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200 results for “Agroecosystem”
Carbon decomposition and nitrogen mineralization in Marsden agroecosystem diversification experiment, Iowa, 2021
This dataset contains measurements of soil carbon decomposition and nitrogen mineralization from 36 soils in the Marsden agroecosystem diversification experiment. Soil samples were collected in fall 2021 from a long-term experiment initiated in 2002 at Iowa State University's Marsden Farm. The dataset includes laboratory data on soil organic carbon (SOC) content, soil total nitrogen content, the carbon-to-nitrogen ratio of soil, time-series CO2 fluxes from SOC decomposition in a 13.5-month lab incubation, and soil nitrogen mineralization rates. It also provides model simulations of SOC decomposition from different carbon pools using three process-based models: the Agricultural version of the Integrated Biosphere Simulator (Agro-IBIS), CN-SIM, and the Microbial-ENzyme Decomposition (MEND) models.
Accompanying dataset; 'Agroforestry enhances biological activity, diversity and soil-based ecosystem functions in mountain agroecosystems of Latin America: A meta-analysis.'
<p>The database created as part of the meta-analysis is designed to facilitate the comparison of biological activity, diversity (BIAD), and ecosystem functions (EFs) between agroforestry systems (AFS) and other land-use types. It incorporates data extracted from selected studies, each record comprising a mean value, sample size, and a variance measure to compute standard deviation. The database also categorizes data according to 22 explanatory variables, including geographical coordinates, climate classification, soil type, AFS classification, and more, to characterize the sites and management systems involved. This detailed classification enables a nuanced analysis of how different factors might influence the BIAD and EFs in the context of AFS. The database supports the meta-analysis by allowing for the estimation of effect sizes using response ratios, which compare the relative difference in BIAD and EFs between AFS and other land uses. Data extraction from primary studies was meticulous, employing both direct and indirect methods such as graph digitizing software, and missing data were supplemented using reliable sources or direct communication with the original study authors. The comprehensive nature of this database ensures that the analysis can account for a wide range of variables that may affect the outcomes of interest in the meta-analysis. </p><p>For an in-depth exploration of the study's findings and methodology, refer to the comprehensive meta-analysis available in Global Change Biology (2024), entitled "<i>Agroforestry Enhances Biological Activity, Diversity, and Soil-Based Ecosystem Functions in Mountain Agroecosystems of Latin America: A Meta-Analysis</i>."</p>
Data for: Diversity of Functional Edaphic Macrofauna in Musa acuminata x Musa balbisiana (AAB) Agroecosystems
<p>The Dataset is linked to the article <strong>Diversity of Functional Edaphic Macrofauna in <em>Musa acuminata x Musa balbisiana</em> (AAB) Agroecosystems.</strong> The collected individuals were analyzed by order, and family and quantified and identified by gender (Database (Oxford). 2020: baaa062. PubMed: 32761142 PMC: PMC7408187.), The collection and taxonomic identification phase is explained in the protocol.i (dx.doi.org/10.17504/protocols.io.rm7vzby75vx1/v1).</p> <p>This dataset was a modification as was indicated for the #GlobalSoilMacroFauna | Official template to report Data to the MACROFAUNA database (<a href="../records/7691884">#GlobalSoilMacroFauna | Official template to report Data to the MACROFAUNA database (zenodo.org)</a>) cited by [Mathieu, J., Antunes, A. C., Barot, S., Bonato Asato, A. E. ., Bartz, M. L. C. ., Brown, G. G., Calderon-Sanou, I., Decaëns, T., Fonte, S. J., Ganault, P., Gauzens, B., Gongalsky, K. B., Guerra, C. A., Hengl, T., Lavelle, P., Marichal, R., Mehring, H., Peña-Venegas, C. P., Castro, D., Potapov, A., Thébault, E., Thuiller, W., Witjes, M., Zhang, C., & Eisenhauer, N. (2022). sOilFauna - a global synthesis effort on the drivers of soil macrofauna communities and functioning: WORKSHOP REPORT . <em>SOIL ORGANISMS</em>, <em>94</em>(2), 111–126. https://doi.org/10.25674/so94iss2id282]</p>
Quantitative Representativeness and Constituency of the Long-Term Agroecosystem Research Network
<p><strong>Data Description</strong>:</p> <p>The USDA Long-Term Agroecosystem Research (LTAR) Network coordinates agricultural research across 18 research sites in the conterminous United States (CONUS). However, it is unclear how well these sites represent the totality of agricultural working lands within the CONUS. Therefore, we performed a quantitative analysis of the 18 sites, based on 15 climatic and edaphic characteristics, to produce maps of representativeness and constituency across the CONUS. Representativeness shows how well the combination of environmental drivers at each CONUS location was represented by the LTAR sites’ environments, while constituency shows which LTAR site was the closest match for each location.</p> <p>Files in collection (22):</p> <p>Collection contains 11 geospatial rasters and 11 PNGs visualizing them.</p> <p>TIF files:</p> <p>├── conus_ltar_constituency_workinglands.tif [Constituency of LTAR network]<br> ├── conus_ltar_representativeness_workinglands.tif [Representativeness of LTAR network]<br> ├── conus_ltar_v5.pc1.tif [Principal Component 1]<br> ├── conus_ltar_v5.pc2.tif [Principal Component 2]<br> ├── conus_ltar_v5.pc3.tif [Principal Component 3]<br> ├── conus_ltar_v5.pc4.tif [Principal Component 4]<br> ├── conus_ltar_v5.pc5.tif [Principal Component 5]<br> ├── conus_ltar_v5.pc6.tif [Principal Component 6]<br> ├── conus_ltar_v5.pc7.tif [Principal Component 7]<br> ├── LTAR_NEON_LTER_bestnetwork_workinglands.tif [Raster identifying best network, among LTAR, NEON, LTER, representing the location]<br> └── LTAR_NEON_LTER_representativeness_workinglands.tif [Representativeness of combined LTAR + NEON + LTER networks]</p> <p>PNG files:</p> <p>├── conus_ltar_constituency_workinglands.png [Constituency of LTAR network]<br> ├── conus_ltar_representativeness_workinglands.png [Representativeness of LTAR network]<br> ├── conus_ltar_v5.pc1.png [Principal Component 1]<br> ├── conus_ltar_v5.pc2.png [Principal Component 2]<br> ├── conus_ltar_v5.pc3.png [Principal Component 3]<br> ├── conus_ltar_v5.pc4.png [Principal Component 4]<br> ├── conus_ltar_v5.pc5.png [Principal Component 5]<br> ├── conus_ltar_v5.pc6.png [Principal Component 6]<br> ├── conus_ltar_v5.pc7.png [Principal Component 7]<br> ├── LTAR_NEON_LTER_bestnetwork_workinglands.png [Raster identifying best network, among LTAR, NEON, LTER, representing the location]<br> └── LTAR_NEON_LTER_representativeness_workinglands.png [Representativeness of combined LTAR + NEON + LTER networks]</p> <p><strong>Data format</strong>:</p> <p>Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution, while the geospatial visualizations are provided in PNG format.</p> <p><strong>Geospatial projection</strong>: </p> <pre><code class="language-bash">GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS["wgs84", DATUM["WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]] </code></pre> <p><strong>Category labels for Constituency data</strong>:</p> <table> <caption> </caption> <thead> <tr> <th scope="col">Cat</th> <th scope="col">LTAR Siite</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>Archbold-University of Florida</td> </tr> <tr> <td>2</td> <td>Central Mississippi River Basin</td> </tr> <tr> <td>3</td> <td>Central Plains Experimental Range</td> </tr> <tr> <td>4</td> <td>Eastern Corn Belt</td> </tr> <tr> <td>5</td> <td>Great Basin</td> </tr> <tr> <td>6</td> <td>Gulf Atlantic Coastal Plain</td> </tr> <tr> <td>7</td> <td>Jornada Experimental Range</td> </tr> <tr> <td>8</td> <td>Kellogg Biological Station</td> </tr> <tr> <td>9</td> <td>Lower Chesapeake Bay</td> </tr> <tr> <td>10</td> <td>Lower Mississippi River Basin</td> </tr> <tr> <td>11</td> <td>Northern Plains</td> </tr> <tr> <td>12</td> <td>Platte River High Plains Aquifer</td> </tr> <tr> <td>13</td> <td>R.J. Cook Agronomy Farm</td> </tr> <tr> <td>14</td> <td>Southern Plains</td> </tr> <tr> <td>15</td> <td>Texas Gulf</td> </tr> <tr> <td>16</td> <td>Upper Chesapeake Bay</td> </tr> <tr> <td>17</td> <td>Upper Mississippi River Basin</td> </tr> <tr> <td>18</td> <td>Walnut Gulch Experimental Watershed</td> </tr> </tbody> </table> <p><strong>Paper describing data and methods</strong>:</p> <p>Kumar, J., Coffin, A. W., Baffaut, C., Ponce-Campos, G. E., Witthaus, L., & Hargrove, W. W. (2023). Quantitative Representativeness and Constituency of the Long-Term Agroecosystem Research Network and Analysis of Complementarity with Existing Ecological Networks. In Environmental Management. Springer Science and Business Media LLC. https://doi.org/10.1007/s00267-023-01834-9</p> <p> </p>
Data and code for "Optimizing cover crop practices as a sustainable solution for global agroecosystem services"
<p>Data and code for "Optimizing cover crop practices as a sustainable solution for global agroecosystem services" (Qiu et al. 2025), including source data, R scripts, and output results.</p>
Code and data for "KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: A Case Study of Estimating N2O Emission using Data from Mesocosm Experiments "
<p>This is code and data for manuscript: <br> "KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: <br> A Case Study of Estimating N<sub>2</sub>O Emission using Data from Mesocosm Experiments"<br> Licheng Liu, Shaoming Xu, Zhenong Jin*, Jinyun Tang, Kaiyu Guan, Timothy J. Griffis, <br> Matt D. Erickson, Alexander L. Frie, Xiaowei Jia, Taegon Kim, Lee T. Miller, Bin Peng, Shaowei Wu, Yufeng Yang, Wang Zhou, Vipin Kumar</p> <p>All the files belong to Prof. Zhenong Jin, University of Minnesota, UA. jinzn@umn.edu<br> "code" foler includes code for data processing, model training, and results plotting.<br> "trained_model_saved" includes all trained model so you can use to reproduce the results showed in the study;<br> "data" includes all data presented in the study. Finetuning data is refering to Miller, L.T. , Griffis, T. J., Erickson, M. D., Turner, P. A., Deventer, M. J., Chen, Z., Yu, Z., Venterea, R.T., Baker, J. M., and Frie, A. L. (2021). Response of nitrous oxide emissions to future changes in precipitation and individual rain events. Journal of Environmental Quality, In review</p>
Valuing diversification benefits through intercropping in Mediterranean agroecosystems: A choice experiment approach.
<p>This data set contains information from a choice experiment survey developed to value the socio-economic benefits of intercropping practices in Mediterranean agroecosystems.</p> <p>These data correspond to the open-access article "Valuing diversification benefits through intercropping in Mediterranean agroecosystems: A choice experiment approach" published in Ecological Economics. (<a href="https://doi.org/10.1016/j.ecolecon.2020.106593">https://doi.org/10.1016/j.ecolecon.2020.106593</a>), funded by he European Commission Horizon 2020 project Diverfarming [grant agreement 728003]. </p>
Code and Source Data for "Knowledge-Guided Machine Learning can improve C cycle quantification in agroecosystems"
<p>Datasets for code and Source Data for the study "Knowledge-Guided Machine Learning can improve C cycle quantification in agroecosystems" https://doi.org/10.1038/s41467-023-43860-5. All files belong to Licheng Liu and Zhenong Jin at University of Minnesota. deposit_code_v2.zip contains packaged codes and sample runs for KGML-ag-Carbon training, validation and implementations. Source Data.zip contains data for generating the figures inside the study. </p> <p>Note: We used Pytorch 1.6.0 (<a href="https://pytorch.org/get-started/previous-versions/">https://pytorch.org/get-started/previous-versions/</a>, last access: 21 Oct 2023) and Python 3.7.11 (<a href="https://www.python.org/downloads/release/python-3711/">https://www.python.org/downloads/release/python-3711/</a>, last access: 21 Oct 2023) as the programming environment for model development. Statistical analysis, such as linear regression, was conducted using Statsmodels 0.14.0 (<a href="https://github.com/statsmodels/statsmodels/">https://github.com/statsmodels/statsmodels/</a>, last access: 21 Oct 2023) In order to use a GPU to speed-up the training process, we installed the CUDA Toolkit 10.1.243 (<a href="https://developer.nvidia.com/cuda-toolkit">https://developer.nvidia.com/cuda-toolkit</a>, last access: 21 Oct 2023). </p> <p><strong>To use the full kgml_lib function, please create a new environment with the same python and libs above.</strong></p>
Database for: Meta-analysis of the impact of land use intensification on earthworms in global agroecosystems
<p>The dataset comprises a compilation of studies investigating the impact of land use intensification on earthworms across global agroecosystems. Extracted from peer-reviewed publications, the dataset includes various fields such as climate characteristics are described using the Köppen-Geiger climate classification system. Soil properties such as type, texture, pH, and organic content are documented. Additionally, details regarding experimental parameters like replicates, sampling depth, and extraction methods are provided. Furthermore, the dataset encompasses information on agricultural practices including herbicide, insecticide, pesticide usage, fertilizer type and rate, grazing, tillage methods, and days after tillage for earthworm collection. Abundance, diversity, and their associated metrics are recorded for both control and treatment sites.</p>
Fig. 4 in Helminth communities from amphibians inhabiting agroecosystems in the Pampean Region (Argentina)
Fig. 4. Helminth prevalence (A), abundance (B) and richness (C) at infracommunity level, in relation to land use and host species. B. pulchella (Bp), L. latrans (Ll), R. fernandezae (Rf).
Fig. 1. Study area. C1-5 in Helminth communities from amphibians inhabiting agroecosystems in the Pampean Region (Argentina)
Fig. 1. Study area. C1-5: crop sites, L1- 4: livestock sites (C1: 34°55'13''S; 58°06'33''O; C2: 34°55'54''S; 58°04'29''O; C3: 34°57'36''S; 58°04'57''O; C4: 35°01'42''S; 57°59'44''O; C5: 35°03'06''S; 57°58'35''O; L1: 35°04'27''S; 57°57'23''O; L2: 35°07'46''S; 57°53'11''O; L3: 35°02'22,94''S; 57°48'58,8''O; L4: 35°02'23,2''S; 57°48'58,2''O).
Fig. 3 in Black cherry as a host plant for stink bugs (Hemiptera: Pentatomidae) in agroecosystems in Georgia, USA
Fig. 3. Mean number of Chinavia hilaris adults and nymphs detected per scout sample in black cherry tree in 2016 (A) and 2018 (B). Fl = flowering; Gr = green fruit; Pi = pink fruit; Rd = red fruit; Pu = purple fruit; Go = fruit gone.
Fig. 2 in Black cherry as a host plant for stink bugs (Hemiptera: Pentatomidae) in agroecosystems in Georgia, USA
Fig. 2. Mean number of Chinavia hilaris adults and nymphs captured per pheromone-baited trap in black cherry in 2016 (A), 2017 (B), and 2018 (C).
Fig. 1 in Black cherry as a host plant for stink bugs (Hemiptera: Pentatomidae) in agroecosystems in Georgia, USA
Fig. 1. Mean number of Euschistus servus and Euschistus tristigmus adults and nymphs captured per pheromone-baited trap in black cherry in 2016 (A), 2017 (B), and 2018 (C).
Data: Local and landscape factors affect sunflower pollination in a Mediterranean agroecosystem
<p>Data and analysis in .Rmd file for the paper "Local and landscape factors affect sunflower pollination in a Mediterranean agroecosystem".</p>
Figure 3. The 95 in Effects of agroecosystems on insect and insectivorous bat activity: a preliminary finding based on light trap and mist net captures
Figure 3. The 95% family-wise confidence level for multiple comparisons test based on insectivorous bat species analyses. Left: H. aff. ruber; right: H. jonesi.
Figure 2. The 95 in Effects of agroecosystems on insect and insectivorous bat activity: a preliminary finding based on light trap and mist net captures
Figure 2. The 95% family-wise confidence level for multiple comparisons test based on insect order analyses. Left: Lepidoptera; right: Diptera.
Fig. 3 in Diversity and density-dependence relationship between hymenopteran egg parasitoids and the corn leafopper (Hemiptera: Cicadellidae) in maize agroecosystem vs. teosinte wild habitat
Fig. 3. Relationship between the number of exposed Dalbulus maidis eggs and number of Anagrus virlai within the crop maize habitat on (A) maize sentinel plants, and (B) teosinte sentinel plants.
Fig. 1 in Diversity and density-dependence relationship between hymenopteran egg parasitoids and the corn leafopper (Hemiptera: Cicadellidae) in maize agroecosystem vs. teosinte wild habitat
Fig. 1. Relationship between the number of exposed Dalbulus maidis eggs and number of adult parasitoids (of any species) found in the crop maize habitat on (A) maize sentinel plants, and (B) teosinte sentinel plants.
Fig. 3 in Spatiotemporal distribution of the glassy-winged sharpshooter, Homalodisca vitripennis (Hemiptera: Cicadellidae), in a southeastern agroecosystem
Fig. 3. Spatiotemporal distribution patterns of glassy-winged sharpshooters in Gadsden County, Florida, USA. Top = images display red-blue plots based on interpolation of the cluster index for individuals from 2001 to 2003. Red areas indicate significant aggregations (greater than 1.5), and blue areas indicate significant gaps (less than −1.5). Bottom = interpolated density maps display seasonal distribution patterns of glassywinged sharpshooters collected in traps during 2001 to 2003, according to habitat.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.