Soil最新文献

{"title":"Cultivation reduces quantities of mineral-organic associations in the form of amorphous coprecipitates","authors":"Floriane Jamoteau, Emmanuel Doelsch, Nithavong Cam, Clément Levard, Thierry Woignier, Adrien Boulineau, François Saint-Antonin, Sufal Swaraj, Ghislain Gassier, Adrien Duvivier, Daniel Borschneck, Marie-Laure Pons, Perrine Chaurand, Vladimir Vidal, Nicolas Brouilly, Isabelle Basile-Doelsch","doi":"10.5194/egusphere-2024-2933","DOIUrl":"https://doi.org/10.5194/egusphere-2024-2933","url":null,"abstract":"<strong>Abstract.</strong> Mineral-organic associations are crucial carbon and nutrient reservoirs in soils. However, soil cultivation disrupts these associations, leading to carbon loss and reduced soil fertility. Although, identifying the specific type(s) of mineral-organic associations susceptible to destruction or transformation upon cropping remains challenging, it is essential for devising strategies to preserve organic matter in croplands. Here we aimed to determine the predominant mineral-organic associations and to identify which types of associations are transformed upon cultivation. To achieve this, we sampled an andosol from both a forested and a cultivated area. We then analyzed cultivation-induced changes in soil physicochemical parameters and characterized mineral-organic associations using an array of spectro-microscopic techniques (TEM-EDX, TEM-EELS, and STXM), for comprehensive structural and compositional analysis. At the micro and nanoscale, we observed mineral-organic associations in the form of coprecipitates composed of amorphous oligomers containing Al, Si, and Fe (referred to as nanoCLICs for nanosized coprecipitates of inorganic oligomers with organics). Down to a few hundred nanometers, the nanoCLICs displayed elemental enrichments with C+Al+Si, C+Fe+Al+Si, or Al+Si dominance with less C. In contrast, organic matter exhibited various C speciation without compound-specific enrichments. These findings suggest that mineral-organic associations in andosols are nanoCLICs-type coprecipitates rather than organic matter associated solely with secondary minerals. NanoCLICs were present in both forest and crop andosols, and while cropping led to a 50 % decrease in nanoCLICs, it did not alter their nature. This novel conceptualization of mineral-organic associations as nanoCLICs shifts our understanding of their persistence in andosols and demonstrates their vulnerability to crop-induced changes.","PeriodicalId":48610,"journal":{"name":"Soil","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142360116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Insights into the prediction uncertainty of machine-learning-based digital soil mapping through a local attribution approach","authors":"Jeremy Rohmer, Stephane Belbeze, Dominique Guyonnet","doi":"10.5194/soil-10-679-2024","DOIUrl":"https://doi.org/10.5194/soil-10-679-2024","url":null,"abstract":"Abstract. Machine learning (ML) models have become key ingredients for digital soil mapping. To improve the interpretability of their predictions, diagnostic tools such as the widely used local attribution approach known as SHapley Additive exPlanations (SHAP) have been developed. However, the analysis of ML model predictions is only one part of the problem, and there is an interest in obtaining deeper insights into the drivers of the prediction uncertainty as well, i.e. explaining why an ML model is confident given the set of chosen covariate values in addition to why the ML model delivered some particular results. In this study, we show how to apply SHAP to local prediction uncertainty estimates for a case of urban soil pollution – namely, the presence of petroleum hydrocarbons in soil in Toulouse (France), which pose a health risk via vapour intrusion into buildings, direct soil ingestion, and groundwater contamination. Our results show that the drivers of the prediction best estimates are not necessarily the drivers of confidence in these predictions, and we identify those leading to a reduction in uncertainty. Our study suggests that decisions regarding data collection and covariate characterisation as well as communication of the results should be made accordingly.","PeriodicalId":48610,"journal":{"name":"Soil","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142329939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cr(VI) reduction, electricity production, and microbial resistance variation in paddy soil under microbial fuel cell operation","authors":"Huan Niu, Xia Luo, Peihan Li, Hang Qiu, Liyue Jiang, Subati Maimaitiaili, Minghui Wu, Fei Xu, Heng Xu, Can Wang","doi":"10.5194/egusphere-2024-2771","DOIUrl":"https://doi.org/10.5194/egusphere-2024-2771","url":null,"abstract":"<strong>Abstract.</strong> Microbial fuel cell (MFC) is an efficient in-situ approach to combat pollutants and generate electricity. This study constructed a soil MFC (SMFC) to reduce Cr(VI) in paddy soil and investigate its influence on microbial community and microbial resistance characteristics. Fe₃O₄ nanoparticle as the cathodic catalyst effectively boosted power generation (0.97 V, 102.0 mW/m²), whose porous structure and reducibility also contributed to Cr reduction and immobilization. After 30 days, 93.67 % of Cr(VI) was eliminated. The bioavailable Cr decreased by 97.44 % while the residual form increased by 88.89 %. SMFC operation greatly changed soil enzymatic activity and microbial structure, with exoelectrogens like <em>Desulfotomaculum</em> (3.32 % in anode) and Cr(VI)-reducing bacteria like <em>Hydrogenophaga </em>(2.07 % in cathode) more than 1000 folds of soil. In particular, SMFC operation significantly enhanced the abundance of heavy metal resistance genes (HRGs). Among them, <em>chrA, chrB, and chrR</em> increased by 99.54~3314.34 % in SMFC anode than control, probably attributed to the enrichment of potential tolerators like <em>Acinetobacter, Limnohabitans, </em>and <em>Desulfotomaculum</em>. These key taxa were positively correlated with HRGs but negatively correlated with pH, EC, and Cr(VI), which could have driven Cr(VI) reduction. This study provided novel evidence for bioelectrochemical system application in contaminated paddy soil, which could be a potential approach for environmental remediation and detoxification.","PeriodicalId":48610,"journal":{"name":"Soil","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142329937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benchmarking soil multifunctionality","authors":"E. R. Jasper Wubs","doi":"10.5194/egusphere-2024-2851","DOIUrl":"https://doi.org/10.5194/egusphere-2024-2851","url":null,"abstract":"<strong>Abstract.</strong> Healthy soils provide multiple functions that importantly contribute to human wellbeing, including primary production, climate and water regulation, and supporting biodiversity. These functions can partially be combined and some functions also clearly trade-off: this motivates soil multifunctionality research. Society needs scientists to help assess which soils are best for which soil functions and to determine appropriate long-term management of any given soil for optimal function delivery. However, for both tasks science lacks coherent tools and in this paper I propose a way forward. Critically, we lack a common measurement framework that pins soil functioning measurements on a common scale. Currently the field is divided with respect to the methods we use to measure and assess soil functioning and indicators thereof. Only three indicator variables (SOM, acidity, and available P) were commonly measured (&gt;70 % of schemes) across 65 schemes that aim to measure soil health or quality, and no biological measure is implemented in more than 30 % of the 65 schemes. This status quo prevents us from systematically comparing across and within soils; we lack a soil multifunctionality benchmark. We can address this limitations systematically by setting a common measurement system. To do this, I propose to use latent variable modelling based on a common set of functional measurements, to develop a common ‘IQ test for soils’. I treat soil functions as latent variables, because they are complex processes that cannot be measured directly, we can only detect drivers and consequences of these complex processes. Latent variable modelling has a long history in social, economic and psychometric fields, where it is known as factor analysis. Factor analysis aims to derive common descriptors – the factors – of hypothesized constructs by linking measurable response variables together on a common scale. Here, I explain why such a new approach to soil multifunctionality and soil health is needed and how it can be operationalized. The framework developed here is only an initial proposal, the issue of soil multifunctionality is too complex and too important to be addressed in one go. It needs to be resolved iteratively by bands of scientist working intensively together. We need to bring our best science together, in a collaborative effort, to develop progressively more refined ways of sustainably managing one of humanity’s most precious resources: our soils.","PeriodicalId":48610,"journal":{"name":"Soil","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142321499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Depth extrapolation of field-scale soil moisture time series derived with cosmic-ray neutron sensing (CRNS) using the soil moisture analytical relationship (SMAR) model","authors":"Daniel Rasche, Theresa Blume, Andreas Güntner","doi":"10.5194/soil-10-655-2024","DOIUrl":"https://doi.org/10.5194/soil-10-655-2024","url":null,"abstract":"Abstract. Ground-based soil moisture measurements at the field scale are highly beneficial for different hydrological applications, including the validation of space-borne soil moisture products, landscape water budgeting, or multi-criteria calibration of rainfall–runoff models from field to catchment scale. Cosmic-ray neutron sensing (CRNS) allows for the non-invasive monitoring of field-scale soil moisture across several hectares around the instrument but only for the first few tens of centimeters of the soil. Many of these applications require information on soil water dynamics in deeper soil layers. Simple depth-extrapolation approaches often used in remote sensing may be used to estimate soil moisture in deeper layers based on the near-surface soil moisture information. However, most approaches require a site-specific calibration using depth profiles of in situ soil moisture data, which are often not available. The soil moisture analytical relationship (SMAR) is usually also calibrated to sensor data, but due to the physical meaning of each model parameter, it could be applied without calibration if all its parameters were known. However, its water loss parameter in particular is difficult to estimate. In this paper, we introduce and test a simple modification of the SMAR model to estimate the water loss in the second layer based on soil physical parameters and the surface soil moisture time series. We apply the model with and without calibration at a forest site with sandy soils. Comparing the model results with in situ reference measurements down to depths of 450 cm shows that the SMAR models both with and without modification as well as the calibrated exponential filter approach do not capture the observed soil moisture dynamics well. While, on average, the latter performs best over different tested scenarios, the performance of the SMAR models nevertheless meets a previously used benchmark RMSE of ≤ 0.06 cm3 cm−3 in both the calibrated original and uncalibrated modified version. Different transfer functions to derive surface soil moisture from CRNS do not translate into markedly different results of the depth-extrapolated soil moisture time series simulated by SMAR. Despite the fact that the soil moisture dynamics are not well represented at our study site using the depth-extrapolation approaches, our modified SMAR model may provide valuable first estimates of soil moisture in a deeper soil layer derived from surface measurements based on stationary and roving CRNS as well as remote sensing products where in situ data for calibration are not available.","PeriodicalId":48610,"journal":{"name":"Soil","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142276730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of moss restoration on soil erosion and soil water content in a temperate vineyard","authors":"Corinna Gall, Silvana Oldenburg, Martin Nebel, Thomas Scholten, Steffen Seitz","doi":"10.5194/egusphere-2024-2504","DOIUrl":"https://doi.org/10.5194/egusphere-2024-2504","url":null,"abstract":"<strong>Abstract.</strong> Soil erosion is a serious problem worldwide, as it jeopardizes soil fertility and thus food security. At the same time, agriculture itself is one of the biggest drivers of soil erosion, and vineyards in particular are vulnerable due to often steep slopes, fragile soils, and management practices. Therefore, the search for alternative management practices becomes vital. Since soil erosion is reduced by vegetation cover, this also applies to moss cover. However, research on the restoration and protection of bare soil using mosses as erosion control is still in its infancy. In this study, the restoration of mosses was investigated by applying artificially cultivated moss mats in a temperate vineyard. The effects of moss restoration on surface runoff and sediment discharge were examined compared to bare soil and cover crops using rainfall simulations at three measurement times during one year (April, June, and October). Additionally, soil water content was monitored for each treatment during all rainfall simulations. Mosses initially showed considerable desiccation in summer, whereupon their growth declined. In October, the mosses recovered and re-established themselves in the vineyard, showing a high level of resistance. Moss restoration significantly reduced surface runoff by 71.4 % and sediment discharge by 75.8 % compared to bare soils. While moss restoration had a slightly better effect on reducing runoff and a slightly lower effect on reducing erosion than cover crops (68.1 % and 87.7 %, respectively), these differences were not statistically significant. Sediment discharge varied seasonally for moss restoration, especially from April to June, which is most likely due to the decline in moss cover and the foliage of the vines in June, as concentrated canopy drip points have formed on the leaves and woody surfaces of the vines, increasing erosion. In April and June, the different treatments do not significantly impact soil water content, while in October, bare soil had the highest and moss restoration the lowest soil water content. According to this, the influence of soil cover varies seasonally, with moss restoration not having a detrimental effect on the soil water content in the drier summer months, but retaining the least water in October. Overall, moss restoration proved to be an appropriate and low-maintenance alternative for erosion control, as it requires no mowing and does not reduce near-surface soil water content during summer.","PeriodicalId":48610,"journal":{"name":"Soil","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142236337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using 3D observations with high spatio-temporal resolution to calibrate and evaluate a process-focused cellular automaton model of soil erosion by water","authors":"Anette Eltner, David Favis-Mortlock, Oliver Grothum, Martin Neumann, Tomas Laburda, Petr Kavka","doi":"10.5194/egusphere-2024-2648","DOIUrl":"https://doi.org/10.5194/egusphere-2024-2648","url":null,"abstract":"<strong>Abstract.</strong> Future global change is likely to give rise to novel combinations of the factors which enhance or inhibit soil erosion by water. Thus there is a need for erosion models, necessarily process-focused, which are able to reliably represent rates and extents of soil erosion under unprecedented circumstances. The process-focused cellular automaton erosion model RillGrow is, given initial soil surface microtopography on a plot-sized area, able to predict the emergent patterns produced by runoff and erosion. This study explores the use of Structure-from-Motion photogrammetry as a means to calibrate and validate this model by capturing detailed, time-lapsed data for soil surface height changes during erosion events. Temporally high-resolution monitoring capabilities (i.e. 3D models of elevation change at 0.1 Hz frequency) permit validation of erosion models in terms of the sequence of formation of erosional features. Here, multi-objective functions, using three different spatio-temporal averaging approaches, are assessed for their suitability in calibrating and evaluating the model's output. We used two sets of data, from field- and laboratory-based rainfall simulation experiments lasting 90 and 30 minutes, respectively. By integrating 10 different calibration metrics, the output of 2000 and 2400 RillGrow runs for the field and laboratory experiments respectively, were analysed. No single model run was able to adequately replicate all aspects of either field and laboratory experiments. The multi-objective approaches highlight different aspects of model performance, indicating that no single objective function can capture the full complexity of erosion processes. They also highlight different strengths and weaknesses of the model. Depending on the focus of the evaluation, an ensemble of objective functions may not always be necessary. These results underscore the need for more nuanced evaluation of erosion models, e.g. by incorporating spatial pattern comparison techniques to provide a deeper understanding of the model’s capabilities. Such evaluations are an essential complement to the development of erosion models which are able to forecast the impacts of future global change.","PeriodicalId":48610,"journal":{"name":"Soil","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142170442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gully rehabilitation in southern Ethiopia – value and impacts for farmers","authors":"Wolde Mekuria, Euan Phimister, Getahun Yakob, Desalegn Tegegne, Awdenegest Moges, Yitna Tesfaye, Dagmawi Melaku, Charlene Gerber, Paul D. Hallett, Jo U. Smith","doi":"10.5194/soil-10-637-2024","DOIUrl":"https://doi.org/10.5194/soil-10-637-2024","url":null,"abstract":"Abstract. Gully erosion can be combatted in severely affected regions like sub-Saharan Africa using various low-cost interventions that are accessible to affected farmers. For successful implementation, however, biophysical evidence of intervention effectiveness needs to be validated against the interests and priorities of local communities. Working with farmers in a watershed in southern Ethiopia, we investigated (a) the effectiveness of low-cost gully rehabilitation measures to reduce soil loss and upward expansion of gully heads; (b) how farmers and communities view gully interventions; and (c) whether involving farmers in on-farm field trials to demonstrate gully interventions improves uptake, knowledge, and perceptions of their capacity to act. On-farm field experiments, key-informant interviews, focus group discussions, and household surveys were used to collect and analyse data. Three gully treatments were explored, all with riprap, one with grass planting, and one with grass planting and check-dam integration. Over a period of 26 months, these low-cost practices ceased measurable gully head expansion, whereas untreated gullies had a mean upward expansion of 671 cm, resulting in a calculated soil loss of 11.0 t. Farmers had a positive view of all gully rehabilitation measures explored. Ongoing rehabilitation activities and on-farm trials influenced the knowledge and understanding of similar gully treatments among survey respondents. On-farm experiments and field day demonstrations empowered farmers to act, addressing pessimism from some respondents about their capacity to do so.","PeriodicalId":48610,"journal":{"name":"Soil","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142170443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An ensemble estimate of Australian soil organic carbon using machine learning and process-based modelling","authors":"Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, Raphael A. Viscarra Rossel","doi":"10.5194/soil-10-619-2024","DOIUrl":"https://doi.org/10.5194/soil-10-619-2024","url":null,"abstract":"Abstract. Spatially explicit prediction of soil organic carbon (SOC) serves as a crucial foundation for effective land management strategies aimed at mitigating soil degradation and assessing carbon sequestration potential. Here, using more than 1000 in situ observations, we trained two machine learning models (a random forest model and a k-means coupled with multiple linear regression model) and one process-based model (the vertically resolved MIcrobial-MIneral Carbon Stabilization, MIMICS, model) to predict the SOC stocks of the top 30 cm of soil in Australia. Parameters of MIMICS were optimised for different site groupings using two distinct approaches: plant functional types (MIMICS-PFT) and the most influential environmental factors (MIMICS-ENV). All models showed good performance with respect to SOC predictions, with an R2 value greater than 0.8 during out-of-sample validation, with random forest being the most accurate; moreover, it was found that SOC in forests is more predictable than that in non-forest soils excluding croplands. The performance of continental-scale SOC predictions by MIMICS-ENV is better than that by MIMICS-PFT especially in non-forest soils. Digital maps of terrestrial SOC stocks generated using all of the models showed a similar spatial distribution, with higher values in south-eastern and south-western Australia, but the magnitude of the estimated SOC stocks varied. The mean ensemble estimate of SOC stocks was 30.3 t ha−1, with k-means coupled with multiple linear regression generating the highest estimate (mean SOC stocks of 38.15 t ha−1) and MIMICS-PFT generating the lowest estimate (mean SOC stocks of 24.29 t ha−1). We suggest that enhancing process-based models to incorporate newly identified drivers that significantly influence SOC variation in different environments could be the key to reducing the discrepancies in these estimates. Our findings underscore the considerable uncertainty in SOC estimates derived from different modelling approaches and emphasise the importance of rigorous out-of-sample validation before applying any one approach in Australia.","PeriodicalId":48610,"journal":{"name":"Soil","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142160438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing soil data needs and data gaps in catchment-scale environmental modelling: the European perspective","authors":"Brigitta Szabó, Piroska Kassai, Svajunas Plunge, Attila Nemes, Péter Braun, Michael Strauch, Felix Witing, János Mészáros, Natalja Čerkasova","doi":"10.5194/soil-10-587-2024","DOIUrl":"https://doi.org/10.5194/soil-10-587-2024","url":null,"abstract":"Abstract. To effectively guide agricultural management planning strategies and policy, it is important to simulate water quantity and quality patterns and to quantify the impact of land use and climate change on soil functions, soil health, and hydrological and other underlying processes. Environmental models that depict alterations in surface and groundwater quality and quantity at the catchment scale require substantial input, particularly concerning movement and retention in the unsaturated zone. Over the past few decades, numerous soil information sources, containing structured data on diverse basic and advanced soil parameters, alongside innovative solutions to estimate missing soil data, have become increasingly available. This study aims to (i) catalogue open-source soil datasets and pedotransfer functions (PTFs) applicable in simulation studies across European catchments; (ii) evaluate the performance of selected PTFs; and (iii) present compiled R scripts proposing estimation solutions to address soil physical, hydraulic, and chemical data needs and gaps in catchment-scale environmental modelling in Europe. Our focus encompassed basic soil properties, bulk density, porosity, albedo, soil erodibility factor, field capacity, wilting point, available water capacity, saturated hydraulic conductivity, and phosphorus content. We aim to recommend widely supported data sources and pioneering prediction methods that maintain physical consistency and present them through streamlined workflows.","PeriodicalId":48610,"journal":{"name":"Soil","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142158979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}