Geoderma最新文献

{"title":"Quantification of sources and input-output pathways of heavy metals in soils from an abandoned mining watershed using Cd isotope tracing and inventory analysis","authors":"Qiumei Wu ,&nbsp;Wenyou Hu ,&nbsp;Kang Tian ,&nbsp;Ya’nan Fan ,&nbsp;Khalid Saifullah Khan ,&nbsp;Hans Christian Bruun Hansen ,&nbsp;Biao Huang","doi":"10.1016/j.geoderma.2025.117359","DOIUrl":"10.1016/j.geoderma.2025.117359","url":null,"abstract":"<div><div>Quantitative analysis of regional-scale soil heavy metal (HM) sources presents significant challenges. The reliability of the widely used source apportionment model (positive matrix factorization, PMF) remains to be validated. Moreover, PMF are limited in their ability to dynamically assess source-sink changes and their impact on HM accumulation trends based solely on soil concentrations. Therefore, Cd isotopic data (analyzed using both the MixSIAR Bayesian model and a three-End-Member model), the PMF model, and input/output inventories were integrated to jointly quantify the fluxes and sources of HMs in the soils from an abandoned mining watershed. The total annual input flux of soil HMs (2160 g/ha/y) was significantly higher than the output flux (486 g/ha/y), resulting in net annual increase rates of Cd, Cu, and Zn of –0.495, 359, and 43.6 µg/kg/y, respectively. Irrigation water (1776 g/ha/y) and leaching water (417 g/ha/y) were the main input and output pathways for all HMs. Soil Cr and Ni originate from natural sources, Cd, Cu, and Zn mainly from irrigation water, and Pb from atmospheric deposition. A strong source relationship of Cd among upstream water, compound fertilizers, and surface soils was found. Combination of the three models showed good consistency in quantitative and source specific data for influx of Cd, indicating that irrigation water affected by open pits is the major Cd source (79 %, 62 % and 77 % for PMF, three-End-Member, and MixSIAR-Bayesian models, respectively). This comprehensive analytical framework provides a robust and broadly applicable strategy for quantifying HM sources and elucidating their multi-phase transport dynamics within complex agroecosystems subject to dual geogenic-anthropogenic influences.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"459 ","pages":"Article 117359"},"PeriodicalIF":5.6,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Elucidating the role of ferrihydrite and goethite in the aggregation and stability of small soil microaggregates: An experimental study on arable Luvisols under different management","authors":"Ni Tang ,&nbsp;Nina Siebers ,&nbsp;Stefan Dultz ,&nbsp;Erwin Klumpp","doi":"10.1016/j.geoderma.2025.117351","DOIUrl":"10.1016/j.geoderma.2025.117351","url":null,"abstract":"<div><div>Iron oxides, exhibiting positive surface charge in most acidic to neutral soils, are key inorganic agents for microaggregate formation, especially via their electrostatic interactions with negatively charged surfaces on organic and inorganic soil compounds. Yet, little is known on the influence of Fe oxide properties, i.e., size, shape, and surface charge on the formation of soil microaggregates (SMA). Here the aggregation of &lt; 20 μm small SMA fractions in the presence of either ferrihydrite or goethite as well as the stability of the resulting aggregates were examined. Water stable small SMA fractions were isolated from Ap-horizons of Stagnic Luvisols under different management (cropped and bare fallow), and both had an average diameter of ∼ 6 μm. Ferrihydrite and goethite, respectively, were added as suspensions to small SMA fractions at 1 or 5 wt%. For comparison, humic acid (HA), a common fraction of soil organic matter, was added at 1 wt% in solution. Laser diffraction was applied to determine changes in the hydrodynamic diameter and the stability of the resulting aggregates. Addition of Fe oxides facilitated the formation of 3 − 10 μm SMA, which probably resulted from their aggregation with &lt; 3 μm particles in the small SMA fractions via electrostatic attraction. Moreover, changes in the particle size distribution also suggested that the addition of Fe oxides decreased the share of &gt; 10 μm SMA, thereby increasing the abundance of 3 − 10 μm small SMA as well. Here it is likely that attachment of Fe oxides on SMA caused a rearrangement of their structure leading to a closer packing of particles. A generally higher decrease in the abundance of &gt; 10 μm SMA in the ferrihydrite addition implied a more efficient compacting effect of ferrihydrite than that of goethite. This was presumably due to the smaller size of ferrihydrite, which can decrease the steric hindrance and provide more contact points. Changes in the size distribution of small SMA fractions were more pronounced after the addition of 5.0 wt% Fe oxides compared to the 1.0 wt% ones. In contrast, adsorption of the added HA on SMA increased their negative surface charges and steric hindrance between them, thereby favoring their dispersion rather than aggregation. In the stability test, both ferrihydrite and goethite showed a less effective stabilizing effect on SMA at the bare fallow site than the cropped one. However, ferrihydrite generally revealed a better ability to stabilize &lt; 1 μm colloids in the small SMA fraction than goethite for both sites. Here, our study provides new insights into the abilities of different Fe oxides to form and stabilize aggregates in soil microaggregation.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"459 ","pages":"Article 117351"},"PeriodicalIF":5.6,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional carbon pools and microbial communities in persistent carbon rich erosion buried topsoils","authors":"Mitchell A.D,&nbsp;Helgason B.L","doi":"10.1016/j.geoderma.2025.117353","DOIUrl":"10.1016/j.geoderma.2025.117353","url":null,"abstract":"<div><div>Storing more C in subsoils can offset greenhouse gas emissions and C-rich buried horizons provide a unique opportunity to investigate the nature of subsurface C persistence. We identified five sites with varying soil texture across a climatic gradient that had C-rich buried surface horizons. We profiled the microbial community and characterized the soil organic matter of surface Ah, buried surface Ahb, and buried subsoil Bwb horizons to gain insight into these C-rich subsoils. Similar concentrations of C remained in Ahb horizons relative to Ah horizons, despite significant viable microbial biomass in Ahb horizons capable of decomposing the C at depth where little fresh C input has occurred in the decades since burial. The microbial community composition had shifted in Ahb horizons to be more similar to subsoil Bwb than surface Ah communities, indicating the SOC composition in Ahb horizons and conditions at depth strongly influenced their current status. Ahb horizons stored a greater proportion of C in mineral-associated organic matter (MAOM) than Ah horizons. Although similar microbial necromass quantity and concentration in Ah vs. Ahb soils, muramic acid was reduced in Ahb horizons, indicating significant recycling of microbial necromass. Commonalities in C-rich buried surface horizons between microbial and C profiles across a wide range of textures and climates, demonstrates that the general processes of C cycling and persistence are ubiquitous across buried surface horizons. These soils may provide an opportunity to examine microbial communities associated with persistent C that is difficult to isolate due to low concentration in surface soils.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"459 ","pages":"Article 117353"},"PeriodicalIF":5.6,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven soil salinization mapping: risk prediction and uncertainty quantification based on Bayesian inference","authors":"Yujian Yang ,&nbsp;Ying Zhao ,&nbsp;Rongjiang Yao ,&nbsp;Xueqin Tong","doi":"10.1016/j.geoderma.2025.117344","DOIUrl":"10.1016/j.geoderma.2025.117344","url":null,"abstract":"<div><div>Soil salinization poses a serious global threat to agricultural production and has emerged as a critical issue of land degradation. To comprehensively investigate the risks and uncertainty quantification associated with soil salinization, Yucheng County, a typical fluvo-aquic soil area located in Shandong Province, China, was selected as the case study region. In October 2021, soil samples were collected from 101 sampling sites utilizing the Global Navigation Satellite System (GNSS) for precise positioning. Soil electrical conductivity (EC) was measured at these sites using a PR-3001-TRREC-N01 sensor. The performance of Bayesian inference using Integrated Nested Laplace Approximation with the Stochastic Partial Differential Equation (INLA-SPDE) approach for predicting soil salinization at unsampled locations was compared with that obtained using Kriging. The results indicated that the maps generated by the Kriging interpolation and INLA-SPDE approach showed similar distribution patterns for soil salinization but differed in detail. High EC values corresponded to specific regions, while low EC values were consistent across both methods. The posterior mean, together with the lower and upper limits of the 95 % credible intervals, effectively quantified the uncertainty associated with soil salinization risk. Both Fangsi township and Xindian township are identified as high-risk areas for soil salinization with exceedance probability map for policymaking. Correspondingly, the implementation of an optimized farmland irrigation and drainage system is recommended, particularly in low-lying areas, to mitigate soil salinization. Additionally, No-U-Turn Sampler (NUTS), highest-posterior density interval (HDI), Kernel density estimation (KDE), rank plots and trace plots enhanced the transparency and interpretability of soil salinization prediction. KDE of 100 groups of predicted values showed a good fit based on data-driven soil EC, higher levels of uncertainty associated with soil EC correspond to areas where the gaussian distributions overlap using Theano, as PyMC3 core component based on deep learning principles.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"459 ","pages":"Article 117344"},"PeriodicalIF":5.6,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Responses of microbial respiration rate and active bacterial communities to antecedent soil moisture on the Loess Plateau","authors":"Xiaodong Nie ,&nbsp;Panpan Jiao ,&nbsp;Lei Yang ,&nbsp;Zhongwu Li","doi":"10.1016/j.geoderma.2025.117348","DOIUrl":"10.1016/j.geoderma.2025.117348","url":null,"abstract":"<div><div>Previous studies on microbial respiration responses to soil moisture have primarily focused on the influence of current moisture conditions, largely overlooking the role of antecedent soil moisture and its effects on the soil carbon (C) pool. In this study, we investigated the effect patterns of antecedent moisture on bacterial 16S rRNA composition, microbial respiration and the soil extractable organic carbon (EOC) pool by examining soils subjected to rewetting, drying and maintenance at constant moisture conditions. Bacterial community composition depended on both antecedent and current soil moisture. Specifically, rewetting decreased the relative abundance of <em>Actinobacteriota</em> and increased that of <em>Proteobacteria</em>, <em>Myxococcota</em>, <em>Acidobacteriota</em> and <em>Gemmatimonadota</em>, while that of <em>Actinobacteriota</em>, <em>Proteobacteria</em>, <em>Chloroflexi</em> and <em>Entotheonellaeota</em> increased after drying. The drying and rewetting treatments significantly affected substrate availability, as rewetting from antecedent drought facilitated the enrichment of soil EOC and induced a pulse in the respiration rate compared to drying from antecedent wetting. Additionally, most of the rewetting treatments increased the relative abundances of aromatic compounds. Soil moisture contents finally at 80 % water holding capacity (WHC) were most suitable for microbial respiration. Except for extreme drying, drying and rewetting treatments reduced C loss as drying promoted the integration of organic matter into the EOC pool via the desorption of aromatic substances and/or the lysis of microbial cells, which stimulated microbial respiration. The mechanistic and quantitative insights into the effects of antecedent moisture conditions on the soil C dynamic provided by this study will help reduce uncertainty in predicting soil C using current models.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"458 ","pages":"Article 117348"},"PeriodicalIF":5.6,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-quality straw over high-quality straw preferred for mineral-associated organic matter formation","authors":"Xiaofang Ji ,&nbsp;Dengchun Xing ,&nbsp;Xin Guan ,&nbsp;Yugang Wang ,&nbsp;Gilles Colinet ,&nbsp;Wenting Feng","doi":"10.1016/j.geoderma.2025.117342","DOIUrl":"10.1016/j.geoderma.2025.117342","url":null,"abstract":"<div><div>The formation of mineral-associated organic matter (MAOM) from plant litter decomposition is crucial for climate change mitigation. However, the way in which plant litter of varying qualities influences MAOM formation and decomposition, particularly regarding the quantity of litter inputs, remains largely unclear. This study aimed to determine how the quality of straw, specifically low-quality wheat (<em>Triticum aestivum</em> L.) versus high-quality milk vetch (<em>Astragalus sinicus</em> L.), and its quantity (input level) affect MAOM formation and decomposition. We conducted a 420-day laboratory incubation experiment using low-quality wheat versus high-quality milk vetch straws added to artificial soil (pure quartz vs. soil with reactive minerals (sandy soil: 5 % clay, 10 % silt, and 85 % sand)) at input levels of 0, 3, 6, 18, 26, 31, and 35 g C kg⁻¹ soil. Different from the Microbial Efficiency-Matrix Stabilization theory, our research indicates that adding low-quality wheat straw led to significantly greater MAOM content than high-quality milk vetch. Notably, the MAOM stabilization efficiency declined at high input levels (26, 31, and 35 g C kg⁻¹ soil) for wheat than for milk vetch. This is further supported by the evidence that reactive minerals slowed the decomposition rate of low-quality wheat straw more effectively than that of high-quality milk vetch. Moreover, the lower C/N ratio of the MAOM fraction, the reduced C/N ratio of dissolved organic matter (DOM), and a higher fluorescence index of DOM (higher values indicating greater contribution of microbial sources) after adding milk vetch than adding wheat straw suggest the significant role of plant-derived organic matter in MAOM formation. Our findings disclose that reactive minerals preferentially protect low-quality litter over high-quality litter through direct interaction with plant-derived organic matter, providing a critical pathway for MAOM formation distinct from microbial assimilation. This study highlights the key role of low-quality straw in the efficient and long-term stabilization of soil C within agricultural practices.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"459 ","pages":"Article 117342"},"PeriodicalIF":5.6,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The addition of chemical compounds extracted from leaf litter leachates enhances short term methane uptake by forest soils","authors":"Vincent Arricastres ,&nbsp;Dorine Desalme ,&nbsp;Thomas Z. Lerch ,&nbsp;Marie-Noëlle Vaultier ,&nbsp;Caroline Plain","doi":"10.1016/j.geoderma.2025.117347","DOIUrl":"10.1016/j.geoderma.2025.117347","url":null,"abstract":"<div><div>Upland forest soils are recognized as the primary biological sink for methane. The influence of litter on soil methane uptake has not been clearly elucidated: litter could reduce methane uptake, have no influence or enhance it. Until now, the role of litter has only been studied for the diffusion of gases. The chemical influence of leachate compounds from litter is a dominant process in forest ecosystems. In this study, we investigated this influence on soil methane fluxes. We extracted leaf litter compounds from four temperate tree species (beech, oak, pine and spruce) and determined their biochemical composition by spectrophotometry. The leachates, or pure water for the control treatment, were added to three different types of sieved forest soil (alocrisol, cambisol and luvisol) to determine their influences on methane fluxes. The methane fluxes were monitored for 48-h. We found that the chemical compounds leached from leaf litter enhanced methane uptake by 8.2 % with no significant effect of the species from which the leachates were extracted. The enhancement depended on the type of soil and was correlated to initial methane uptake. These results indicate that the role played by litter in the methane balance of forest soils, which has so far been thought to affect only the availability of the substrate (methane and dioxygen), is more complex than that.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"459 ","pages":"Article 117347"},"PeriodicalIF":5.6,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing a multi-criteria assessment model for soil primary productivity in double cropping systems: Insights from the North China Plain","authors":"Yizan Li ,&nbsp;Carmen Vazquez ,&nbsp;Jiyu Jia ,&nbsp;Jiangzhou Zhang ,&nbsp;Ron de Goede ,&nbsp;Marko Debeljak ,&nbsp;Fusuo Zhang ,&nbsp;Junling Zhang ,&nbsp;Rachel Creamer","doi":"10.1016/j.geoderma.2025.117346","DOIUrl":"10.1016/j.geoderma.2025.117346","url":null,"abstract":"<div><div>Soil, one of the Earth’s most critical natural resources, supports global agricultural production and underpins key ecosystem services. Among the multiple functions soil performs, primary productivity stands out as a crucial element, pivotal for ensuring food security as the basis of the agricultural system. This study aimed to develop a multi-criteria assessment model for soil primary productivity at field scale, drawing insights from the winter wheat − summer maize rotation systems in the North China Plain. The development of the model followed the Decision Expert (DEX) methodology, using an integrated approach that combines knowledge graph and data mining techniques. We systematically structured the knowledge underpinning soil primary productivity. Utilising datasets derived from long-term field experiments and smallholder farms, the model was subjected to an iterative process of calibration and validation, enhancing both its predictive accuracy and operational applicability. The developed DEX model consists of 28 input attributes that encompass soil properties, field management practices, and meteorological conditions. The model achieved an accuracy of 71% in assessing soil primary productivity in the experimental field dataset after calibration, and 62% in the smallholder farm dataset as model validation. The developed model can effectively assess soil primary productivity function and facilitate the improvement of soil management. The innovative integration of knowledge-based and data-driven approaches proved to be effective. It is expected that the developed model can be integrated with other soil function models into a soil health decision support system that provides a holistic approach to soil health assessment and optimisation of field practices.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"459 ","pages":"Article 117346"},"PeriodicalIF":5.6,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aggregate pore and shape properties were more strongly correlated to soil organic carbon in large aggregates: Evidence from a long-term management-induced soil carbon gradient","authors":"Weijun Zhang ,&nbsp;Lars J. Munkholm ,&nbsp;Richard J. Heck ,&nbsp;Christopher W. Watts ,&nbsp;Johannes L. Jensen","doi":"10.1016/j.geoderma.2025.117357","DOIUrl":"10.1016/j.geoderma.2025.117357","url":null,"abstract":"<div><div>The interplay between soil structure and soil organic carbon (SOC) is complex and affects key soil functions. There is limited knowledge on how this relationship changes with the size of the structural unit studied. The objective of this study was to quantify the pore and shape characteristics of soil aggregates of varying sizes, and their relationships with SOC under different soil management regimes. Soils were sampled in March 2015 from the Highfield Ley-Arable Long-Term Experiment at Rothamsted Research. This experiment includes bare fallow, continuous arable rotation, ley-arable rotation, and grass treatments. A total of 24 aggregates from each treatment and size class (2–4, 4–8, and 8–16 mm) were subjected to X-ray micro-CT scanning at 40 μm voxel resolution. Results showed that permanent grass not only increased SOC accumulation, but also promoted pore connectivity of soil aggregates compared to bare fallow, regardless of aggregate size. Additionally, the pore and shape characteristics of larger aggregates (4–8 and 8–16 mm) were more sensitive to soil management compared to smaller aggregates (2–4 mm). The relationships between SOC and aggregate structural characteristics were strong for both 8–16 and 4–8 mm aggregates but weak for 2–4 mm aggregates. Furthermore, the responses of pore connectivity and sphericity to SOC increased with aggregate size. The results suggest that organic matter input plays an essential role in shaping aggregate structural characteristics and aggregate rearrangement (especially in larger aggregates).</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"459 ","pages":"Article 117357"},"PeriodicalIF":5.6,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping","authors":"Jonas Schmidinger ,&nbsp;Sebastian Vogel ,&nbsp;Viacheslav Barkov ,&nbsp;Anh-Duy Pham ,&nbsp;Robin Gebbers ,&nbsp;Hamed Tavakoli ,&nbsp;Jose Correa ,&nbsp;Tiago R. Tavares ,&nbsp;Patrick Filippi ,&nbsp;Edward J. Jones ,&nbsp;Vojtech Lukas ,&nbsp;Eric Boenecke ,&nbsp;Joerg Ruehlmann ,&nbsp;Ingmar Schroeter ,&nbsp;Eckart Kramer ,&nbsp;Stefan Paetzold ,&nbsp;Masakazu Kodaira ,&nbsp;Alexandre M.J.-C. Wadoux ,&nbsp;Luca Bragazza ,&nbsp;Konrad Metzger ,&nbsp;Martin Atzmueller","doi":"10.1016/j.geoderma.2025.117337","DOIUrl":"10.1016/j.geoderma.2025.117337","url":null,"abstract":"<div><div>Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking studies on multiple datasets are needed to reveal strengths and limitations of commonly used methods. Existing DSM studies usually rely on a single dataset with restricted access, leading to incomplete and potentially misleading conclusions. To address these issues, we introduce an open-access dataset collection called Precision Liming Soil Datasets (LimeSoDa). LimeSoDa consists of 31 field- and farm-scale datasets from various countries. Each dataset has three target soil properties: (1) soil organic matter or soil organic carbon, (2) clay content and (3) pH, alongside a set of features. Features are dataset-specific and were obtained by optical spectroscopy, proximal- and remote soil sensing. All datasets were aligned to a tabular format and are ready-to-use for modeling. We demonstrated the use of LimeSoDa for benchmarking by comparing the predictive performance of four learning algorithms across all datasets. This comparison included multiple linear regression (MLR), support vector regression (SVR), categorical boosting (CatBoost) and random forest (RF). The results showed that although no single algorithm was universally superior, certain algorithms performed better in specific contexts. MLR and SVR performed better on high-dimensional spectral datasets, likely due to better compatibility with principal components. In contrast, CatBoost and RF exhibited considerably better performances when applied to datasets with a moderate number (&lt;20) of features. These benchmarking results illustrate that the performance of statistical methods can be highly context-dependent. LimeSoDa therefore provides an important resource for improving the development and evaluation of statistical methods in DSM.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"459 ","pages":"Article 117337"},"PeriodicalIF":5.6,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}