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102 results for “soil conditioning”
Organic carbon dynamics in clay soils: impact of management practices on microorganism structure and abundance under semi-arid conditions
<p>Proper management of soil organic matter in arid and semi-arid regions improves organic carbon storage in the soil, helps in compact soil degradation, mitigates climate change impacts, and preserves ecosystem functionality and sustainability food security. This study aims to provide a better insight into the biogeochemical processes that drive the organic carbon dynamics of saline clay soil in a semi-arid area. The study is not intended to be exhaustive but contributes to analyzing the relationship between bacterial microflora, physicochemical properties, and organic carbon dynamics as a function of different soil management modes. The monitoring was carried out on three different plots located at the National Institute of Agronomic Research of Algeria. A physicochemical characterization of the soils was performed. A metagenomic study was also conducted to identify bacterial biodiversity using PCR-amplified DNA sequencing. The study results show that the control plot has the highest average organic carbon stock value at 47 Mg ha-1. This was followed by the amended plot and the conventional plot, respectively, with 43 Mg ha-1 and 38 Mg ha-1. In the context of this study, organic carbon dynamics would appear to depend on the interaction of several biotic and abiotic factors. Soil management methods would impact the density and diversity of bacterial microflora. This, in turn, affects the soil's physicochemical properties and, more specifically, organic carbon dynamics and storage.</p>
Data from: Plant community evenness responds to spatial plant-soil feedback heterogeneity primarily through the diversity of soil conditioning
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Interspecific trait variability and local soil conditions modulate grassland model community responses to climate
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Soil conditions drive belowground trait space in temperate agricultural grasslands
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Data from: Soil conditioning affects interactions between native and invasive exotic perennials of semi-natural grasslands
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Data from: Organization of biogeochemical nitrogen pathways with switch-like adjustment in fluctuating soil redox conditions
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Root morphological responses to population density vary with soil conditions and growth stages
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Data from: No evidence that plant-soil feedback effects of native and invasive plant species under greenhouse conditions are reflected in the field
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Groundwater and Soil Moisture Conditions from GRACE and GRACE-FO Data Assimilation L4 7-days 0.125 x 0.125 degree U.S. V4.0 (GRACEDADM_CLSM0125US_7D) at GES DISC
Scientists at NASA Goddard Space Flight Center generate groundwater and soil moisture drought indicators each week. They are based on terrestrial water storage observations derived from GRACE satellite data and integrated with other observations, using a sophisticated numerical model of land surface water and energy processes.This data product is GRACE Data Assimilation for Drought Monitoring (GRACE-DA-DM) U.S. Version 4.0 data product and supersedes the GRACE-DA-DM Version 2.0. The GRACE-DA-DM U.S. V4.0 is based on the Catchment Land Surface Model (CLSM) Fortuna 2.5 version simulation that was created within the Land Information System data assimilation framework (Kumar et al., 2016). This simulation used the latest GRACE RL06 (GRACE; 2002-2017) and GRACE Follow On (GRACE-FO; 2018-present) Mascon solutions version 2, at 0.25 degree resolution, from the University of Texas at Austin (Save et al., 2016; Save, 2020). The CLSM soil parameters were updated to address a soil moisture dry limit issue found near Zapata, Texas. Because the root zone soil moisture frequently reaches the dry limit there, drought conditions are often “normal” when the area should be in drought. The new soil parameters resolved the issue, and the root zone soil moisture now matches closely the in-situ observation near Zapata. In the data assimilation, the baseline for Terrestrial Water Storage anomaly computation was updated to the 2003-2019 mean, whereas previous simulations used the 2003-2016 mean. The percentile computation was switched to a 7-day moving average climatology, instead of monthly, to improve the temporal transition of drought/wetness conditions.The GRACE-DA-DM V1.0 was created by the stand alone CLSM (an older version) using the GRACE-Tellus 1 degree data from the Center for Space Research at University of Texas. The GRACE data assimilation (DA) is executed on a grid-to-grid basis in V2.0, while a basin scale average was used in V1.0 (Zaitchik et al. 2008). The V2.0 data were reprocessed (on June 14, 2017), using the GRACE RL05 Mascon solutions version 1 data set from UT CSR, for the entire period from April 1, 2002 to June 5, 2017. The reprocessing included fixes in the DA and increased the bedrock depth by 3 meters to enhance the drought indicator calculations. The GRACE-DA-DM U.S. V4.0 uses the same configuration as the V2.0 for the DA scheme and increased bedrock depth, with the updates previously mentioned, thus supersedes the previous versions. The GRACE-DA-DM U.S. V4.0 data product contains three drought indicators: Groundwater Percentile, Root Zone Soil Moisture Percentile, and Surface Soil Moisture Percentile. These drought indicators express wet or dry conditions as a percentile, indicating the probability of occurrence within the period of record from 1948 to 2014. The drought indicator data are daily, but available only one day (Monday) per week. The data have a spatial resolution of 0.125 x 0.125 degree over North America and range from April 1, 2002 to present (with a 3-6 months latency). The data are archived in NetCDF format.
Groundwater and Soil Moisture Conditions from GRACE and GRACE-FO Data Assimilation L4 7-days 0.25 x 0.25 degree Global V3.0 (GRACEDADM_CLSM025GL_7D) at GES DISC
Scientists at NASA Goddard Space Flight Center generate groundwater and soil moisture drought indicators each week. They are based on terrestrial water storage observations derived from GRACE-FO satellite data and integrated with other observations, using a sophisticated numerical model of land surface water and energy processes.This data product is GRACE Data Assimilation for Drought Monitoring (GRACE-DA-DM) Global Version 3.0 from a global GRACE and GRACE-FO data assimilation and drought indicator product generation (Li et al., 2019). It varies from the other GRACE-DA-DM products which are from the U.S. GRACE-based drought indicator product generation (Houborg et al., 2012).The GRACE-DA-DM Global V3.0 is similar to the GRACE-DA-DM U.S. V4.0 product. Both products are based on the Catchment Land Surface Model (CLSM) Fortuna 2.5 version simulation that was created within the Land Information System data assimilation framework (Kumar et al., 2016). GRACE-DA-DM Global V3.0 drought indicator maps are derived from the GLDAS_CLSM025_DA1_D product, at 0.25 degree resolution, forced by ECMWF meteorological data, and assimilated RL06 GRACE and GRACE-FO data from the University of Texas at Austin (Save et al., 2016; Save, 2020). The GRACE-DA-DM U.S. V4.0 is at 0.125 degree, which is based on a model simulation (not published at GES DISC) forced by NLDAS-2 meteorological data and assimilated with RL06 GRACE/GRACE-FO data. More information on GRACE-DA-DM U.S. V4.0 and previous versions of the data can be found in the README.The GRACE-DA-DM Global V3.0 data product contains three drought indicators: Groundwater Percentile, Root Zone Soil Moisture Percentile, and Surface Soil Moisture Percentile. These drought indicators express wet or dry conditions as a percentile, indicating the probability of occurrence within the period of record from 1948 to 2014. The drought indicator data are daily, but available only one day (Monday) per week. The data have a spatial resolution of 0.25 x 0.25 degree with global coverage (60S, 180W, 90N, 180E), and a temporal range from February 2003 to present (with a 3-6 month latency). The data are archived in NetCDF format.The GRACE-DA-DM is an operational project which produces groundwater and soil moisture drought indicators each week. The operational data is available weekly with a 2-9 day latency from the NASA GRACE project home page found under the Documentation tab. The GRACE-DA-DM data distributed here at GESDISC is the final archive version, which is generated after the latest GRACE-FO data are available.
iPOTs: Internet of Things-based pot system controlling optional treatment of soil water condition for plant phenotyping under drought stress
GEO Series GSE171578. Oryza sativa. 36 samples. Type: Expression profiling by high throughput sequencing.
Transcriptional plasticity of a soil arthropod across different ecological conditions
GEO Series GSE21213. Folsomia candida. 112 samples. Type: Expression profiling by array.
Single-cell RNA-seq of Kitaake rice roots grown in gel, and non-compacted soil conditions
GEO Series GSE251706. Oryza sativa. 18 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome assembly and microarray construction for Enchytraeus crypticus, a model oligochaete to assess stress response mechanisms derived from soil conditions
GEO Series GSE51838. Enchytraeus crypticus. 8 samples. Type: Expression profiling by array.
Expression analysis in roots and leaves of asparagus bean accesions B47 and B128 under well-watered and soil drought conditions
GEO Series GSE63636. Vigna unguiculata; Vigna unguiculata subsp. sesquipedalis. 24 samples. Type: Expression profiling by array.
Gene expression in droughted and well-watered maize roots in diverse, differentially conditioned soil microbiomes
GEO Series GSE282587. Zea mays. 132 samples. Type: Expression profiling by high throughput sequencing.
Global expression analysis of differential expression of genes working under Soil Water Stress condition in different genotypes of rice in Vegetative and Grain-filling stage
GEO Series GSE63719. Oryza sativa. 24 samples. Type: Expression profiling by array.
Gene expression in droughted and well-watered eastern gamagrass roots in diverse, differentially conditioned soil microbiomes
GEO Series GSE282586. Tripsacum dactyloides. 133 samples. Type: Expression profiling by high throughput sequencing.
Global expression analysis of differential expression of genes working under Soil Water Stress condition in different genotypes of rice in Vegetative and Grain-filling stage [set 1]
GEO Series GSE49364. Oryza sativa. 12 samples. Type: Expression profiling by array.
Global expression analysis of differential expression of genes working under Soil Water Stress condition in different genotypes of rice in Vegetative and Grain-filling stage [set 2]
GEO Series GSE63718. Oryza sativa. 12 samples. Type: Expression 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.
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DANDI Archive for NWB datasets
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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.