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42 results for “soil texture”
Georgia Salt Marsh: Soil Organic Carbon, Nitrogen, Bulk Density, Moisture, and Texture
As part of project predicting soil carbon at depth from that found at the surface using remote sensing, 28 soil cores were taken from six salt marshes along the Georgia coastline. Cores were taken as deep as possible (25 – 165 cm) and sectioned into 5 cm depths. Soils were analyzed for organic carbon (SOC), total nitrogen (N), bulk density (BD), and particle size (by horizon). Stable carbon isotopes were obtained in three marshes on Sapelo Island; a subset was also analyzed for radiocarbon.
WSC - Gridded sample points at Wibu field site including yield, soil texture, water table depth, and estimated soil water retention parameters
A variety of data from gridded sampling points at the Wibu field site. The gridded sampling scheme is described in the Point Locations dataset. This dataset includes 2012 and 2013 absolute and normalized yield, soil textural characteristics (organic content, porosity, bulk density, particle size metrics, % sand/silt/clay), a variety of water table depth metrics (mean, percentiles, sum exceedance values, moving averages), and soil water retention parameters estimated using the Rosetta pedotransfer function. It was collected as part of a study of the impacts of water table depth, soil texture, and growing season weather conditions on corn production at the Wibu field site, described in Zipper et al. (in review). The Wibu field site is a commercial agricultural field, which grew corn in the 2012, 2013, and 2014 growing seasons. See Zipper and Loheide (2014) Ag. For. Met. for more information about the field site.
Soil texture classes (USDA system) for 6 soil depths (0, 10, 30, 60, 100 and 200 cm) at 250 m
<p>Soil texture classes (USDA system) for 6 standard soil depths (0, 10, 30, 60, 100 and 200 cm) at 250 m. Derived from predicted soil texture fractions using the soiltexture package in R. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antartica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>texture.class = variable: soil texture class,</li> <li>usda = determination method: USDA texture triangle,</li> <li>c = factor,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Textural soil data, Colombia, 0 - 100 cm
The textural soil data is a harmonized and structured data set with information related to soil particle-size fractions (PSF) such as clay, sand, and silt, as transformed data. The transformed data was obtained through additive log-ratio transformation because the PSF are compositional data. The textural soil data was fitted to five standard depths using the quadratic function with equal areas (spline). Standard depths were: 0 - 5 cm, 5 - 15 cm, 15 - 30 cm, 30 - 60 cm, and 60 - 100 cm. The original data was obtained from Sistema de Información de Suelos de Latinoamérica y el Caribe - SISLAC, a soil information system developed by the Food and Agriculture Organization of the United Nations.
Textural soil maps, Colombia, 0 - 100 cm
These are the first texture maps of Colombia, obtained from national and global digital soil mapping products. The maps were developed at five standard depths (0-5, 5-15, 15-30, 30-60, and 60-100 cm) and standardized with Additive log-ratio (ALR) transformation. The maps were harmonized at 1 square km of spatial resolution. The data packages include the following set maps: texture maps obtained through the Ensemble Machine Learning (EML) algorithms called landmap and MACHISPLIN; texture maps obtained from SoilGrids platform; residual maps of the texture of the algorithms referenced above; and finally texture maps obtained through spatial ensemble technique.
[[Deprecated]] DIGITAL SOIL TEXTURE MAPS OF ARGENTINA
<p>A new version has been uploaded by Guillermo Schulz.</p>
DIGITAL SOIL TEXTURE MAPS OF ARGENTINA
<p>Soil fractions of Argentina in g/100g, Clay, Silt and Sand, for 4 standard depth intervals (0–15, 15-30, 30–60, 60–100) at 1000 m resolution. Including textural classes for the four standard layers and error estimation using random forest.</p> <p>Global accuracy based on cross-validation</p> <table> <tbody> <tr> <td> <p><strong>sp</strong></p> </td> <td> <p><strong>RMSE</strong></p> </td> <td> <p><strong>Rsquared</strong></p> </td> <td> <p><strong>MAE</strong></p> </td> </tr> <tr> <td> <p><strong>Sand 0-15 cm</strong></p> </td> <td> <p><strong>16.189</strong></p> </td> <td> <p><strong>0.640</strong></p> </td> <td> <p><strong>11.069</strong></p> </td> </tr> <tr> <td> <p><strong>Sand 15-30 cm</strong></p> </td> <td> <p><strong>16.320</strong></p> </td> <td> <p><strong>0.629</strong></p> </td> <td> <p><strong>11.213</strong></p> </td> </tr> <tr> <td> <p><strong>Sand 30-60 cm</strong></p> </td> <td> <p><strong>16.676</strong></p> </td> <td> <p><strong>0.618</strong></p> </td> <td> <p><strong>11.364</strong></p> </td> </tr> <tr> <td> <p><strong>Sand 60-100 cm</strong></p> </td> <td> <p><strong>16.762</strong></p> </td> <td> <p><strong>0.587</strong></p> </td> <td> <p><strong>11.472</strong></p> </td> </tr> <tr> <td> <p><strong>silt 0-15 cm</strong></p> </td> <td> <p><strong>12.011</strong></p> </td> <td> <p><strong>0.638</strong></p> </td> <td> <p><strong>8.352</strong></p> </td> </tr> <tr> <td> <p><strong>silt 15-30 cm</strong></p> </td> <td> <p><strong>11.807</strong></p> </td> <td> <p><strong>0.608</strong></p> </td> <td> <p><strong>8.388</strong></p> </td> </tr> <tr> <td> <p><strong>silt 30-60 cm</strong></p> </td> <td> <p><strong>11.504</strong></p> </td> <td> <p><strong>0.561</strong></p> </td> <td> <p><strong>8.168</strong></p> </td> </tr> <tr> <td> <p><strong>silt 60-100 cm</strong></p> </td> <td> <p><strong>11.728</strong></p> </td> <td> <p><strong>0.583</strong></p> </td> <td> <p><strong>8.263</strong></p> </td> </tr> <tr> <td> <p><strong>clay 0-15 cm</strong></p> </td> <td> <p><strong>8.766</strong></p> </td> <td> <p><strong>0.475</strong></p> </td> <td> <p><strong>5.721</strong></p> </td> </tr> <tr> <td> <p><strong>clay 15-30 cm</strong></p> </td> <td> <p><strong>10.723</strong></p> </td> <td> <p><strong>0.452</strong></p> </td> <td> <p><strong>7.432</strong></p> </td> </tr> <tr> <td> <p><strong>clay 30-60 cm</strong></p> </td> <td> <p><strong>11.211</strong></p> </td> <td> <p><strong>0.557</strong></p> </td> <td> <p><strong>7.842</strong></p> </td> </tr> <tr> <td> <p><strong>clay 60-100 cm</strong></p> </td> <td> <p><strong>11.005</strong></p> </td> <td> <p><strong>0.536</strong></p> </td> <td> <p><strong>7.734</strong></p> </td> </tr> </tbody> </table> <p> </p> <p> </p>
Fig. 1 in Infection of Anastrepha ludens (Diptera: Tephritidae) adults during emergence from soil treated with Beauveria bassiana under various texture, humidity, and temperature conditions
Fig. 1. Adult mortality of Anastrepha ludens infected with different concentrations of Beauveria bassiana conidia, afer emerging from treated soil. Different letters indicate significant differences among treatments based on 1-way ANOVA followed by the Tukey Honest Significant Difference test, P <0.05).
Soil texture dataset from the publication: "Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula'
<p>Clay, silt and sand distribution in Antarctic soils modeled and predicted through Machine Learning approaches, legacy soil data and environmental covariates. The coefficient of variation and quantile data represent the spatial uncertainty of the predictions. For more information about the methodology used, users are referred to the article: </p> <p>Siqueira, R.G., Moquedace, C.M., Francelino, M.R., Schaefer, C.E.G.R., Fernandes-Filho, E.I., 2023. Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula. Geoderma 432, 116405. https://doi.org/10.1016/j.geoderma.2023.116405</p> <p>The .zip file has the following folders:</p> <p>1) soil_texture_antarctica: soil texture information containing clay, silt and sand contents</p> <p>2) soil_texture_coefficient_variation: uncertainty from the coefficient of variation of the soil texture prediction</p> <p>3) soil_texture_prediction_interval: uncertainty from the prediction interval 90% (Q95% - Q5%) of the soil texture prediction</p> <p>4) soil_texture_quantile05: quantile 5% of the soil texture prediction</p> <p>5) soil_texture_quantile95: quantile 95% of the soil texture prediction</p>
iSDAsoil: soil texture class (USDA system) for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil texture classes derived from sand, silt and clay fractions at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_texture.class_c_30m_*..*cm_2001..2017_v0.13_wgs84.tif = soil texture class,</li> </ul> <p>Classes:</p> <pre><code>Code,Name,Value,Color Cl,clay,1,#d5c36b SiCl,silty clay,2,#b96947 SaCl,sandy clay,3,#9d3706 ClLo,clay loam,4,#ae868f SiClLo,silty clay loam,5,#f86714 SaClLo,sandy clay loam,6,#46d143 Lo,loam,7,#368f20 SiLo,silt loam,8,#3e5a14 SaLo,sandy loam,9,#ffd557 Si,silt,10,#fff72e LoSa,loamy sand,11,#ff5a9d Sa,sand,12,#ff005b NODATA,,255,#ffffff </code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
Soil grid data for agricultural fields in Spain for STEROPES (EJP Soil) project (ECe, texture, soil organic carbon, pH)
<p><span>Soil data collected in an agricultural area with vegetable crops in Spain (Campo de Cartagena). The data refers to soil properties of 141 soil samples collected at a depth of 0-10 cm, considering a regular sampling grid, in different commercial fields with varying soil salinity. The samples were collected at a period when the soil was bare, during two consecutive summers, following the harvest of the annual crops, and pictures of the soil surface were taken for eventual correction of corresponding remote sensing imaging. The data includes: soil organic carbon (SOC) (Walkley-Black method), soil water content, electric conductivity of the saturated soil paste (ECe), EC1:5, soil texture, stone content and pH1:2.5. </span></p> <p><span> </span></p> <p><span>The data may be representative of the soil conditions of the area, which is an intensive productive agricultural low land, potentially prone to the development of soil salinity as a result of the rise of saline groundwater and/or irrigation. The data can be used to establish relations between soil salinity (ECe) and other soil properties as well as build prediction models of the soil properties from remote sensing namely, for developing models for SOC prediction under the STEROPES project (WP3, WP5 and WP6).The aim of the collected dataset was to be able to analyze the influence of soil salinity in SOC prediction from remote sensing.</span></p> <p><span> </span></p> <p><span>Data in the form of MS Excel file (xlsx).</span></p> <p><span> </span></p> <p><span> </span></p>
Plant litter chemistry controls coarse-textured soil carbon dynamics
<p>1. As soils store more carbon (C) than the Earth's atmosphere and terrestrial biomass together, the balance between soil C uptake in the form of soil organic matter and release as CO2 upon its decomposition is a critical determinant in the global C cycle regulating our planet's climate. Although plant litter is the predominant source of C fueling both soil C build-up and losses, the issue of how litter chemistry influences this balance remains unresolved.</p> <p>2. As a contribution to solving that issue, we traced the fate of C during near-complete decomposition of 13C-labelled leaf and root litters from 12 plant species in a coarse-textured soil. We separated the soil organic carbon (SOC) into mineral-associated organic matter (MAOM) and particulate organic matter (POM) pools, and investigated how 14 litter chemical traits affected novel SOC formation and native SOC mineralization (i.e., the priming effect) in these soil fractions.</p> <p>3. We observed an overall net increase in SOC due to the addition of litter, which was stronger for root than for leaf litters. The presumed stable MAOM-C pool underwent both substantial stabilization and mineralization, whereas the presumably less stable POM-C pool showed substantial stabilization and reduced mineralization. Overall, the initial increase in soil C mineralization was fully counterbalanced by a later decrease in native soil C mineralization. POM-C formation as well as MAOM-C formation and mineralization were positively related to the initial litter lignin concentration and negatively to that of the nitrogen leachates, whereas the opposite was observed for POM-C mineralization.</p> <p>4. Synthesis. Our results highlight the importance of litter chemical traits for SOC formation, and stabilization, destabilization, and mineralization. In our coarse-textured soil, the amount of MAOM-C did not change despite large C fluxes through this pool. The litter chemical traits that drove these processes differed from those frequently reported for fine-textured soils far from mineral-associated C saturation. To account for these discrepancies, we propose an integrative perspective in which litter quality and soil texture interactively control soil C fluxes by modulating several SOC stabilization and destabilization mechanisms. Irrespective, our results open new critical perspectives for managing soil C pools globally.</p>
Soil C saturation in tropical agricultural systems: soil fertility, climate, texture and N controls on SOM fractions
<p><span>Nature-based solutions for C sequestration in tropical croplands are paramount strategies in a changing climate. Soybean-maize-forage intercropped systems associated with soil acidity alleviation and nitrogen (N) fertilization effectively accumulate carbon (C) in weathered soils. However, the soil saturation status and the controls of C stabilization up to 40 cm soil depth into particulate (POM) and mineral-associated organic matter (MAOM) fractions are poorly understood in tropical croplands. This study tested the hypothesis of C saturation in the MAOM fraction by comparing field and published data focused on Brazilian tropical soils. We assessed the N fertilization effect on C stabilization in POM and MAOM pools, and climate, soil texture, and chemical attributes controls on POM and MAOM formation in tropical croplands using a machine-learning Random Forest (RF) modeling approach. Our findings do not support C saturation in the MAOM regardless of contrasting soil contents of silt plus clay and depths. C in the MAOM and POM fractions were not affected by N fertilizer. However, legume inclusion in the system resulted in higher total soil C and lower soil C:N ratio compared with N fertilization, which indicates that C dynamics differ whether synthetic or organic-N forms are applied to tropical croplands. Our RF model showed robust predictive performance for the MAOM but poorer for the POM fraction. Total organic carbon (TOC), total N, silt plus clay, phosphorus (P) and soil depth, zinc (Zn), cation exchange capacity, and TOC covariates were the most important variables for predicting MAOM and POM fractions, respectively. Our findings show, besides the well-known effect of calcium, that other nutrients such as P, Zn, manganese and copper play a key role in either MAOM or POM formation. Our work provides key insights on C saturation status in tropical croplands and land-management C sequestering potential, and N, climate, soil chemical attributes and texture controls on C distribution into MAOM and POM fractions up to 40 cm soil depth in tropical conservation agricultural systems. We advocate for upscaling C saturation concept nationally and further research investigating the role of climate and micronutrients contribution on MAOM and POM formation in broad scenarios and N fertilizer responsiveness for C stabilization in tropical croplands.</span></p>
Data from: Volatile fatty acid concentration, soil pH and soil texture during anaerobic soil conditions affect germination of Athelia (Sclerotium) rolfsii sclerotia
<p>Anaerobic growth chamber trials were conducted to evaluate effects of VFA and VFA concentration, and interactions with soil pH and soil texture, on <i>A. rolfsii</i> sclerotia germination. In the first objective, sclerotia were exposed to 4, 8, or 16 mmol/kg soil of acetic or <i>n</i>-butyric acids in sandy soil; soil pH was buffered to 5, 6, or 7. In the second objective, sclerotia in sandy or sandy loam soil were exposed to 4 or 16 mmol VFA/kg soil at soil pH 5 or 6. VFAs are probable important factors in <i>A. rolfsii </i>suppression<i> </i>due to ASD treatment in many soil environments, and activity is dependent on VFA concentration, soil solution pH, and soil texture.</p>
Data from: Volatile fatty acid concentration, soil pH and soil texture during anaerobic soil conditions affect germination of Athelia (Sclerotium) rolfsii sclerotia
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Plant litter chemistry controls coarse-textured soil carbon dynamics
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Shortgrass Steppe site, station Owl Creek, coarse texture soil, study of aboveground net primary productivity in units of gramsPerMeterSquaredPerYear on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Shortgrass Steppe (SGS) contains aboveground net primary productivity measurements in gramsPerMeterSquaredPerYear units and were aggregated to a yearly timescale.
Shortgrass Steppe site, station Sec 25, fine texture soil, study of aboveground net primary productivity in units of gramsPerMeterSquaredPerYear on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Shortgrass Steppe (SGS) contains aboveground net primary productivity measurements in gramsPerMeterSquaredPerYear units and were aggregated to a yearly timescale.
1996 Ring soil texture, pH and Cation Exchange Capacity (CEC):BioCON : Biodiversity, Elevated CO2, and N Enrichment
BioCON (Biodiversity, CO2, and Nitrogen) is an ecological experiment started in 1997 at the University of Minnesota's Cedar Creek Ecosystem Science Reserve. BioCON's goal is to explore the ways in which plant communities will respond to three environmental changes that are known to be occurring on a global scale: increasing nitrogen deposition, increasing atmospheric CO2, and decreasing biodiversity. Why Biodiversity, CO2, and Nitrogen? While there are many uncertainties in global change biology, there are also some well documented facts. Some of these are: 1. The amount of carbon dioxide (CO2) in the atmosphere is rising. Since the industrial revolution, the CO2 concentration in the atmosphere has increased from approximately 275 parts per million (ppm) to about 378 ppm today. This has been largely the result of fossil fuel burning. It is expected that CO2 levels will continue to rise, and that by the year 2050 these levels will be approximately 550 ppm. CO2 is the raw material for photosynthesis and is known to affect plant growth and development. 2. The amount of nitrogen moving through terrestrial ecosystems has increased in the recent past. While natural "background" levels of nitrogen fixation have remained constant, human additions to the system through fertilizer production and fossil fuel use have increased dramatically. Nitrogen is a key nutrient for plant growth and plays a critical role in plant community structure and composition in many environments. 3. Biodiversity levels are falling. While the research and data are not as complete as they are for CO2 and nitrogen, data indicate that the number of species globally, is being reduced. Perhaps more important for ecosystem function, diversity levels on local to regional scales have fallen due to land use change, biotic invasion and many other drivers. While much is known about how each of these factors affects ecosystem functioning, many questions remain. There is also little data on how these issues affe
2007 pre-treatment soils pH, nutrients, texture:Nutrient Network: A cross-site investigation of bottom-up control over herbaceous plant community dynamics and ecosystem function.
This experiment is one implementation of a globally distributed experiment, known as the Nutrient Network. At Cedar Creek, as in over 70 other sites in grasslands around the world, the experiment aims to describe impacts of increased nutrients (nitrogen, phosphorus, potassium, sulfur and other metals) and decreased herbivory (removal of mammals by fencing). Two overarching questions are being explored with these manipulations: 1. To what extent are plant production and diversity co-limited by multiple nutrients in herbaceous-dominated communities? 2. Under what conditions do grazers or fertilization control plant biomass, diversity, and composition? By utilizing identical protocols at diverse grassland sites around the world, NutNet aims to uncover both the generalities in ecosystem functioning, and the contingencies or differences which can obscure those common mechanisms. In addition to the standard NutNet protocol, e247 includes an additional low Nitrogen gradient (1 gram Nitrogen per meter squared per year and 5 grams Nitrogen per meter squared per year in addition to the standard 10 grams Nitrogen per meter squared per year).
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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