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66 results for “soil pH”
Root Biomass, Fine Root Production, Soil Mass, and Soil pH in Limed and Control Plots at the Woods Lake Watershed, Adirondack Park, NY, USA, 2021-2022
In 1989, 6.89 Mg/ha of pelletized lime (CaCO3) was applied by helicopter to two subcatchments at the Woods Lake Watershed in Adirondack Park, New York, USA to ameliorate ecosystem acidification. Two unlimed (control) subcatchments were paired with limed subcatchments. In the same year, 99 permanent plots (20 m x 20 m) were established. Between 2008 and 2010, tree inventory and soil physicochemical measurements were made in five plots in each of the four subcatchments (20 plots total). This dataset contains soil physicochemical properties (dry mass, depth, and pH); root biomass (<1 mm, 1-2 mm, and >2 mm diameter); and annual fine root production (<1 mm and 1-2 mm) measurements made between 2021 and 2022 in 19 of these same plots (5 plots per control subcatchment and 4 or 5 plots per limed subcatchment). Data include measurements for all properties for Oe, Oa, and 0-10 cm mineral soil samples collected from 5 locations within each plot.
iSDAsoil: soil pH for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil pH (1:1 Soil-Water Suspension) predicted 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_ph_h2o_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil pH mean value,</li> <li>sol_ph_h2o_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil pH model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: ph_h2o R-square: 0.818 Fitted values sd: 0.972 RMSE: 0.459 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -5.5939 -0.2328 -0.0066 0.2222 4.7477 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.113440 1.164473 0.956 0.338986 regr.ranger 1.032918 0.003138 329.116 < 2e-16 *** regr.xgboost -0.014201 0.004185 -3.393 0.000691 *** regr.cubist 0.049667 0.003709 13.392 < 2e-16 *** regr.nnet -0.188570 0.188214 -1.002 0.316398 regr.cvglmnet -0.059763 0.003636 -16.438 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.4591 on 133378 degrees of freedom Multiple R-squared: 0.8176, Adjusted R-squared: 0.8176 F-statistic: 1.195e+05 on 5 and 133378 DF, p-value: < 2.2e-16</code></pre> <p>To back-transform values (y) to index use:</p> <pre><code>index = y / 10</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 pH in H2O [-] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: soil pH in H2O;</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Soil pH in H2O at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Soil pH in H2O in × 10 at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica 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>ph.h2o = variable: soil pH in H2O,</li> <li>usda.4c1a2a = determination method: laboratory method code,</li> <li>m = mean value,</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>
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera VI: Mineral Soil Sample and pH Data 2022
This dataset contains field- and lab-measured characteristics for post-fire mineral soil samples collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Lab analyses were conducted in fall of 2022 at NAU.
Effects of Nitrogen Fertilization on Litter and Soil Decomposition: Soil pH
The influence of inorganic nitrogen (N) inputs on decomposition is poorly understood. Some prior studies suggest that N may reduce the decomposition of substrates with high concentrations of lignin via inhibitory effects on the activity of lignin-degrading enzymes, although such inhibition has not always been demonstrated. The purpose of E145 was to study the effects of nitrogen (N) addition on decomposition of seven substrates ranging in initial lignin concentrations (from 7.4 - 25.6%) over five years in eight different grassland and forest sites in central Minnesota.
Soil pH, developmental stages and geographical origin differently influence the root metabolomic diversity and root-related microbial diversity of Echium vulgare from native habitats
<p>R Studio codes and ASV table used to analyze the microbiome data of our Echium vulgare microbial ecology experiment. </p>
Post-fire succession in 1994 Hajdukovich Creek burn: Measurements of soil Ph
This dataset contains soil pH measurements. Soil cores were collected in the field in July 2009 from the 1994 Hajdukovich Creek Burn and frozen until lab analysis.
Soils pH: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Soil pH measurements from 9 Hillslope Project sites in Macon County, North Carolina, within the Upper Little Tennessee River Basin
Acidity of soil was analyzed as part of the hillslope plots in Macon County, North Carolina. There were 9 hillslope sites representing a gradient of development, including forested, valley agriculture, and mountain housing developments. There were 12 10 x 10-m plots at each site. A soil probe was used to collect soils from 3 depths at each plot: 0-10 cm, 10-30 cm, and 30 + cm. Soil was then dried, processed, and analyzed for pH at the Coweeta Analytical Laboratory.
Data from: Monitoring microarthropods assemblages along a pH gradient in a forest soil over a 60 years' time period
<p>The goal of this study was to assess the development, over 60 years, of microarthropod communities over a pH gradient in forest soil.</p> <p>Site Description</p> <p>Hackfort is an oak coppice grove in the East-Southeast of the city of Zutphen in the province of Gelderland, the Netherlands, 52°06′09.7″ N, 6°15′56.0″ E (see Figure 1). The experimental area is about 1.5 ha and is divided in a 10 m × 10 m grid. Vegetation is dominated by common oak (<em>Quercus robur</em>), mixed with birch (<em>Betula pendula</em>), and had in 1959, an understory of wood sage plugs (<em>Teucrium scorodonia</em>), wood anemone (<em>Anemone nemorosa</em>), bracken (<em>Pteridium aquilinum</em>), and wavy-hair grass (<em>Deschampsia flexuosa</em>). In later years, the understory became more dominated by bramble species (<em>Rubus fruticosus </em>and<em> R. idaeus</em>) and common nettles (<em>Urtica dioica</em>) at the edges of the forest, due to increased N deposition from adjacent farmland. The forest is situated at the transition from western riverine deposits and eastern periglacial cover sands. The soil is a riverine deposit with a few elevation differences, making a number of gradients in clay and loam content, which results in many short-distance gradients in soil types, varying from typic haplaquolls with the largest loam contents, via psammaquentic haplorthods to humaqueptic spodic psammaquents, slightly elevated and low in loam contents.</p> <p>Microarthropod Sampling and pH Measurement</p> <p>In 1959, samples were taken at three subsequent dates: 11 September, 9 October, and 30 October. Samples in 1987 were taken on one date, 9 October, just as on 30 October 2019. Samples were taken following a standard procedure, developed at the Institute for Applied Biological Research in Nature, Wageningen, the Netherlands (later merged into the Research Institute for Nature management, Institute for Forestry and Nature Research and Alterra resp., now known as Wageningen Environmental Research); this procedure has been published by Siepel and van de Bund in 1988 (Siepel and van de Bund, 1988). Each mineral soil sample has 100 cc: a volume of 5 cm diameter and 5 cm depth plus litter on top. In 1959, two samples per date were taken on each plot, making a total of 6 samples (only pooled data are available); in 1987 and in 2019, 4 and 5 samples for each plot were taken on, respectively (data per sample available).</p> <p>Soil cores were put on a Tullgren funnel for 1 week, during which temperature was increased from 35 to 45 °C, and then, microarthropods were collected in 70% alcohol and later put into 20% lactic acid for clarification and identification (Siepel, 1990; Siepel and van de Bund, 1988). The Tullgren funnel used for extraction (Siepel, 1990) has been used ever since 1936 and efficiency has not changed as the tool and protocol was the same all over the years.</p> <p>Identification was done to the species level as much as possible using at present the keys for Oribatida(Weigmann and G., 2006), for Gamasina (Lehtinen, 1994), for Uropodina (Karg, 1989), and for Collembola (Hopkin, 2007). Material from the extractions of 1959 and 1987 was re-examined as far as possible to check the correct species identification. In the 1959 and 1987 samples, only oribatid mites were identified to the species level, whereas in 1959, all species of <em>Quadroppiidae, Oppiidae</em>, and <em>Suctobelbidae</em> were pooled. In 2019, all microarthropods were identified to the species level.</p> <p>Sorting and identification of the 1959 microarthropods was carried out by an experienced acarologist (J.G. de Gunst), in 1987, this was done by a student (C. Arnold) and completed and checked by the second author. For the 2019 samples, we decided to demonstrate the potential difference in picking out the microarthropods from the extraction fluid into the slides for identification as part of the experiment: the first author made a first series of slides including all distinguished animals (dataset 2019 a), while the second author made an extra set of slides with the animals missed by the first (dataset 2019 b). The first author did know since the beginning that the second author would check all samples after her sorting session. In this way, we intended to demonstrate the potential difference in this crucial part of the procedure by a starting and an experienced professional. In the analysis, we compare dataset (2019 a) with (2019 a + b), in order to highlight the difference between a starting and an experienced acarologist. Nomenclature adopted was updated according to current standards, following, e.g., the checklists for Oribatida (Siepel et al., 2009), for Astigmatina (Siepel et al., 2016), and for Mesostigmata (Siepel, 2018). Values of pH-KCl were measured in the core material after the extraction of the microarthropods, both in 1959, 1987, and 2019.</p> <p> </p> <p>We have four data files:</p> <p>1959 hackfort microarthropods data.csv</p> <p>1989 hackfort microarthropods data.csv</p> <p>2019 hackfort microarthropods data.csv</p> <p>pH data Hackfort 1959-2019.csv.</p> <p> </p> <p>Explanation of the variables in the datasets:</p> <p>higher taxon: Oribatida, Astigmata, Mesostigmata, Prostigmata, Collembola or Protura</p> <p>Name in De Gunst 1959: taxonomic identification by De Gunst in 1959</p> <p>Valid name: Henk Siepel re-checked these species names in 2019</p> <p>Plot: plot 1, plot 2, plot 3, plot 4, plot 5</p> <p>a: identified by Yuxi Guo</p> <p>b: re-checked by Henk Siepel from remaining soil microarthropods in slide</p> <p>pH(KCL) and pH(H2O): pH values based on indicated methods</p> <p> </p>
Phosphorous fertilization and soil pH affect the growth of deciduous trees in a temperate hardwood forest
<p>To better understand how a forest’s response to P limitation and acidic deposition can change over time, we added P, limestone to raise pH, and a cross-treatment where both P and limestone were added to 3 different northeastern Ohio forest stands over a 12-year period. Internally, we call this experiment APEX, which stands for Acid Precipitation EXperiment. We tracked diameter at breast height (DBH) of the trees annually, conducted foliar nutrient analyses, and collected tree roots to assess treatment impacts on mycorrhizal colonization. We analyzed our dataset in three sections: the first 6 years after manipulation, the latter 6 years, and the entire 12-year period. These sections allowed us to compare differences between early responses to manipulation and later responses. The R code included here shows how these sections of data were analyzed using linear mixed effect models and Tukey post hoc tests (with the R packages lme4 and multcomp, respectively) and graphed (with the package ggplot2). The three R code files include analyses of 1) litter biomass and chemistry (APEX_leaf_litter_R_code.R), 2) ectomycorrhizal (EM) and arbscular mycorrhizal (AM) fungal colonization and root biomass estimates from trees associated with these mycorrhizal types (APEX_mycorrhizal_roots_R_code.R), and 3) relative basal area increment that was calculated for different tree species and mycorrhizal association types using DBH measurements (APEX_RBAI_R_code.R). All input csv files are included here.</p>
Map sheet 25-132 Lipník n. B. - Results of soil chemical analyses and pH of soil leachate
<p>Results of soil chemical analyses and pH of soil leachate from the 25-132 Lipník nad Bečvou map sheet</p>
Extractable NH4-N and NO3-N (2 N KCl), PO4-P (0.025 N HCl) and pH (0.01 M CaCl2) were measured on soils from a transect along the Dalton road, Arctic LTER 1991.
Extractable NH4-N and NO3-N (2 N KCl), PO4-P (0.025 N HCl) and pH (0.01 M CaCl2) were measured on soils from a transect along the Dalton road. Sites are Gus Shaver flowering sites and Arctic LTER sites.
Soil pH: 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
Soil pH: Effect of Burning Patterns on Vegetation in the Fish Lake Burn Compartments
This study examines the effects of long-term prescribed burning treatments on vegetation structure and composition, productivity, and nutrient cycling in upland oak savanna and woodland vegetation. The basis for the study is an ongoing, experimental prescribed burning program begun in 1964 at Cedar Creek, and a similar program operating since 1962 on the adjacent Helen Allison Savanna property (owned by The Nature Conservancy). These prescribed burning programs are designed to subject upland oak communities (and some old fields) to different burn frequencies and patterns of burning, with the ultimate objectives of 1) restoring and maintaining the historically important savanna and open woodland vegetation, and 2) providing information about the effects of different burning patterns on vegetation structure and composition. This study addresses the latter of these two purposes and expands on it by also investigating possible influences of fire on resource availability (nutrients, water, and light) and net primary productivity. This study represents a continuation and expansion of experiments 015 and 094.
Measurements of Soil pH and Soil Cations Along an Elevational Gradient
This project is part of a larger examination of site productivity along an elevational gradient. Soil pH was measured at each site. A subset of soil samples were analyzed for soil cations, e.g., K, Ca, Mg, and PO4-P, at each of the five terrestrial gradient plots located at Coweeta Hydrologic Lab, Otto, NC.
Soil pH on the Main Cropping System Experiment at the Kellogg Biological Station, Hickory Corners, MI (1989 to 2013)
Dataset Abstract Soil pH was measured on field-moist soils from the LTER Main Site beginning in 1989 and from Successional and Forested sites beginning in 1991. original data source http://lter.kbs.msu.edu/datasets/31
Data from: Environmental filtering by pH and soil nutrients drives community assembly in fungi at fine spatial scales
Whether niche processes, like environmental filtering, or neutral processes, like dispersal limitation, are the primary forces driving community assembly is a central question in ecology. Here, we use a natural experimental system of isolated tree "islands" to test whether environment or geography primarily structures fungal community composition at fine spatial scales. This system consists of isolated pairs of two distantly-related, congeneric pine trees established at varying distances from each other and the forest edge, allowing us to disentangle the effects of geographic distance versus host and edaphic environment on associated fungal communities. We identified fungal community composition with Illumina sequencing of ITS amplicons, measured all relevant environmental parameters for each tree - including tree age, size, and soil chemistry - and calculated geographic distances from each tree to all others and to the nearest forest edge. We applied generalized dissimilarity modeling to test whether total and ectomycorrhizal fungal (EMF) communities were primarily structured by geographic or environmental filtering. Our results provide strong evidence that, as in many other organisms, niche and neutral processes both contribute significantly to turnover in community composition in fungi, but environmental filtering plays the dominant role in structuring both free-living and symbiotic fungal communities at fine spatial scales. In our study system, we found pH and organic matter primarily drive environmental filtering in total soil fungal communities and that pH and cation exchange capacity – and, surprisingly, not host species - were the largest factors affecting EMF community composition. These findings support an emerging paradigm that pH may play a central role in the assembly of all soil mediated systems.
Soil grid data for 4 agricultural fields in PT (ECe; soil organic carbon, pH)
<p>Soil data collected in an agricultural area with annual crops in Portugal (Lezíria Grande). The data refers to soil properties of 63 soil samples collected at a depth of 0-20 cm, considering a regular sampling grid, in four fields with varying soil salinity (field areas between 2 and 34 ha). The samples were collected at a period when the soil was bare, 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, and pH1:5. </p><p>The data may be representative of the soil conditions of the area, which is a highly productive agricultural low land, 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 (WP5 (WP5-T3) and WP2 (WP2-T3)).The aim of the collected dataset was to be able to analyze the influence of soil salinity in SOC prediction from remote sensing.</p><p>Data in the form of MS Excel files (xlsx), pictures of the soil surface in jpg. format. </p>
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