the genetic programming model predicted ECeover the study area accurately (R2 = 0.87, ME = −1.04 and RMSE = 16.36 dSm−1). Overall, it is suggested that similar applications of this technique could be used for mapping soil salinity in other arid regions of Iran. [ABSTRACT FROM AUTHOR]

This study evaluates the performances of a combination of genetic programming and soil depth functions to map the three-dimensional distribution of cation exchange capacity (CEC) in a semiarid region located in Baneh region, Iran. Using the conditioned Latin hypercube sampling method, the locations of 188 soil profiles were selected, which were then sampled and analyzed. In general, results showed that equal-area quadratic splines had the highestR2, 89%, in fitting the vertical CEC distribution compared to power and logarithmic functions withR2of 81% and 84%, respectively. Our findings indicated some auxiliary variables had more influence on the prediction of CEC. Normalized difference vegetation index (NDVI) had the highest correlation with CEC in the upper two layers. However, the most important auxiliary data for prediction of CEC in 30–60 cm and 60–100 cm were topographic wetness index and profile curvature, respectively. Validation of the predictive models at each depth interval resulted inR2values ranging from 66% (0–15 cm) to 19% (60–100 cm). Overall, results indicated the topsoil can be reasonably well predicted; however, the subsoil prediction needs to be improved. We can recommend the use of the developed methodology in mapping CEC in other parts in Iran. [ABSTRACT FROM PUBLISHER]

Digital soil mapping (DSM) involves acquisition of field soil observations and matching them with environmental variables that can explain the distribution of soils. The harmonization of these data sets, through computer-based methods, are increasingly being found to be as reliable as traditional soil mapping practices, but without the prohibitive costs. Therefore, the present research developed decision tree models for spatial prediction of soil classes in a 720 km2area located in an arid region of central Iran, where traditional soil survey methods are difficult to undertake. Using the conditioned Latin hypercube sampling method, the locations of 187 soil profiles were selected, which were then described, sampled, analyzed, and allocated to six Great Groups according to the USDA Soil Taxonomy system. Auxiliary data representing the soil forming factors were derived from a digital elevation model (DEM), Landsat 7 ETM+images, and a map of geomorphology. The accuracy of the decision tree models was evaluated using overall, user, and producer accuracy based on an independent validation data set. Our results showed some auxiliary variables had more influence on the prediction of soil classes which included: topographic wetness index, geomorphological map, multiresolution index of valley bottom flatness, elevation, and principal components of Landsat 7 ETM+images. Furthermore, the results have confirmed the DSM model successfully predicted Great Groups with overall accuracy up to 67.5%. Our results suggest that the developed methodology could be used to predict soil classes in the arid region of Iran. [ABSTRACT FROM PUBLISHER]

The time-integrated normalized difference vegetation index (iNDVI) provides key remote-sensing-derived information on the interactions between vegetation growth, climatic and soil conditions, and land use. Using a time-series of Landsat imagery obtained for Queensland, Australia, it has been demonstrated how robust geostatistics can be used to predict iNDVI. This approach is novel because it explicitly quantifies the uncertainty of prediction and uses Winsorizing, a data-censoring method, to minimize the distorting effects of outliers. Robust prediction of iNDVI, as opposed to non-robust prediction, was justifiable in 79% of the study area, highlighting the need for methods that deal with outliers in time-series analysis of remotely sensed imagery. There was a strong coarse-scale association between Queensland's bioregions and iNDVI, and also between bioregion and the rain-induced difference in iNDVI through time (effects that were significant atp< 0.001 in both cases). At a finer spatial scale, prediction of iNDVI also appeared to be a promising way to distinguish long-term cropping land from adjacent long-term grazing land (effect significant atp< 0.001). The method is tied to a set of assumptions concerning image radiometry, cloud detection, variogram estimation, and variable additivity. The first two are fundamental remote-sensing issues that can be improved with additional labour; the last two can be improved statistically but would greatly increase the processing time per pixel. Robust geostatistical analysis of time-series has immediate relevance to gap-filling of SLC-off Landsat 7 Enhanced Thematic Mapper Plus (ETM+) imagery, and for generating novel covariates for digital soil mapping. [ABSTRACT FROM PUBLISHER]

Hungary has long traditions in soil survey and mapping. Large amount of soil information is available in various dimensions and generally presented in maps, serving different purposes as to spatial and/or thematic aspects. Increasing the proportion of soil-related data has been digitally processed and organized into various spatial soil information systems (SSISs). The most current countrywide ones have been elaborated by and available at Research Institute for Soil Science and Agricultural Chemistry of the Hungarian Academy of Sciences (RISSAC HAS). The existing maps, data and systems served the society for many years; however, the available data are no longer fully satisfactory for the recent needs of policy making. There were numerous initiatives for the digital processing, completion, improvement and integration of the existing soil datasets. In our paper, we briefly present the national SSISs developed and maintained by our institute and some examples are given regarding how their functionality was extended by digital soil mapping for the solution of specific soil-related demands. [ABSTRACT FROM AUTHOR]

Sampling design plays an important role in spatial modeling. Existing methods often require a large amount of samples to achieve desired mapping accuracy, but imply considerable cost. When there are not enough resources for collecting a large set of samples at once, stepwise sampling approach is often the only option for collecting the needed large sample set, especially in the case of field surveying over large areas. This article proposes an integrative hierarchical stepwise sampling strategy which makes the samples collected at different stages an integrative one. The strategy is based on samples' representativeness of the geographic feature at different scales. The basic idea is to sample at locations that are representative of large-scale spatial patterns first and then add samples that represent more local patterns in a stepwise fashion. Based on the relationships between a geographic feature and its environmental covariates, the proposed sampling method approximates a hierarchy of spatial variations of the geographic feature under concern by delineating natural aggregates (clusters) of its relevant environmental covariates at different scales. The natural occurrence of such aggregates is modeled using a fuzzyc-means clustering method. We iterate through different numbers of clusters from only a few to many more to be able to reveal clusters at different spatial scales. At a particular iteration, locations that bear high similarity to the cluster prototypes are identified. If a location is consistently identified at multiple iterations, it is then considered to be more representative of the general or large-scale spatial patterns. Locations that are identified less during the iterations are representative of local patterns. The integrative stepwise sampling design then gives higher sampling priority to the locations that are more representative of the large-scale patterns than local ones. We applied this sampling design in a digital soil mapping case study. Different representative samples were obtained and used for soil inference. We started with samples that are the most representative of the large-scale patterns and then gradually included the samples representative of local patterns. Field evaluation indicated that the additions of more samples with lower representativeness lead to improvements of accuracy with a decreasing marginal gain. When cost-effectiveness is considered, the representative grade could provide essential information on the number and order of samples to be sampled for an effective sampling design. [ABSTRACT FROM AUTHOR]

After several years of digital processing of legacy soil data collected by the Kreybig soil survey, the nationwide development of the digital Kreybig soil information system (DKSIS) made possible the compilation of soil property and function maps for the territory of Hungary at a scale of approximately 1:25,000-1:50,000. The Kreybig legacy data are spatially most detailed nationwide dataset related to soils which covers the whole area of the country. It simultaneously contains two types of geometric datasets: approximately 100,000 soil mapping units (SMUs) and 250,000 sampling plots. SMUs are characterized by several complex soil physical and chemical categories and detailed soil properties which are provided for soil profiles whose description in the digital environment is supported by a specific relational database. Primary digital soil maps can be compiled based on the polygons-type entities, while suitable spatial inference of profile-related variables makes the composition of secondary, regionalized digital soil maps possible, too. In our paper, we present example for both types. [ABSTRACT FROM AUTHOR]

Fuzzy classification typically assigns a location or an area to a category with some estimated degree of uncertainty. There are strong incentives for depicting uncertainty along with category, and numerous authors have recommended that this be done using progressive desaturation of the entity's color with increasing uncertainty. This article shows that such recommendations cannot be naively applied using color models widely used in computer graphics because colors equally ‘saturated’ do not appear equally certain. We demonstrate that models based on color perception are preferred, particularly if one wishes to compare uncertainties across classes. We discuss geometrical complications arising with perceptual models that are not present with models closely tied to hardware. An algorithm for selecting colors is presented and illustrated using the model. [ABSTRACT FROM AUTHOR]