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4 results for “pedogenesis”
Elemental behaviour during weathering and pedogenesis
<p>Geochemical data of six soil profiles, soil and bedrock samples were analyzed to ascertain the source of red soil materials and, investigate elemental behaviour and weathering intensity across different lithologies in Indonesia. The tested parameters to include major, trace and rare earth elements were done by Beijing Research Institute of Uranium Geology. The concentrations of major elements were determined by an X-ray fluorescence spectrograph (XRF) using the Philips PW2404 X-ray fluorescence spectrometer.</p>
Supplementary Data for "Assessing weathering, pedogenesis, and silt generation in granitoid-hosted soils from contrasting hydroclimates"
<p>Supplementary data includes original sample names and locations, raw geochemical and granulometric data, and calculated geochemical data represented as figures for the manuscript titled "<strong>Assessing weathering, pedogenesis, and silt generation in granitoid-hosted soils from contrasting hydroclimates"</strong>. All datasets are in the same excel file on separate tabs. All data were processed according to established procedures cited in the manuscript text. All software used is open access: ImageJ, adobe illustrator, and Microsoft excel.</p>
Tracing topography-dependent diagenesis and pedogenesis with iron oxides and magnetic particles on a volcano
<p>Data supporting this research are available for people who engage in scientific research. Note that the data can not be used for any commercial activities.</p>
Data from: Turfgrass pedogenesis under low maintenance: an experimental analysis with Festuca rubra subspecies at different fertilization levels
<p>Fine-leaved turfgrass (sub)species can deal with stress situations resulting from climate change and a legislatory ban on chemical pest control, without losing turfgrass playability in golf. Low maintenance, including very low N-input and non-disturbance, is suggested to support turfgrass playability via control of excessive plant biomass growth to overcome soil layering. In a pot experiment, the development of visible plant biomass (VPB) in distinctive soil layers, thatch, mat and deeper soil layers, was analyzed in response to low N fertilization levels and non-disturbance maintenance, in three monocultures of fine-leaved <em>Festuca rubra</em> subspecies over the first 4 years of establishment. </p> <p>The experiment was designed as a factorial randomized block design with 4 replicates, consisting three N-fertilizer application levels (30, 60 and 90 kg N.ha<sup>-1</sup>.year<sup>-1</sup>), 3 <em>Festuca rubra</em> subspecies and three varieties per subspecies (Table 5.1). Varieties grew during 4 years in outdoor placed PVC pots (12.5 l, Ø 20 cm) constructed as a sandy USGA green with 30 cm rootzone over 10 cm gravel layer (USGA, 2015) at Lumbricus research facility in Herveld, The Netherlands (51°54’10.017”N, 5°44’10.586”E). The rootzone consisted a 95/5 v/v sand/heather compost mixture containing sand with a median grain size of 330 µm. For practical reasons pots were mown manually with a scissor, once a week at 6 mm height during growing season with clippings removed. Pots were only irrigated with tap water during dry warm periods when soil moisture content in the upper 6 cm dropped below 8% v/v,.</p> <p>An indication of growth rate was obtained from the collection of turfgrass clippings per pot during 7 months of the growing period in 2019. Fresh weight was measured monthly by collecting 4 clipping rounds per pot and stored in a fridge at 4 °C until measurements on a compound sample per pot at the end of the month were taken. In each pot, cores of the top 20 cm of the soil including aboveground biomass were taken with a core sampler (diameter 28 mm) in May/June 2018, 2019, 2020 and 2021 and in October 2018 and 2019. Cores were immediately divided into 4 distinctive layers, thatch + aboveground biomass, mat, remaining upper 10 cm soil and 10-20 cm soil. Distinction of the 4 layers followed the protocol of Evers et al. (2024). Aboveground biomass was separated with a scissor from the thatch. Sediment from thatch, mat, remaining upper 10 cm of soil and 10-20 cm soil was then carefully washed out with tap water, after which the remaining below-ground (dead and living) visible plant biomass and aboveground biomass was dried at 65 °C until stable weight and weighed. Total C and N analyses were carried out, from which C and N concentrations (in % of dry matter of plant biomass) and C to N ratios (CN ratio) were calculated. Immediately after the last moment of measurements in June 2021, one cultivar per subspecies of all these three cultivars were selected for microbial analyses in thatch and mat layers in both the low and high N-fertilizer input treatments, in all 4 replicates. Profiling of microbial communities via phospholipid fatty acid (PLFA) analysis was carried out following the protocol of Buyer and Sasser (2012). The MIDI Sherlock PLFA Software Package (MIDI, Newark, Delaware, USA) was used for peak identification.</p> <p>Per turfgrass subspecies or variety, the best fitted line for thatch and mat depth development, i.e., with the smallest <em>rse</em>, was iteratively calculated by linear least square regression with the lm function in R (version 4.1.3; 2022-03-10) and by non-linear least square regression with the nls function in R. For the fitting of the statistical models, either a linear function (<em>F </em>(χ) = αχ +β), a logistic function (<em>F </em>(χ) = α/(1+e<sup>-(βχ+γ)</sup>) or a hyperbolic function (<em>F </em>(χ) = αχ/(β + χ)), was used, based on the best fit for the respective turfgrass species or variety.Best fit curves for the thatch and mat depth were determined from RMSE with the RMSE function in the qpcR package of R giving the possibility to compare the best fit between linear and non-linear models, and <em>p</em> values after examination of various possible models. With SPSS the effect of subspecies and N-fertilizer over time on growth rate, visible plant biomass concentration and CN ratio in a distinctive soil layer was analyzed with general linear models with time as repeated measurement. Mauchly’s test of sphericity was used with p < 0.05 for choosing the correct test of within-subject effects. When sphericity was not assumed, statistical correction of chi-square was done conservatively with Greenhouse-Giesser method when epsilon < 0.75 and with the lower-bound method when epsilon > 0.75. Multiple comparison for means across groups was done with Bonferroni's post hoc test when equal variances between groups were assumed.</p>
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Allen Brain Atlas
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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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