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237 results for “Soil properties”
Data from: Soil hydraulic properties determined by inverse modeling of drip infiltrometer experiments extended with pedotransfer functions
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Slope position affects growth and allometry of the endangered conifer Calocedrus macrolepis by mediating soil properties and microbial communities
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Data from: Spatial mid-domain effect overrides climate, soil properties and microbes on a cosmopolitan non-native plant across elevation
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Soil properties shape the heterogeneity of denitrification and N2O emissions across large-scale flooded paddy soils
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Soil properties and plant functional traits have different importance in shaping rhizosphere soil bacterial and fungal communities in a meadow steppe
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Data from: Productivity and related soil properties mediate the population-level consequences of rodent seed predation on Blanketflower, Gaillardia aristata
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Large-scale drivers of relationships between soil microbial properties and organic carbon across Europe
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Effects of plant traits and ecosystem properties on wave attenuation and soil carbon content
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Soil property, microbial abundance, and plant and invertebrate biomass data across a natural soil temperature gradient in Iceland from August 2018
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Woody debris removal modifies carbon stocks and soil properties in a fragmented tropical rainforest
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Data from: Photodegradation modifies microplastic effects on soil properties and plant performance
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Tree communities and soil properties influence fungal community assembly in neotropical forests
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Data from: Can the soil seed bank of Rumex obtusifolius in productive grasslands be explained by management and soil properties?
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Data from: Above-belowground linkages of functionally dissimilar plant communities and soil properties in a grassland experiment
<p>Changes in plant community composition can have long-lasting consequences for ecosystem functioning. However, how the duration of plant growth of functionally distinct grassland plant communities influences abiotic and biotic soil properties and thus ecosystem functions is poorly known. In a field experiment, we established identical experimental subplots in two successive years comprising of fast- or slow-growing grass and forb community mixtures with different forb:grass ratios. After one and two years of plant growth, we measured above- and belowground biomass, soil abiotic characteristics (pH, organic matter, soil nutrients), soil microbial properties (respiration, biomass, community composition), and nematode abundance. Fast- and slow-growing plant communities did not differ in above- and belowground biomass. However, fast-and slow-growing plant communities created distinct soil bacterial communities, whereas soil fungal communities differed most in 100% forb communities compared to other forb:grass ratio mixtures. Moreover, soil nitrate availability was higher after two years of plant growth, whereas the opposite was true for soil ammonium concentrations. Furthermore, total nematodes and especially bacterial-feeding nematodes were more abundant after two years of plant growth. Our results show that plant community composition is a driving factor in soil microbial community assembly and that the duration of plant growth plays a crucial role in the establishment of plant community and functional group composition effects on abiotic and biotic soil ecosystem functioning under natural field conditions.</p>
Data from Soil chemistry turned upside down: a meta-analysis of invasive earthworm effects on soil chemical properties
<p>Recent studies have shown that invasive earthworms can dramatically reduce native biodiversity, both above and below the ground. <a name="_Hlk9515878">However, we still lack a synthetic understanding of the underlying mechanisms behind these changes, such as whether earthworm effects on soil chemical properties drive such relationships</a>. Here, we investigated the effects of invasive earthworms on soil chemical properties (pH, water content, and the stocks and fluxes of carbon, nitrogen, and phosphorus) by conducting a meta-analysis. Invasive earthworms generally increased soil pH, indicating that the removal of organic layers and the upward transport of more base-rich mineral soil caused a shift in soil pH. Moreover, earthworms significantly decreased soil water content, suggesting that the burrowing activities of earthworms may have increased water infiltration of and/or increased evapo-transpiration from soil. Notably, invasive earthworms had opposing effects on organic and mineral soil for carbon and nitrogen stocks, with decreases in organic, and increases in mineral soil. Nitrogen fluxes were higher in mineral soil, whereas fluxes in organic soil were not significantly affected by the presence of invasive earthworms, indicating that earthworms mobilize and redistribute nutrients among soil layers and increase overall nitrogen loss from the soil. Invasive earthworm effects on element stocks increased with ecological group richness only in organic soil. Earthworms further decreased ammonium stocks with negligible effects on nitrate stocks in organic soil, whereas they increased nitrate stocks but not ammonium stocks in mineral soil. Notably, all of these results were consistent across forest and grassland ecosystems underlining the generality of our findings. However, we found some significant differences between studies that were conducted in the field (observational and experimental settings) and in the lab, such as that the effects on soil pH decreased from field to lab settings, calling for a careful interpretation of lab findings. Our meta-analysis provides strong empirical evidence that earthworm invasion may lead to substantial changes in soil chemical properties and element cycling in soil. Furthermore, our results can help explain the dramatic effects of invasive earthworms on native biodiversity, e.g., shifts towards the dominance of grass species over herbaceous ones, as shown by recent meta-analyses.</p>
Plant communities, grazing intensity, soil properties and decomposers in grasslands across elevation in the Eastern Carpathians in Ukraine
<p>This data set contains information on plant community properties, grazing intensity, elevation, soil properties and density of soil decomposers collected in 2006 and 2007 from the 31 semi-natural grasslands exposed to cattle grazing along elevation gradient at large topographic scale ranging from the Carpathian Mountains, across the adjacent foothills to the plain areas. Plant community properties are represented by the following variables for each of the 31 study grassland: number of species (species number 100 m<sup>-2</sup>), number of functional groups 100 m<sup>-2 </sup>(that is, legumes, grasses, rushes and sedges, and non-legume forbs), proportion of undesirable weeds (%), number of species of legumes (species number 100 m<sup>-2</sup>), number of species of grasses (species number 100 m<sup>-2</sup>), number of species of rushes and sedges (species number 100 m<sup>-2</sup>), and number of non-legume forbs (species number 100 m<sup>-2</sup>). Grazing intensity is measured as cattle density (livestock units h<sup>-1</sup>). Soil properties are represented for each 31 study grassland by bare soil exposure (%) , soil organic carbon content (%), and soil pH. Density of soil decomposers are represented by biomass of earthworms g m<sup>-2</sup> and abundance of soil microorganisms cells × 10<sup>8</sup> g (dry soil)<sup>-1</sup>. Plant community composition is based on individual species' canopy cover. This dataset also contain information on the following plant traits for each plant species (n=175 species) found across the 31 study pastures across 2006 and 2007 sampling years: family of the plant species, belonging to legumes, grasses, rushes and sedges, non-legume forbs, and undesirable weeds. All data are averages across the two sampling years. </p>
Soil morphology and properties of forest soils in the Odra valley (SW Poland, Forest District Wołów).
<p>The presented dataset contains the raw data concerning soil morphology and physical and physicochemical properties of forest soils in the Odra valley (SW Poland, Forest District Wołów).</p> <p> </p>
Data from: Genes, geology, and germs: gut microbiota across a primate hybrid zone are explained by site soil properties, not host species
Gut microbiota in geographically isolated host populations are often distinct. These differences have been attributed to between-population differences in host behaviors, environments, genetics, and geographic distance. However, which factors are most important remains unknown. Here we fill this gap for baboons by leveraging information on 13 environmental variables from 14 baboon populations spanning a natural hybrid zone. Sampling across a hybrid zone allowed us to additionally test whether phylosymbiosis (codiversification between hosts and their microbiota) is detectable in admixed, closely related primates. We found little evidence of genetic effects: neither host genetic ancestry, host genetic relatedness, nor genetic distance between host populations were strong predictors of baboon gut microbiota. Instead, gut microbiota were best explained by the baboons' environments, especially the soil's geologic history and exchangeable sodium. Indeed, soil effects were 15 times stronger than those of host-population FST, perhaps because soil predicts which foods are present, or because baboons are terrestrial and consume soil microbes incidentally with their food. Our results support an emerging picture in which environmental variation is the dominant predictor of host-associated microbiomes. We are the first to show that such effects overshadow host species identity among members of the same primate genus.
SoilCompDB: Global soil compressive properties database. Version 1.0
<p><strong>Data collection and processing</strong></p><p>Our data collection comprised published journal articles sourced from Web of Science and Scopus databases, using search terms such as 'soil precompression stress,' 'soil compression index,' 'soil compaction index,' 'soil recompression index,' 'soil swelling index,' 'soil precompaction stress,' and 'preconsolidation pressure' for articles published up to February 2022. A total of 1235 publications were found. Duplicate records were eliminated using the Endnote Web citation management application. The remaining references were exported to Rayyan software for title and abstract screening based on predefined criteria for full-text selection. After a careful review, we identified 128 papers where the data on soil compressive properties (precompression stress, compression index, and swelling index) were reported in numerical format or legible graphical format and considered suitable for inclusion in the database. We employed the WebPlotDigitizer software to extract data from figures within the original publications. For each chosen study, we systematically recorded data concerning soil compressive properties and collected information on soil properties, soil conditions, site characteristics, and experimental settings. We compiled 4,743 individual data entries.</p><p><strong>Time and place</strong></p><p>The database includes data from 128 independent studies published between 1992 and 2021. Each study reported between 1 and 360 measurements, with a study median of 14 measurements and a mean of 38 measurements, totalling 4743 database entries. Our database includes data from 20 countries, with a significant concentration of the data originating from Brazil, followed by Germany, Switzerland, Sweden, and Denmark. The majority of the data came from arable soils, representing approximately 72% of data entries. </p><p><strong>Instruments</strong></p><p>The soil compressive properties included in the database were based on soil compressive tests performed in the laboratory by uniaxial method. The procedure used for stress application on soil samples was mainly the stepwise stress application method, while the constant strain rate method was applied in few studies (less than 2% of the data). The component of the compressive curve related to the soil packing state was represented by soil bulk density, void ratio, and strain. The stress component of the curve was represented in a logarithmic form in the entirety of the database. The database also comprised eight different methods for calculating precompresion stress: Casagrande (1936), Dias Junior and Pierce (1995), Lamandé et al. (2017), Sullivan and Robertson (1996), Casini (2012), Culley and Larson (1987), Pacheco Silva (1990), Gregory et al. (2006).</p><p><strong>Resources</strong></p><p>Web of Science, Scopus – literature search</p><p>Endnote Web – removal of duplicates</p><p>Rayyan software – initial paper selection based on title and abstract</p><p>WebPlotDigitizer – data extraction from figures</p><p>Microsoft Access – database platform</p><p><strong>Description of the collected data (column, unit, and description)</strong></p><p>Sample ID- A unique identification number assigned to each individual sample within the database </p><p>Study ID- Identification number assigned to each research study in the database</p><p>Reference - Research paper reference</p><p>Year - Year of research paper publication </p><p>Language - Language of the research paper </p><p>Soil classification (SiBCS) - Soil Classification according to the Brazilian System (SiBCS), as described in portuguese-language papers</p><p>Soil classification (original in paper) - Soil classification described in research paper </p><p>Soil classification (convertion to Soil Taxonomy orders) - Soil classification aligned with the Soil Taxonomy system developed by the United States Department of Agriculture (USDA) </p><p>Location - Study location country </p><p>Texture classification (USDA) - Soil textural classification according USDA</p><p>Texture classification USDA (letter code) - Letter code for soil textural classification according USDA: S=sand; LS=loamy sand; SL=sandy loam; SiL=silt loam; Si=silt; L=loam; SCL= Sandy clay loam; SiCL=Silty clay loam; CL=clay loam; SC=Sandy clay; SiC=Silty clay; C=clay</p><p>Clay (USDA) - % - Soil clay content (weight based) - (<0.002 mm) </p><p>Silt (USDA) - % - Soil silt content (weight based) - (0.002 < x < 0.05 mm, interpolated for European samples where needed using the k-nearest neighbor technique by Nemes et al. 2006) </p><p>Sand (USDA) - % - Soil sand content (weight based) - (0.05 < x < 2 mm, interpolated for European samples where needed using the k-nearest neighbor technique by Nemes et al. 2006)</p><p>USDA PSD interpolated - =0 if the data was NOT interpolated; =1 if the data was interpolated</p><p>Published texture class - Texture classification provided in the source publication when the values for clay, silt and sand were not available</p><p>Clay - g kg-1 - Soil clay content - original in the paper</p><p>Clay class upper boundary - µm - The clay class upper boundary informed in source publication</p><p>Silt - g kg-1 - Silt clay content - original in the paper</p><p>Silt class upper boundary - µm - The silt class upper boundary informed in source publication</p><p>Sand - Soil sand content - original in the paper</p><p>Sand class upper boundary - µm - The sand class upper boundary informed in source publication</p><p>Particle size data flag - =0 if no issues; =1 if there are issues (summing)</p><p>Sum particle size- g kg-1 - Sum of clay, silt, and sand content</p><p>Soil depth FROM – cm - When soil depth is presented as a range (e.g., 0-10cm), it indicates the minimum depth at which soil samples were collected </p><p>Soil depth TO – cm - When soil depth is presented as a range (e.g., 0-10cm), it indicates the maximum depth at which soil samples were collected </p><p>Depth – cm -Specific depth value as presented in paper, or when soil depth is showed as a range (e.g., 0-10cm), it indicates the average depth at which soil samples were collected (e.g 5cm) </p><p>SOC - g kg-1 - Soil organic carbon content informed in research paper or soil organic carbon content calculate from soil organic matter content by multiplying by 0,58 </p><p>SOC converted from SOM - 1= yes for soil organic carbon derived from soil organic matter content calculations</p><p>Particle density - Mg m-3 - Soil particle density </p><p>Initial matric potential – hPa - Soil water matric potential before loading</p><p>log Initial matric potential - Soil water matric potential expressed by log </p><p>Wetness (based on initial matric potential) - 1=if initial matric potential (MP)<100 hPa; 2= if 100<=initial MP<1000 hPa; 3= initial MP>=1000 hPa</p><p>Initial gravimetric water content - g g-1 - Gravimetric soil water content before loading provided by source publication, or calculated by volumetric water content divided by soil bulk density</p><p>Initial volumetric water content - m3 m-3 - Volumetric soil water content before loading, when the soil bulk density was not reported</p><p>Initial water content data source - Graph or table from where the data was collected, or explanation on calculation used</p><p>Matric potential type - Compressive tests performed on soil samples under different conditions: 1= equilibrated at matric potential; 2= field matric potential; 3= air-dried samples </p><p>Initial bulk density - Mg m-3 - Soil bulk density before loading </p><p>Initial BD data source - Graph or table from where the data was collected, or explanation on calculation used </p><p>Initial volumetric water content calculated - m3 m-3 - Soil volumetric water content calculated by multiplying soil gravimetric water content by soil bulk density</p><p>Precompression stress – kPa - Precompression stress </p><p>Precompression stress (SD) – kPa - Standard deviation for precompression stress values reported in paper </p><p>Precompression stress data source - Graph or table from where the data was collected, or explanation on calculation used</p><p>Compression index - Compression index </p><p>Compression index (SD) - Standard deviation of compression index values reported in paper </p><p>Compression index data source - Graph or table from where the data was collected, or explanation on calculation used</p><p>Swelling index - Swelling index </p><p>Swelling index (SD) - Standard deviation of swelling index values reported in paper </p><p>Swelling index data source - Graph or table from where the data was collected, or explanation on calculation used</p><p>N - Number of replicates used for calculating precompression stress, compression index, and swelling index when mean values are reported</p><p>Land use (paper) - Land use described in the research paper</p><p>Land use (categories) - Land use categorized</p><p>Land use standardized - Land use classified as: arable, forest, grassland, and native vegetation. The latter includes forest, grassland, and savanna</p><p>Land use (number code) - Number code for land use: 1=Arable, 2= forest, 3= grassland, and 4= native vegetation</p><p>Tillage system - Tillage system</p><p>Tillage system (arable soils) - Tillage system for arable soils classified as "conventional" and "conservation"</p><p>Coordinates - Geographical coordinates of study location</p><p>Climate - Climatic region classification: temperate, tropical, subtropical</p><p>Climatecod - Code number assigned to each climatic region: 1=temperate, 2=tropical, 3=subtropical</p><p>Sampling position (paper) - Field position where soil samples were collected with details described in the paper</p><p>Sampling position - Field position where soil samples were collected standardized</p><p>Treatment - Experimental treatment type where the soil samples were collected</p><p>Stress rate - kPa - Stress applied in compressive tests </p><p>Minimum stress – kPa - Minimum stress applied in compressive tests</p><p>Maximum stress – kPa - Maximum stress applied in compressive tests</p><p>Number of stress rate steps - Number of steps in stepwise stress application procedure</p><p>Stess application type - 1=Stepwise stress 2=one sample per stress 3=Strain controlled</p><p>Stess application type – min - Time for stress application in each step in stepwise stress application procedure</p><p>Degree of deformation at the end of loading - % - Degree of deformation at the end of compressive test</p><p>Sample diameter – cm - Diameter of the soil samples</p><p>Sample height – cm - Height of the soil samples</p><p>Ratio sample diameter and height - Ratio between diameter and height of the soil samples</p><p>Sample volume - cm3 -Sample volume when the sample diameter and height are nor presented</p><p>Precompression stress calculation method - Calculation method of precompression stress</p><p>Precompression stress calculation method (number code) - Number code for calculation method PC:1=Casagrande (1936); 2=Dias Junior and Pierce (1995); 3= Lamandé et al. (2017); 4=O`Sullivan and Robertson (1996); 5=Casini (2012); 6=Culley and Larson (1987);7=ABNT (1990); 8=Gregory et al. (2006)</p><p>Description of precompression stress calculation - Brief explanation of precompression stress calculation</p><p>Soil compressive curve components - Component of the soil compression curve related to the soil packing state: soil bulk density, void ratio, and strain. </p><p>Soil compressive curve components (number code) - Number code for component of the soil compressive curve related to the soil packing state: 1= soil bulk density; 2= strain; 3= void ratio</p><p>Curve components source - Source of the component of the soil compressive curve related to the soil packing state: 1= showed in the paper, 2= according to original method for precompression stress calculation, 3= described in method, but not clear in the paper</p><p>Compressive curve available - Original soil compressive curve available in the paper: 1= No 2=Yes</p><p>Comments - Brief comments on the paper</p><p><strong>Issues and remarks</strong></p><p>We sought out important information not included in the paper by directly communicating with the authors whenever possible. In cases where multiple papers covered the same experiment, we prioritized the one offering more comprehensive details. If two papers complemented each other, we included both. When analyzing studies comparing various methods for calculating soil precompression stress, we exclusively gathered data calculated using the widely accepted Casagrande (1936) method. To ensure comparability across studies, we standardized the collected data by converting it to the same unit. The standardization process involved: i) assuming that 58% of soil organic matter (SOM) was soil organic carbon (SOC) when only SOM was reported, ii) calculating soil bulk density using a soil particle density of 2.65 Mg m-3 when only total porosity data were provided, and iii) harmonizing all texture data to the USDA classification system, which defines the silt/sand boundary as 50 μm, utilizing the k-nearest neighbor approach (referred to as "similarity method" by Nemes et al. (1999). </p><p><strong>Reference</strong></p><p>Associação Brasileira de Normas Técnicas - ABNT. NBR 12007: Ensaio de adensamento unidimensional. Rio de Janeiro: 1990.</p><p>Casagrande, A., 1936. Determination of the preconsolidation load and its practical significance. In: Proceedings of the International Conference on Soil Mechanics and Foundation Engineering, vol. III, Harvard University, Cambridge, MA, pp. 60–64.Casini, F. 2012. Deformation induced by wetting: A simple model. Can. Geotech. J. 49:954–960 10.1139/T2012-054. doi:10.1139/t2012-054</p><p>Culley, J.L.B., Larson, W.E., 1987. Susceptibility to compression of a clay loam Haplaquoll. Soil Sci. Soc. Am. J. 51, 562–567.</p><p>Dias Junior, M.S., Pierce, F.J., 1995. A simple procedure for estimating preconsolidation pressure from soil compression curves. Soil Technology 8, 139–151. doi:10.1016/0933-3630(95)00015-8</p><p>Gregory, A.S., Whalley, W.R., Watts, C.W., Bird, N.R.A., Hallett, P.D., Whitmore, A.P., 2006. Calculation of the compression index and pre-compression stress from soil compression test data. Soil Till Res. 89:45-57. doi:10.1016/j.still.2005.06.012</p><p>Lamandé, M., Schjønning, P., Labouriau, R., 2017. A novel method for estimating soil precompression stress from uniaxial confined compression tests. Soil Sci. Soc. Am. J. 81 https://doi.org/10.2136/sssaj2016.09.0274.</p><p>Nemes, A., Wösten, J.H.M., Lilly, A., Oude Voshaar, J.H., 1999. Evaluation of different procedures to interpolate the cumulative particle-size distribution to achieve compatibility within a soil database. Geoderma 90: 187-202. 129 </p><p>O'Sullivan, M.F., Robertson, E.A.G., 1996. Critical state parameters from intact samples of two agricultural topsoils. Soil Tillage Res 39(3 – 4):161 – 173.</p>
Dataset for Estimating soil hydraulic properties from oven-dry to full saturation using inverse modeling and shortwave infrared imaging
<p>In this repository, we provide all the datasets that are needed to reproduce the analysis conducted in the paper entitled "Estimating soil hydraulic properties from oven-dry to full saturation using inverse modeling and shortwave infrared imaging."</p> <p><br> codes: This folder contains Python codes to run the forward and inverse modeling. Install the following packages.<br> notebook, fenics, numpy, pandas, matplotlib, scipy, numdifftools, and lmfit for inverse modeling (needs to be run on Linux).<br> data: This directory contains data used in the inverse modeling.<br> gif: This directory contains GIF movies of the upward infiltration experiments.</p> <p>readme.xlsx: This file explains which data are used for each figure in the paper.</p>
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