Soil Database Research Articles

ESTABLISHING THE FIRST DATABASE OF SOIL HYDRAULIC PROPERTIES IN TUNISIA BASED ON PEDOTRANSFER FUNCTIONS

Soil hydraulic properties play a crucial role in the unsaturated soil water supply process. Currently, Tunisia lacks a database containing essential values for soil retention properties and soil saturated hydraulic conductivity. The main objective of this study is to create the inaugural soil hydraulic properties database through the utilization of open-access data and pedotransfer functions (PTFs). To achieve this goal, the harmonized world soil database (FAO) was employed to identify 752 measurement points across Tunisia. Subsequently, the soil texture, organic carbon content, and bulk density were determined at each point. These acquired values were then entered into the CalcPTF software to estimate the van Genuchten soil retention parameters. The calculation of soil saturated hydraulic conductivity was accomplished using two widely recognized pedotransfer functions (Saxton and Rosetta). The outcomes facilitated the creation of a catalog containing soil hydraulic parameter value for each soil texture. Significance of discrepancies between values obtained from the PTFs was assessed using a Tukey test. The spatial variability of each soil hydraulic property was studied using the simple kriging. In conclusion, the establishment of this significant soil hydraulic properties database holds diverse applications in agricultural, hydrological, and environmental studies in Tunisia.

Possible Integration of Soil Information into Land Degradation Analysis for the United Nations (UN) Land Degradation Neutrality (LDN) Concept: A Case Study of the Contiguous United States of America (USA)

Soil makes important contributions to the United Nations (UN) Land Degradation Neutrality (LDN) concept and targets; however, currently, soil is not integrated into measurable information (e.g., indicators, metrics) to monitor land degradation (LD) patterns and trends. This study examines the role of soil in LDN in the UN Convention to Combat Desertification (UNCCD), and UN Sustainable Development Goal (SDG 15: Life on Land). This study is specifically focused on the LDN and biodiversity loss as they relate to an indicator 15.3.1 Proportion of land that is degraded over total land area. Tracking of LD status can be improved by using detailed soils databases combined with satellite-derived land cover maps. This study has applied these newly improved methods to quantify and map the anthropogenic LD status and trends in the contiguous United States of America (USA), as well as to identify potential land areas for nature-based solutions (NBS) to compensate for LD. Anthropogenic LD in 2016 in the contiguous USA affected over two million square kilometers, about one-third of the country’s total area, with high variability by state. Between 2001 and 2016, LD in the USA showed an overall increase of 1.5%, with some states exhibiting increases in degraded land while other states had overall improvements to their land. All ten soil orders present in the contiguous USA have been anthropogenically degraded, with Mollisols, Alfisols, and Vertisols having the highest LD levels. Compensating for LD requires a variety of strategies and measures (e.g., NBS), which often require additional land. In 2016, the potential land area for NBS was over two million square kilometers, an area approximately equal to that of degraded land. Some of the states that have high proportions of land available for potential NBS are dominated by soils (Aridisols) typical of deserts and therefore may have less promise for NBS. The variability of LD needs to be evaluated at finer spatial scales for realistic LDN analysis.

Modelling Soil Erosion Using RUSLE and GIS for Kadalundi River Basin in Kerala, India

The main objective of the study was to estimate the potential average annual soil loss from Kadalundi river basin using RUSLE model and to prepare the spatial distribution map of soil erosion hazard using GIS to suggest suitable soil conservation and management measures for the basin. The individual factor maps for each factor in the RUSLE equation were prepared and multiplied in raster calculator to obtain the spatial distribution map of soil erosion with the help of ArcGIS. The daily rainfall data, basic soil data, Digital Elevation Model (DEM) and satellite imageries were used as the input data for the calculation of rainfall erosivity factor, soil erodibility factor, topographic factor, cover management factor and conservation practice factor. The results indicated an average annual erosion of 5.48 t·ha-1·yr-1 in the basin during the period of 2001 to 2021. The total quantity of soil washed away from the basin was 694836.60 t·yr-1. The estimated values of R-factor ranged from 938.97 MJ·mm·ha-1·h-1·yr-1 to 1102.65 MJ·mm·ha-1·h-1·yr-1, and the computed erodibility factor values varied between 0.0075 to 0.025 t·h·MJ⁻1·mm⁻1. The topographic factor value of the basin was found between 0 to 9.07. The basin had C-factor values of 0.08 to 1.10 and the P-factor values varied from 0.1 to 1. The spatial distribution map of erosion suggest that major part of the basin is under slight erosion class and only a minor part is under very severe erosion class. Areas in the very severe erosion class is characterised by moderate to steep slopes, or in other words areas with less slope is having slight erosion. As most of the area of the basin is having slight erosion, simple agronomical measures can be adopted for the mitigation purposes.

Heavy metals concentration in soils across the conterminous USA: Spatial prediction, model uncertainty, and influencing factors

Assessment and proper management of sites contaminated with heavy metals require precise information on the spatial distribution of these metals. This study aimed to predict and map the distribution of Cd, Cu, Ni, Pb, and Zn across the conterminous USA using point observations, environmental variables, and Histogram-based Gradient Boosting (HGB) modeling. Over 9180 surficial soil observations from the Soil Geochemistry Spatial Database (SGSD) (n = 1150), the Geochemical and Mineralogical Survey of Soils (GMSS) (n = 4857), and the Holmgren Dataset (HD) (n = 3400), and 28 covariates (100 m × 100 m grid) representing climate, topography, vegetation, soils, and anthropic activity were compiled. Model performance was evaluated on 20 % of the data not used in calibration using the coefficient of determination (R2), concordance correlation coefficient (ρc), and root mean square error (RMSE) indices. Uncertainty of predictions was calculated as the difference between the estimated 95 and 5 % quantiles provided by HGB. The model explained up to 50 % of the variance in the data with RMSE ranging between 0.16 (mg kg−1) for Cu and 23.4 (mg kg−1) for Zn, respectively. Likewise, ρc ranged between 0.55 (Cu) and 0.68 (Zn), respectively, and Zn had the highest R2 (0.50) among all predictions. We observed high Pb concentrations near urban areas. Peak concentrations of all studied metals were found in the Lower Mississippi River Valley. Cu, Ni, and Zn concentrations were higher on the West Coast; Cd concentrations were higher in the central USA. Clay, pH, potential evapotranspiration, temperature, and precipitation were among the model's top five important covariates for spatial predictions of heavy metals. The combined use of point observations and environmental covariates coupled with machine learning provided a reliable prediction of heavy metals distribution in the soils of the conterminous USA. The updated maps could support environmental assessments, monitoring, and decision-making with this methodology applicable to other soil databases, worldwide.

Upscaling Soil Organic Carbon Measurements at the Continental Scale Using Multivariate Clustering Analysis and Machine Learning

AbstractEstimates of soil organic carbon (SOC) stocks are essential for many environmental applications. However, significant inconsistencies exist in SOC stock estimates for the U.S. across current SOC maps. We propose a framework that combines unsupervised multivariate geographic clustering (MGC) and supervised Random Forests regression, improving SOC maps by capturing heterogeneous relationships with SOC drivers. We first used MGC to divide the U.S. into 20 SOC regions based on the similarity of covariates (soil biogeochemical, bioclimatic, biological, and physiographic variables). Subsequently, separate Random Forests models were trained for each SOC region, utilizing environmental covariates and SOC observations. Our estimated SOC stocks for the U.S. (52.6 ± 3.2 Pg for 0–30 cm and 108.3 ± 8.2 Pg for 0–100 cm depth) were within the range estimated by existing products like Harmonized World Soil Database, HWSD (46.7 Pg for 0–30 cm and 90.7 Pg for 0–100 cm depth) and SoilGrids 2.0 (45.7 Pg for 0–30 cm and 133.0 Pg for 0–100 cm depth). However, independent validation with soil profile data from the National Ecological Observatory Network showed that our approach (R2 = 0.51) outperformed the estimates obtained from Harmonized World Soil Database (R2 = 0.23) and SoilGrids 2.0 (R2 = 0.39) for the topsoil (0–30 cm). Uncertainty analysis (e.g., low representativeness and high coefficients of variation) identified regions requiring more measurements, such as Alaska and the deserts of the U.S. Southwest. Our approach effectively captures the heterogeneous relationships between widely available predictors and the current SOC baseline across regions, offering reliable SOC estimates at 1 km resolution for benchmarking Earth system models.

Relative and absolute difference in soil organic carbon stocks in grassland soils in Ireland: Impact of rock fragments, bulk density and calculation methods

On-farm management measures can sequester carbon by removing carbon dioxide from the atmosphere and storing it securely for long periods, in our soils. Estimates of soil organic carbon (SOC) stocks differ by calculation methods in terms of how they account for the coarse >2 mm fraction (i.e. rock fragments) of the soil. Irish grassland soils have variable volumetric stone contents and the extent of overestimation is unknown. Without best practice guidance, calculations of SOC in these soils are vulnerable to overestimation. The objectives of the present study for grassland soils in Ireland were to: 1) calculate mean SOC stocks using different methods by soil horizon and volumetric stone content class; 2) highlight relative differences in SOC stocks between these calculation methods and 3) compare normalised layer (Ly, fixed depth) versus horizon (Hz) type derived sampling data in terms of SOC stocks, to establish a best practice standardised sampling protocol. Five methods (M, 1–3 overestimate SOC stocks, whereas 4–5 are equivalent and are closest to reality) to calculate SOC stocks were used in combination with three soil databases i.e. Irish Soil Information System (I-SIS), the Heavy Soils Programme (HSP) and the Soil Quality Assessment Research Project (SQUARE). Objectives 1 and 2 were assessed using all three databases across methods, whereas objective 3 used the SQUARE database and M4. These data were sorted into volumetric stone content (%) classes (< 5%, 5–10%, 10–20%, 20–30% and > 30%) and sampling horizon (O, A, E, A/B, B, B/C, and C) with SOC stocks calculated using M1–5 equations. Results confirmed the high volumetric stone content assumption of grassland soils in Ireland (exception O horizons). Volumetric stone contents ranged from 0% to 36% for all horizons. When M1–5 are considered the average relative difference in SOC stocks between M1 and M5 was 18.9% (max: 388%), M2 and M5 was 7.7% (max: 60.7%) and M3 and M5 was 8.9 (max: 310%). In terms of Ly versus Hz sampling, no significant differences were found for the A horizons sampled. However, in Ireland, best practice should still utilise elements from both Ly (using an auger to get fixed depth soil samples; analysed for organic C) and Hz (using a soil profile pit to extract representative BD cores and to gather bulk samples for volumetric stone content analysis) approaches in the field. Laboratory methods used must fit with the needs and assumptions of equations M4/5 to calculate SOC stocks (thereby minimising overestimation and indeed underestimation).

Development of an information database of saline and wetlands in semi-desert and dry steppe zones of Abai, Pavlodar and Karaganda

In this article, the authors have developed and used soil morphogenetic indicators in the course conducted ground field work to determine saline and waterlogged soils in the semi-desert and dry steppe, zones of the Abay, Pavlodar and Karagandy regions of Kazakhstan. In Kazakhstan, there are no practical and scientific provisions for monitoring and managing saline and waterlogged soils based on digital technologies. This development makes it possible to determine the location of such lands depending on the location of soil and climatic zones. And also, the development of a cartographic model of these soils with the determination of the degree of their salinity makes it possible to develop recommendations for their development (improvement) with the subsequent preservation of productive longevity. The study of the current state of saline and waterlogged soils was carried out through field work along routes covering the territory of 3 administrative regions of the republic. Descriptions of the state of soils were carried out at 28 base points. A database of saline and waterlogged soils of the semi-desert and dry steppe (latitudinal), zones has been compiled, including the following indicators: soil type and subtype, profile morphology, content of humus and nutrients, water-soluble salts, particle size distribution, absorbed bases, and cation exchange capacity.

Evaluation and analysis of liquefaction potential of gravelly soils using explainable probabilistic machine learning model

Majority of the presently available machine learning (ML) models employed to assess the liquefaction potential of soils are for sands or sands containing silt fraction. In the current study, an explainable ML (EML) model has been developed using the updated liquefaction database of gravelly soils. The Chinese dynamic cone penetration test (DPT) and shear wave velocity test results of gravelly soils are used in the analysis. A new empirical correlation between these two in-situ tests’ results is developed using the final processed database. The light gradient boosting machine (LightGBM) is trained using this processed dataset and further tuned using Fast and Lightweight AutoML library (FLAML). The final tuned model shows relatively better deterministic and probabilistic predictive performance for the test sites as compared to the conventional method. An EML technique, SHapley Additive exPlanations (SHAP) is applied to provide further comprehension into the predictions. The developed LightGBM-SHAP model has achieved a right balance between explainability and accuracy. The obtained SHAP plots are consistent with almost all the existing domain knowledge (DK) in gravelly soil liquefaction. The developed model thus fills the gap between the ML-based procedures and the traditional DK of gravelly soil liquefaction.

Soil monitoring infrastructure in response to war

ABSTRACT Soil degradation is not the least impact of the Russian aggression. War degradation of soils includes compaction, erosion, biodiversity loss, contamination, physical destruction of soils etc. Explosions release harmful chemicals and metals that can persist in the environment affecting the quality of both soil and water resources; and fires at the site of burning military equipment impact soil. Assessment of soil to support soil management requires a well-functioning soil monitoring infrastructure which we describe in terms of: 1) legislative support, 2) soil testing laboratories, 3) a soil information system, and 4) finance. Gaps in Ukrainian legislation make effective soil monitoring impossible; there is need for decree to officially recognise military soil degradation and establish an effective monitoring. The main body of soil sampling and analysis can be carried out within the network of the State Institute “Institute of Soil Protection of Ukraine” but a publicly accessible soil database is needed to hold and transmit the updated information. Novadays funding is hard to come by; we would seek it firstly through cooperation with international donors. If these the times, visiting scientists who participate in the survey and assessment of battlefields will gain experience that finds widespread international application.

Space-time mapping of soil organic carbon stock and its local drivers: Potential for use in carbon accounting

The measurement and tracking of changes in soil organic carbon (SOC) stocks and its underpinning drivers are important in greenhouse gas accounting and carbon trading schemes. While repeated direct measurement of a location over time is the ‘gold standard’ for SOC stock change estimation, this is currently unfeasible at scale due to sample collection and analysis costs. Here, we used a data-driven, space–time digital soil mapping modelling approach (ST-DSM) coupled with an interpretive machine learning (IML) technique to both predict and quantify the relative contributions of covariates to annual SOC stocks and their uncertainty at 0–30 cm depth and at a 90 m spatial resolution for Australia between 1990 and 2018. A quantile regression forest model was calibrated using data from a datacube comprising of SOC stock values collected over the prediction years and a suite of dynamic and static environmental covariates. The evaluation of the ST-DSM model on a test set was satisfactory. Long-term annual mean (Mg C/ha), and total (Pg C) topsoil SOC stock were 37.6 (12.3 – 101.1, 90 % PI) and 28.3 (9.25 – 83.1, 90 % PI) respectively. The mean annual rate of SOC stock change over the Australian continent was 0.11 (±0.22) Mg C/ha yr−1 and the total net change was 23.5 Tg C yr−1, indicating Australia’s soils were a net sink of SOC over the 29-year period. However, SOC stocks declined in the montane ecoregion, a great concern given its fragility and high SOC stock. The long-term mean fraction of photosynthetic vegetation was the most important individual local contributor to SOC stock prediction in many parts of Australia. The aggregated effect of covariates based on the soil forming factors indicates vegetation as the principal local driver of SOC stock across most of the Australian continent, followed by climate. The evaluation of our results against independent, temporally paired field data and estimates from a nationally accepted SOC process-based model yielded promising results. Our approach demonstrates the use of readily available and inexpensive covariates to estimate SOC stock changes, as well as the underlying drivers over a larger area but at a spatial resolution suitable for decision making. This approach presents a new monitoring and verification possibility for carbon projects and national carbon accounting especially with the growing number of existing profile observations in soil databases.

Ecological Suitability Assessment Methods of Waste Pile-Up along Railway Routes Based on Machine Learning Algorithms

Waste pile-up along railway routes poses an important threat to the regional ecological environment. However, there is a lack of methods that assess the ecological suitability of waste pile-up (ESWP) at a macro scale, which is crucial for informed decision-making. We define the ESWP and propose a methodology to measure the level of ESWP along railway routes. Specifically, we focus on the Ya’an to Nyingchi section of the railway, selecting a 30-km buffer zone on either side as the study area. To develop ESWP maps, we employed Landsat 8, digital elevation model (DEM), soil database, land use, and meteorological data. We tested 3 machine learning methods—random forest (RF), deep neural network (DNN), and extreme gradient boosting (XGBoost)—using 7 key indicators as input parameters. The performance of these models was evaluated using overall accuracy and the Kappa index. Additionally, we analyzed the relative importance of each indicator on the results. The study reached the following results: Firstly, the combination of selected indicators with machine learning methods effectively assesses the ESWP along railways. Secondly, among the tested methods, DNN demonstrated superior performance, achieving an accuracy of 86.49%, outperforming RF (80.31%) and XGBoost (79.54%). Thirdly, the indicators with the greatest impact on the assessment were biological richness (weight is 0.23), vegetation coverage (weight is 0.20), and soil nutrients (weight is 0.16). These findings provide a novel approach to assessing the ecological suitability and identifying low-risk sites for waste pile-up along railway routes.

Mapping soil corrosivity potential to exclusion fencing using pedotransfer functions and open‐source soil data

AbstractSoil corrosivity is a term used to describe the corroding susceptibility (risk) of metal infrastructure in different soil environments. Soil corrosivity mapping is a crucial step in identifying potentially problematic, high‐maintenance fence lines and can help improve fence longevity by identifying soil environments where the use of more expensive, corrosion‐resistant materials would be more cost‐effective in the long term. Soil corrosion damage sustained on exclusion fences can be a serious management issue for conservation programs and initiatives, as it weakens the fence netting and provides opportunities for invasive animal migration and occupation (e.g. feral cats and foxes) into areas of high conservation value. The increasing accessibility of geospatial analysis software and the availability of open‐source soil data provide land managers with the opportunity to implement digital soil databases and pedotransfer functions to produce fence corrosion risk maps using commonly measured soil attributes. This paper uses open‐source government agency soil data (shapefiles) to map fence corrosion risk in the southern part of the Yorke Peninsula in South Australia, with the intention to assist with the installation of a new barrier (exclusion) fence as part of the Marna Banggara rewilding project. The risk classifications (low, moderate and high risk) made by this map were compared with rates of zinc corrosion (μm/year zinc loss) observed at field sites and correctly predicted the amount of fence damage sustained at five of the eight sites. The mapping approach outlined in this study can be implemented by environmental managers in other areas to inform strategies for enhancing fence longevity.

Spatial Evaluation of Land and Soil Properties with Geography Information Systems (GIS): The Case Study from Meriç District of Thrace Region in Türkiye

This research was carried out within the scope of spatial evaluation of the land and soil properties of Edirne-Meriç district, located in the Thrace region (Türkiye) by using GIS. Arc GIS 10.3.1 software was used in the classification of soil and land features. Digital soil maps (1/25.000 scale) were used to determine soil and land properties. Alos Palsar (12.5m) satellite images were used to determine land elevation and aspect distributions. As a result of the research, it was seen that the soil class with the largest area in terms of large soil groups in Meriç district is lime-free brown forest soils (261.2 km2). III. It was determined that class lands (153.7 km2) cover the largest area. In general, it was observed that the soil depth was greater than 150 cm (261.9 km2). It was determined that 23.3 km2 of the Meriç district lands were exposed to severe water erosion. It was determined that the study area consists of lands with a steep slope of 12-20% (126.7 km2). It has been observed that the height distribution of Meriç district lands varies between 4.7-120.5 m. It was defined that the majority of the lands were in the southwestern direction group. It is thought that the soil and land information database created as a result of the research will make significant contributions to researchers and the public, institutions and organizations.

Pedological formations on old mountain geomorphological surfaces of central Spain

The chemo-morphological properties of soils on ancient landforms (quarzitic pliocene alluvial fans, pleistocene terraces), namely “rañizos”, on middle-high mountains (Eastern-Central System, Iberian Peninsula, Rio Negro, Cogolludo) were investigated. Several properties were analyzed by standard procedures. A detailed soil diagnostics and classification on “rañizos” were done, unlike those widely studied on “rañas” (similar landform), by identifying parallel pedogenetic processes in both formations. The genetic and geographic features of Alfisols, Ultisols and Inceptisols are closely related to the nature of their parent materials, based on quartzite and quartz conglomerates, sometimes with an arkosic matrix, red shale, polygenic gravels and pebbles. Soil features were determined by genuine soil-forming inherent lithological rock properties. Other driving factors were flat topography and enough soil formation time to allow intense pedogenesis. The main soil-forming processes were intense weathering, clay enrichment horizons with illuviation, red color caused by iron oxide dehydration and signs of pseudogleyzation processes. Such pedological formations can be considered endemisms; that is, “rare” soils and, up to a point, “relict”. The soil reaction is acid/slightly acid with low base saturation. Despite lying on mountains, soils are characterized by moderate-low organic matter content. Soil conditions and climate provide good vine production requirements despite acidity levels. A common feature of all Rio Negro soils is the presence of gravel (size up to 3–5 cm), which is evenly distributed on arable layers. The results can be used to assess vineyard soil use in a potential Pago (Protected Denomination of Origin) and to extend the database of vineyard soils from poorly studied Mediterranean continental mountain regions.

A survey of soil elemental variability in Singapore parks for forensic purposes

Soil, as a surviving trace after contact and transfer between contacting surfaces at a crime scene, can be recovered and analyzed to infer the presence of persons or tools at the scene and prior activity leading to its deposition. With its vast diversity and heterogeneity, it varies in composition from place to place, providing a basis for trace examiners to distinguish visually similar-colored soils. Unlike countries with native topsoil, Singapore’s urban lands are commonly filled with man-altered and man-transported soil, with relatively little known about the variations of soil’s characteristics within a small, localized area. The current study surveyed the soil’s elemental variability in Singapore parks, which are green spaces for public leisure use. Past installations of recreational facilities and landscaping with fast-growing vegetation in parks inevitably cause disturbance to the original natural soil and mixing with extraneous soil, further contributing to the heterogeneity of the park’s topsoil composition. In our sampling approach, visually similar-colored surface soils were collected from within a 1-m2 site, sites in proximity within a park, and parks across Singapore. The collected soils were dried and sieved into clay- and silt-size fractions for elemental analysis using WDXRF and SEM/EDS. To examine the extent of the spatial elemental variability, we adopted three-sigma interval match criteria and a discriminative model incorporating relative data, square root values and the Canberra distance measure for data processing and pairwise comparison of soil samples. The study also aimed to develop soil databases encompassing soils across Singapore with the intent of understanding the value of soil evidence within a local context.

Experience in creating an information system for the rational use of soil resources

The structure and functions of the “SoilGeographical Database of Russia Information System” (ISSGDBR) implemented on the platform of the Soil Science Faculty of the Lomonosov Moscow State University are considered. The current state of national soil databases and developing on that base international soil information systems are described. The created information system consists of three sections. The first section includes soil maps of different scales and maps of the natural conditions of the territory of the Russian Federation. The second section is represented by a naturalattribute database, which is based on a soil profile with associated analytical characteristics. In the third section, with the help of software tools, the collected varied information about the soil cover becomes the basis for modeling the spatial distribution of various indicators, performing calculations in applied projects for a wide range of users. The ISSGDBR is considered as an expert and analytical center, which acts as a coordinator and supports the functioning of a distributed network, as well as the development and distributing of internationally relevant standards for description, storage, presentation of soil information and data exchange through the use of specialized programs adapted to regional features. In addition, the purpose of the center is the implementation of communication between primary digital resources of natural and soil information localized in various organizations, agrochemicalservice regional centers, and federal agencies accumulating information necessary for making managerial decisions.

Evaluating mobile applications for estimating soil properties: Quality of current apps, limitations and future directions

The estimation of soil properties is an important factor in agriculture. With the advancement of smartphones and artificial intelligence, estimating soil properties such as the Munsell soil color, pH, soil organic matter and moisture is tangible, alleviating the need for laboratories, which are time-consuming and costly. While there are several smartphone applications available in different app stores that claim to provide this functionality, an evaluation of the limitations and quality is required for these apps. The objective of this study is to identify existing soil property estimation apps, devise a soil apps rating scale to evaluate the functionality along with the software quality characteristics, and provide design guidelines for improving apps of this type. A thorough search was conducted in two prominent app stores: the Google Play and Apple App store. The selected apps were then rated by four individual raters. A total of 337 apps were found in the search and from this list, 32 selected apps were reviewed using the devised app rating scale. The rating scale evaluates the apps against ten sub-scales: app metadata, aesthetics, general app features, performance and efficiency, usability, functionality, subjective quality, transparency, user comments, and the perceived impact of the app on users. The inter-rater and intra-rater reliability are calculated along with the internal consistency of the rating scale to ensure the validity of the results. The evaluation results that returned the highest-ranking mean sub-scales were performance, usability, and aesthetics. Adversely, the lowest-ranging sub-scales were functionality and general features. There was also an evident absence of the functionality to calculate any type of soil properties in all but four of the apps reviewed. Several key design considerations were identified, such as the need for automated estimation of soil properties, an improvement of general features, and the inclusion of a shared soil database.

A Research - Civil Society Joint Approach for Soil Conservation in High Natural Value Areas

Since the existing information on soils level of fertility in the High Natural Value (HNV) eligible area is scarce, a partnership was concluded between a mixed team of soil scientists and civil society environmentalists within the frame of a research project aiming to: (i) appraise the stakeholders perception and views on the importance of soil conservation and environmental friendly land management, (ii) assess the level of fertility in HNV eligible areas and start building-up the first national HNV soil data base and (iii) identify the most relevant soil indicators for designing an expeditious HNV soil monitoring system. The results revealed the strong agreement of the civil society on the recently issued agricultural policies supporting practices under agri-environment measures and a fair existing level of the soil conservation under HNV practices, as long as land and soil careful administration and resource-efficient management are constantly pursued.

Multinational prediction of soil organic carbon and texture via proximal sensors

AbstractNovel technologies help to monitor the environmental impact of human activities, but tests involving datasets from several countries, encompassing a large variability of soil properties, are still scarce. This study utilized proximal sensors to predict soil organic carbon (OC) and soil texture of samples from Brazil, France, India, Mozambique, and United States. A total of 1749 samples were analyzed by portable X‐ray fluorescence (pXRF) spectrometry and visible near‐infrared diffuse reflectance spectroscopy. Sand (R2 = 0.89), silt (0.87), and clay (0.84) predictions were very accurate, despite contrasting climates, soil parent materials, and weathering degrees. Soil OC predictions were similarly successful (0.74) using samples from five countries. pXRF was the optimal sensor for soil texture predictions. The addition of international data may improve local models. Proximal soil sensing can be successfully used with a multinational soil database offering a clean, rapid, and accurate alternative to estimate soil texture and OC with international datasets.

Modelling and prediction of major soil chemical properties with Random Forest: Machine learning as tool to understand soil-environment relationships in Antarctica

Digital Soil Mapping includes quantitative estimations of soil attributes in areas with little or no soil information, being important in remote landscapes such as the Antarctic ice-free areas. Our objective was to predict the spatial distribution of major soil chemical attributes in two important Antarctic Peninsula regions; as well as to identify the main environmental drivers of these attributes. For this, we compiled one of the largest soil databases of Antarctica and applied the machine learning algorithm Random Forest to predict seven soil chemical attributes. We also used covariates selection and partial dependence analysis to better understand the relationships of the attributes with the environmental covariates. Bases sum was the attribute which presented the highest prediction performance, whereas Na and P predictions presented the lowest. Both accuracy and uncertainty indicators showed the difficulties of Random Forest in handling natural outliers. The soil attributes distribution was related to the mean annual temperature and annual precipitation, multispectral bands and indexes related to the vegetation response, rookeries distance expressing the birds’ activity, and topographic attributes. The attributes showed a climatic gradient, with higher values of bases sum, pH, and Na in Northern Antarctic Peninsula, and higher total organic carbon, H + Al and P in Maritime Antarctic. This shows the climate covariates influence the soil variability in a macroscale, whereas terrain predictors control the soil at local scales. This study showed the Digital Soil Mapping potential to surpass the limitations of conventional mapping and indicated the feasibility in obtaining interpretable predictions which can be directly associated with the soil forming factors. Finally, the data generated can be used as reference to monitor the impacts of the climate changes on the soils of Antarctica.