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14 results for “Artificial soil”
Manual soil moisture measurements from ten artificial forest gaps at the Coweeta Hydrologic Laboratory, North Carolina, 2000-2018
Ten artificial forest gaps were created in March 2002 at Coweeta, following two years of pretreatment data collection. Experimental gaps were created by pulling canopy trees with a winch until they were down. Trees, saplings, and seedlings were censused and tracked as part of a demography study. Soil moisture data was collected during the growing season as an explanatory variable for tree survivorship and mortality.
CASM: A long-term Consistent Artificial-intelligence based Soil Moisture dataset based on machine learning and remote sensing
<p>Paper to cite: Skulovich, O., Gentine, P. A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset. <em>Sci Data</em> 10, 154 (2023). https://doi.org/10.1038/s41597-023-02053-x</p> <p> </p> <p>The Consistent Artificial Intelligence (AI)-based Soil Moisture (CASM) dataset is a global, consistent, and long-term, remote sensing soil moisture (SM) dataset created using machine learning. It is based on the NASA Soil Moisture Active Passive (SMAP) satellite mission SM data as a target and is aimed at extrapolating SMAP-like quality SM data back in time with previous satellite microwave platforms. Machine learning approach, such as neural network (NN) has the advantage of being both nonlinear, and state-dependent, and naturally imposing a global distribution matching between the source and the target data. Utilizing this, the new CASM dataset was created using high-quality SMAP SM as a target and Soil Moisture and Ocean Salinity (SMOS) or Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E/2) brightness temperature as a source, which allowed extrapolating SM data 13 years back from before SMAP mission launch. CASM represents SM in the top soil layer, defined on a global 25 km EASE-2 grid and covers 2002-2020 with a 3-day temporal resolution. The resulting dataset exhibits excellent spatial and temporal homogeneity, without compromising the interannual variability, and is in excellent agreement with the SMAP data (with a mean correlation of 0.97 between the SMAP and CASM SM for the period when the two overlap). Moreover, the input and target datasets were divided into seasonal cycle and residuals, with the NN trained on the residuals. This approach ensures that the high performance does not mask a simple seasonal cycle matching but rather exemplifies the skill targeted at predicting extremes; with the NN achieving a correlation of 0.75 on the test data for the residuals. Comparison to 367 global in-situ SM monitoring sites shows a SMAP-like median correlation of 0.66 between station SM and CASM SM from the corresponding grid cell. Additionally, the SM product uncertainty was assessed, and both aleatoric and epistemic uncertainties were estimated and included in the dataset. Mean epistemic uncertainty, related to the NN model structure, ranges from 0.007 m<sup>3</sup>/m<sup>3</sup> to 0.014 m<sup>3</sup>/m<sup>3</sup> and on average is close to a desired SM product stability threshold of 0.01 m<sup>3</sup>/m<sup>3</sup> per year. Aleatoric uncertainty, defined as input noise propagated through the system, depends on the introduced level of noise. With 10% noise applied to the residuals, the resulting mean standard deviation of the model outputs rises from 0.005 to 0.007 m<sup>3</sup>/m<sup>3</sup>. </p>
Artificial humic acid diminishes the effect of drought on the soil microbiome
<p><span lang="EN-US">Humic substances have an enormous potential for regenerative agriculture to improve soil quality and plant growth. Recently developed technologies called hydrothermal humification enabled the conversion of waste into artificial humic acids, that would allow for sustainable and large-scale applications. However, not much is known about the effect of artificially produced humic acid on the soil microbiome, and its effect on drought-exposed soil. Therefore, we studied the effect of drought stress and artificial humic acid on the soil microbiota in sandy soil in a controlled experimental design. Analyses of 16S rDNA amplicon libraries by bioinformatics and statistics revealed that both, drought and artificial humic acid application, influenced bacterial community composition significantly, but only artificial humic acid affected bacterial diversity. Bacterial families like <em>Pseudomonadaceae</em>, <em>Peptostreptococcaceae</em> and <em>Moraxellaceae</em> enriched under artificial humic acid conditions, suggest an adaptation and selection of the soil bacterial microbiome. Under drought stress, artificial humic acid treatment kept bacterial diversity stable in the changed bacterial community composition. We propose that artificial humic acid application in sandy soil can improve the soil bacterial community, diminish drought stress, favor plant growth-promoting taxa, and bring enormous potential to sequestrate carbon in soil.</span></p>
Raw data for the submitted manuscript: The Effect of Clay Type on the Toxicity of Carbendazim and Imidacloprid to the Earthworm Eisenia andrei in Artificial Soils
<p>Raw data obtained from toxicity tests with the earthworm <em>Eisenia </em><em>andrei </em>exposed for 56 days to carbendazim and imidacloprid in artificial soils created with kaolin and bentonite clay. Tests were performed following OECD guideline 222. The file includes data on earthworm starting and ending weights, survival, and reproduction.</p>
Surface and subsurfaca data collected during artificial rainfall experimetns on tilled soil with wheel tracks
<p>This dataset contains data measured during the artificial rainfall experiment conducted within the project LTC18030 - The effects of land use changes on soil erosion, sediment transport, water quality and runoff conditions in 2018 and 2019 at experimental site Řisuty, Czech Republic.</p> <p>This dataset contains raw data (xlsx, csv) and images of more qualitative data such as electrical resistivity tomography profiles.</p> <p>For further information about the experiments and analysis please check: Jeřábek, J., Zumr, D., Laburda, T., Krása, J., Dostál, T., 2022. Soil surface connectivity of tilled soil with wheel tracks and its development under simulated rainfall. J. Hydrol. 613. https://doi.org/10.1016/j.jhydrol.2022.128322</p> <p><strong>The dataset consists of following files: </strong></p> <ul> <li>experimental_plots_overview.pdf: overview of the setup and map of the experimental site</li> <li>electrical_resistivity_tomography.pdf: overview of the ERT measurement is provided in this file</li> <li>penetrometry.pdf: penetrometry was done in and in the vicinity of the experimental plot</li> <li>basic_variables_of_experiments.xlsx: basic information about each of the experiments</li> <li>measuted_soil_hydraulic_properties.xlsx: summarised the measured soil hydraulic propertie for all used method and for each plot</li> <li>optimized-parameters-all.xlsx: optimal parameters for all optimized scenarios</li> <li>optimized-parameters-stats.xlsx: statistics of optimal parameters for each soil layer and plot</li> <li>vol1-soil-water-retention-data.xlsx: soil water retention data from the sand tank and pressure chamber measurement of the vol1 experiment</li> <li>vol2-soil-water-retention-data.xlsx: soil water retention data from the sand tank and pressure chamber measurement of the vol2 experiment</li> <li>soil_loss.csv: soil loss time series</li> <li>surface_runoff.csv: surface runoff time series</li> <li>soil_water_pressure.csv: soil water pressure time series</li> <li>soil_water_pressure_positions.csv: position of tensiometers on each plot</li> <li>volumetric_water_content.csv: volumetric water content time series</li> <li>volumetric_water_content_positions.csv: position of vwc probe on each plot</li> <li>start_stop_rainfall_info.csv: information about the time span of each experiment</li> </ul>
Interpretable Geotechnical Artificial Intelligence (XGeoT-AI) Application to Demystify Image Recognition of Soil Cracks [Datasets]
<p>Here is the test data for the paper "Interpretable Geoscience Artificial Intelligence (XGeoS-AI): Application to Demystify Image Recognition".</p>
Simulated winter snow, soil thermal regime, and artificial drainage dynamics under climate change.
<p>This dataset provides simulated winter subsurface drainage outputs from the calibrated Root Zone Water Quality Model–Simultaneous Heat and Water (RZ-SHAW) and four machine-learning models (Cubist, LSTM, Multilinear, SVM) for five agricultural research sites across Eastern Canada (Alfred, Harrow, Kentville, Ottawa-Greenbelt, and St-Emmanuel). The simulations span from 1950 to 2100 under the high-emission climate change scenario RCP8.5, derived from the Canadian Regional Climate Model Large Ensemble (CANRCM4 LE).</p> <p>Included in this repository are:</p> <ul> <li> <p>Winter drainage volume's future projections from 1950 to 2100 the RZ-SHAW and machine-learning models.</p> </li> <li> <p>RZ-SHAW simulated future projections from 1950 to 2100 in snow cover, soil thermal conditions, subsurface drainage volume and frequency, evaporation, surface runoff, and soil water storage.</p> </li> </ul> <p>This dataset also contains simulated annual crop yield projections (kg/hectare) from 1950 to 2100 the five sites Alfred (corn), Harrow (corn and soybean), Kentville (corn), Ottawa (corn), St-Emmanuel (corn). Simulations were performed using the RZ-SHAW model under RCP8.5 climate change scenario. Each site's data includes annual grain yield values and Sen's slope trend analysis with Mann-Kendall significance test results.</p> <p>Users should note the following important limitations when interpreting these yield projections:</p> <ol> <li>Simplified Crop Growth Parameters: The crop module was implemented primarily to account for crop residue effects on winter soil conditions, not for yield prediction. No calibration was performed for crop growth parameters.</li> <li>No Nutrient Stress: Simulations were run without nitrogen or phosphorus limitations for simplicity, which may overestimate yields in scenarios where nutrient availability would be constraining.</li> <li>Fixed Management Practices: Planting and harvest dates were held constant throughout the simulation period (1950-2100) based on historical averages, which becomes increasingly unrealistic under future climate conditions.</li> <li>No CO₂ Fertilization Effect: Atmospheric CO₂ concentration changes were not incorporated, potentially underestimating photosynthetic responses in future climate scenarios.</li> <li>Limited Stress Responses: The model's representation of heat stress, drought tolerance, and other physiological responses to extreme conditions may not fully capture crop responses under future climate scenarios.</li> <li>No Adaptation Strategies: The simulations do not account for potential adaptations such as cultivar changes, shifting planting dates, or irrigation implementation that would likely occur in response to changing conditions.</li> </ol>
Supplementary material 2 from: Niemi M, Pöyry J, Heiskanen I, Uotinen V, Nieminen M, Erkomaa K, Wallenius K (2014) Variability of soil enzyme activities and vegetation succession following boreal forest surface soil transfer to an artificial hill. Nature Conservation 8: 1-25. https://doi.org/10.3897/natureconservation.8.6369
Figure S1: Explanation note: The studied sites at the onset of the study in June 2003: a) Top b) Grove c) Middle d) North e) Alder f) Spruce.
Supplementary material 5 from: Niemi M, Pöyry J, Heiskanen I, Uotinen V, Nieminen M, Erkomaa K, Wallenius K (2014) Variability of soil enzyme activities and vegetation succession following boreal forest surface soil transfer to an artificial hill. Nature Conservation 8: 1-25. https://doi.org/10.3897/natureconservation.8.6369
Figure S4: Explanation note: The studied s ites after a decade in June 2013: a) Top, b) Grove c) Middle d) North.
Vedioes of artificial rainfall experiment of wide-grade colluvium slope and vegetated soil slope
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Supplementary material 3 from: Niemi M, Pöyry J, Heiskanen I, Uotinen V, Nieminen M, Erkomaa K, Wallenius K (2014) Variability of soil enzyme activities and vegetation succession following boreal forest surface soil transfer to an artificial hill. Nature Conservation 8: 1-25. https://doi.org/10.3897/natureconservation.8.6369
Figure S2: Explanation note: The studied sites in August 2003: a) Top b) Grove c) Middle d) North.
Supplementary material 4 from: Niemi M, Pöyry J, Heiskanen I, Uotinen V, Nieminen M, Erkomaa K, Wallenius K (2014) Variability of soil enzyme activities and vegetation succession following boreal forest surface soil transfer to an artificial hill. Nature Conservation 8: 1-25. https://doi.org/10.3897/natureconservation.8.6369
Figure S3: Explanation note: The studied s ites in August 2005: a) Top b) Grove c) Middle d) North.
Supplementary material 1 from: Niemi M, Pöyry J, Heiskanen I, Uotinen V, Nieminen M, Erkomaa K, Wallenius K (2014) Variability of soil enzyme activities and vegetation succession following boreal forest surface soil transfer to an artificial hill. Nature Conservation 8: 1-25. https://doi.org/10.3897/natureconservation.8.6369
Table S1: Explanation note: Vegetation data used in the ordination analyses.
Artificial reforestation has less diverse soil nitrogen-cycling genes than natural restoration
GEO Series GSE100379. Bacteria; Viruses; Archaea; Fungi. 42 samples. Type: Genome variation profiling by array.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.