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101 results for “cover crops”
Images, data, and statistical analysis scripts for review article on cover crop roots
<p>Images, data, and statistical analysis scripts for review article on cover crop roots.</p> <blockquote> <p><strong>Optimization of root traits to provide enhanced ecosystem services in agricultural systems: a focus on cover crops</strong> - [<a href="https://doi.org/10.1111/pce.14247">https://doi.org/10.1111/pce.14247</a>]</p> </blockquote> <ul> <li>Research site, planting, and growth <ul> <li>10/2020 - 04/26/2021 cover crop field trial. DDPSC FRS at Planthaven Farm, O'Fallon, MO 63366 (latitude 38.848240°, longitude -90.686640°). </li> <li>The field was tilled before sowing of cover crops. Seed for each cover crop were spread in using a push seed spreader and were lightly irrigated.</li> <li>Alfalfa (<em>Medicago sativa</em>), dundale pea (<em>Pisum sativum</em>), milkvetch (<em>Astragalus canadensis</em>, <em>Astragalus bisulcatus</em>), crimson clover (<em>Trifolium incarnatum</em>), hairy vetch (<em>Vicia villosa</em>), mustard (<em>Brassica junce</em>a var Mighty Mustard, var Kodiak), barley (<em>Hordeum vulgare</em>), wheat (<em>Triticum aestivum</em>, winter, spring), winter rye (<em>Secale cereale</em>), and triticale (× T<em>riticosecale</em> Wittmack).</li> </ul> </li> <li> <p>Field harvest measurements</p> <ul> <li> <p>Four canopy images were taken across each cover crop row using a Canon 5DS R camera. Images were taken from above each plot at 5ft height manually. Green color was thresholded from the canopy images in batch using OpenCV python script and the percent green cover calculated (Jupiter notebook).</p> </li> <li> <p>Five soil monoliths were excavated using a "shovelomics" approach with an average monolith size of 25.4cm x 25.4cm x 20 cm. The remaining four soil monoliths were destructively analyzed.</p> </li> <li> <p>One soil monolith was imaged using a Canon 50D DLSR camera in a photogrammetry shed. All photogrammetric analysis was conducted using Pix4D mapper software (Pix4D S.A. Prilly, Switzerland), and point cloud cleaning was conducted in CloudCompare V2. 10.2.</p> </li> <li> <p>Cover crop shoots from the remaining soil monoliths were cut and placed into a paper bag for dry biomass determination (60oC for 5 days). A cover crop shoot count was conducted for each monolith with each tiller considered as a shoot for the grasses (barley, wheat, triticale). After cover crop shoot harvesting, a photo was then taken of each soil monolith with remaining weed biomass. A weed score was assigned to each image by one trained researcher with a score 1 low weeds to 5 high weed presence.</p> </li> <li> <p>Soil monoliths were the soaked briefly in water and then the soil washed using a hose keeping the roots. Roots were then scanned on an Epson Expression 12000XL Photo Scanner with transparency unit. Images labeled with "_part" were samples with too many roots for scanning and so were separately weighed. Dry root biomass was taken for the scanned and unscanned roots separately. Root length was determined from images using software RhizoVision Explorer (https://doi.org/10.5281/zenodo.4095629), total root length was estimated using scanned root length and scanned dry biomass with unscanned root biomass.</p> </li> <li> <p>Along each cover crop plot a 10ft trench was dug using a Yanmar Excavator Vi020-6 perpendicular to the row with each trench fully bisecting the plot. Trench was one bucket wide (19 inches) and approximately 36 inches deep in the middle of the row. The five deepest roots that could be observed in the trench wall was measured manually with a tape measure for each cover crop. A garden trowel and shovel were used to excavate and confirm roots in trench wall.</p> </li> <li> <p>Data was analyzed using R Statistics script and raw data used for data processing and figure generation (2021PlantHavenCovercrop_dataprocessing.R). PCA analysis was conducted using the “FactoMineR” package (Husson <em>et al</em>. 2019) to explore the relationships between the traits within the dataset and clustered by family.</p> </li> </ul> </li> </ul> <p>Individual ZIP file contents:</p> <ul> <li><code><strong>2021PlantHavenCovercrop_CanopyImages.zip</strong></code> – Raw canopy images, processed percent green cover images, and Jupiter notebook python script (2021PlantHavenCovercrop_ImageBatchColorThreshold.ipynb).</li> <li><code><strong>2021PlantHavenCovercrop_RootFlatbedImages.zip</strong></code> – Raw flatbed root scans of cover crops and processed images using RhizoVision Explorer.</li> <li><code><strong>2021PlantHavenCovercrop_SoilMonolithWeedImages.zip</strong></code> – Images of soil monoliths after cover crop shoot biomass was removed.</li> <li><code><strong>2021PlantHavenCovercrop_dataprocessing.zip</strong></code> – R Statistics script and raw data used for data processing and figure generation (2021PlantHavenCovercrop_dataprocessing.R).</li> <li><code><strong>2021PlantHavenCovercrop_ShootPhotogrammetry.zip</strong></code> – 3D models of cover crop shoots from excavated soil monoliths. The .bin files can be opened using CloudCompare app.</li> </ul> <p> </p> <p> </p>
Average cover crop adoption rates in the U.S. Midwest in 2000-2010 and 2011-2021
<p>Cover crops have critical significance for agroecosystem sustainability and have long been promoted in the U.S. Midwest. Knowledge of the variations of cover cropping and the impacts of government policies remains very limited. We developed an accurate and cost-effective approach utilizing multi-source satellite fusion data, environmental variables, and machine learning to quantify cover cropping in corn and soybean fields from 2000 to 2021 in the U.S. Midwest. We found that cover crop adoption in most counties has significantly increased in the recent 11 years from 2011 to 2021. The adoption percentage of 2021 is 3.3 times that of 2011, which was highly correlated to the increased funding for federal and state conservation programs. However, the percentage of cover crop adoption is still low (7.2%). The averaged county-level cover crop adoption rates in 2000-2010 and 2011-2021 are publicly available on Dryad.</p>
Early Stage Crop/ Land Cover Classifcation Results Datasets
<p>This dataset provides results related to the work and report of work done by NPA in a close collaboration with project the Sentinels for Common Agricultural Policy - Sen4CAP consortium. The report (cross referenced below) gives an overview of a collaboration between NPA and Sen4CAP focussed on the 2020 claim year. The main idea is to check if any results generated before and during applications submission period could be used as preliminary data. By using results of land cover classification, crop classification and activity monitoring it would lead to availability to check if it provides benefit in the beginning and during declaration period as preliminary data of crop type (summer, winter, land cover, land use). </p>
Data for the manuscript 'Cover crop root morphology rather than quality controls the fate of root and rhizodeposition C into distinct soil C pools'
<p><strong>Data for manuscript</strong></p> <p>The data provided in the present document corresponds to the manuscript:</p> <p>Engedal, T., Magid, J., Hansen, V., Rasmussen, J., Sørensen, H., Jensen, L. S. (2023): Cover crop root morphology rather than quality controls the fate of root and rhizodeposition C into distinct soil C pools. <em>Global Change Biology, in press</em>.</p> <p> </p> <p><strong>Short abstract</strong></p> <p>In order to investigate the fate of cover crop-derived belowground C as rhizodeposition and, over time, into the distinct soil organic carbon pools of particulate- and mineral-associated organic carbon (POC and MAOC), a column trial was esblished with 0.25 m top soil and 0.25 m sub soil. Four cover crops were grown for 3 months and 14CO2-labelled twice a week. Four out of eight replicate columns were destructively harvested to quantify root C and the carbon lost via rhizodeposition in absolute (qClvR) and relative terms (%ClvR) in bulk soil and rhizosphere soil from top- and subsoil (t1). The other four replicate columns were harvested for undisturbed incubation for one year, before final sampling (t2). Bulk soil from both sampling times were subject to a simple fractionation protocol by size, where particles larger from 50 microns were assigned to POC and smaller than 50 microns assigned to MAOC after dispersion in NaHMP. All fractions were dried, weighed and analyzed for 14C activity as disintegrations per minute (DPM).</p> <p> </p> <p><strong>Further details</strong></p> <p>Column ID 1-16 refer to columns sampled at t1, while column ID 17-32 refer to columns sampled at t2. Underlying assumptions and detailed descriptions of the different fractions are to be found in the manuscript.</p>
Data from: Cover cropping history affects cotton boll distribution, lint yields, and fiber quality
<p>This is digital research data corresponding to a published manuscript, Cover cropping history affects cotton boll distribution, lint yields, and fiber quality, in Crop Science, Vol. 63 p. 1209–1220. </p> <p>There has been limited introduction of new cover crop species into cotton (<em>Gossypium</em> <em>hirsutum</em> L.) production within the last 30 years. Mounting evidence shows that traditional cover cropping species may be detrimental to cotton production, either by depleting soil fertility with crop removal, immobilizing minerals from high carbon residue, or excessive quantity of residue remaining at planting. The objective of this study was to determine the effects of growing a novel cover crop species, carinata (<em>Brassica</em> <em>carinata</em> A. Braun), as a winter annual cover crop for cotton rotation in the southeastern Coastal Plain. Over a 2-year period, carinata, winter wheat (<em>Triticum</em> <em>aestivum</em> L.), and fallow covers were maintained over winter months, then rotated into cotton. Each year, seedcotton and lint yields were collected, along with subsamples for ginning and subsequent fiber quality analyses. Additionally, end-of-season plant mapping was conducted on plants from 1-m of row per plot to determine cover crop effects on boll formation, retention, and distribution, as well as canopy architecture.</p>
Data from: Resilience of an integrated crop-livestock system to climate change: a simulation analysis of cover crop grazing in southern Brazil
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Orchard floor plant communities: Multispecies cover crops in commercial almond orchards
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Data for: Effect of cover cropping and biosolarization on eggplant growth, soil pests, and soil nitrogen
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Long-term tillage and cover cropping differentially influenced soil nitrous oxide emissions from cotton cropping system
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Data from: Cover cropping history affects cotton boll distribution, lint yields, and fiber quality
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Data from: 42 years of no-tillage and cover cropping improved soil oxygen availability and resilience
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Soil physical, biological, chemical, and carbon data and cover crop biomass data from Sac Valley almond orchard comparing multiple cover crop compositions with resident vegetation for effects on soil health and nematodes
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Average cover crop adoption rates in the U.S. Midwest in 2000-2010 and 2011-2021
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Forest cover and fruit crop size differentially influence frugivory of select rainforest tree species in Western Ghats, India (Part I)
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Synergies and trade-offs between ecosystem services and economics in dryland cover crop systems
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Efficacy of cover crops for pollinator habitat provision and weed suppression
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Data from: Early impacts of cover crop selection on soil biological parameters during a transition to organic agriculture
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Climate change mitigation potential of widespread cover crop adoption in U.S.
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Data from: Higher avian biodiversity, increased shrub cover, and proximity to continuous forest may reduce pest insect crop loss in small-scale oil palm farming
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Soil structure changes under reduced tillage and cover cropping enhance carbon mineralization in Mediterranean croplands
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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.