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Carbon is an essential element and contributor to healthy soil conditions as well as ecological soil function and productivity. Additionally, carbon is a component of all plants and animals on the planet and is a necessary component of life. Natural vegetation serves as a significant but highly dynamic carbon sink. When vegetation is removed quicker than it can regenerate, for example by harvesting crops or timber, soil carbon is depleted. Thus, understanding the environmental effects and dynamics of loss of vegetation is a crucial prerequisite to turning our natural resource management from a carbon emitter to a carbon sink to avoid that and achieve sustainability. At the same time, the spatial distribution of soil organic carbon is also highly heterogeneous, with variations in climate, other soil characteristics, and land use/land cover affecting how our ecosystem reacts to the loss of vegetation. Thus, to effectively improve green metrics and contribute to the creation of future policies, it is required to conduct research on the changes in vegetation and their effect on soil organic carbon and provide regionally appropriate management advice. Here, in this research, our goal is to examine the "individual treatment effects" (ITE), which are a personalized or individualized effect estimation of one variable on the output, and utilize causal inference to address them. Using the LUCAS dataset, we explore the heterogeneous treatment effect of percent tree coverage (PTC), as a parameter of the density of trees on the ground, on the soil organic carbon content in Germany. We do this by leveraging some parameters, such as climate data, land use/land cover information, and other information from the soil. We thus offer a data-driven viewpoint for focusing on sustainable behaviors and effectively increasing soil organic carbon content levels.

Optimal soil nitrogen-to-phosphorus-to-potassium (NPK) stoichiometry is critical for agricultural production in Africa because it presents the appropriation of input materials to avoid limited or excessive fertilizer applications. Furthermore, an optimum nutrient supply to the plants is crucial to mitigate or eliminating Africa’s food crisis. However, what drivers influence its levels, distribution and variability across different landscapes and scales? Insights regarding these aspects are necessary for (1) the derivation of robust policies associated with crop production and food security, (2) monitoring of changes associated with hotspot areas between NPK stoichiometry and designated drivers, (3) instigation of suitable and well-targeted efforts to ensure that areas with optimal soil NPK levels are maintained since these eventually affect and influence crop yield output, and (4) identification of areas that are imbalanced in NPK content of the soils and thus may need fertilization. Freely accessible major soil nutrient data [i.e., nitrogen (N), phosphorus (P) and potassium (K)] for Africa were obtained from the iSDAsoil platform, aggregated to 250 m, and used to compute the NPK stoichiometry estimate. In addition, similar NPK stoichiometry estimates were derived for national-scale major food crop exporters, including South Africa, Ethiopia, and Malawi. All these across-scale NPK stoichiometry estimates coupled with different driver estimates (e.g., human activities/agricultural activities/cropping systems, soil texture, soil pH, etc.) provided the data used to assess pairwise mechanistic and explainable model insights using structural equation models [SEM(s)] plus partial dependence plots (PDPs) respectively. Climate-related factors along with topography were the main direct drivers of NPK stoichiometry connected to the topsoil of Africa (i.e., the entire continent including some selected nations). Human-related activities contributed less to soil NPK stoichiometry. Interestingly, aboveground biomass was discovered to be interdependent with NPK stoichiometry. This cross-scale benchmark alludes to the variations in NPK stoichiometry under both changing climatic conditions and topography in Africa. Keywords: NPK Stoichiometry, Soil Nutrients, Climate Change, Food Security, Structural Equation Modeling, Topographic Effects, Nitrogen, Phosphorus, Potassium, Africa

Potentially toxic elements in agricultural soils are primarily derived from anthropogenic and geogenic sources. This study aims to predict and map antimony (Sb) concentration in soil using multiple regression kriging in two distinct modeling approaches, namely Sb prediction using data fusion coupled with regression kriging (scenario 1) and Sb prediction using data fusion, terrain attributes, and regression kriging (scenario 2). Cubist regression kriging (cubist_RK), conditional inference forest regression kriging (CIF_RK), extreme gradient boosting regression kriging (EGB_RK) and random forest regression kriging (RF_RK) were the modeling techniques used in the estimation of Sb concentration in agricultural soil. The validation results suggested that in scenario 1, EGB_RK was the optimal modeling approach for Sb prediction in agricultural soil with root mean square error (RMSE) = 1.31 and mean absolute error (MAE) = 0.61, bias = 0.37, and high coefficient of determination R

The current study assesses and predicts cadmium (Cd) concentration in agricultural soil using two Cd datasets, namely legacy data (LD) and preferential sampling-legacy data (PS-LD), along with four streams of auxiliary datasets extracted from Sentinel-2 (S2) and Landsat-8 (L8) bands. The study was divided into two contexts: Cd prediction in agricultural soil using LD, ensemble models, 10 and 20 m spatial resolution of S2 and L8 (context 1), and Cd prediction in agricultural soil using PS-LD, ensemble models and 10 and 20 m spatial resolution of S2 and L8 (context 2). In context 1, ensemble 1, L8 with PS-LD was the cumulative optimal approach that predicted Cd in agricultural soil with a higher R

Zinc (Zn) is a vital element required by all living creatures for optimal health and ecosystem functioning. Therefore, several researchers have modeled and mapped its occurrence and distribution in soils. Nonetheless, leveraging model predictive performances while coupling information derived from visible near-infrared (Vis-NIR) and soils (i.e. chemical properties) to estimate potential toxic elements (PTEs) like Zn in agricultural soils is largely untapped. This study applies two methods to rapidly monitor Zn concentration in agricultural soil. Firstly, employing Vis-NIR and machine learning algorithms (MLAs) (Context 1) and secondly, applying Vis-NIR, soil chemical properties (SCP), and MLAs (Context 2). For the Vis-NIR information, single and combined pretreatment methods were applied. The following MLAs were used: conditional inference forest (CIF), partial least squares regression (PLSR), M5 tree model (M5), extreme gradient boosting (EGB), and support vector machine regression (SVMR) respectively. For context 1, the results indicated that M5-MSC (M5 tree model-multiplicative scatter correction) with coefficient of determination (R

Background Human activities considerably contribute to polluting potentially toxic element (PTEs) levels in soils, especially agricultural soils. The consistent introduction of PTEs in the environment and the soil pose health-related risks to humans, flora and fauna. One hundred and fifteen samples were collected in the district of Frydek Mistek (Czech Republic) in a regular grid form. The soil samples were air-dried, and the concentrations of PTEs (i.e. lead, arsenic, chromium, nickel, manganese, cadmium, copper, and zinc) were determined by ICP-OES (inductively coupled plasma optical emission spectrometry). The purpose of this study is to create digitized soil maps that expose the human-related health risks posed by PTEs, estimate pollution indices, ascertain the spatially distributed patterns of PTEs, source apportionment and quantify carcinogenic and non-carcinogenic health risks using the sample location approach. Results The results revealed that the pollution assessment of the soils in the study area using diverse pollution assessment indexes (pollution index, pollution load index, ecological risk and risk index), based on the application of the local background value and the European average value, displayed a range of pollution levels due to differences in the threshold limits from differing geochemical background levels. The principal components analysis and positive matrix factorization, respectively, identified the sources of pollution and the distribution of PTE sources. Mapping the health index and total carcinogenic risk highlighted hotspots of areas within the study area that require immediate remediation. The self-organizing map (SeOM) revealed a diversified colour pattern for the factor scores. A single neuron exhibited a high hotspot in all factor loadings on different blocks of neurons. Children’s CDItotal (Chronic Daily Intake total) values for non-carcinogenic risk and carcinogenic risk were found to be greater than adults’, as were their HQ (hazard quotients) and CR (carcinogenic risk) values. According to the health index of non-carcinogenic risk, 6.1% of the study area sampled posed a potential risk to children rather than adults. Corresponding to the sampled pointwise health risk assessment, 13.05% of the sampled locations are carcinogenic to children. The estimated health risk in the agricultural soil was high, with both carcinogenic and non-carcinogenic risks that could threaten persons living in the study area, particularly children. Conclusion In general, the continuous application of agriculturally related inputs such as phosphate fertilizers and other anthropogenic activities (e.g., steel industry) can increase the level of PTEs in soils. The use of mean, maximum, and minimum values in health risk estimation does not provide a comprehensive picture of a research area’s health state. This study recommends using a sampled pointwise or location health risks assessment approach, which allows researchers to identify high-risk environments that exceeds the recommended threshold as well as areas on the verge of becoming high risk, allowing for rapid remedial action.

The study intended to assess the level of pollution of potential toxic elements (PTEs) at different soil depths and to evaluate the source contribution in agricultural soil. One hundred and two soil samples were collected for both topsoil (51), and the subsoil (51) and the content of PTEs (Cr, Cu, Cd, Mn, Ni, Pb, As and Zn) were determined using inductively coupled plasma-optical emission spectroscopy (ICP-OES). The concentrations of Zn and Cd in both soil horizons indicated that the current study levels were higher than the upper continental crust (UCC), world average value (WAV), and European average values (EAV). Nonetheless, the concentration values of PTEs such as Mn and Cu for EAV, As, Cu, Mn, and Pb for UCC, and Pb for WAV were lower than the average values of the corresponding PTEs in this study. The single pollution index, enrichment factor, and ecological risk revealed that the pollution level ranged from low to high. The pollution load index, Nemerow pollution index, and risk index all revealed that pollution levels ranged from low to high. The spatial distribution confirmed that pollution levels varied between the horizons; that is, the subsoil was considered slightly more enriched than the topsoil. Principal component analysis identified the PTE source as geogenic (i.e. for Mn, Cu, Ni, Cr) and anthropogenic (i.e. for Pb, Zn, Cd, and As). PTEs were attributed to various sources using enrichment factor-positive matrix factorization (EF-PMF) and positive matrix factorization (PMF), including geogenic (e.g. rock weathering), fertilizer application, steel industry, industrial sewage irrigation, agrochemicals, and metal works. Both receptor models allotted consistent sources for the PTEs. Multiple linear regression analysis was applied to the receptor models (EF-PMF and PMF), and their efficiency was tested and assessed using root-mean-square error (RMSE), mean absolute error (MAE), and R