Soil Data Research Articles

Estimation of Cation Exchange Capacity for Low-Activity Clay Soil Fractions Using Experimental Data from South China

The cation exchange capacity (CEC) of the clay fraction (<2 μm), denoted as CECclay, serves as a crucial indicator for identifying low-activity clay (LAC) soils and is an essential criterion in soil classification. Traditional methods of estimating CECclay, such as dividing the whole-soil CEC (CECsoil) by the clay content, can be problematic due to biases introduced by soil organic matter and different types of clay minerals. To address this issue, we introduced a soil pedotransfer functions (PTFs) approach to predict CECclay from CECsoil using experimental soil data. We conducted a study on 122 pedons in South China, focusing on highly weathered and strongly leached soils. Samples from the B horizon were used, and eight models and PTFs (four machine learning methods, multiple linear regression (MLR) and three PTFs from publication) were evaluated for their predictive performance. Four covariate datasets were combined based on available soil data and environmental variables and various parameters for machine learning techniques including an artificial neural network, a deep belief network, support vector regression and random forest were optimized. The results, based on 10-fold cross-validation, showed that the simple division of CECsoil by clay content led to significant overestimation of CECclay, with a mean error of 14.42 cmol(+) kg−1. MLR produced the most accurate predictions, with an R2 of 0.63–0.71 and root mean squared errors (RMSE) of 3.21–3.64 cmol(+) kg−1. The incorporation of environmental variables improved the accuracy by 2–10%. A linear model was fitted to enhance the current calculation method, resulting in the equation: CECclay = 15.31 + 15.90 × (CECsoil/Clay), with an R2 of 0.41 and RMSE of 4.48 cmol(+) kg−1. Therefore, given limited soil data, the MLR PTFs with explicit equations were recommended for predicting the CECclay of B horizons in humid subtropical regions.

An Ecoinformatic Model Using Rényi's Entropy Predicts Soil Chemistry Effect on Tree Species Abundance Distributions

ABSTRACTAimAn ecological community consists of species of various abundances that reflect their responses to the environmental conditions. A classic macroecological pattern, the species abundance distribution (SAD), has been studied for diverse taxa and communities and integrated into numerous modelling tools. Despite its widespread use, a mathematical model that can capture variations in the empirical SAD and describe its response to environmental changes is still lacking. By integrating the Maximum Entropy Theory of Ecology (METE) with a generalised entropy called Rényi's entropy, we aim to develop a new ecoinformatic model that can predict the variation of empirical SAD along multiple environmental gradients.LocationPanama.TaxonAngiosperms.MethodsWe extend the METE using the Rényi's entropy as an uncertainty measure. We apply this extended METE, called Rényi model, to the tree abundance data from 49 plots in Panama and predict the SAD within each plot. We estimate Rényi's parameter q by fitting the predicted SAD to the empirical SAD in each plot. We further compile climate and soil data from the Panama plots and analyse their relationships with the estimated q using multiple regressions.ResultsRényi model provides adequate description of the empirical SADs and outperforms lognormal or log‐series models in 40 of the 49 tree plots, according to the Akaike information criterion. Variations in Renyi's q estimates (from 1/2 to 1) reflect shifts in the empirical SADs. Multiple regressions reveal that P, Al and NH4, three soil chemicals that are important for tree growth and species distribution, significantly affect Renyi's q across plots.Main ConclusionsThese findings suggest that the Rényi model and Rényi's q can characterise the SAD of communities under environmental changes. They also indicate the potential of using generalised entropies to predict macroecological patterns in stressed ecosystems.

Optimal vegetation coverage from the perspective of ecosystem services in the Qilian Mountains

Abstract The Qilian Mountains (QLMs) serve as a vital ecological security barrier in the northwest China, determining the optimal fractional vegetation cover (FVC) of different land-use types is crucial for ecological restoration and protection. Based on the normalized vegetation index, meteorological data, soil data and land-use data, the InVEST model, bivariate spatial autocorrelation and elastic analysis, we calculated the water conservation, soil conservation and carbon storage, analyzed the spatio-temporal distribution characteristics of FVC and ecosystem services (ES) and the correlation between them. This study aims to evaluate the optimal FVC of the different land-use types and vegetation restoration’s effect on it in the QLMs. The results showed that the FVC and ES showed an increasing trend from 2000 to 2020, with a spatial distribution pattern of high values in the southeast and low values in the northwest. There was a significant and positive correlation between FVC and ecosystem service functions (p < 0.05), FVC had the most obvious effect on soil conservation, followed by water conservation. The optimal FVC varied by land-use type, with its value between 0.4 and 0.48 for forest land, 0.24 and 0.30 for grassland and 0.19 to 0.20 for unused land. In the eastern of the QLMs, nearly 70% of the forests and 50% of the grasslands exceeded the optimal vegetation coverage, while 36% of the grasslands and 62% of the unused lands were under-covered in the western of the QLMs. The findings suggest that differentiated ecological restoration and management measures should be formulated according to the status of vegetation restoration, to enhance the comprehensive capacity of ecosystem services in the QLMs.

Comparative assessment of SoilGrids system with regional soil data for advancing ecosystem reporting in Bulgaria

The aim of this study is to test the suitability of the SoilGrids system for ecosystem reporting, research and monitoring. The study is conducted in the Rila Mountains in Bulgaria, an area characterised by diverse ecological factors. We propose a methodological approach to compare SoilGrids predictions with independent point observations, addressing issues of inconsistency across survey layers when combining data from different sources. The comparative analysis is discussed in respect to point data, soil type, altitude and climate. The results show that the SoilGrids represents the main soil parameters well in terms of their dynamics over the altitudinal range. There is a good agreement between the observed and predicted values for the averages of the parameters - bulk density, coarse fraction, soil organic carbon (SOC) content and SOC stock. The average measured SOC stock (0-30 cm) is 58.54 t/ha, while the average predicted SOC stock (0-30 cm) is 55.38 t/ha. However, the study also showed that the predicted values for nitrogen content are almost two times higher than the observed figures and the pH values from the SoilGrids are less acidic than those measured in the field.

Testing the contribution of vertebrate predators and leaf traits to mainland–island differences in insect herbivory on oaks

Abstract Ecological theory predicts that herbivory should be weaker on islands than on mainland based on the assumption that islands have lower herbivore abundance and diversity. However, empirical tests of this prediction are rare, especially for insect herbivores, and those few tests often fail to address the mechanisms behind island–mainland divergence in herbivory. In particular, past studies have not addressed the relative contribution of top‐down (i.e. predator‐driven) and bottom‐up (i.e. plant‐driven) factors to these dynamics. To address this, we experimentally excluded insectivorous vertebrate predators (e.g. birds, bats) and measured leaf traits associated with herbivory in 52 populations of 12 oak (Quercus) species in three island–mainland sites: The Channel Islands of California vs. mainland California, Balearic Islands vs. mainland Spain, and the island Bornholm vs. mainland Sweden (N = 204 trees). In each site, at the end of the growing season, we measured leaf damage by insect herbivores on control vs. predator‐excluded branches and measured leaf traits, namely: phenolic compounds, specific leaf area, and nitrogen and phosphorous content. In addition, we obtained climatic and soil data for island and mainland populations using global databases. Specifically, we tested for island–mainland differences in herbivory, and whether differences in vertebrate predator effects or leaf traits between islands and mainland contributed to explaining the observed herbivory patterns. Supporting predictions, herbivory was lower on islands than on mainland, but only in the case of Mediterranean sites (California and Spain). We found no evidence for vertebrate predator effects on herbivory on either islands or mainland in any study site. In addition, while insularity affected leaf traits in some of the study sites (Sweden‐Bornholm and California), these effects were seemingly unrelated to differences in herbivory. Synthesis. Our results suggest that vertebrate predation and the studied leaf traits did not contribute to island–mainland variation patterns in herbivory, calling for more nuanced and comprehensive investigations of predator and plant trait effects, including measurements of other plant traits and assessments of predation by different groups of natural enemies.

Landslide Studies in the Context of Disaster Management in Bangladesh—A Systematic Literature Review

Landslides and their resulting impacts on property and human life have become an ongoing challenge in the hilly regions of Bangladesh. This study aims to systematically review diverse landslide studies in Bangladesh, particularly focusing on landslide disaster management (LDM) from 2008 to 2023, encompassing the pre-disaster, syn-disaster, and post-disaster phases. Several key attributes of landslide studies were considered, including general trends, data types, study scales, contributing factors, methodologies, results, and validation approaches, to investigate challenges and subsequently identify research gaps. This study evaluated 51 research articles on LDM using a systematic literature review (SLR) technique that adhered to the Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) framework. Our finding revealed that articles on LDM were dominated by the pre-disaster (76%) and the syn-disaster phases (12%), with the post-disaster phase (12%) receiving equal attention. The SLR revealed a growing number of studies since 2020 that used data-driven methods and secondary spatial data, often focused on medium-scale analyses (district level) that, however, often lacked field-based validation. From the factors examined in various landslide studies, topographical and hydrological factors were found to be the most significant attributes in assessment. This study identified key challenges, such as insufficient landslide inventories including poor site accessibility and a lack of high-resolution geological, soil, and rainfall data. It also highlighted critical research gaps, including the need for advanced technologies in susceptibility mapping for national hazard atlas, the investigation of underexplored causative factors, effective early warning systems, detailed post-event characterization, health impact assessment, risk-sensitive land use planning, and interactive web portals for landslide prone areas. This study would thus aid researchers in understanding the depth of existing knowledge and provide insights into how landslides fit into broader disaster management frameworks, facilitating interdisciplinary approaches.

The nature of information and the implication for making predictions about soil properties using soil maps

Information theory is one method to evaluate how soil data can be utilised to estimate the amount of information produced by grouping or classifying the soil into classes or spatial polygons. Information theory provides the means of making “information” a measurable quantity. This paper explores how information theory can be applied to an existing, conventional soil map for the Lower Macquarie alluvial plain in New South Wales. It uses stepwise interpretation tables to estimate information levels for pH, salinity, ESP and shrink swell potential. An estimate is made of the amount of information produced for each of the soil profile classes (SPCs) and the soil map units. The amount of information produced varied between soil properties and for different soil depths. It also varied between the SPCs and soil map units. The soil map identified high amounts of available information for some soil properties, particularly in the surface soils for salinity and ESP. Moderate levels of information were generally available for pH. For other soil properties, only low amounts of information were available, especially for salinity and shrink swell potential in the deep subsoils. The overall estimate indicated that the soil map provided 50% of the total potential available information. The amount of available information produced for the soil properties was shown to be highly correlated to the standard deviation of the soil properties. The application of information theory and the use of stepwise interpretation tables was shown to be a useful method of evaluating the amount of information produced by a soil map for a range of soil properties.

The performance of two urban flood regulation models using different input data

Increasing climate change has led to an increase in urban flood events. Events with a return period of twenty years become events with a period of two to three years. The primary objectives of this study are to evaluate the performance of flood regulation models using different input data and compare their performance for flood regulation supply (FRS) assessment. The assessment of the ecosystem services of urban FRS was performed using two models: Urban Flood Risk Mitigation (UFRM) by Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) and IMECOGIP Waterflow Regulation (Retention Model) (WRRM). They rely on relatively accessible open-source data, require a short time to model each scenario and provide opportunities to interpret results in different spatial and measurement units. We performed several improvements to the input data, including updating the national ecosystem-type assessment for the case study area by connecting it with the Urban Atlas typology and proposing an approach for soil data refinement. The outputs of the two models show significant differences in FRS values for each land-use/land-cover (LULC) class. The UFRM assesses different impervious urban classes with different values, varying from very low to medium. In contrast, the WRRM assesses the FRS in densely sealed areas with one fixed value for low supply (where nearly all rainfall is transformed into runoff).

Runoff Dynamics and Soil Erosion Assessment using a SWAT Model at Upper Cauvery River Basin

The increasing availability of geospatial datasets on watershed characteristics and hydro-meteorological variables emphasises the importance of integrated hydrological models for effective catchment management. Climatic and physiographic determinants such as topography, land use, soil properties, and anthropogenic interventions substantially influence a catchment's hydrological equilibrium. The SWAT model was used to elucidate rainfall-runoff dynamics in the Upper Cauvery River Basin, Karnataka, India (36,682 km²), applying the SCS Curve Number (CN) method for runoff estimation. Runoff was estimated for 2012–2021 using rainfall data from the Indian Meteorological Department (IMD), soil data from the FAO, and land use/land cover and slope datasets.Soil erosion, exacerbated by intensified agricultural practices, was evaluated using the Revised Universal Soil Loss Equation (RUSLE) model integrated with Geographic Information Systems (GIS). The SWAT model results showed that runoff accounted for 15-20% of the total precipitation, with an annual soil loss of 2027.95 tons. The predominance of agricultural land use, covering 66.29% of the basin, significantly contributed to high runoff, whereas the forested areas (26.48%) demonstrated a low runoff potential. About 38% of the basin exhibited a very low soil loss risk, while 11% was classified as high risk and 9% as very high risk. The SWAT model is recognized for its robustness, and this research aims to leverage its capabilities to validate its effectiveness in estimating runoff and erosion, providing crucial insights for advancing sustainable catchment resource management.

An “Ion Harvester” Battery in Soil Empowered by a Microfluidic Pump and Interlayer Confinement within Micro‐Swiss‐Rolls

AbstractA significant challenge arises in powering distributed tiny sensors that gather high‐resolution soil data are crucial for advancing digital and geospatial technologies in the field. Traditional batteries and solar cells are unsuitable due to size mismatches. Microbial fuel cells lack stability in delivering constant power. Soil naturally contains redox‐active ions, such as Zn2+ and Mn2+, which can be harvested and utilized for electrochemical energy storage. To facilitate access to soluble ions, a microfluidic pump based on micro‐Swiss‐roll structures via micro‐origami is developed. Spontaneous capillary flow allows for efficient liquid extraction. The micro‐Swiss‐roll architecture shows significant improvements in redox reversibility and charge transfer within micrometer‐thick interlayer space. Notably, the interference of Zn0/2+ and Mn(II)/Mn(IV) reactions is suppressed. Consequently, the battery using a micro‐Swiss‐roll electrode shows high voltage and energy efficiency at high currents up to 10 µA, with cycling stability of 300 times. A twin‐micro‐Swiss‐roll architecture of 0.385 ± 0.006 mm2 is used for energy storage in wet soil, yielding over 40 times more energy as compared to soil‐microbial fuel cells. A conceptual demonstration of an integrated irrigation water sensor and micro‐Swiss‐roll battery highlights the potential of microbattery development in addressing the critical challenge of powering tiny sensors in the soil.

A New Approach to Differentiate the Causes of Excessive Cadmium in Rice: Soil Cadmium Extractability or Rice Variety

In order to effectively decrease cadmium (Cd) in rice grains in contaminated paddy soil and maintain the safe production of rice, identifying excessive Cd in rice caused by rice varieties or soil Cd is critical, but it is currently lacking. In the present study, the soil ethylenediaminetetraacetic acid (EDTA)-extractable Cd (EDTA-Cd) and the bioaccumulation factors of rice based on EDTA-Cd (BCFEDTA-Cd) were used to develop an approach to identify excessive Cd in rice caused by rice varieties or soil Cd. Based on an empirical soil–plant transfer model and species sensitivity distribution (SSD), BCFEDTA-Cd and EDTA-Cd were divided into five grades. The results showed that the five grades of the EDTA-Cd (minimum value less than 0.11 mg/kg and maximum value greater than 2.93 mg/kg) and BCFEDTA-Cd (minimum value less than 0.09 and maximum value greater than 1.40) were classified in the normal soil pH range. Further, the conversion equation between EDTA-Cd and diethylene triamine pentaacetic acid (DTPA)-Cd was obtained through linear regression analysis using 67 sets of soil data from the literature. In addition, the four selected rounding thresholds for the percentage of EDTA-Cd to total soil Cd (EDTA-Cd) (%) were 52.5, 67.5, 82.5, and 97.5%. A selected soil EDTA-Cd (%) (about 75%) can be used to identify the status of soil bioavailability, especially in soil with high background Cd. Finally, a set of 1084 pairs of rice and soil data for Cd-contaminated soils was used to investigate the respective contributions of rice varieties and soil Cd when Cd in rice exceeds the limit (0.2 mg/kg). Based on field experiment data, a systematic identification approach for the causes of rice Cd exceeding the limit, soil Cd or rice variety, was established and applied. In conclusion, under Cd exposure conditions, the importance of the causes of Cd in soil and rice varieties can be identified, and their contributions can be distinguished, thus helping to identify the causes of Cd contamination in rice.

Modelling Climate Change Impacts on Location Suitability for Cultivating Avocado and Blueberry in New Zealand

Regional suitability for growing avocados and blueberries may alter with climate change. Modelling can provide insights into potential climate change impacts, thereby informing industry and government policy decisions to ameliorate future risks and capitalise on future opportunities. We developed continuous/sliding-scale models that used soil, terrain and weather data to assess location suitability for cultivating avocado and blueberry, based on physiological and phenological considerations specific to each crop. Using geographical information system (GIS) data on soil, slope and weather, we mapped cultivation suitability for avocado and blueberry across New Zealand, and, for accuracy, “ground-truthed” these maps in an iterative process of expert validation and model recalibration. We modelled the incremental changes in location suitability that could occur through climate change using “future” GIS-based weather data from climate model simulations for different greenhouse gas (GHG) pathways that ranged from stringent GHG mitigation to unabated GHG emissions. Changes in maps over time showed where suitability would increase or decrease and to what extent. These results indicate where avocado and blueberry might replace other crops that become less suitable over time, and where avocado might displace blueberry. The approach and models can be applied to other countries or extended to other crops with similar growing requirements.

Mapping of soil erosion risk in Ezeagu Local Government Area of Enugu State, Nigeria

This study centres on mapping soil erosion risk in Ezeagu Local Government Area of Enugu State, Nigeria. A multi-criteria research design was adopted in carrying out this study by using remote sensed images, topographic, soil and rainfall data. K Factor, R Factor, P Factor, K Factor and Digital Elevation Model were generated. SWAT model was used to generate surface runoff and sediment yield and AHP model were used to identify and map levels of soil erosion risk in the study area. Loamy sandy soil with a K Factor value of 0.23 – 0.34 (t h MJ-1 mm-1) and loam soil with a K factor Value of 0.08 – 0.14 (t h MJ-1 mm-1) dominates the study area. The organic matter contents for all the soils were rated low with an average measured organic matter of 0.97 (%). The rainfall erosivity factor (R-factor) results showed variations across the study area from 1029.295 to 1343.092, 1343.092 to 1589.713, 1589.713 to 1744.922, 1744.922 to 1965.777, and 1965.777 to 2060.864 MJ mm ha year-1, h-1 year-1. Areas with dense vegetation had lower P-factors indicating better protection against erosion, while areas under cultivation, especially those with poor management practices and without cover crops show higher P-factors. Almost all the area has a moderate (0.5-0.6) C factor which means moderate protection, areas with higher protection (0.6-1) C factor occupied a small portion towards the east while patches of low C factors are found scattered in the north, west northwest, south and eastern part of the study area. The eastern part of the study area are more susceptible to soil erosion with value ranging from 3.2-5.8, 5.9-9.9 and 10.0-19. Slope steepness and slope length is lower in the northern, western and south-west of the study area. Areas with annual soil loss between 0 to 10 grams, 10 to 20 grams and 30 to 40 grams dominates the northern, western and southern parts of the study area. The erosion risk map depicts that very low-risk area coverage of 24.06%, low risk covers 20.29%, moderate risk occupies 17.75% and high risk erosion risk covers 21.30% while very high risk occupies 16.61% of the total area. This study recommends terracing and gabions in areas identified at risk of erosion, promotion and adoption of agronomic soil conservation methods of mulching, cover cropping and planting plants to enhance soil stability and reduce erosion.

Multi-Sensor Soil Probe and Machine Learning Modeling for Predicting Soil Properties.

We present a data-driven, in situ proximal multi-sensor digital soil mapping approach to develop digital twins for multiple agricultural fields. A novel Digital Soil CoreTM (DSC) Probe was engineered that contains seven sensors, each of a distinct modality, including sleeve friction, tip force, dielectric permittivity, electrical resistivity, soil imagery, acoustics, and visible and near-infrared spectroscopy. The DSC System integrates the DSC Probe, DSC software (v2023.10), and deployment equipment components to sense soil characteristics at a high vertical spatial resolution (mm scale) along in situ soil profiles up to a depth of 120 cm in about 60 s. The DSC Probe in situ proximal data are harmonized into a data cube providing vertical high-density knowledge associated with physical-chemical-biological soil conditions. In contrast, conventional ex situ soil samples derived from soil cores, soil pits, or surface samples analyzed using laboratory and other methods are bound by a substantially coarser spatial resolution and multiple compounding errors. Our objective was to investigate the effects of the mismatched scale between high-resolution in situ proximal sensor data and coarser-resolution ex situ soil laboratory measurements to develop soil prediction models. Our study was conducted in central California soil in almond orchards. We collected DSC sensor data and spatially co-located soil cores that were sliced into narrow layers for laboratory-based soil measurements. Partial Least Squares Regression (PLSR) cross-validation was used to compare the results of testing four data integration methods. Method A reduced the high-resolution sensor data to discrete values paired with layer-based soil laboratory measurements. Method B used stochastic distributions of sensor data paired with layer-based soil laboratory measurements. Method C allocated the same soil analytical data to each one of the high-resolution multi-sensor data within a soil layer. Method D linked the high-density multi-sensor soil data directly to crop responses (crop performance and behavior metrics), bypassing costly laboratory soil analysis. Overall, the soil models derived from Method C outperformed Methods A and B. Soil predictions derived using Method D were the most cost-effective for directly assessing soil-crop relationships, making this method well suited for industrial-scale precision agriculture applications.

BEpipeR: a user-friendly, flexible, and scalable data synthesis pipeline for the Biodiversity Exploratories and other research consortia

Abstract Background Large research consortia can generate tremendous amounts of biological information, including high-resolution soil, vegetation, and climate data. While this knowledge stock holds invaluable potential for answering evolutionary and ecological questions, making these data exploitable for modelling remains a daunting task due to the many processing steps required for synthesis. This might result in many researchers to fall back to a handful of ready-to-use data sets, potentially at the expense of statistical power and scientific rigour. In a push for a more stringent approach, we introduce BEpipeR, an R pipeline that allows for the streamlined synthesis of plot-based Biodiversity Exploratories data. Methods BEpipeR was designed with flexibility and ease of use in mind. For instance, users simply choose between aggregating forest or grassland data, or a combination thereof, effectively allowing them to process any experimental plot data of this research consortium. Additionally, instead of coding, they parse most processing information in a user-friendly way through parameter sheets. Processing includes, among others, the creation of a spatially explicit plot-ID template, data wrangling, quality control, plot-wise aggregations, the calculation of derived metrics, data joining to a large composite data set, and metadata compilation. Results With BEpipeR, we provide a feature-rich pipeline that allows users to process Biodiversity Exploratories data in a flexible and reproducible way. This pipeline might serve as a starting point for aggregating the numerous data sets of this and potentially similar research consortia. In this way, it might be a primer for the construction of consortia-wide composite data sets that take full advantage of the consortia’s rich information stocks, ultimately boosting the visibility and participation of individual research projects. Conclusions The BEpipeR pipeline permits the user-friendly processing and plot-wise aggregation of Biodiversity Exploratories data. With modifications, this framework may be easily adopted by other research consortia.

Analysis of Soil Viability Monitoring System for In-House Plantation Growth Using an Internet of Things Approach

Houseplant cultivation has become increasingly popular, allowing individuals to bring nature into their homes. However, successful indoor gardening requires careful monitoring of soil parameters to ensure optimal plant growth. To address this need, sensor technology and Internet of Things (IoT) devices are utilized to monitor soil temperature and moisture levels, which play crucial roles in plant growth. Various soil factors are sensed and collected using an IoT-based microcontroller, with data transmission facilitated by a Message Queue Telemetry Transport (MQTT) broker. Visualization of the data is achieved through the Node-RED programming tool, simplifying dashboard creation for easy monitoring. Furthermore, the collected data is stored in a MySQL server, enabling further analysis through SQL queries. The day is divided into four quarters with six-hour intervals, allowing for soil data collection using temperature and moisture sensors. The resulting information on the dashboard facilitates informed decision-making to enhance soil conditions for optimal indoor plant growth. Experimentation has revealed a reduction in soil temperature of 3°C during daytime due to air conditioning operation, while soil moisture content remains consistently between 60 to 65% during early mornings and late evenings. Additionally, emphasis is placed on remote management using IoT systems, enabling monitoring of plant growth even when access is limited. Overall, monitoring soil factors using IoT technology offers a promising approach to optimizing indoor gardening practices and minimizing environmental resource consumption.

Providing quality-assessed and standardised soil data to support global mapping and modelling (WoSIS snapshot 2023)

Abstract. Snapshots derived from the World Soil Information Service (WoSIS) are served freely to the international community. These static datasets provide quality-assessed and standardised soil profile data that can be used to support digital soil mapping and environmental applications at broad scale levels. Since the release of the preceding snapshot in 2019, refactored ETL (extract, transform and load) procedures for screening, ingesting and standardising disparate source data have been developed. In conjunction with this, the WoSIS data model was overhauled, making it compatible with the ISO 28258 and Observations and Measurements (O&M) domain models. Additional procedures for querying, serving and downloading the publicly available standardised data have been implemented using open software (e.g. GraphQL API). Following up on a short discussion of these methodological developments we discuss the structure and content of the “WoSIS 2023 snapshot”. A range of new soil datasets was shared with us, registered in the ISRIC World Data Centre for Soils (WDC-Soils) data repository and subsequently processed in accordance with the licences specified by the data providers. An important effort has been the processing of forest soil data collated in the framework of the EU-HoliSoils project. We paid special attention to the standardisation of soil property definitions, description of the soil analytical procedures and standardisation of the units of measurement. The 2023 snapshot considers soil chemical properties (total carbon, organic carbon, inorganic carbon (total carbonate equivalent), total nitrogen, phosphorus (extractable P, total P and P retention), soil pH, cation exchange capacity and electrical conductivity) and physical properties (soil texture (sand, silt and clay), bulk density, coarse fragments and water retention), grouped according to analytical procedures that are operationally comparable. Method options are defined for each analytical procedure (e.g. pH measured in water, KCl or CaCl2 solution, molarity of the solution, and soil / solution ratio). For each profile we also provide the original soil classification (i.e. FAO, WRB and USDA system with their version) and pedological horizon designations as far as these have been specified in the source databases. Three measures for “fitness for intended use” are provided to facilitate informed data use: (a) positional uncertainty of the profile's site location, (b) possible uncertainty associated with the operationally defined analytical procedures and (c) date of sampling. The most recent (i.e. dynamic) dataset, called wosis_latest, is freely accessible via various web services. To permit consistent referencing and citation, we also provide a static snapshot (in this case, December 2023). This snapshot comprises quality-assessed and standardised data for 228 000 geo-referenced profiles. The data come from 174 countries and represent more than 900 000 soil layers (or horizons) and over 6 million records. The number of measurements for each soil property varies (greatly) between profiles and with depth, this generally depending on the objectives of the initial soil sampling programmes. In the coming years, we aim to gradually fill gaps in the geographic distribution of the profiles, as well as in the soil observations themselves, this subject to the sharing of a wider selection of “public” soil data by prospective data contributors; possible solutions for this are discussed. The WoSIS 2023 snapshot is archived and freely available at https://doi.org/10.17027/isric-wdcsoils-20231130 (Calisto et al., 2023).

Elevation, Soil and Environmental Factors Determine the Spatial and Quantitative Distribution of Qinghai Spruce Recruitment Biomass in Mountainous (Alpine) Watersheds

Soil heterogeneity observed in the alpine environment plays a very important role in the growth of forest recruitment. However, the mechanisms by which the biomass accumulation and allocation patterns of forest recruitment respond to such environmental differences are unclear, which hinders a thorough understanding of climate change’s impact on forest biomass. We hypothesized that soil heterogeneity influences the distribution of Qinghai spruce recruitment biomass along with elevation. In the frame of this study, carried out in the northern Tibetan Plateau, forest Qinghai spruce recruitment data were combined with soil data derived from 24 sample plots, while permutation multifactor ANOVA and multiple linear regression were utilized to reveal the characteristics of forest recruits’ above- and below-ground biomass and their allocation patterns in response to soil heterogeneity. According to the results, the soil heterogeneity mainly affected the distribution characteristics of recruits’ above- and below-ground biomass at different elevations, while the recruits’ root–shoot ratio variability was influenced by a combination of soil and other environmental factors. Soil organic carbon (SOC) had the greatest effect on the variability of the above- and below-ground biomass of spruce recruits, with R2 of 0.280 and 0.257, respectively. Soil organic carbon and soil moisture content (SMC) had a significant effect on the variability of the root–shoot ratio, with R2 of 0.168 and 0.165, respectively. Soil total nitrogen (TN) and soil organic carbon were the main influencing factors of the above-ground biomass of forest recruits, with contribution rates of 43.15% and 35.28%, respectively. Soil total nitrogen and soil organic carbon were also the main factors influencing the below-ground biomass of forest recruits, with contribution rates of 42.52% and 37.24%, respectively, and both of them had a positive effect on biomass accumulation, and the magnitude of the influence varied with the elevation gradient. Soil moisture content was the main influence factor of spruce recruits’ root–shoot ratio, with a contribution rate of 54.12%. Decreasing soil moisture content would significantly increase the root–shoot ratio of spruce recruits and promote plants to allocate more biomass to root growth. Changes in elevation not only affected the intensity of the effect of soil factors on spruce recruitment biomass and its allocation pattern but even led to a change in the positive and negative effects.

Spatial distribution characteristics and influencing factors of soil organic carbon based on the geographically weighted regression model.

Quantifying the effects of environmental factors on soil organic carbon and spatial distribution is fundamental to soil quality regulation, restoration, and response to climate change. The present study aims to explore the spatial distribution characteristics of the soil organic carbon (SOC) contents in Anhui Province, China, based on national soil data. In addition, we used the geographically weighted regression (GWR) model to quantify the influence degrees of environmental factors on the soil organic carbon density (SOCD). The results showed that the spatial distribution of SOCD in Anhui Province in both 1985 and 2018 was characterized by high in the south and low in the north. The GWR model prediction results of the 0-30cm SOCD showed local coefficients of determination (local R2) ranging from 0.21 to 0.96 and 0.14 to 0.96 in 1985 and 2018, respectively. Therefore, the predicted results were effective in evaluating the overall spatial distribution of the SOCD in Anhui Province. The regression coefficients of the normalized difference vegetation index (NDVI) and air temperature ranged from - 0.39 to 5.67 and - 0.17 to 3.11, respectively, demonstrating their strong controlling effects on the spatiotemporal variations in the 0-30cm SOCD in Anhui Province.

Leveraging extensive soil, vegetation, fire, and land treatment data to inform restoration across the sagebrush biome

ContextWidespread ecological degradation has prompted calls for massive global investments in ecological restoration, yet limited resources necessitate efficient application of restoration efforts. In western North America, altered fire regimes are increasing the scale of restoration needed to preserve the sagebrush (Artemisia species) biome but prioritizing and implementing effective restoration is complicated by the vast and heterogeneous sagebrush landscape, which includes gradients in climate, disturbance, and species composition.ObjectivesTo develop spatially explicit and context-dependent estimates of treatment efficacy and sagebrush recovery rates.MethodsWe leveraged a suite of spatio-temporally extensive datasets to evaluate the influence of restoration treatments and environmental conditions on trends in post-disturbance sagebrush cover, with an emphasis on understanding differences between sites recovering naturally and sites receiving restoration treatments. We used estimates from these models to develop spatially explicit projections for sagebrush recovery, conditional on disturbance, restoration practice, and environmental conditions.ResultsWe found seeding Artemisia spp. increased sagebrush cover over time relative to natural recovery, but this relationship depended on spring soil moisture availability and treatment methods. Natural recovery was positively influenced by soil moisture and sagebrush cover and negatively influenced by cumulative burns and annual herbaceous cover, while the influence of perennial herbaceous cover varied with soil moisture.ConclusionsOur results provide biome-wide insights and spatially explicit tools that can inform economic cost-effectiveness analyses, restoration prioritization tools, and other scientific endeavors to ensure managers have the tools and information needed to effectively steward the sagebrush biome in a rapidly changing world.