Distribution Of Soil Properties Research Articles

Fertility and quality of arable soils in Poland: spatial–temporal analysis of long-term monitoring

Soil quality is a globally important issue from the point of view of food production, maintenance of appropriate environmental quality and ecological balance. Assessing its quality and fertility using indices is one of the methods that make it possible to formulate recommendations for good soil management. The aim of the study was, therefore, to determine the spatial and temporal distribution of soil properties in the context of soil quality assessment (SQI) and soil fertility assessment (SFI, SEF, NIV, and SSFI) using the example of a Central European country (Poland). The inverse distance-weighted spatial interpolation technique was used to estimate the spatial distribution of soil quality and fertility indicators. So far, no research has been carried out that would include a comprehensive analysis of soil fertility and quality indicators in such a large research area over many years. The results of 216 points of monitoring the chemistry of arable soils in Poland from the period 1995–2020 for 28 physicochemical soil parameters were considered. The analyses carried out showed significant variability in the properties of the described soils in Poland, as evidenced by the principal component analysis (PCA) and the coefficient of variation (exceptions: bulk density, pH). It was found that heavy metals (Zn, Cd, Cu) were responsible for the deterioration of soil properties. The results obtained for different indices were different (SFI – most soils with good fertility, SEF – high fertility, NIV – soils highly enriched with nutrients, SQI – average soil quality). It was found that the SSFI is the most appropriate indicator among the analyzed indicators for Polish conditions in relation to arable soils, as it is adapted and calibrated to the specifics of this area. The Spearman correlation matrix showed that the largest group of statistically highly significant parameter pairs belonged to heavy metals (Ni/Zn, Zn/Pb, Zn/Cu, Ni/Cu). In contrast, the strongest correlations were recorded for Ni (with effective cation exchange capacity (ECEC), Mn, Mgav, Zn, Cu). The assessment revealed limiting factors and temporal trends in the overall evaluation of the quality of the country’s agricultural soils, which are important from the point of view of both sustainable development and agricultural production.

Soil Fertility Mapping using Geo-spatial Technique for Khapradih Farm, DKS College of Agriculture and Research Station, Bhatapara, Chhattisgarh, India

The present study aimed to assess various chemical properties and available macro and micronutrients in the soil. Collection of 101 geo-referenced soil surface samples at 100-meter intervals of the Khapradih Farm of DKS College of Agriculture & Research Station, district Balodabazar-Bhatapara of Chhattisgarh state and examined in the laboratory for pH, electrical conductivity (EC), organic carbon (OC), and available macro-nutrients (N, P, K, S) and micro- nutrients (Fe, Mn, Cu, Zn, B). The results showed that the soil was generally neutral in pH, with a range of 5.7 to 7.6. The electrical conductivity indicated non-saline, varying from 0.11 to 0.26 dSm-1. Available nutrients were categorized as low, medium, or high based on nutrient index values. The study revealed that the soil in the area had low levels of available N and S, medium levels of available P and K, and high levels of available Fe, Mn, and Cu. Zn was found to be in the medium category, while B was low. Significant correlations were observed between various physico-chemical properties and available macro and micro nutrients. Using ArcGIS 10.4.1, thematic maps were created, representing the spatial distribution of soil properties and nutrient status. Based on these findings, fertilizer recommendations were developed for major crops grown in the area, resulting in enhanced crop productivity. The study concludes that GPS and GIS-based tools are valuable for soil fertility mapping, monitoring, and site-specific nutrient management, leading to sustainable and optimal crop yields.

Geo-spatial sensing of physical properties a leeway to agricultural soil assessment

The demand for economical means of evaluating soil nutrients’ unpredictability triggered the use of physical factors against the costlier, laborious, and time-consuming chemical approach. This drive led to resolving its capability in evaluating intricate soil properties as a productivity checker. This study aimed at assessing the physical parameters as a useful alternative to the conventional chemical examination of nutrient inconsistency. A petrographic examination was conducted on four rock samples for their classifications. Apparent Electrical Conductivity (ECa) measurements were seasonally executed in the wet (912-station) and dry (906-station). Ten cored soil samples were subjected to a permeability test. Twenty soil samples were examined for pH, Electrical Conductivity (EC), available phosphorus, acidity, Na, Mg, K, and Ca using standard soil science procedures. The mineralogical composition of six samples was determined with X-ray diffraction. The rock is biotite granite gneiss containing plagioclase (22%), microcline (24%), orthoclase (4%), quartz (25%), biotite (7%), and others (18%). The soils ECa were 10-344 µS/cm; categorised as low (1-49 µS/cm), moderate (50-99 µS/cm), and high (>100 µS/cm). The ECa distribution varied from moderate (61%) to high (64%) suggesting a heterogeneous pattern of soil attributes. The infiltration rate was slow in high ECa (5.56x10-5-1.67x10-4 cm/s) signifying good retention capability whereas the low and moderate ECa (moderate-moderately rapid) sections promote nutrient leaching. The cation exchangeable capacity was low (2.99 cmol/kg) in the low ECa and moderate (3.30-4.85 cmol/kg) in the moderate and high ECa; with varying basic cation saturation in the high (81.38%), moderate (73.34%) and low (71.89%) ECa regions and high ECa had higher fertility status. The high ECa had low quartz (41.3%) and microcline (15.7%), but high kaolinite (31.1%) had an affinity to ads orb more cations compared to other ECa regions. ECa variability is practicable in predicting the spatial distribution of soil properties and delineating the management zones. Key words: Granite gneiss, electrical conductivity, permeability, soil composition, mineral assemblages

Looking for Optimal Maps of Soil Properties at the Regional Scale

Around 70% of surface in Extremadura, Spain, faces a critical risk of degradation processes, highlighting the necessity for regional-scale soil property mapping to monitor degradation trends. This study aimed to generate the most reliable soil property maps, employing the most accurate methods for each case. To achieve this, six different machine learning (ML) techniques were tested to map nine soil properties across three depth intervals (0–5, 5–10 and > 10 cm). Additionally, 22 environmental covariates were utilized as inputs for model performance. Results revealed that the Random Forest (RF) model exhibited the highest precision, followed by Cubist, while Support Vector Machine showed effectiveness with limited data availability. Moreover, the study highlighted the influence of sample size on model performance. Concerning environmental covariates, vegetation indices along with selected topographic indices proved optimal for explaining the spatial distribution of soil physical properties, whereas climatic variables emerged as crucial for mapping the spatial distribution of chemical properties and key nutrients at a regional scale. Despite providing an initial insight into the regional soil property distribution using ML, future work is warranted to ensure a robust, up-to-date, and equitable database for accurate monitoring of soil degradation processes arising from various land uses.

Predicting wetland soil properties using machine learning, geophysics, and soil measurement data

PurposeMachine learning models can improve the prediction of spatial variation of wetland soil properties, such as soil moisture content (SMC) and soil organic matter (SOM). Their performance, however, relies on the quantity of data used to train the model, limiting their use with insufficient data. In this study, we assessed the use of synthetic data constrained by limited field data for training an eXtreme Gradient Boosting (XGBoost) algorithm used to predict the distribution of soil properties based on geophysical measurements constrained by soil samples.Materials and methodsA spatial distribution of soil apparent electrical conductivity (ECa) and laboratory measurements of SOM and SMC from twenty-two core samples were acquired at the St. Michael restored wetland near Defiance, Ohio. The correlations between ECa, SOM, and SMC were explored for predicting the spatial distribution of SOM and SMC. We used a Beta Variational AutoEncoder (β-VAE) approach to synthetically generate over 70,000 training data from the original twenty-two data from soil cores. The training data samples were taken from the latent space. The XGBoost algorithm was then trained on the β-VAE generated data and used to predict the spatial distribution of SOM and SMC at the site. We also validated the accuracy of the XGBoost predictions using an original holdout model validation technique.Results and discussionsThe generated synthetic data using the β-VAE include both soil attributes and ECa, which are larger and more diverse than the original training set with an absolute mean reconstructed error for SMC and SOM ranging from 0.018 to 0.022 and 0.026 to 0.041, respectively. This indicates that the β-VAE successfully generated a realistic synthetic dataset and overcame the technical barrier of using limited datasets. In addition, using generated data to expand the original training data helps the XGBoost model make more accurate predictions compared to training on the original data. The XGBoost prediction performance yielded average Lin’s concordance correlation coefficient (LCCC) values of 0.82 and 0.85 for SOM and SMC and a ratio of performance to deviation (RPD) values of 1.92 and 2.22 respectively, indicating a good performance.ConclusionsThis study validated the use of β-VAE to successfully generate synthetic wetland soil datasets with attributes of the original field data that can be effectively used to train the machine learning XGBoost model. The proposed framework offers an efficient solution for mapping the spatial variability of soil property in data-scarce wetland soil environments.

The Western Greece Soil Information System (WΕSIS)—A Soil Health Design Supported by the Internet of Things, Soil Databases, and Artificial Intelligence Technologies in Western Greece

Soil quality is vital for ecosystem stability, impacting human, plant, and animal health. Traditional soil quality assessments are labor-intensive and costly, making them unsuitable for smart agriculture. To overcome this, Internet of Things (IoT) and artificial intelligence (AI) technologies are employed for sustainable agriculture, enabling real-time data collection and analysis, trend identification, and soil health optimization. The Western Greece Soil Information System (WΕSIS) offers open-access data and services for soil health and sustainability. It includes modules for soil quality indicators, sustainable fertilization management zones, soil property distribution, prediction, mapping, statistical analysis, water management, land use maps, digital soil mapping, and crop health calculation. Integrating the IoT and AI allows for real-time and remote monitoring of soil conditions, managing soil interventions adaptively and in a data-driven way, enhancing soil resources’ efficiency and sustainability, and increasing crop yield and quality. AI algorithms assist farmers and regional stakeholders in optimizing production lines, methodologies, and field practices, reducing costs and increasing profitability. This promotes a circular economy, a soil- and climate-resilient future, biodiversity protection targets, and enhanced soil fertility and productivity. The proposed IoT/AI technical architecture can underpin the development of soil health monitoring platforms, integrating data from various sources, automating data collection, and providing decision support tools.

A Systematic Review on Digital Soil Mapping Approaches in Lowland Areas

Digital soil mapping (DSM) around the world is mostly conducted in areas with a certain relief characterized by significant heterogeneities in soil-forming factors. However, lowland areas (e.g., plains, low-relief areas), prevalently used for agricultural purposes, might also show a certain variability in soil characteristics. To assess the spatial distribution of soil properties and classes, accurate soil datasets are a prerequisite to facilitate the effective management of agricultural areas. This systematic review explores the DSM approaches in lowland areas by compiling and analysing published articles from 2008 to mid-2023. A total of 67 relevant articles were identified from Web of Science and Scopus. The study reveals a rising trend in publications, particularly in recent years, indicative of the growing recognition of DSM’s pivotal role in comprehending soil properties in lowland ecosystems. Noteworthy knowledge gaps are identified, emphasizing the need for nuanced exploration of specific environmental variables influencing soil heterogeneity. This review underscores the dominance of agricultural cropland as a focus, reflecting the intricate relationship between soil attributes and agricultural productivity in lowlands. Vegetation-related covariates, relief-related factors, and statistical machine learning models, with random forest at the forefront, emerge prominently. The study concludes by outlining future research directions, highlighting the urgency of understanding the intricacies of lowland soil mapping for improved land management, heightened agricultural productivity, and effective environmental conservation strategies.

GIS Interpolation and Mapping of Soil Physicochemical Properties in Deep Medium Black Soils of Established Citrus Orchards

Soil properties are an important factor for orchard establishment, precise nutrient management and sustainable production of fruit crops. Therefore, it is important to assess the spatial distribution of fundamental soil properties in well-established orchards. Hence an attempt has been made to assess the extent of soil properties and its spatial distribution in citrus orchards in medium black soils of Madhya Pradesh. The present study was conducted for the assessment of the spatial distribution of physicochemical properties viz. pH, electrical conductivity (EC) and soil organic carbon (SOC) of citrus orchards in medium-deep black soils of India. Results revealed that soil pH ranged from 6.83-8.84 (mean 7.80), soil EC varied from 0.07-0.34dS m-1 (mean 0.18 dS m-1) and soil organic carbon ranged from 0.13-0.89% (mean 0.47%) in 0-20 cm of surface soil layer. Geostatistical analysis showed that the slope of the prediction function of best-fit model (exponential) for soil pH, EC and SOC was 0.31, 0.22 and 0.77, respectively. The corresponding values of root mean square error (RMSE) were 0.35, 0.03, and 0.14. Interpolation of soil properties indicated that 89.2 % area had soil pH between 7.20 to 8.00, 83.4 % area had soil EC between 0.10 to 0.20 dS m-1, while>90 % area had SOC content ranged from 0.25 to 0.75%. Geo-statistical analysis revealed that spatial dependency was moderate for pH and strong spatial dependency was estimated for EC and SOC content. Based on RMSE and slope of prediction function, an exponential model was best-fit model in ordinary kriging for interpolation of measured soil properties.

A new approach to measure spatial variability of soil parameters and field technique to test-value specific fertilizer recommendations

<p>Information on the distribution of soil properties is important to know the status of nutrients in the soils based on which fertilizer nutrients are recommended. Given the variability of nutrients in the soils, making a site-specific fertilizer recommendation seems to be a compelling work. To determine the spatial variability of soil nutrients and to make judicious and precise fertilizer recommendations, new measures are designed with this study. These measures are tested against the soil samples (n = 43) for total nitrogen (N), organic matter (OM), phosphorus (P2O5), and potassium (K2O) in the study area. The descriptive statistical analysis indicated an average of low nitrogen and organic matter, while phosphorus was found to be very high and the level of potassium was high. The spread of nutrients across the data sets, however, included low, medium, high, and very high levels of ratings. The Deviation Square Index was developed and applied for the variability measurement and found that the largest variation was with phosphorus distribution, followed by potassium, nitrogen, and organic matter. The coefficient of variation (CV%) analysis also exhibited similar trends in nutrient distributions. Nitrogen was the main determinant explaining the variations in rice yield, while phosphorus and potash were negatively related to the yield. An index of fertilizer nutrient recommendation called Test-Value Specific Dose (TVSD) was developed and used to calculate the nutrient recommendation for each sampled location. This new method gave easy and more accurate doses of fertilizer over the blanket recommendation to fit the variations across the soil samples.</p>

Soil buffering capacity enhances maize yield resilience amidst climate perturbations

CONTEXTMaize production in China is facing a multitude of challenges such as climate change, increasing demand, and limited cropland availability. Soil properties, beyond supporting crop growth and development, can play a critical role in buffering the impacts of climate crisis on crop yields. However, little information is known about such buffering capacities and their spatial distributions of soil properties in buffering the impacts of climate perturbations on maize yields in China's maize belt (CMB). OBJECTIVEThe objective of this study was to identify the responses of different regions within the CMB to climate perturbations in terms of maize yields, as well as their spatial differences and magnitudes. Additionally, we aimed to quantify the capacities of soil properties in buffering the impacts of climate perturbations. METHODSWe quantified the impacts of climate perturbations (climate fluctuations deviating from the mean state) on maize yields using the APSIM across the China's maize belt. We then used the random forest model to interpret the buffering effects of typical soil properties to the adverse impacts of climate perturbations. RESULTS AND CONCLUSIONSOur results reveal that temperature variability was the predominant climatic perturbation affecting maize yields, with rising temperature leading to significant yield losses in most regions. Perturbations in solar radiation and precipitation exhibited relatively stronger positive yield impacts only in areas with inadequate solar radiation and dry conditions, respectively. Notably, soil properties explained ∼40% to ∼70% of the spatial variations for the impacts of climate perturbations. Soils with high soil organic carbon content (SOC) can significantly reduce the negative impacts of temperature increase, highlighting the crucial buffering effect of SOC in response to global warming. Soil texture, field capacity and bulk density were also critical soil buffering properties for interpreting the spatial patterns of the impacts of climate perturbations. SIGNIFICANCEOur results underscore the importance of soil quality improvement to ensure stable food production, which is an effective strategy for enhancing agricultural system resilience amidst future climate crises.

Topographic correction of visible near‐infrared reflectance spectra for horizon‐scale soil organic carbon mapping

AbstractUnderstanding soil organic carbon (SOC) response to global change has been hindered by an inability to map SOC at horizon scales relevant to coupled hydrologic and biogeochemical processes. Standard SOC measurements rely on homogenized samples taken from distinct depth intervals. Such sampling prevents an examination of fine‐scale SOC distribution within a soil horizon. Visible near‐infrared hyperspectral imaging (HSI) has been applied to intact monoliths and split cores surfaces to overcome this limitation. However, the roughness of these surfaces can influence HSI spectra by scattering reflected light in different directions posing challenges to fine‐scale SOC mapping. Here, we examine the influence of prescribed surface orientation on reflected spectra, develop a method for correcting topographic effects, and calibrate a partial least squares regression (PLSR) model for SOC prediction. Two empirical models that account for surface slope, aspect, and wavelength and two theoretical models that account for the geometry of the spectrometer were compared using 681 homogenized soil samples from across the United States that were packed into sample wells and presented to the spectrometer at 91 orientations. The empirical approach outperformed the more complex geometric models in correcting spectra taken at non‐flat configurations. Topographically corrected spectra reduced bias and error in SOC predicted by PLSR, particularly at slope angles greater than 30°. Our approach clears the way for investigating the spatial distributions of multiple soil properties on rough intact soil samples.

Spatial Distribution of Soil Carbon and Nitrogen Content in the Danjiangkou Reservoir Area and Their Responses to Land-Use Types

Understanding the spatial distribution of soil properties is essential for comprehending soil fertility, predicting ecosystem productivity, enhancing environmental quality, promoting sustainable agriculture, and addressing global climate change. This study focuses on investigating the spatial distribution and influencing factors of soil carbon (C) and nitrogen (N) in the Danjiangkou Reservoir area, a vital water source for the South-to-North Water Transfer Project. Utilizing both geostatistical and traditional statistical methods, this research explores the impact of various land-use types—such as orchards, drylands, paddy fields, and Hydro-Fluctuation Belts (HF belts)—on soil C and N content. The findings reveal predominantly low levels of soil organic carbon (SOC) (ranging from 2.95 to 21.50 g·kg−1), total nitrogen (TN) (ranging from 0.27 to 2.44 g·kg−1), and available nitrogen (AN) (ranging from 18.20 to 170.45 mg·kg−1), mostly falling into deficient categories. Notably, spatial variability is observed, especially in agriculturally developed regions, leading to areas of enrichment. Paddy fields and HF belts are identified as influential contributors to increased SOC and nitrogen content compared to orchards and drylands. Correlation and stepwise regression analyses unveil intricate interactions among SOC, TN, AN, and environmental factors, underscoring the necessity for a holistic approach to soil management. This study emphasizes the critical role of adopting rational land-use types and sustainable agricultural practices for effective soil management in the Danjiangkou Reservoir area.

The influence of spatial heterogeneity of the properties of agricultural fields on productivity

The use of differentiated technologies in crop production ensures high yields while minimizing environmental impacts and significant resource savings. To optimize technological impacts, it is necessary to know the distribution of soil properties and their changes in space and time. The purpose of the work is to assess the variability of soil characteristics and the impact on crop yields. The article presents studies of the variability of soil properties, the unevenness of soil moisture during the operation of wide-range sprinkler equipment and the influence of spatial heterogeneity of properties and the use of differentiated technologies on crop yields.

SUBSTANTIVE-GENETIC PRINCIPLES OF SOIL CLASSIFICATION AND DIAGNOSTICS IN THE SCIENTIFIC HERITAGE OF NABOKIKH O.G.

Problem Statement and Purpose. Alexander Nabokikh (1874–1920), who criticized the model of soil classification and diagnostics by V. Dokuchaev and M. Sibirtsev, formulated the original water-mode concept of soil formation. This concept provided for the quantitative assessment of individual soil formation factors, as well as the classification and diagnosis of soils according to their characteristics and properties. Nabokikh ideas and methods were rejected by V. Dokuchaev’s students and have not been improved throughout the history of genetic soil science, but today they are clearly reflected in the «newest» substantive principles of soil diagnosis and classification. The methodological innovations of O. Nabokikh are far-sighted and relevant to this day and require a thorough study. Data & Methods. General scientific and historical methods were used. The research materials are the scientific works of O. Nabokikh, as well as archival materials of the Soil Museum of Odesa State Agrarian University. The maps of «selected major soil properties» of 1914–1916 were analyzed – detailed plans of organic matter content in the soils of the southern region of Ukraine. Results. O. G. Nabokikh proposed the Three-phase soil survey, which was based on methods of soil diagnostics by their properties, as well as the method of mapping the so-called “selected major soil properties”. Nabokikh foresaw the importance of detailed maps of the distribution of soil properties for soil monitoring. A comparative analysis of the data on organic matter content in the early twentieth century and in the early twenty-first century along two (n=94) of O. Nabokikh’s routes in the Odesa region revealed a decrease in organic matter in the surface layer of soils by an average of 0.6%. Nabokikh O. G. remained a supporter of the factor concept of soil formation, but modified it by identifying the “dominant factor” (an inseparable pair of precipitation and relief). In the matter of soil diagnostics and classification, he prefers the substantive principle. When identifying “variants of chernozems,” he cites the following substantive “main features of chernozems and the main modifications of these features”: uniformity of distribution of silicate minerals; accumulation of humus; illuvium of hydrates of one and a half oxides; illuvium of calcium carbonate salts; relict features. Determination of the main features of soils became the basis of the deep-profile method of soil research used by Nabokikh O. G.

Characterization of Microplastics and Associated Heavy Metals in Urban Soils Affected by Anthropogenic Littering: Distribution, Spatial Variation, and Influence of Soil Properties

ABSTRACT Microplastics (MPs) are ubiquitous in terrestrial and marine environments. This research work studied the occurrence, distribution of MPs and heavy metals associated with these MPs in soils of different land- use types viz. residential, commercial, and industrial areas. We identified 15 types of MP polymers, of which polypropylene (15.51%), polyethylene (12.28%), polyamide (9.57%) were dominant. MPs abundance ranged from 8.54 × 104 to 8.51 × 105 particles/kg of soil. Average abundance of MPs in the three fractions viz. < 1 mm, 1–2 mm, and 2–5 mm were 1.85 × 105, 1.31 × 105, and 697.5 particles/kg of soil, respectively. Among the three land use types, MPs abundance in < 1 mm and 1–2 mm soil fractions were significantly higher in residential areas than that in commercial and industrial areas, i.e. <1 mm: 3.4 × 105, 1.26 × 104 and 9.0 × 104, and 1–2 mm: 2.35 × 105, 8.36 × 104, and 7.50 × 104 particles/kg, respectively, for residential, commercial and industrial areas respectively. The average concentration of Cr, Fe, Mn, Cd, Co, Ni, Cu and Pb in soils were 31.79, 11047.29, 423.92, 0.05, 11.86, 17.53, 125.95 and 28.29 mg/kg, respectively, whereas that in MPs were 6.27, 2181.64, 277.36, 0.24, 6.43, 8.29, 47.05 and 9.24 mg/kg, respectively. Concentration of Cd sorbed on MPs was higher than those found in soils. Also, Pb and Cd in MPs were positively correlated to MPs abundance, indicating MPs can be vectors of heavy metals in soils. The synergistic effect of MPs and heavy metals might put soil organisms at risk.

Estimation of site-specific multivariate probability distribution of soil properties using a mixed sampling technique

Estimation of multivariate probability density function (PDF) of soil properties based on limited site investigation data plays an important role in data driven-based geotechnical engineering analysis and design. However, this estimation is often intractable because the site-specific investigation data generally are multivariate, uncertain and unique, sparse, incomplete, sometimes corrupt and spatially variable. The estimation is more challenging when the uncertainties in model parameters of the distribution (i.e., mean, standard deviations and scale of fluctuation of soil properties) are taken into consideration. To address these difficulties, this study proposed a mixed sampling technique by combing the Gibbs sampler and transitional Markov Chain Monte Carlo sampler to effectively estimate the multivariate probability density function of soil properties. The proposed method is demonstrated through a virtual site example while the effect of statistical uncertainty in the scale of fluctuation on reliability analyses is illustrated in an infinite slope example. It is shown that the proposed method can not only handle the complex features of site investigation data but also effectively quantify the statistical uncertainties of the mean values, standard deviations and scale of fluctuation of the investigated soil properties. Ignoring the statistical uncertainties of scale of fluctuation can affect the estimation of mean and standard deviations of the soil parameters. When considering the statistical uncertainty of scale of fluctuation, the 95% confidence intervals for the profiles of the soil properties can rationally cover the actual soil data at the unobserved points. Ignoring the statistical uncertainty of scale of fluctuation can lead to the underestimation of the probability of failure of the infinite slope.

Delineation of Productive Zones in Eastern China Based on Multiple Soil Properties

Accurate soil management has long been the focus of research in agroecology. Crop productivity can be enhanced while reducing environmental threats from excessive fertilization by fully comprehending the spatial variability of soil properties and delineating management zones (MZs). A field investigation was carried out at experimental sites outside Hefei City’s administrative districts in China to study the spatial variability of soil properties and the delineation of MZs. A total of 9601 soil samples were collected in the study area. A variety of soil properties were analyzed, including the pH, organic matter, total nitrogen, alkali-hydrolyzable nitrogen, available phosphorus, available potassium, slowly released potassium, available sulfur, available boron, available copper, available zinc, available iron, and available manganese. The coefficient of variation for various soil properties exhibited a wide range, spanning from 12.2% to 100.5%. The geostatistical results show that most soil properties have moderate to strong spatial autocorrelation, and the ordinary kriging method is used to map the distribution of soil properties. The principal component analysis method was used to reduce the dimension of 13 soil properties to 4 principal components, and the fuzzy c-means clustering method was used to delineate MZs. The calculation results of the fuzzy performance index and normalized classification entropy show that the optimum number of MZs is five. In the study area, the western part exhibits the highest soil fertility, primarily attributed to its elevated organic matter content. Additionally, organic matter emerges as a key factor influencing sustainable agricultural production in this region. These results form the basis for soil managing areas outside the administrative districts of Hefei City.

Predictive geochemical mapping using machine learning in western Kenya

Digital soil mapping techniques represent a cost-effective method for obtaining detailed information regarding the spatial distribution of chemical elements in soils. Machine learning (ML) algorithms using random forest (RF) models have been developed for classification, pattern recognition and regression tasks, they are capable of modelling non-linear relationships using a range of datasets, identifying hierarchical relationships, and determining the importance of predictor variables. In this study, we describe a framework for spatial prediction based on RF modelling where inverse distance weighted (IDW) predictors are used in conjunction with ancillary environmental covariates. The model was applied to predict the total concentration (mg kg−1) and assess the prediction uncertainty of 56 elements, soil pH and organic matter content using 466 soil samples in western Kenya; the results of iodine (I), selenium (Se), zinc (Zn) and soil pH are highlighted in this work. These elements were selected due to contrasting biogeochemical cycles and widespread dietary deficiencies in sub-Saharan Africa, whilst soil pH is an important parameter controlling soil chemical reactions. Algorithm performance was evaluated determining the relative importance of each predictor variable and the model's response using partial dependence profiles. The accuracy and precision of each RF model were assessed by evaluating out-of-bag predicted values. The models R2 values range from 0.31 to 0.64 whilst CCC values range from 0.51 to 0.77. The IDW predictor variables had the greatest impact on assessing the distribution of soil properties in the study area, however, the inclusion of ancillary environmental data improved model performance for all soil properties. The results presented in this paper highlight the benefits of ML algorithms which can incorporate multiple layers of data for spatial prediction, uncertainty assessment and attributing variable importance. Additional research is now required to ensure health practitioners and the agri-community utilise the geochemical maps presented here for assessing the relationship between environmental geochemistry, endemic diseases and preventable micronutrient deficiency.

Spatial modeling of geotechnical soil parameters: Integrating ground-based data, RS technique, spatial statistics and GWR model

Ensuring the availability of a cost-effective method to predict soil behavior is imperative for newly developed construction sites. This research aims to create a model that can estimate the spatial distribution of soil properties and compaction characteristics using the Inverse Distance Weighting (IDW) and Geographic Weighted Regression (GWR) methods within the study area, specifically in Koya city, situated in Erbil, Iraq. To determine these soil parameters, 27 soil samples were collected from the fields based on stratified random sampling, and then tested and analyzed in the laboratory. The IDW spatial interpolation technique and GWR method were then used to create a spatial distribution map of soil properties and compaction characteristics. In the GWR model, the calculated soil properties and compaction characteristics served as the dependent variable, while the Modified Normalized Difference Water Index (MNDWI) derived from the Landsat8 satellite image was the independent variable. This process resulted in a spatial distribution map showing the soil properties and compaction characteristics. The results indicated a strong correlation between the MNDWI water indexes and various soil parameters, including water content, liquid limit, plastic limit, optimum moisture content, and maximum dry density, with respective coefficient of determination (R2) values of 0.91, 0.97, 0.98, 0.95, and 0.96. Additionally, the assessment of the precision in this correlation indicates that the results maintain a satisfactory level of accuracy, as demonstrated by the Root Mean Square Error (RMSE) values, which are 2.86 for water content, 5.4 for liquid limit, and 3.85 for plastic limit, 2.9 for optimum moisture, and 13.86 for maximum dry density. By integrating satellite-derived MNDWI water indexes with soil parameters, a fast, accurate, and cost-effective method for estimating soil parameters and modeling their spatial distribution in the study area can be developed. Additionally, the findings suggest that the IDW method, implemented using spatial analyst tools, performed exceptionally well for mapping the study area. In conclusion, the results of this research can be utilized by land use planners, municipalities, policymakers, and engineers to develop practical and effective plans.

Impact of landuse systems on soil organic carbon and its projection in northwestern India

AbstractThe evaluation of the impact of landuse on soil organic carbon (SOC) is required to understand the long‐term productivity and possible sustainability of a landuse system. The effect of landuse on SOC was studied in the Shaheed Bhagat Singh Nagar district of Punjab (India). Surface (0–15 cm) and subsurface (15–30 cm) soil samples were collected under different land uses (cropland, forest, scrubland, and plantation) using stratified random sampling. Soil samples were analyzed for soil texture, bulk density, pH, electrical conductivity, organic carbon, Olsen P (available P), NH4OAc–extractable K (available K), and CaCl2 extractable S (available S) using standard methods. Ordinary kriging was used to study the distribution of soil properties in different land uses. Soil organic carbon under future climate was simulated using Rothamsted carbon (RothC) model with current landuse and management practices. Organic carbon in surface and subsurface soils was in the order: Forest &gt; scrubland &gt; plantation &gt; cropland. The content of organic carbon, available P, K, and S decreased with increasing soil depth. The simulated SOC content in cropland soils under future climate may reduce, and in soils under forest, plantation and scrubland may increase by the year 2100. These results suggest that the landuse system is one of the major factors affecting SOC stocks. The study of organic carbon in different landuses could help the famers and policy makers to take measures for increasing SOC stocks to mitigate the climate change and also for sustainable agriculture.