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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Sanderman, Jonathan; Smith, Colleen; Safanelli, José L.; Mitu, Sadia Mannan; +3 Authors

    Up-to-date information on soil properties and the ability to track changes in soil properties over time are critical for improving multiple decisions on soil security at various scales, ranging from global climate change modeling and policy to national level environmental and development planning, to farm and field level resource management. Diffuse reflectance infrared spectroscopy has become an indispensable laboratory tool for the rapid estimation of numerous soil properties to support various soil mapping, soil monitoring, and soil testing applications. Recent advances in hardware technology have enabled the development of handheld sensors with similar performance specifications as laboratory-grade near-infrared (NIR) spectrometers. Here, we've compiled a hand-held NIR spectral library (1350-2550 nm) using the NeoSpectra Handheld NIR Analyzer developed by Si-Ware. Each scanner is fitted with Fourier-Transform technology based on the semiconductor Micro Electromechanical Systems (MEMS) manufacturing technique, promising accuracy, and consistency between devices. This library includes 2,106 distinct mineral soil samples scanned across 9 of these portable low-cost NIR spectrometers (indicated by serial no). 2,016 of these soil samples were selected to represent the diversity of mineral soils found in the United States, and 90 samples were selected across Ghana, Kenya, and Nigeria. 519 of the US samples were selected and scanned by Woodwell Climate Research Center. These samples were queried from the USDA NRCS NSSC-KSSL Soil Archives as having a complete set of eight measured properties (TC, OC, TN, CEC, pH, clay, sand, and silt). They were stratified based on the major horizon and taxonomic order, omitting the categories with less than 500 samples. Three percent of each stratum (i.e., a combination of major horizon and taxonomic order) was then randomly selected as the final subset retrieved from KSSL's physical soil archive as 2-mm sieved samples. The remaining 1,604 US samples were queried from the USDA NRCS NSSC-KSSL Soil Archives by the University of Nebraska - Lincoln to meet the following criteria: Lower depth <= 30 cm, pH range 4.0 to 9.5, Organic carbon <10%, Greater than lower detection limits, Actual physical samples available in the archive, Samples collected and analyzed from 2001 onwards, Samples having complete analyses for high-priority properties (Sand, Silt, Clay, CEC, Exchangeable Ca, Exchangeable Mg, Exchangeable K, Exchangeable Na, CaCO3, OC, TN), & MIR scanned. All samples were scanned dry 2mm sieved. ~20g of sample was added to a plastic weighing boat where the NeoSpectra scanner would be placed down to make direct contact with the soil surface. The scanner was gently moved across the surface of the sample as 6 replicate scans were taken. These replicates were then averaged so that there is one spectra per sample per scanner in the resulting database. The repository contains: Neospectra_database_column_names.csv: describes the variables (columns) of site and soil data, and the range of NIR and MIR spectra. Both Neospectra_WoodwellKSSL_avg and Neospectra_WoodwellKSSL_reps share the same columns. The CSV is composed of the file name, column name, type, example, and description with measurement unit. Neospectra_project_summary.txt: the summary of the project with purpose, the origin of soil samples, instrumentation, and brief SOP. Neospectra_WoodwellKSSL_avg_MIR.csv: the equivalent MIR spectra of neospectra samples' list that was fetched from the KSSL database and formatted to the OSSL specifications. Neospectra_WoodwellKSSL_avg_soil+site+NIR.csv: soil, site, and Neospectra's NIR. Each row contains the averaged spectra for a given scanner and soil sample (1 spectra per scanner per soil sample). Soil and site info is filled within the same soil sample. Neospectra_WoodwellKSSL_reps_soil+site+NIR.csv: soil, site, and Neospectra's NIR. Each row contains one replicated spectra of a given scanner (6 repeats per scanner per soil sample). Soil and site info is filled within the same soil sample. We thank the USDA NRCS National Soil Survey Center for providing access to their soil archives and for continuing to promote the use of soil spectroscopy. This project was funded by USDA National Institute of Food and Agriculture Award # 2020-67021-32467; USDA National Institute of Food and Agriculture Award # 2018-67007-28529; Woodwell Fund for Climate Solutions; and Foodshot Global.

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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Saby, Nicolas P.A.; Lemercier, Blandine; Arrouays, Dominique; Walter, Christian; +4 Authors

    In France, farmers commission about 250,000 soil-testing analyses per year to assist them managing soil fertility. The number and diversity of origin of the samples make these analyses an interesting and original information source regarding cultivated topsoil variability. Moreover, these analyses relate to several parameters strongly influenced by human activity (macronutrient contents, pH...), for which existing cartographic information is not very relevant. Compiling the results of these analyses into a database makes it possible to re-use these data within both a national and temporal framework. A database compilation relating to data collected over the period 1990-2014 has been recently achieved. So far, commercial soil-testing laboratories approved by the Ministry of Agriculture have provided analytical results from more than 3,600,000 samples. After the initial quality control stage, analytical results from more than 1,900,000 samples were available in the database. The anonymity of the landholders seeking soil analyses is perfectly preserved, as the only identifying information stored is the location of the nearest administrative city to the sample site. We present in this dataset a set of statistical parameters of the spatial distributions for several agronomic soil properties. These statistical parameters are calculated for 4 different nested spatial entities (administrative areas: e.g. regions, departments, counties and agricultural areas) and for 5 time periods (1990-1994, 1995-1999, 2000-2004, 2005-2009, 2010-2014). Two kinds of agronomic soil properties are available: the first one correspond to the quantitative variables like the organic carbon content, and the second one corresponds to the qualitative variables like the texture class. For each spatial unit and temporal period, we calculated the following statistics sets: the first set is calculated for the quantitative variables and corresponds to the number of samples, the mean, the standard deviation and, the 2-,4-,10-quantiles; the second set is calculated for the qualitative variables and corresponds to the number of samples, the value of the dominant class, the number of samples of the dominant class, the second dominant class, the number of samples of the second dominant class.

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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Recherche Data Gouvarrow_drop_down
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Demattê, José A. M.; Novais, Jean Jesus; Rosin, Nicolas Augusto; Rosas, Jorge T. F.; +3 Authors

    Abstract: NEW VERSION V.002 (Some Lat Long Coordinates added). Soil spectroscopy has emerged as a solution to the limitations associated with traditional soil surveying and analysis methods, addressing the challenges of time and financial resources. Analyzing the soil's spectral reflectance enables to observe the soil composition and simultaneously evaluate several attributes because the matter, when exposed to electromagnetic energy, leaves a "spectral signature" that makes such evaluations possible. The Soil Spectral Library (SSL) consolidates soil spectral patterns from a specific location, facilitating accurate modeling and reducing time, cost, chemical products, and waste in surveying and mapping processes. Therefore, an open access SSL benefits society by providing a fine collection of free data for multiple applications for both research and commercial use. BSSL Description and Usefulness The Brazilian Soil Spectral Library (BSSL), available at https://bibliotecaespectral.wixsite.com/english, is a comprehensive repository of soil spectral data. Coordinated by JAM Demattê and managed by the GeoCiS research group, the BSSL was initiated in 1995 and published by Demattê and collaborators in 2019. This initiative stands out due to its coverage of diverse soil types, given Brazil's significance in the agricultural and environmental domains and its status as the fifth largest territory in the world (IBGE, 2023). In addition, a Middle Infrared (MIR) dataset has been published (Mendes et al., 2022), part of which is included in this repository. The database covers 16,084 sites and includes harmonized physicochemical and spectral (Vis-NIR-SWIR and MIR range) soil data from various sources at 0-20 cm depth. All soil samples have Vis-NIR-SWIR data, but not all have MIR data. The BSSL provides open and free access to curated data for the scientific community and interested individuals. Unrestricted access to the BSSL supports researchers in validating their results by comparing measured data with predicted values. This initiative also facilitates the development of new models and the improvement of existing ones. Moreover, users can employ the library to test new models and extract information about previously unknown soil properties. With its extensive coverage of tropical soil classes, the BSSL is considered one of the most significant soil spectral libraries worldwide, with 42 institutions and 61 researchers participating. However, 47 collaborators from 29 institutions have authorized the data opening. Other researchers can also provide their data upon request through the coordinator of this initiative. The data from the BSSL project can also help wet labs to improve their analytical capabilities, contributing to developing hybrid wet soil laboratory techniques and digital soil maps while informing decision-makers in formulating conservation and land use policies. The soil's capacity for different land uses promotes soil health and sustainability. Coverage The BSSL data covers all regions of Brazil, including 26 states and the Federal District. It is in a .xlsx format and has a total size of 305 Mb. The table is structured in sheets with rows for observations, and columns, representing various soil attributes in the surface layer, from 0 to 20 cm depth. The database includes environmental and physicochemical properties (22 columns and 16,084 rows), Vis-NIR-SWIR spectral bands (2151 columns and 16,084 rows), and MIR channels (681 columns and 1783 rows). An ID unique column can merge the sheet for each attribute or spectral range. Accessing original data source Using these data requires their reference in any situation under copyright infringement penalty. Three mechanisms are available for users to reach the original and complete data contributors: a) Refer to sheet two for name and code-based searches; b) Visit the website https://bibliotecaespectral.wixsite.com/english/lista-de-cedentes or locate the contributors' list by Brazilian state; c) Visit the website of the Brazilian Soil Spectral Service – Braspecs http://www.besbbr.com.br/, an online platform for soil analysis that uses part of the current SSL (Demattê et al., 2022) - It was developed and managed by GeoCiS. There, owners from all over the country can be found. Proceeding to data analysis We registered and organized the samples at the ESALQ/USP Soil Laboratory. Some samples arrived without preliminary data analyses, so we analyzed them for soil organic matter (SOM), granulometry, cation exchange capacity (CEC), pH in water, and the presence of Ca, Mg, and Na, following the recommendations of Donagemma et al. (2011). The GeoCiS research group performed spectral analyses following the procedures described by Bellinaso et al. (2010). Demattê et al. (2019) provide detailed methods for sampling, preparation, and soil analyses, including reflectance spectroscopy. Latitude and longitude data can be requested directly from the data owner. In summary, the following steps are involved in data acquisition. a) We subjected the soil samples to a preliminary treatment, which involved drying them in an oven at 45°C for 48 hours, grinding them, and sieving them through a 2mm mesh; b) We placed the samples in Petri dishes with a diameter of 9 cm and a height of 1.5 cm; c) We homogenized and flattened the surface of the samples to reduce the shading caused by larger particles or foreign bodies, making them ready for spectral readings; d) The spectral analyses took place in a darkened room to avoid interference from natural light. We used a computer to record the electromagnetic pulses through an optical fiber connected to the sensor, capturing the spectral response of the soil sample; e) We obtained reflectance data in the Visible-Near Infrared-Shortwave Infrared (Vis-NIR-SWIR) range using a FieldSpec 3 spectroradiometer (Analytical Spectral Devices, ASD, Boulder, CO), which operates in the spectral range from 350 to 2500 nm; f) The sensor had a spectral resolution of 3 nm from 350-700 nm and 10 nm from 700-2500 nm, automatically interpolated to 1 nm spectral resolution in the output data, resulting in 2151 channels (or bands); and g) We positioned the lamps at 90° from each other and 35 cm away from the sample, with a zenith angle of 30°. The sensor captured the light reflected through the fiber optic cable, which was positioned 8 cm from the sample's surface. We used two 50W halogen lamps as the power source for the artificial light. It's important to note that we took three readings for each sample at different positions by rotating the Petri dish by 90°. Each reading represents the average of 100 scans taken by the sensor. From these three readings, we calculated the final spectrum of the samples. Notably, the laboratory's equipment and procedures for soil sample spectral analyses followed the ASD's recommendations, particularly about sensor calibration using a white spectralon plate as a 100% reflectance standard. For the analysis in the Middle Infrared (MIR) spectral region, we followed the procedures outlined by Mendes et al. (2022). We milled the soil fraction smaller than 2 mm, sieved it to 0.149 mm, and scanned it using a Fourier Transform Infrared (FT-IR) alpha spectroradiometer (Bruker Optics Corporation, Billerica, MA 01821, USA) equipped with a DRIFT accessory. The spectroradiometer measured the diffuse reflectance using Fourier transformation in the spectral range from 4000 cm-1 to 600 cm-1, with a resolution of 2 cm-1. We conducted these measurements in the Geotechnology Laboratory of the Department of Soil Science at Esalq-USP. We took the average of 32 successive readings to obtain a soil spectrum. Sensor calibration took place before each spectral acquisition of the sample set by standardizing it against the maximum reflectance of a gold plate. Dataset characterization The database, named BSSL_DB_Key_Soils, has five sheets containing the key soil attributes, Vis-NIR-SWIR and MIR datasets, descriptions of the contributors and the proximal sensing methods used for spectral soil analysis. The sheets can be linked by "ID_Unique" columns, which bring the corresponding rows according to the data type. Some cells are empty because collaborators have already provided data in this way. However, we have decided to keep them in the database because they have other soil key attributes. Every Column in the data sheets is described as follows: Sheet 1. BSSL_Soil_Attributes_Dataset Column 1. ID_unique: Sequential code assigned to every record; Column 2. Owner code: Acronym assigned to each contributor who allowed access to their proprietary data; Column 3. Vis_NIR_SWIR_availability: availability of spectral data in visible, near-infrared, and shortwave infrared ranges; Column 4. MIR_availability: availability of spectral data in the middle infrared range; Column 5. Sampling: type of soil sampling; Column 6. Depth_cm: soil surface layer depth in centimeters; Column 7. Lat: Latitude; Column 8. Lat: Longitude; Column 9. Region: Brazilian geographical region of samples' source; Column 10. Municipality: Brazilian municipality of samples' source; Column 11. State: Brazilian Federation Unit of samples' source; Column 12. Vegetation: type of vegetal covering; Column 13. Biome: groupings of ecosystems that share similar characteristics and span different regions; Column 14. Geology: type of rock matter from local soil sampling; Column 15. Sand_gkg: Content of the soil fraction with grain size between 2 and 0.053 mm, expressed in grams per kilogram; Column 16. Clay_gkg: Content of soil fraction with grain size smaller than 0.002 mm, expressed in grams per kilogram; Column 17. SOM_gkg: Soil organic matter content, expressed in grams per kilogram; Column 18. pH_H2O: Soil hydrogen ion potential measured in water; Column 19. Ca_mmolkg: Exchangeable calcium content in the soil, expressed in millimoles per kilogram; Column 20. Mg_mmolkg: Exchangeable magnesium content in the soil, expressed in millimoles per kilogram; Column 21. Na_mmolkg: Exchangeable sodium content in the soil, expressed in millimoles per kilogram; and Column 22. CEC_Ph7_mmolkg: Cation exchange capacity of the soil at neutral pH, expressed in millimoles per kilogram. Sheet 2. BSSL_Vis_NIR_SWIR_Dataset Column 1. ID_Unique: Sequential code assigned to every record; Column 2. Owner code: Acronym assigned to each contributor who allowed access to their proprietary data; and Column 3 – 2153. 350 – 2500: Reflectance in 2151 spectral bands in nanometers from visible and near-infrared to shortwave infrared range (350 – 2500 nm). Sheet 3. BSSL_MIR_Dataset Column 1. ID_Unique: Sequential code assigned to every record; Column 2. Owner_code: Acronym assigned to each contributor who allowed access to their proprietary data; and Column 3 – 683. 4000 – 600: Reflectance in 681 spectral bands in centimeters in the middle infrared range (4000 – 600 cm-1). Sheet 4. Contributors Column 1. Owner_code: Acronym assigned to each contributor who allowed access to their proprietary data, which identifies and links it to datasets; Column 2. Owner: Name of the collaborator who agreed to the availability of the data; Column 3. E-mail: Contact the e-mail of the owner for more information or a data request; Column 4. Institution: Contributor's affiliation; Column 5. Samples NIR: Number of Vis-NIR-SWIR samples sent to the BSSL collection; Column 6. Samples MIR: Number of MIR samples sent to the BSSL collection; Sheet 5. Metadata Column 1. Material and Methods: Description of procedures performed for soil data analyses Expectation and Social Relevance These data can impact various disciplines such as soil surveying, soil attribute mapping, soil analysis, soil mineralogy, soil management zones, precision agriculture, development of new datasets and scientific groups, and others. We expect this contribution to be valuable and useful to the soil research community in promoting this non-renewable natural resource's conservation and sustainable use. {"references": ["DEMATT\u00ca, J. A. M.; DOTTO, A. C.; PAIVA, A. F. S.; SATO, M. V.; DALMOLIN, R. S. D.; ARA\u00daJO, M. do S. B.; \u2026 NORONHA, N. C. (2019). The Brazilian Soil Spectral Library (BSSL): A general view, application and challenges. Geoderma, 113793. doi:10.1016/j.geoderma.2019.05.043.", "DEMATT\u00ca, J. A. M.; PAIVA, A. F. S.; POPPIEL, R. R.; ROSIN, N. A.; RUIZ, L. F. C.; MELLO, F. A. O.; MINASNY, B. \u2026 SILVERO, N. E. Q. The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication. Remote Sensing. 2022, 14, 740. https://doi.org/10.3390/rs14061459", "MENDES, W. S.; DEMATT\u00ca, J.A.M.; ROSIN, N. A.; TERRA, F. S.; POPPIEL, R. R.; URBINA-SALAZAR, D.F.; BOECHAT, C. L.; SILVA, E. B.; CURI, N.; SILVA, S. H. G.; SANTOS, U. J.; VALLADARES, G. S. 2022. The Brazilian soil Mid-infrared Spectral Library: The Power of the Fundamental Range. Geoderma, V. 415, 2022, 115776, doi:10.1016/j.geoderma.2022.115776", "IBGE. Brasil em s\u00edntese: Territ\u00f3rio. Rio de Janeiro: Instituto Brasileiro de Geografia e Estat\u00edstica. 2021. Available in: https://brasilemsintese.ibge.gov.br/territorio/dados-geograficos.html accessed in July 1 2023", "DONAGEMMA, G.K., CAMPOS, D.V.B. DE, CALDERANO, S.B., TEIXEIRA, W.G., VIANA, J.H.M., 2011. Manual de m\u00e9todos de an\u00e1lise de solo, 2 rev. ed, Embrapa Solos.", "BELLINASO, H., DEMATT\u00ca, J.A.M., ROMEIRO, S.A., 2010. Soil spectral library and its use in soil classification. Revista Brasileira de Ci\u00eancia Solo 34, 861\u2013870. https://doi.org/10.1590/S0100-06832010000300027"]} Acknowledgments Geotechnologies in Soil Sciences Research Group - GeoCiS Brazilian Society of Soil Science - SBCS Research Support Foundation of the State of São Paulo - FAPESP grant number #2021/05129-8 Higher Education Personal Improvement Coordination - CAPES National Council for Scientific and Technological Development CNPq Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo. Grupo de Pesquisa Geotecnologias em Ciências do Solo - GeoCiS Sociedade Brasileira de Ciência do Solo - SBCS Fundação de Amparo à Pesquisa do Estado de São Paulo - FAPESP (#2021/05129-8) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES Conselho Nacional de Desenvolvimento Científico e Tecnológico CNPq Departamento de Ciência do Solo, Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo.

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    ZENODO
    Dataset . 2023
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    ZENODO
    Dataset . 2023
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    ZENODO
    Dataset . 2023
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      ZENODO
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      License: CC BY
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      ZENODO
      Dataset . 2023
      License: CC BY
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      ZENODO
      Dataset . 2023
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    Authors: Rashid, Kausar; Rashid, Sufiya; Islam, Tajamul; Ganie, Aijaz; +2 Authors

    Understanding the vegetation and soil ecology of natural habitats harbouring threatened species is critical in conservation planning and restoration. The present study investigated the vegetation composition and soil physico-chemical attributes of natural habitats of Trillium govanianum – a threatened Himalayan endemic species. We laid 120 quadrats across eight randomly selected sites where the species was growing in the Kashmir Himalaya. We collected the soil samples from these sites and determined soil physico-chemical attributes using standard methods. Across all the sites, we found a total of 57 plant species with dominance of Rosaceae and Ranunculaceae. The IVI results revealed that Fragaria nubicola, Corydalis diphylla, Galium aparine, and Leucanthemum vulgare, were the dominant species in T. govanianum communities. The density, abundance and IVI of 3-leaf vegetative plants was higher than 1-leaf vegetative and 3-leaf reproductive plants across all the study sites. We found that T. govanianum alone forms 23.5 % positive, 0 negative, 76.4% random co-occurrences with other associated species in its community. Our results reveal that the variations in vegetation composition among the sites was influenced by differences in soil properties. Principal component analysis revealed that several soil parameters such as organic carbon, nitrogen, potassium, and sulphur were concentrated in five sites, namely Dara, Drung, Bangus, Gulmarg, and Doodhpathri, which also showed the highest density, frequency, and abundance of T. govanianum. Overall, our study contributes quantitative information on the vegetation and soil ecology of T. govanianum-assemblages, which in turn can help in developing conservation strategies for this threatened species, and its sustainable management and habitat restoration. Data was collected by carrying out field surveys across the different sampling sites. The data was written on already prepared datasheets and arranged on spreadsheets for further ananlysis using different softwares.

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2023
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    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC 0
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      DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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    Authors: Nobile, Cécile M.; Bravin, Matthieu N.; Tillard, Emmanuel; Becquer, Thierry; +1 Authors

    Dataset of the paper entitled "Phosphorus sorption capacity and availability along a toposequence of agricultural soils: distinct effects of soil type and decadal fertilizer applications". Five field trials where mineral and organic fertilizers were applied for a decade along a toposequence (two andosols, one andic cambisol, one nitisol and one arenosol) were investigated. Legacy phosphorus (i‧e. phosphorus accumulated in soil with fertilizer applications minus phosphorus lost by plant uptake, erosion and leaching) was used to study the effect of fertilizer application rate. Phosphorus availability was determined by classic extractions and by the diffusive gradients in thin films technique. The solid-solution partitioning coefficient of inorganic phosphorus was determined to assess the soil Pi sorption capacity.

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    CIRAD Dataverse
    Dataset . 2018
    Data sources: CIRAD Dataverse
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      CIRAD Dataverse
      Dataset . 2018
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    Authors: Nobile, Cécile N; Bravin, Matthieu N; Becquer, T; Paillat, Jean-Marie;

    Dataset of the paper entitled "Phosphorus sorption and availability in an andosol after a decade of organic or mineral fertilizer applications: importance of pH and organic carbon modifications in soil as compared to phosphorus accumulation". We conducted a 10-years-old field experiment on an andosol and compared fields that had been amended with mineral or organic (dairy slurry and manure compost) fertilizers against a non-fertilized control. Water and Olsen extractions and inorganic phosphorus sorption experiments were realized on soils sampled after 6 and 10 years of trial. We also realized an artificial and ex situ alkalization of the control soil to isolate the effect of pH on the sorption capacity of inorganic phosphorus.

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    CIRAD Dataverse
    Dataset . 2019
    Data sources: CIRAD Dataverse
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      CIRAD Dataverse
      Dataset . 2019
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    Authors: Laurent, Céline; Bravin, Matthieu; Crouzet, Olivier; Pelosi, Céline; +3 Authors

    Dataset of the paper entitled "Increased soil pH and dissolved organic matter after a decade of organic fertilizer application mitigates copper and zinc availability despite contamination" Seventy-four soil samples were collected over time from fields corresponding to three soil types upon which no, mineral, or organic fertilization had been applied over a decade, and thus exhibited a gradient of Cu and Zn contamination, pH, and organic matter concentration. Soil Cu and Zn contamination (i‧e. total and DTPAextractable Cu and Zn concentration), soil solution chemistry (i‧e. pH and dissolved organic matter concentration and aromaticity) and Cu and Zn availability (i‧e. total concentration and free ionic activity in solution and DGT-available concentration in soil) levels were measured. The Windermere humic aqueous model (WHAM) was used to estimate Zn2+ activity and dissolved organic matter (DOM) binding properties in soil solution.

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    CIRAD Dataverse
    Dataset . 2019
    Data sources: CIRAD Dataverse
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      CIRAD Dataverse
      Dataset . 2019
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    Authors: Laurent Céline; Bravin Matthieu; Crouzet Olivier; Lamy Isabelle;

    We assessed the effect of a decade of agronomically realistic organic fertilization on copper (Cu) and zinc (Zn) availability in the rhizosphere and their phytoavailability. Using a laboratory biotest, Festuca arundinacea was exposed to 34 soil samples collected from three agricultural field trials that had received no, mineral, or organic fertilization for a decade. Dissolved organic matter (DOM) properties (i‧e., concentration, aromaticity, and binding properties toward Cu), pH, and Cu and Zn availability (i‧e., total dissolved concentration and free ionic activity) were determined in the rhizosphere solutions. Cu and Zn phytoavailability was measured as the plant uptake flux. Contrary to bulk soils, organic fertilization induced very few changes in the chemistry and Cu and Zn availability in the rhizosphere solutions compared to no and mineral fertilization. Consistently, Cu and Zn phytoavailability did not increase with organic fertilization, but it was mostly driven by soil properties rather than by fertilization. Despite increasing soil Cu and Zn contamination, a decade of soil organic fertilization did not increase Cu and Zn phytoavailability, presumably due to the root-mediated levelling of Cu and Zn availability in the rhizosphere.

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    CIRAD Dataverse
    Dataset . 2023
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      CIRAD Dataverse
      Dataset . 2023
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    Authors: Laurent, Céline; Bravin, Matthieu; Blanchart, Eric; Crouzet, Olivier; +2 Authors

    Dataset of the paper entitled "Does a decade of soil organic fertilization promote copper and zinc bioavailability to epi-endogeic earthworms?" While long-term organic fertilizer (OF) application tends to decrease copper (Cu) and zinc (Zn) availability in agricultural soils, earthworm bioturbation tends to have the opposite effect. Consequences in terms of Cu and Zn bioavailability to earthworms in bioturbated, OF-amended soils are hence still a matter of debate. Accordingly, we assessed the effect of a decade of agronomically-realistic OF applications on Cu and Zn availability in earthworm-inhabited soils and their consequence on Cu and Zn bioavailability to earthworms. An epi-endogeic species (Dichogaster saliens) was exposed in microcosms to three soils receiving at field scale either no, mineral or organic fertilization for a decade. Dissolved organic matter (DOM) properties (i‧e. concentration, aromaticity, and binding properties towards Cu), pH, and Cu and Zn availability (i‧e. total concentration and free ionic activity) were determined in the solution of earthworm-inhabited soils. Cu and Zn bioavailability was assessed by measuring the net accumulation and concentration of Cu and Zn in earthworms. Despite soil Cu and Zn contamination induced by a decade of OF applications, the organic fertilization-induced increase in pH and DOM properties drove the mitigation of Cu and Zn availability in earthworm-inhabited soils, while bioturbation had little effect. Consistently, Cu and Zn bioavailability did not increase with OF applications. From an ecotoxicological perspective, our results suggest that agronomically-realistic applications of OF for a decade should not constitute a risk for earthworms in terms of bioaccumulation and toxicity of Cu and Zn.

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    CIRAD Dataverse
    Dataset . 2022
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    Authors: SNOECK, Didier; KOKO, Louis; N'GUESSAN, Kouamé; KASSIN, Émmanuel;

    Les échantillons de sols et feuilles des cacaoyers (Theobroma cacao L.) ont été prélevés dans des plantations de cacaoyers adultes sélectionnées dans toutes les unités pédologiques de toutes les régions du sud de la Côte d'Ivoire. Les échantillons de sol ont été prélevés dans l'horizon de 0 à 30 cm à 1 m des cacaoyers. Les échantillons de sol ont été analysés pour connaître leur teneur en argile et leurs paramètres chimiques; à savoir pH, C, N, P, K, Ca, Mg, CEC, Al, Zn, B, Mo, Mn. Les échantillons de feuilles ont été prélevées en mars 2015. Les récoltes de l'année 2015 ont été estimées à partir du comptage des cabosses. Chaque parcelle de cacaoyer échantillonnée a été géolocalisée pour être associée à l'unité pédologique du sol correspondante. L'information a ensuite été utilisée pour construire une carte thématique fournissant des recommandations d'engrais localisées pour la culture du cacao (voir publication N'Guessan et al., 2017). The soil and cacao (Theobroma cacao L.) leaf samples were collected in adult cacao plantations selected in all pedological units of all regions of Southern Côte d'Ivoire. Soil samples were taken in the 0-30 cm horizon at 1 m from the cacao trees. The soil samples were analysed for their clay content and chemical parameters; i‧e. pH, C, N, P, K, Ca, Mg, CEC, Al, Zn, B, Mo, Mn. Leaf samples were collected in March 2015. The harvests for the year 2015 were estimated from pod counting. Each sampled cacao plot was geolocated to be associated with the corresponding soil pedological unit. The information was further used to build a thematic map providing localised fertilizer recommendations for cacao cultivation (see N'Guessan et al., 2017). Données sur les analyses chimiques des échantillons de sols et feuilles des cacaoyers (Theobroma cacao L.) prélevés dans des plantations de cacaoyers adultes sélectionnées dans toutes les régions du sud de la Côte d'Ivoire. ............ ................ Data of chemical analysis of soil and cacao (Theobroma cacao L.) leaf samples collected in adult cacao plantations selected in all regions of Southern Côte d'Ivoire. --------------------------

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    CIRAD Dataverse
    Dataset . 2017
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    Authors: Sanderman, Jonathan; Smith, Colleen; Safanelli, José L.; Mitu, Sadia Mannan; +3 Authors

    Up-to-date information on soil properties and the ability to track changes in soil properties over time are critical for improving multiple decisions on soil security at various scales, ranging from global climate change modeling and policy to national level environmental and development planning, to farm and field level resource management. Diffuse reflectance infrared spectroscopy has become an indispensable laboratory tool for the rapid estimation of numerous soil properties to support various soil mapping, soil monitoring, and soil testing applications. Recent advances in hardware technology have enabled the development of handheld sensors with similar performance specifications as laboratory-grade near-infrared (NIR) spectrometers. Here, we've compiled a hand-held NIR spectral library (1350-2550 nm) using the NeoSpectra Handheld NIR Analyzer developed by Si-Ware. Each scanner is fitted with Fourier-Transform technology based on the semiconductor Micro Electromechanical Systems (MEMS) manufacturing technique, promising accuracy, and consistency between devices. This library includes 2,106 distinct mineral soil samples scanned across 9 of these portable low-cost NIR spectrometers (indicated by serial no). 2,016 of these soil samples were selected to represent the diversity of mineral soils found in the United States, and 90 samples were selected across Ghana, Kenya, and Nigeria. 519 of the US samples were selected and scanned by Woodwell Climate Research Center. These samples were queried from the USDA NRCS NSSC-KSSL Soil Archives as having a complete set of eight measured properties (TC, OC, TN, CEC, pH, clay, sand, and silt). They were stratified based on the major horizon and taxonomic order, omitting the categories with less than 500 samples. Three percent of each stratum (i.e., a combination of major horizon and taxonomic order) was then randomly selected as the final subset retrieved from KSSL's physical soil archive as 2-mm sieved samples. The remaining 1,604 US samples were queried from the USDA NRCS NSSC-KSSL Soil Archives by the University of Nebraska - Lincoln to meet the following criteria: Lower depth <= 30 cm, pH range 4.0 to 9.5, Organic carbon <10%, Greater than lower detection limits, Actual physical samples available in the archive, Samples collected and analyzed from 2001 onwards, Samples having complete analyses for high-priority properties (Sand, Silt, Clay, CEC, Exchangeable Ca, Exchangeable Mg, Exchangeable K, Exchangeable Na, CaCO3, OC, TN), & MIR scanned. All samples were scanned dry 2mm sieved. ~20g of sample was added to a plastic weighing boat where the NeoSpectra scanner would be placed down to make direct contact with the soil surface. The scanner was gently moved across the surface of the sample as 6 replicate scans were taken. These replicates were then averaged so that there is one spectra per sample per scanner in the resulting database. The repository contains: Neospectra_database_column_names.csv: describes the variables (columns) of site and soil data, and the range of NIR and MIR spectra. Both Neospectra_WoodwellKSSL_avg and Neospectra_WoodwellKSSL_reps share the same columns. The CSV is composed of the file name, column name, type, example, and description with measurement unit. Neospectra_project_summary.txt: the summary of the project with purpose, the origin of soil samples, instrumentation, and brief SOP. Neospectra_WoodwellKSSL_avg_MIR.csv: the equivalent MIR spectra of neospectra samples' list that was fetched from the KSSL database and formatted to the OSSL specifications. Neospectra_WoodwellKSSL_avg_soil+site+NIR.csv: soil, site, and Neospectra's NIR. Each row contains the averaged spectra for a given scanner and soil sample (1 spectra per scanner per soil sample). Soil and site info is filled within the same soil sample. Neospectra_WoodwellKSSL_reps_soil+site+NIR.csv: soil, site, and Neospectra's NIR. Each row contains one replicated spectra of a given scanner (6 repeats per scanner per soil sample). Soil and site info is filled within the same soil sample. We thank the USDA NRCS National Soil Survey Center for providing access to their soil archives and for continuing to promote the use of soil spectroscopy. This project was funded by USDA National Institute of Food and Agriculture Award # 2020-67021-32467; USDA National Institute of Food and Agriculture Award # 2018-67007-28529; Woodwell Fund for Climate Solutions; and Foodshot Global.

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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Saby, Nicolas P.A.; Lemercier, Blandine; Arrouays, Dominique; Walter, Christian; +4 Authors

    In France, farmers commission about 250,000 soil-testing analyses per year to assist them managing soil fertility. The number and diversity of origin of the samples make these analyses an interesting and original information source regarding cultivated topsoil variability. Moreover, these analyses relate to several parameters strongly influenced by human activity (macronutrient contents, pH...), for which existing cartographic information is not very relevant. Compiling the results of these analyses into a database makes it possible to re-use these data within both a national and temporal framework. A database compilation relating to data collected over the period 1990-2014 has been recently achieved. So far, commercial soil-testing laboratories approved by the Ministry of Agriculture have provided analytical results from more than 3,600,000 samples. After the initial quality control stage, analytical results from more than 1,900,000 samples were available in the database. The anonymity of the landholders seeking soil analyses is perfectly preserved, as the only identifying information stored is the location of the nearest administrative city to the sample site. We present in this dataset a set of statistical parameters of the spatial distributions for several agronomic soil properties. These statistical parameters are calculated for 4 different nested spatial entities (administrative areas: e.g. regions, departments, counties and agricultural areas) and for 5 time periods (1990-1994, 1995-1999, 2000-2004, 2005-2009, 2010-2014). Two kinds of agronomic soil properties are available: the first one correspond to the quantitative variables like the organic carbon content, and the second one corresponds to the qualitative variables like the texture class. For each spatial unit and temporal period, we calculated the following statistics sets: the first set is calculated for the quantitative variables and corresponds to the number of samples, the mean, the standard deviation and, the 2-,4-,10-quantiles; the second set is calculated for the qualitative variables and corresponds to the number of samples, the value of the dominant class, the number of samples of the dominant class, the second dominant class, the number of samples of the second dominant class.

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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Recherche Data Gouvarrow_drop_down
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    Authors: Demattê, José A. M.; Novais, Jean Jesus; Rosin, Nicolas Augusto; Rosas, Jorge T. F.; +3 Authors

    Abstract: NEW VERSION V.002 (Some Lat Long Coordinates added). Soil spectroscopy has emerged as a solution to the limitations associated with traditional soil surveying and analysis methods, addressing the challenges of time and financial resources. Analyzing the soil's spectral reflectance enables to observe the soil composition and simultaneously evaluate several attributes because the matter, when exposed to electromagnetic energy, leaves a "spectral signature" that makes such evaluations possible. The Soil Spectral Library (SSL) consolidates soil spectral patterns from a specific location, facilitating accurate modeling and reducing time, cost, chemical products, and waste in surveying and mapping processes. Therefore, an open access SSL benefits society by providing a fine collection of free data for multiple applications for both research and commercial use. BSSL Description and Usefulness The Brazilian Soil Spectral Library (BSSL), available at https://bibliotecaespectral.wixsite.com/english, is a comprehensive repository of soil spectral data. Coordinated by JAM Demattê and managed by the GeoCiS research group, the BSSL was initiated in 1995 and published by Demattê and collaborators in 2019. This initiative stands out due to its coverage of diverse soil types, given Brazil's significance in the agricultural and environmental domains and its status as the fifth largest territory in the world (IBGE, 2023). In addition, a Middle Infrared (MIR) dataset has been published (Mendes et al., 2022), part of which is included in this repository. The database covers 16,084 sites and includes harmonized physicochemical and spectral (Vis-NIR-SWIR and MIR range) soil data from various sources at 0-20 cm depth. All soil samples have Vis-NIR-SWIR data, but not all have MIR data. The BSSL provides open and free access to curated data for the scientific community and interested individuals. Unrestricted access to the BSSL supports researchers in validating their results by comparing measured data with predicted values. This initiative also facilitates the development of new models and the improvement of existing ones. Moreover, users can employ the library to test new models and extract information about previously unknown soil properties. With its extensive coverage of tropical soil classes, the BSSL is considered one of the most significant soil spectral libraries worldwide, with 42 institutions and 61 researchers participating. However, 47 collaborators from 29 institutions have authorized the data opening. Other researchers can also provide their data upon request through the coordinator of this initiative. The data from the BSSL project can also help wet labs to improve their analytical capabilities, contributing to developing hybrid wet soil laboratory techniques and digital soil maps while informing decision-makers in formulating conservation and land use policies. The soil's capacity for different land uses promotes soil health and sustainability. Coverage The BSSL data covers all regions of Brazil, including 26 states and the Federal District. It is in a .xlsx format and has a total size of 305 Mb. The table is structured in sheets with rows for observations, and columns, representing various soil attributes in the surface layer, from 0 to 20 cm depth. The database includes environmental and physicochemical properties (22 columns and 16,084 rows), Vis-NIR-SWIR spectral bands (2151 columns and 16,084 rows), and MIR channels (681 columns and 1783 rows). An ID unique column can merge the sheet for each attribute or spectral range. Accessing original data source Using these data requires their reference in any situation under copyright infringement penalty. Three mechanisms are available for users to reach the original and complete data contributors: a) Refer to sheet two for name and code-based searches; b) Visit the website https://bibliotecaespectral.wixsite.com/english/lista-de-cedentes or locate the contributors' list by Brazilian state; c) Visit the website of the Brazilian Soil Spectral Service – Braspecs http://www.besbbr.com.br/, an online platform for soil analysis that uses part of the current SSL (Demattê et al., 2022) - It was developed and managed by GeoCiS. There, owners from all over the country can be found. Proceeding to data analysis We registered and organized the samples at the ESALQ/USP Soil Laboratory. Some samples arrived without preliminary data analyses, so we analyzed them for soil organic matter (SOM), granulometry, cation exchange capacity (CEC), pH in water, and the presence of Ca, Mg, and Na, following the recommendations of Donagemma et al. (2011). The GeoCiS research group performed spectral analyses following the procedures described by Bellinaso et al. (2010). Demattê et al. (2019) provide detailed methods for sampling, preparation, and soil analyses, including reflectance spectroscopy. Latitude and longitude data can be requested directly from the data owner. In summary, the following steps are involved in data acquisition. a) We subjected the soil samples to a preliminary treatment, which involved drying them in an oven at 45°C for 48 hours, grinding them, and sieving them through a 2mm mesh; b) We placed the samples in Petri dishes with a diameter of 9 cm and a height of 1.5 cm; c) We homogenized and flattened the surface of the samples to reduce the shading caused by larger particles or foreign bodies, making them ready for spectral readings; d) The spectral analyses took place in a darkened room to avoid interference from natural light. We used a computer to record the electromagnetic pulses through an optical fiber connected to the sensor, capturing the spectral response of the soil sample; e) We obtained reflectance data in the Visible-Near Infrared-Shortwave Infrared (Vis-NIR-SWIR) range using a FieldSpec 3 spectroradiometer (Analytical Spectral Devices, ASD, Boulder, CO), which operates in the spectral range from 350 to 2500 nm; f) The sensor had a spectral resolution of 3 nm from 350-700 nm and 10 nm from 700-2500 nm, automatically interpolated to 1 nm spectral resolution in the output data, resulting in 2151 channels (or bands); and g) We positioned the lamps at 90° from each other and 35 cm away from the sample, with a zenith angle of 30°. The sensor captured the light reflected through the fiber optic cable, which was positioned 8 cm from the sample's surface. We used two 50W halogen lamps as the power source for the artificial light. It's important to note that we took three readings for each sample at different positions by rotating the Petri dish by 90°. Each reading represents the average of 100 scans taken by the sensor. From these three readings, we calculated the final spectrum of the samples. Notably, the laboratory's equipment and procedures for soil sample spectral analyses followed the ASD's recommendations, particularly about sensor calibration using a white spectralon plate as a 100% reflectance standard. For the analysis in the Middle Infrared (MIR) spectral region, we followed the procedures outlined by Mendes et al. (2022). We milled the soil fraction smaller than 2 mm, sieved it to 0.149 mm, and scanned it using a Fourier Transform Infrared (FT-IR) alpha spectroradiometer (Bruker Optics Corporation, Billerica, MA 01821, USA) equipped with a DRIFT accessory. The spectroradiometer measured the diffuse reflectance using Fourier transformation in the spectral range from 4000 cm-1 to 600 cm-1, with a resolution of 2 cm-1. We conducted these measurements in the Geotechnology Laboratory of the Department of Soil Science at Esalq-USP. We took the average of 32 successive readings to obtain a soil spectrum. Sensor calibration took place before each spectral acquisition of the sample set by standardizing it against the maximum reflectance of a gold plate. Dataset characterization The database, named BSSL_DB_Key_Soils, has five sheets containing the key soil attributes, Vis-NIR-SWIR and MIR datasets, descriptions of the contributors and the proximal sensing methods used for spectral soil analysis. The sheets can be linked by "ID_Unique" columns, which bring the corresponding rows according to the data type. Some cells are empty because collaborators have already provided data in this way. However, we have decided to keep them in the database because they have other soil key attributes. Every Column in the data sheets is described as follows: Sheet 1. BSSL_Soil_Attributes_Dataset Column 1. ID_unique: Sequential code assigned to every record; Column 2. Owner code: Acronym assigned to each contributor who allowed access to their proprietary data; Column 3. Vis_NIR_SWIR_availability: availability of spectral data in visible, near-infrared, and shortwave infrared ranges; Column 4. MIR_availability: availability of spectral data in the middle infrared range; Column 5. Sampling: type of soil sampling; Column 6. Depth_cm: soil surface layer depth in centimeters; Column 7. Lat: Latitude; Column 8. Lat: Longitude; Column 9. Region: Brazilian geographical region of samples' source; Column 10. Municipality: Brazilian municipality of samples' source; Column 11. State: Brazilian Federation Unit of samples' source; Column 12. Vegetation: type of vegetal covering; Column 13. Biome: groupings of ecosystems that share similar characteristics and span different regions; Column 14. Geology: type of rock matter from local soil sampling; Column 15. Sand_gkg: Content of the soil fraction with grain size between 2 and 0.053 mm, expressed in grams per kilogram; Column 16. Clay_gkg: Content of soil fraction with grain size smaller than 0.002 mm, expressed in grams per kilogram; Column 17. SOM_gkg: Soil organic matter content, expressed in grams per kilogram; Column 18. pH_H2O: Soil hydrogen ion potential measured in water; Column 19. Ca_mmolkg: Exchangeable calcium content in the soil, expressed in millimoles per kilogram; Column 20. Mg_mmolkg: Exchangeable magnesium content in the soil, expressed in millimoles per kilogram; Column 21. Na_mmolkg: Exchangeable sodium content in the soil, expressed in millimoles per kilogram; and Column 22. CEC_Ph7_mmolkg: Cation exchange capacity of the soil at neutral pH, expressed in millimoles per kilogram. Sheet 2. BSSL_Vis_NIR_SWIR_Dataset Column 1. ID_Unique: Sequential code assigned to every record; Column 2. Owner code: Acronym assigned to each contributor who allowed access to their proprietary data; and Column 3 – 2153. 350 – 2500: Reflectance in 2151 spectral bands in nanometers from visible and near-infrared to shortwave infrared range (350 – 2500 nm). Sheet 3. BSSL_MIR_Dataset Column 1. ID_Unique: Sequential code assigned to every record; Column 2. Owner_code: Acronym assigned to each contributor who allowed access to their proprietary data; and Column 3 – 683. 4000 – 600: Reflectance in 681 spectral bands in centimeters in the middle infrared range (4000 – 600 cm-1). Sheet 4. Contributors Column 1. Owner_code: Acronym assigned to each contributor who allowed access to their proprietary data, which identifies and links it to datasets; Column 2. Owner: Name of the collaborator who agreed to the availability of the data; Column 3. E-mail: Contact the e-mail of the owner for more information or a data request; Column 4. Institution: Contributor's affiliation; Column 5. Samples NIR: Number of Vis-NIR-SWIR samples sent to the BSSL collection; Column 6. Samples MIR: Number of MIR samples sent to the BSSL collection; Sheet 5. Metadata Column 1. Material and Methods: Description of procedures performed for soil data analyses Expectation and Social Relevance These data can impact various disciplines such as soil surveying, soil attribute mapping, soil analysis, soil mineralogy, soil management zones, precision agriculture, development of new datasets and scientific groups, and others. We expect this contribution to be valuable and useful to the soil research community in promoting this non-renewable natural resource's conservation and sustainable use. {"references": ["DEMATT\u00ca, J. A. M.; DOTTO, A. C.; PAIVA, A. F. S.; SATO, M. V.; DALMOLIN, R. S. D.; ARA\u00daJO, M. do S. B.; \u2026 NORONHA, N. C. (2019). The Brazilian Soil Spectral Library (BSSL): A general view, application and challenges. Geoderma, 113793. doi:10.1016/j.geoderma.2019.05.043.", "DEMATT\u00ca, J. A. M.; PAIVA, A. F. S.; POPPIEL, R. R.; ROSIN, N. A.; RUIZ, L. F. C.; MELLO, F. A. O.; MINASNY, B. \u2026 SILVERO, N. E. Q. The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication. Remote Sensing. 2022, 14, 740. https://doi.org/10.3390/rs14061459", "MENDES, W. S.; DEMATT\u00ca, J.A.M.; ROSIN, N. A.; TERRA, F. S.; POPPIEL, R. R.; URBINA-SALAZAR, D.F.; BOECHAT, C. L.; SILVA, E. B.; CURI, N.; SILVA, S. H. G.; SANTOS, U. J.; VALLADARES, G. S. 2022. The Brazilian soil Mid-infrared Spectral Library: The Power of the Fundamental Range. Geoderma, V. 415, 2022, 115776, doi:10.1016/j.geoderma.2022.115776", "IBGE. Brasil em s\u00edntese: Territ\u00f3rio. Rio de Janeiro: Instituto Brasileiro de Geografia e Estat\u00edstica. 2021. Available in: https://brasilemsintese.ibge.gov.br/territorio/dados-geograficos.html accessed in July 1 2023", "DONAGEMMA, G.K., CAMPOS, D.V.B. DE, CALDERANO, S.B., TEIXEIRA, W.G., VIANA, J.H.M., 2011. Manual de m\u00e9todos de an\u00e1lise de solo, 2 rev. ed, Embrapa Solos.", "BELLINASO, H., DEMATT\u00ca, J.A.M., ROMEIRO, S.A., 2010. Soil spectral library and its use in soil classification. Revista Brasileira de Ci\u00eancia Solo 34, 861\u2013870. https://doi.org/10.1590/S0100-06832010000300027"]} Acknowledgments Geotechnologies in Soil Sciences Research Group - GeoCiS Brazilian Society of Soil Science - SBCS Research Support Foundation of the State of São Paulo - FAPESP grant number #2021/05129-8 Higher Education Personal Improvement Coordination - CAPES National Council for Scientific and Technological Development CNPq Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo. Grupo de Pesquisa Geotecnologias em Ciências do Solo - GeoCiS Sociedade Brasileira de Ciência do Solo - SBCS Fundação de Amparo à Pesquisa do Estado de São Paulo - FAPESP (#2021/05129-8) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES Conselho Nacional de Desenvolvimento Científico e Tecnológico CNPq Departamento de Ciência do Solo, Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo.

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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Rashid, Kausar; Rashid, Sufiya; Islam, Tajamul; Ganie, Aijaz; +2 Authors

    Understanding the vegetation and soil ecology of natural habitats harbouring threatened species is critical in conservation planning and restoration. The present study investigated the vegetation composition and soil physico-chemical attributes of natural habitats of Trillium govanianum – a threatened Himalayan endemic species. We laid 120 quadrats across eight randomly selected sites where the species was growing in the Kashmir Himalaya. We collected the soil samples from these sites and determined soil physico-chemical attributes using standard methods. Across all the sites, we found a total of 57 plant species with dominance of Rosaceae and Ranunculaceae. The IVI results revealed that Fragaria nubicola, Corydalis diphylla, Galium aparine, and Leucanthemum vulgare, were the dominant species in T. govanianum communities. The density, abundance and IVI of 3-leaf vegetative plants was higher than 1-leaf vegetative and 3-leaf reproductive plants across all the study sites. We found that T. govanianum alone forms 23.5 % positive, 0 negative, 76.4% random co-occurrences with other associated species in its community. Our results reveal that the variations in vegetation composition among the sites was influenced by differences in soil properties. Principal component analysis revealed that several soil parameters such as organic carbon, nitrogen, potassium, and sulphur were concentrated in five sites, namely Dara, Drung, Bangus, Gulmarg, and Doodhpathri, which also showed the highest density, frequency, and abundance of T. govanianum. Overall, our study contributes quantitative information on the vegetation and soil ecology of T. govanianum-assemblages, which in turn can help in developing conservation strategies for this threatened species, and its sustainable management and habitat restoration. Data was collected by carrying out field surveys across the different sampling sites. The data was written on already prepared datasheets and arranged on spreadsheets for further ananlysis using different softwares.

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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