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101 results for “cover crops”
Reduced erosion augments soil carbon storage under cover crops
This dataset comprises field measurements of soil organic carbon erosion and soil organic carbon stock from 152 paired control and cover crop treatments, collected from 57 published studies worldwide. It also provides related information on the collected study sites, including climate (mean annual temperature and mean annual precipitation), geography (slope and altitude), soil properties (silt+clay and SOC concentration), and agricultural management (cover crop species, tillage intensity and experimental duration). Furthermore, it includes the estimated effect sizes of soil organic carbon erosion reduction induced by cover crops in agricultural lands at the global scale.
3C dataverse: Community capitals, cover crops, & conservation agriculture in the U.S. corn-soybean belt, version 2.2
<p><strong>What? </strong></p> <p>A dataset containing 315 total variables from 33 secondary sources. There are 262 unique variables, and 53 variables that have the same measurement but are reported for a different year; e.g. average farm size in 2017 (CapitalID: N27a) and 2022 (N27b). Variables were grouped by the community capital framework's seven capitals—Natural (96 total variables), Cultural (38), Human (39), Social (40), Political (18), Financial (67), & Built (15)—and temporally and thematically ordered. The geographic boundary is NOAA NCEI's corn and soybean belt (figure below), which stretches across 18 states and includes N=860 counties/observations. Cover crop data for the 80 Crop Reporting Districts in the boundary are also included for 2015-2021.</p> <p><strong>Why? </strong></p> <p>Comprehensively assessing how community capital clustered variables, for both farmers and nonfarmers, impact conservation practices (and perennial groundcover) over time helps to examine county-level farm conservation agriculture practices in the context of community development. We contribute to the robust U.S. cover crop literature a better understanding of how overarching cultural, social, and human factors influence conservation agriculture practices to encourage better farm management practices. Analyses of this Dataverse will be presented as recomendations for farmers, nonfarmers, ag-adjacent stakeholders, and community leaders.</p> <p><strong>How? </strong></p> <p>Variables used in this dataset range 20 years, from 2004-2023, though primary analyses focus on data collected between 2017-2024, primarily 2017 and 2022 (NASS Ag Census years). First, JAM-K requested, accessed, and downloaded data, most of which was already publically available. Next, JAM-K cleaned the data and aggregated into one dataset, and made it publically available on Google Drive and Zenodo. </p> <p><strong>What is 'new' or corrected in version 2.2? </strong></p> <p><em>Edited/amended</em>: Carroll, KY is now spelled correctly (two 'l's, not one); variable names, full and abbreviated, were updated to include the data year; Pike County's (IL) FIPS has been corrected from its wrong 17153 (same as Pulaski County) to 17149 (correct fips), and all Pike County (IL) data has been correctly amended; Farming dependent (ERS) updated for all variables; Data for built capital variables irrCorn17, irrSoy17, irrHcrp17, tractor17, and combine17 were incorrect for v.1, but were corrected for v.2; Several variable labels aggregated by Wisconsin University's Population Health Institute's County Health Rankings and Roadmaps were corrected to have the data's original source and years included, rather than citing CHR&R as the source (except for CHR&R's originally-produced values such as quartiles or rank scores); variables were reorganized by hypothesized community capital clusters (Natural -> Built), and temporally within each cluster. </p> <p><em>Added</em>: 55 variables, mostly from the 2022 Ag Census, and v 2.2 added a .pdf file with descriptives of data sources and years, and a .sav file. </p> <p><em>Omitted</em>: Four variables deemed irrelevant to the study; V1 codebook's "years internally available" column. Variable herbac22 for 55079, Milwaukee, WI, incorrectly had the value 2,049.612. That value was correctly changed to missing, with no data in the cell.</p> <p><strong>CRediT</strong>: conceptualization, CBF, JAM-K; methodology, JAM-K; data aggregation and curation, JAM-K; formal analysis, JAM-K; visualization, JAM-K; supervision, CBF; funding acquisition, CBF; project administration, CBF; resources, CBF, JAM-K</p> <p><strong>Acknowledgements</strong>: This research was funded by the Agriculture and Food Research Initiative Competitive Grant No. 2021-68012-35923 from the United States Department of Agriculture National Institute for Food and Agriculture. Any opinions, findings, conclusions, or recommendations expressed in this presentation are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture. Much thanks to Corteva for granting data access of OpTIS 2.0 (2005-2019), and Austin Landini for STATA code and visualization assistance. </p>
qPCR raw datasets on the assessments of cover crop monocultures and mixtures improving the rhizosphere bacterial abundance and functionality through rerooting
<p>Quantitative PCR (qPCR) raw data includes the source data that corresponds to the the counts of 16S rRNA gene copies per gram of soil for different variations, as discussed in the research article - "Cover crop monocultures and mixtures improve the rhizosphere bacterial abundance and functionality through rerooting". The copy number of the 16S rRNA gene per gram of soil was quantified by SYBR® Green-based qPCR using a 7500 Fast Real-Time PCR System (Applied Biosystems™, Thermo Fisher Scientific, Waltham, MA, USA). Aliquots of the same DNA extract utilized in amplicon sequencing were used in qPCR. Dilutions of template DNA were used to compensate for the effect of PCR inhibitors in the samples. Each sample was analyzed in triplicate. A PCR amplicon of the <i>Escherichia coli</i> V3 region was used as standard. Each reaction of 20 µL contained 1 µL of template DNA, the forward primer 341F (Muyzer et al., 1993), the reverse primer 518R (Muyzer et al., 1993), and Luna® Universal qPCR Master Mix (NEB). Reaction conditions were an initial denaturation for 1 min at 95 °C, followed by 40 cycles of denaturation at 95 °C for 15 s and extension at 60 °C for 30 s. The melting curve was recorded in the temperature range of 60 °C to 95 °C. The 16S rRNA gene copy numbers per gram of soil were calculated using the standard curve method and then normalized against the standard (Adelowo et al., 2018). The average efficiency value was 100.77 ± 3.15 %. The absolute copy numbers for each bacterial phylum were calculated by multiplying the qPCR values by the relative abundance values in percent obtained from the 16S rRNA gene sequencing analyses.</p>
Database to: Cover crops affect pool specific soil organic carbon in cropland – A meta‐analysis
<p>Database to a meta-analysis studying the effects of cover crops on the mineral-associated organic carbon pool (MAOC), the particulate organic carbon pool (POC) and the microbial biomass carbon pool (MBC). Consists of:<br>1. information on the database<br>2. legend<br>3. list of included studies, all extracted data necessary for response ratio calculation and moderator analysis, and additional information</p>
Supplementary data: Winter cover cropping: Effect on soybean and synergistic implications on soil microbiome
<p>Supplementary data: (i) Agronomic and quality data of soybean (2 varieties) grown in 2 years (2020 & 2021) in two management systems (organic & low-input) with different cover crops; (ii) Soil microbiome analysis of the soybean field trials.</p>
Soil C models for evaluating the effect of cover crops
<p>Excel implementation and dataset for three C cycling models: </p> <ul> <li>Yasso20: monthly simulation version</li> <li>SOMIC 1.0: excel implementation with both simple time step and Euler-Heng iteration</li> <li>Single pool simulation model</li> <li>In addition input files for DNDC.Can.9.5.8 for one of the farms. </li> </ul> <p>There are three versions of the SOMIC model: </p> <ol> <li>The Zip file contains the operational version of the files for the cover crop experiment example and an </li> <li>_Euler-Heng version is an alternative version of the numerical simulation, which can be unstable (feel free to improve, do not use for simulation as such)</li> <li>The _MC version is a Monte Carlo uncertainty analysis implementation for one farm. (Using Simulacion 4.0 <a href="https://ucema.edu.ar/~jvarela/index_eng.htm">https://ucema.edu.ar/~jvarela/index_eng.htm</a>) </li> </ol> <p>The models are applied to an cover crop experiment, where four farms tested cover crops for 5 years. The corresponding article is submitted to Soil Use and Management. The C input estimation zip is used to translate recorded yield and cover crop NDVI data to time series of C inputs used for the models. </p> <ul> <li>The Yasso20 model implementation is based on Yasso20 model code: <a href="https://github.com/YASSOmodel/Yasso20/tree/main">https://github.com/YASSOmodel/Yasso20/tree/main</a></li> <li>The SOMIC model implementation is based on: <a href="https://github.com/domwoolf/somic1">https://github.com/domwoolf/somic1</a></li> <li>The single pool model is as described in: <a href="https://doi.org/10.1016/j.still.2021.105204">https://doi.org/10.1016/j.still.2021.105204</a></li> <li>The DNDC.Can model version can be downloaded from: <a href="https://github.com/BrianBGrant/DNDCv.CAN">https://github.com/BrianBGrant/DNDCv.CAN</a></li> </ul> <p>All the models are capable of simulating time series of soil C development over time, as influenced by C inputs, starting SOC and soil temperature and moisture. They are presented here for the purpose of further model development and comparison, not for making accurate forecasts. </p> <p> </p> <p></p> <p></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>
Data and code from: A mixture of grass-legume cover crop species may ameliorate water stress in a changing climate, a greenhouse experiment at Dickinson College in Carlisle, PA, USA, 2021.
Data and R code associated with a greenhouse study investigating the influence of water stress on growth, root traits, and biomass of rye and crimson clover seedlings grown separately or together. Data were collected in the Dr. Inge P. Stafford Greenhouse of Dickinson College (Carlisle PA, USA) in June 2021.
Early-season biomass and weather enable robust cereal rye cover crop biomass predictions
<p>Farmers need accurate estimates of winter cover crop biomass to make informed decisions on termination timing or to estimate potential release of nitrogen from cover crop residues to subsequent cash crops. Utilizing data from an extensive experiment across 11 states from 2016 to 2020, this study explores the most reliable predictors for determining cereal rye cover crop biomass at the time of termination. Our findings demonstrate a strong relationship between early-season and late-season cover crop biomass. Employing a random forest model, we predicted late-season cereal rye biomass with a margin of error of approximately 1,000 kg ha<sup>-1</sup> based on early-season biomass, growing degree days, cereal rye planting and termination dates, photosynthetically active radiation, precipitation, and site coordinates as predictors. Our results suggest that similar modeling approaches could be combined with remotely sensed early-season biomass estimations to improve the accuracy of predicting winter cover crop biomass at termination for decision support tools.</p>
U.S. cereal rye winter cover crop growth database
<p>Winter cover crop performance metrics (i.e., vegetative biomass quantity and quality) affect ecosystem services provisions but vary widely due to differences in agronomic practices, soil properties, and climate. Cereal rye (Secale cereale) is the most common winter cover crop in the United States due to its winter hardiness, low seed cost, and high biomass production. We compiled data on cereal rye winter cover crop performance metrics, agronomic practices, and soil properties across the eastern half of the United States. The dataset includes a total of 5,695 cereal rye biomass observations across 208 site-years between 2001–2022 and encompasses a wide range of agronomic, soil, and climate conditions. Cereal rye biomass values had a mean of 3,428 kg ha−1, a median of 2,458 kg ha−1, and a standard deviation of 3,163 kg ha−1. The data can be used for empirical analyses, to calibrate, validate, and evaluate process-based models, and to develop decision support tools for management and policy decisions.</p>
BSRLC+: An annual land cover dataset for the Baltic Sea Region with crop types and peat bogs at 30 m from 2000 to 2022
<p><strong>(NEW) </strong>Baltic Sea Region Land Cover <em>Urban</em> (BSRLC-U) focusing on urban built-up types now available: <a href="https://zenodo.org/records/17347941">https://zenodo.org/records/17347941 </a></p> <p><strong>Baltic Sea Region Land Cover <em>Plus </em>(BSRLC+) </strong>is annual land cover mapping (30 m) dataset in Europe from 2000 to 2022. The maps contain detailed information of 18 land cover (LC) types, including 9 crop types and 2 peat bog types.</p> <p>Input data : Optical multi-temporal remote sensing imageries (Landsat 5 (TM) / 7 (ETM+) / 8 (OLI) / 9 (OLI+) and Sentinel 2 (A / B ) from 2000 to 2022. Data is processed to surface reflectance and tiled into datacube structure using <a href="https://doi.org/10.3390/rs11091124">Framework for Operational Radiometric Correction for Environmental monitoring - FORCE.</a></p> <p>Mapping method: Maps are produced using data encoding and deep learning classification according to <a href="https://doi.org/10.1016/j.jag.2024.103867">Pham et al. 2024</a></p> <p>Validation: Maps have been rigorously validated using independent in-situ data <a href="https://doi.org/10.1038/s41597-020-00675-z">The Land Use/Cover Area frame Survey (LUCAS)</a>. </p> <p>Traing data and validation data are available: <a href="https://zenodo.org/records/11073291">https://zenodo.org/records/11073291</a></p> <p>This dataset contains:</p> <ul> <li><strong>00_preview.png</strong>: Preview map (2022) of the Baltic Sea region</li> <li><strong>BSRLC_{year}.tif</strong>: Annual map data (30 m) in GeoTIFF format (projection ETRS89 / EPSG:3035)</li> <li><strong>BSRLC_legend.xlss</strong>: Land cover codes and class names</li> <li><strong>BSRLC_qgis_style.qml</strong>: Map style to be used in QGIS</li> <li><strong>BSRLC_arcgis_style.lyrx</strong>: Map style to be used in ArcGIS</li> </ul> <p>Land cover codes (can also be found in <strong>BSRLC_legend.xlss</strong>):</p> <ul> <li>1: Built-up</li> <li>2: Bareland</li> <li>3: Water</li> <li>4: Shrubland</li> <li>5: Broadleaf forest</li> <li>6: Coniferous forest</li> <li>7: Wetland marsh</li> <li>8: Exploited peat bog</li> <li>9: Unexploited peat bog</li> <li>10: Wheat</li> <li>11: Barley</li> <li>12: Rye</li> <li>13: Oat</li> <li>14: Maize</li> <li>15: Seed crops</li> <li>16: Root crops</li> <li>17: Pulses, vegetable</li> <li>18: Grassland</li> <li>255: Nodata</li> </ul> <p> </p> <p><strong>Publication (please cite this publication if you are using the dataset):</strong></p> <ul> <li>Pham, V.-D., de Waard, F., Thiel, F., Bobertz, B., Hellmann, C., Nguyen, D.-V., Beer, F., Arasumani, M., Schwieder, M., Hartleib, J., Frantz, D., & van der Linden, S. (2024). An annual land cover dataset for the Baltic Sea Region with crop types and peat bogs at 30 m from 2000 to 2022. <em>Scientific Data, 11</em>, 1242, <a href="https://doi.org/10.1038/s41597-024-04062-w">https://doi.org/10.1038/s41597-024-04062-w</a></li> </ul> <p> </p> <p><strong>Other related publications:</strong></p> <ul> <li><em>Pham, V.-D., Tetteh, G., Thiel, F., Erasmi, S., Schwieder, M., Frantz, D., & van der Linden, S. (2024). Temporally transferable crop mapping with temporal encoding and deep learning augmentations. International Journal of Applied Earth Observation and Geoinformation, 129, 103867, <a href="https://doi.org/10.1016/j.jag.2024.103867">https://doi.org/10.1016/j.jag.2024.103867</a></em></li> <li><em>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, <a href="https://doi.org/10.3390/rs11091124">https://doi.org/10.3390/rs11091124</a></em></li> </ul> <p> </p> <p><strong>Funding</strong></p> <p>This datatset is created in the frame of the Interdisciplinary Research Center for the Baltic Sea Region Research (IFZO) of University of Greifswald, Germany, and the research project Fragmented Transformations, which is funded by the German Federal Ministry of Education and Research (FKZ 01UC2102). </p>
Data from: Carry-over effect of leguminous winter cover crops and living mulches on winter wheat as a second main crop following white cabbage
<p><strong>Background: </strong>In trials on two strategies for the integration of legumes in a vegetable crop rotation (leguminous winter cover crops and living mulches), data were collected on the two subsequent crops white cabbage and winter wheat. The data on biomass and soil mineral nitrogen content are made publicly available here. </p> <p> </p> <p><strong>Abstract:</strong> <span>The direct effect of winter cover crops (WCC) or living mulches (LM) on a first vegetable crop has already been investigated. However, little is known about the effect on growth and yield of a second cash crop. </span><span>The aim of the study was to assess the carry-over effect of legumes grown as WCC or LM on winter wheat as a second crop after cabbage measured in yield and nitrogen release.</span><span> Two field trials were carried out in Germany between 2019 and 2022. In the WCC trial rye, rye with vetch, vetch, pea and faba bean were used as WCC and compared to bare soil. The WCC biomass was incorporated before cabbage planting in late spring. For the LM trial, perennial ryegrass or white clover were used as LM during cabbage cultivation and compared to bare soil. The LM biomass was incorporated together with the cabbage residues (STU/STT) and compared to an early incorporation of LM biomass before cabbage planting (RT). Winter wheat in both trials was seeded as the second main crop in the rotation in the fall.</span></p>
Figure 1 in Integrating cover crops for weed management in the semiarid U.S. Great Plains: opportunities and challenges
Figure 1. Map of the Great Plains showing three main regions: (1) Northern Great Plains (marked by purple line), (2) Central Great Plains (marked by red line), and (3) Southern Great Plains (marked by light blue line). Adapted from Center for Great Plains Studies, University of Nebraska–Lincoln.
Fig. 3 in Comparison of carabid densities in different cover crop species in north Florida
Fig. 3 Number of Selenophorus palliatus found in 2 sunn hemp germplasm lines in plots planted in Mar 2016 in Tallahassee, Florida (mean ± SE; n = 8).
Fig. 2 in Comparison of carabid densities in different cover crop species in north Florida
Fig. 2 Number of predator carabid species found in 2 sunn hemp germplasm lines in plots planted in Mar 2016 in Tallahassee, Florida (mean ± SE; n = 8).
Fig. 3 in Comparison of bee composition in sunn hemp and other cover crops
Fig. 3. Total number of bees collected in blue vane traps within cover crop plots for 5 dates in 2012, Citra, Florida, USA. SSG = sorghum sudangrass.
Fig. 2 in Comparison of bee composition in sunn hemp and other cover crops
Fig. 2. Total number of bees collected in blue vane traps within cover crop plots for 5 dates in 2012, Quincy, Florida, USA. SSG = sorghum sudangrass.
Fig. 1 in Comparison of bee composition in sunn hemp and other cover crops
Fig. 1. Mean number of total bees collected in bee bowls within cover crop plots for 6 dates in 2012, Quincy, Florida, USA. SSG = sorghum sudangrass.
Data from: Quantifying the impact of crop coverings on honey bee orientation and foraging in sweet cherry orchards using RFID
<p>Advancements in agricultural production have seen the rapid adoption of protected cropping systems globally. Such systems have been optimised for plant growth and efficiency, with little understanding of the potential impacts on key insect pollinators. Here we investigate the effect of netting and polythene rain covers on the health and performance of honey bees (<em>Apis mellifera </em>L.) during the pollination of sweet cherry crops. Over two consecutive seasons, twelve full-strength colonies were equipped with tagged bees and radio frequency identification (RFID) systems. The colonies were equally divided between open control, netted, and polythene (semi-permanent VOEN in 2019 and retractable Cravo in 2020) groups. Over 1,300 individual bees were monitored for the duration of the commercial pollination period to determine behavioural parameters such as foraging commencement age, number and duration of trips, and overall survival. Bees began foraging within the optimum age range (mean 15.7-24.1 days) under all covering types, with little indication of prolonged stress or increased mortality during the short season. Polythene covers (VOEN & Cravo) were found to significantly increase the total time needed for bees to orientate successfully. Once orientated, bees placed under covers conducted up to 155% more foraging trips, with a longer cumulative duration. Covering type was found to significantly impact the amount and type of pollen collected, with the most restrictive system (VOEN) yielding the highest proportion of cherry pollen. Overall, we found little evidence to suggest that protective covers have a detrimental impact on honey bee foraging in cherry crops.</p>
Dataset for "Cover crop inclusion and residue retention improves soybean production and physiology in drought conditions"
<p>Data and code for "Cover crop inclusion and residue retention improves soybean production and physiology in drought conditions"</p> <p><strong>CONTEXT: </strong>Soybean (<em>Glycine max</em> (L.) Merr.) planting has increased in central and western North Dakota despite frequent drought occurrences that limit productivity. Soybean plants need high photosynthetic and transpiration rates to be productive, but they also need high water use efficiency when water is limited. Retaining crop residues and including cover crops in crop rotations are management strategies that could improve soybean drought resilience in the northern Great Plains. </p> <p><strong>OBJECTIVE</strong>: We aimed to examine how a management practice that included cover crops and residue retention impacts agronomic, ecosystem water and carbon dioxide flux, and canopy-scale physiological attributes of soybeans in the northern Great Plains under drought conditions. </p> <p><strong>METHODS</strong>: We compared two soybean fields over two years with business-as-usual and aspirational management that included residue retention and cover crops during a drought year. This comparison was based on yield, aboveground biomass, Phenocam images, and fluxes from eddy covariance and ancillary measurements. These measurements were used to derive meteorological, physical, and physiological attributes with the ‘big leaf’ framework. </p> <p><strong>RESULTS: </strong>Soybean yields were 29% higher under drought conditions in the field managed in a system that included cover crops and residue retention. This yield increase was caused by extending the maturity phenophase by 5 days, increasing agronomic and intrinsic water use efficiency by 27% and 33%, respectively, increasing water uptake, and increasing the rubisco-limited photosynthetic capacity (V<sub>cmax25</sub>) by 42%.</p> <p><strong>CONCLUSIONS:</strong> The inclusion of cover crops and residue retention into a cropping system improved soybean productivity because of differences in water use, phenology timing, and photosynthetic capacity.</p> <p><strong>IMPLICATIONS:</strong> These results suggest that farmers can improve soybean productivity and yield stability by incorporating cover crops and residue retention into their management practices because these practices allow soybean plants to shift to a more aggressive water uptake strategy.</p> <p><strong>Code:</strong></p> <p><strong>1_phenocam.rmd: </strong>Code to download Phenocam data and identify phenophase transition dates.</p> <p><strong>2_Daily_CO2_Water_Fluxes.Rmd: </strong>Code to analyze daily carbon and water fluxes (Figure 1, 2 3 and Table 2).</p> <p><strong>3_Inferring_LAI_and_Height.Rmd: </strong>Code to calculate the predicted LAI and height for each day. The output is used in the big-leaf framework.</p> <p><strong>4_Big_Leaf.Rmd: </strong>Code for the big-leaf ecophysiology estimates (Figure 4, 5 and 6; Table 3 and 4).</p> <p><strong>4_Data_Dictionary_Vairables:</strong> Code to identify the data dictionary variables. </p> <p> </p>
ScienceDex guides
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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)
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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.