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506 results for “crop data”
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: pDSSAT wheat
<p>This is model output from pDSSAT for wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: EPIC-IIASA maize
<p>This is model output from EPIC-IIASA for maize as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: pAPSIM wheat
<p>This is model output from pAPSIM for wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
Model and Data for the T&C-CROP Validation Paper: T&C-CROP: Representing mechanistic crop growth with a terrestrial biosphere model (T&C,v1.5): Model formulation and validation.
<p>Here included is the code used to run T&C-CROP as used for the GMD paper submission alongside with the necessary weather data and raw field data used as part of the validation exercise. </p> <p> </p>
Probabilistic Data Generating Process-based Crop Type Map for the EU 2010-2020
<h3>General Description</h3> <p>This dataset consists of probabilistic crop type maps for the EU-28 for the years 2010-2020 that distinguish 28 crop types at 1km resolution (EPSG:3035). The maps were generated using the Data Generating Process-based procedure developed by Baumert, Heckelei and Storm (2024) [<em><span><a href="https://doi.org/10.1016/j.ecoinf.2024.102836">https://doi.org/10.1016/j.ecoinf.2024.102836</a></span></em>]. We refer to this paper for details on the generation and validation of the maps. The code used to create the maps including a detailed list of the input data can be found here: <a href="https://github.com/JoBaumert/Probabilistic_Crop_Mapping_EU">GitHub - JoBaumert/Probabilistic_Crop_Mapping_EU</a> . </p> <h3>Downloadable Data</h3> <p>The file “EU_expected_crop_shares.zip” consists of 11 raster files, one for each year from 2010 – 2020. The raster files indicate the expected shares for each of the 28 distinguished crop types in a grid cell for the entire EU-28 (see readme.txt contained in the zipped folder). Note that this raster file does not contain uncertainty information.</p> <p>The other 28 zip files contain the entire crop map ensemble (i.e., including uncertainty information), each for one of the EU countries and the United Kingdom. Each of those zip files contain 11 raster files, one for each year from 2010 – 2020. Each raster file has 2830 bands: the first two bands indicate the weight of the cell (proportional to the utilized agricultural area in a cell) and the estimated number of agricultural fields in a cell, respectively. The next 28 bands indicate the expected shares for each of the 28 crops in the respective cell. The remaining 2800 bands compose the crop type map ensemble, i.e., 100 simulated crop shares for each of the 28 crops. The zipped country folder also includes a csv file named “bands” that describes which band refers to which crop. Note that all crop shares were multiplied by 1000 when writing them to the raster files (saving them as integers requires less storage capacity), i.e., if a crop share is 0.325 or 32.5% it will appear as 325 in the raster files. </p> <p>The distinguished crops are (with abbreviation used in "bands.csv"):</p> <ul> <li>Apples and other fruits, nuts and berries (APPL+OFRU)</li> <li>Barley (BARL)</li> <li>Citrus fruits (CITR)</li> <li>Durum wheat (DWHE)</li> <li>Flowers and ornamental plants (FLOW)</li> <li>Grassland (GRAS)</li> <li>Maize (both green maize as well as grain maize, LMAIZ)</li> <li>Rape and turnip (LRAPE)</li> <li>Nurseries (NURS)</li> <li>Oats (OATS)</li> <li>Other cereals (OCER)</li> <li>Other permanent crops (OCRO)</li> <li>Other forage plants (OFAR)</li> <li>Other industrial plants (OIND)</li> <li>Olives (OLIVGR)</li> <li>Rice (PARI)</li> <li>Potatoes (POTA)</li> <li>Pulses (PULS)</li> <li>Fodder roots and brassicas (ROOF)</li> <li>Rye (RYEM)</li> <li>Soybeans (SOYA)</li> <li>Sugar beets (SUGB)</li> <li>Sunflowers (SUNF)</li> <li>Soft/common wheat (SWHE)</li> <li>Other oilseeds and fibre crops (TEXT)</li> <li>Tobacco (TOBA)</li> <li>Fresh vegetables, melons, strawberries (TOMA+OVEG)</li> <li>Vineyards (VINY)</li> </ul> <p> </p> <p> </p>
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>
Data & Code from: Crop mixtures: does niche complementarity hold for belowground resources? an experimental test using rice genotypic pairs
<p>Data & Code for the study "Crop mixtures: does niche complementarity hold for belowground resources? an experimental test using rice genotypic pairs"</p> <p>Data:<br> "Rice_traits.csv": this file contains trait and productivity data measured at the individual plant level. It has one row per plant and one column per trait.</p> <p>Column headers:<br> "IDplant": unique plant identifier (1 to 200)<br> "IDpot": pot identifier with two plants per pot (1 to 100)<br> "Bloc": bloc identifier, with 20 pots per bloc (A, B, C, D, E)<br> "Treatment": P0 vs P+ = no P supply vs P supply<br> "Asso": pot type, either monoculture (M) or mixture (P)<br> "IDcouple": concatenation of the identifiers of the two genotypes in a pot (I64 = IR64, I64+=IR64 introgressed with QTL9, Pdi=Padi, Ktn=Ketan)<br> "IDgeno": focal genotype identifier (I64 = IR64, I64+=IR64 introgressed with QTL9, Pdi=Padi, Ktn=Ketan)<br> "IDnei": neighbour genotype identifier (I64 = IR64, I64+=IR64 introgressed with QTL9, Pdi=Padi, Ktn=Ketan)<br> "BIOM_above": aboveground biomass (g)<br> "Tillers": number of tillers<br> "PH": Plant height (cm)<br> "Biovolume": biovolume (m3)<br> "SLA": Specific Leaf Area (m2/kg)<br> "RB_top": Root biomass between 0 and 20 cm below the soil surface(g)<br> "RB_deep: Root biomass between 20 and 60 cm below the soil surface(g) (!!! Only measured at the pot-level)<br> "D_ad"/"D_bas": Mean root diameter (mm) of adventitious/basal roots, respectively<br> "SRL_ad"/"SRL_bas": Specific Root Length (m/g) of adventitious/basal roots, respectively<br> "RTD_ad"/"RTD_bas": Root Tissue Density (mg/cm3) of adventitious/basal roots, respectively<br> "RBI_ad"/"RBI_bas": Root Branching Intensity (nb tips/cm) of adventitious/basal roots, respectively<br> "PfR_ad"/"PfR_bas": Proportion of fine roots (diameter < 0.1 mm) (%) in adventitious/basal roots, respectively</p> <p>Code:<br> "Rice_mixtures_analysis.R": this file contains the main statisticl analysis presented in the study. It uses "Rice_traits.csv" as an input.</p> <p> </p>
Data of crop yield derived from H2020 Diverfarming project
<p>Crop yield data of a mandarin crop (main crop) and different secondary crops (alley crops) cultivated in different cropping systems (monocrop and diversification) with different treatments (control irrigation and regulated deficit irrigation)</p>
Data for: Improvements in the Land and Crop Modeling over Flooded Rice Fields by Incorporating the Shallow Paddy Water (Submitting to the Journal of Advances in Modeling Earth Systems)
<p>We incorporated the shallow paddy surface water layer into the Noah-MP land surface model to improve its performance of surface heat fluxes over flooded rice paddies. Field measurements from two crop sites, i.e., SAITO (early rice) and SAGA (late rice), were used to initialize and evaluate the modified Noah-MP model (Maruyama, 2021). Additionally, we investigated the roles of some key parameters in the land and crop modeling. </p> <p>Note that, all numerical experiments in this study were conducted at the field scale using the offline version of Noah-MP (Niu et al., 2011) running within the High-Resolution Land Data Assimilation System (HRLDAS v3.9; Chen et al., 2007). Please refer to the official HRLDAS/Noah-MP unified Github repository (<a href="https://github.com/NCAR/hrldas">https://github.com/NCAR/hrldas-release</a>) for the original model codes.</p> <p>The related model code modifications and model outputs were included in this dataset. Surface observations for the nearest AMeDAS or meteorological observatory stations were obtained from the Japan Meteorological Agency website (<a href="https://www.jma.go.jp/jma/indexe.html">https://www.jma.go.jp/jma/indexe.html</a>), and were also provided in this dataset.</p> <p> </p> <p>References</p> <p>Chen, F., Manning, K. W., LeMone, M. A., Trier, S. B., Alfieri, J. G., Roberts, R. D., et al. (2007). Description and evaluation of the characteristics of the NCAR high‐resolution land data assimilation system. <em>Journal of Applied Meteorology and Climatology</em>, 46(6), 694-713. <a href="https://doi.org/10.1175/JAM2463.1">https://doi.org/10.1175/JAM2463.1</a></p> <p>Maruyama, A. (2021). Data for: Coupling land surface and crop models to estimate the effects of changes in the growing season on energy balance and water use of rice paddies (version 2) [Data set]. Mendeley Data. <a href="https://doi.org/10.17632/tv23z95r5g.2">https://doi.org/10.17632/tv23z95r5g.2</a></p> <p>Niu, G., Yang, Z., Mitchell, K., Chen, F., Ek, M., Barlage, M., et al. (2011). The community Noah land surface model with multiparameterization options (Noah‐MP): 1. Model description and evaluation with local‐scale measurements. <em>Journal of Geophysical Research, </em>116, D12109. <a href="https://doi.org/10.1029/2010JD015139">https://doi.org/10.1029/2010JD015139</a></p>
Data from: Maintenance and expansion of genetic and trait variation following domestication in a clonal crop: Enset tGBS individual genotype data
<p class="MsoNormal">Clonal propagation enables favourable crop genotypes to be rapidly selected and multiplied. However, the absence of sexual propagation can lead to low genetic diversity and accumulation of deleterious mutations, which may eventually render crops less resilient to pathogens or environmental change. To better understand this trade-off, we characterise the domestication and contemporary genetic diversity of Enset (<em>Ensete ventricosum</em>), an indigenous African relative of bananas (<em>Musa</em>) and principal starch staple for 20 million Ethiopians. Wild enset is strictly sexually outcrossing, but in cultivation is propagated clonally and associated with diversification and specialisation into hundreds of named landraces. We applied tGBS sequencing to generate genome-wide genotypes for 192 accessions from across enset's cultivated distribution, and surveyed 1340 farmers on enset agronomic traits. Overall, reduced heterozygosity in the domesticated lineage was consistent with a domestication bottleneck that retained 37% of wild diversity. However, an excess of putatively deleterious missense mutations at low frequency present as heterozygotes suggested accumulation of mutational load in clonal domesticated lineages. Our evidence indicates that the major domesticated lineages initially arose through historic sexual recombination associated with a domestication bottleneck, followed by amplification of favourable genotypes through an extended period of clonal propagation. Among domesticated lineages we found significant phylogenetic signal for multiple farmer-identified food, nutrition and disease resistance traits and little evidence of contemporary recombination. Development of future-climate adapted genotypes may require crop breeding, but outcrossing risks exposing deleterious alleles as homozygotes. This trade-off may partly explain the ubiquity and persistence of clonal propagation over recent centuries of comparative climate stability.</p>
Data from: Identification of anti-fungal bioactive terpenoids from the bioenergy crop switchgrass (Panicum virgatum)
<p>Plant derived bioactive small molecules have attracted attention of scientists across fundamental and applied scientific disciplines. We seek to understand the influence of these phytochemicals on functional phytobiomes. Increased knowledge of specialized metabolite bioactivities could inform strategies for sustainable crop production. We hypothesized that – consistent with accumulating evidence that switchgrass genotype impacts microbiome assembly – differential terpenoid accumulation contributes to switchgrass ecotype-specific microbiome composition. An initial in vitro plate-based disc diffusion screen of 18 switchgrass root derived fungal isolates revealed differential responses to upland- and lowland-isolated metabolites. To identify specific fungal growth-modulating metabolites, we tested fractions from root extracts on three ecologically important fungal isolates – <em>Linnemania elongata</em>, <em>Trichoderma</em> sp. and <em>Fusarium</em> sp. Saponins and diterpenoids were identified as the most prominent antifungal metabolites. Finally, analysis of liquid chromatography-purified terpenoids revealed fungal inhibition structure – activity relationships (SAR). Saponin antifungal activity was primarily determined by the number of sugar moieties – saponins glycosylated at a single core position were inhibitory whereas saponins glycosylated at two core positions were inactive. Saponin core hydroxylation and acetylation were also associated with reduced activity. Diterpenoid activity required the presence of an intact furan ring for strong fungal growth inhibition.</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 and R code for: "Global meta-analysis shows reduced quality of food crops under inadequate animal pollination"
<p>R code, data, and metadata for the study "Global meta-analysis shows reduced quality of food crops under inadequate animal pollination"</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 for: Direct and indirect effects of food, fear and management on crop damage by ungulates
<p>Foraging on crops by wild ungulates may create human-wildlife conflicts through reducing crop production. Ungulates interact with and within complex socio-ecological systems, making the reduction of crop damage a challenging task. Aside from ungulate densities, crop damage is influenced by different drivers affecting ungulate foraging behavior: food availability and food quality in the landscape (i.e. the foodscape) as well as fear from hunting and scaring actions (i.e. the landscape of fear) may together affect the degree of damage via both direct and indirect effects. A better understanding of the individual effects of these potential drivers behind crop damage is needed, as is an appreciation of whether the effects are dependent on ungulate density. We investigated this by applying path analysis to test indirect and direct links between ungulate density, foodscape, landscape of fear and, human management goals on crop damage of oats and grass, respectively. Our results suggest that crop type is the major driver behind crop damage, with more damage to oats than to leys, implying that human decisions (i.e changing crop type) influence the level of crop damage. We found that management goals and actions influenced the foodscape and the landscape of fear, by affecting the amount of forage produced in the agricultural landscape and the amount of scaring actions. Additionally, we found that supplementary feeding influenced the local ungulate densities in the area. Our results highlight the importance of including human actions on multiple levels when assessing drivers behind damage by ungulates in managed landscapes. We suggest that more studies using path analysis on multiple scales are needed in order to tackle complex issues such as crop damage and other human-wildlife conflicts.</p>
Pan trap and plant-flower visitor observation data for: Multi-species crop mixtures increase insect biodiversity in an intercropping experiment
<ol> <li><span>Recent biodiversity declines require action across sectors such as agriculture. The situation is particularly acute for arthropods, a species-rich taxon providing important ecosystem services. To counteract negative consequences of agricultural intensification, creating a less hostile agricultural "matrix" through growing crop mixtures can reduce harm for arthropods without yield losses. </span></li> <li><span>While grassland biodiversity experiments showed positive plant biodiversity effects on arthropods, experiments manipulating crop diversity and agrochemical input use to study arthropods are lacking. </span></li> <li><span>Here, we experimentally manipulated crop diversity (1–3 species, fallows), crop species (wheat, faba bean, linseed, oilseed rape) and agrochemical input (high vs. low) and studied responses of arthropod biodiversity. We tested if arthropod responses were affected by crop diversity, mixtures and management. Additionally, we measured crop biomass.</span></li> <li><span>Crop biomass increased with crop diversity under high-input mangement, while under low management intensity, biomass was highest in two-species mixtures.</span></li> <li><span>Increasing crop diversity positively affected arthropod abundance and diversity, both under low- and high-input management. Crop mixtures containing faba bean, linseed or oilseed rape had particularly high arthropod diversity.</span></li> <li><span>Mass-flowering crops attracted more arthropods than legumes or cereals. Integrating intercropping into agricultural systems could increase flower visits by insects up to 15 million per hectare, thus likely also supporting pollination and pest-control ecosystem services.</span></li> <li><span>Flower-visitor network complexity increased in mixtures containing linseed and faba bean, and under low-input management.</span></li> <li><span>Intercropping can counteract insect declines in farmland by creating beneficial matrix habitat without compromising crop yield.</span></li> </ol>
Fitness and cold tolerance of Spodoptera frugiperda fed on corn and two winter crops (Original data)
<p>草地贪夜蛾(FAW)草<em>地</em>贪夜蛾是一种主要的迁徙和多噬性害虫。FAW的越冬、迁徙和定植性状与植被和寄主适应性密切相关。在这项研究中,我们调查了春夏作物(玉米)和两种冬季作物(卷心菜,<em>芸苔</em>属和油菜,油菜,油菜)的产卵偏好,喂养<em>偏好,适应性</em>和耐寒性。结果表明,FAW可以通过喂食卷心菜和油菜来完成其生命周期,尽管健康状况不如玉米。以卷心菜为食的FAW存活率、雌蛹质量和宿主适宜性指数高于油菜,幼虫更喜欢以油菜叶为食,雌性更喜欢在油菜植株上产卵。此外,与玉米和油菜相比,以卷心菜为食的FAW幼虫从寒晕中恢复的时间最短,表明以卷心菜为食可提高FAW幼虫的耐寒性。因此,卷心菜可能是FAW的理想宿主,需要在冬季生存和繁殖。研究结果提高了我们对FAW寄主选择和适应的理解,为预测FAW冬季繁殖区奠定了基础。</p>
Data supplementing Lichtenberg et al. (2023) Differential effects of soil conservation practices on arthropods and crop yields. Journal of Applied Entomology
<p>This dataset contains data and scripts that supplement the publication</p> <p>Lichtenberg et al. Differential effects of soil conservation practices on arthropods and crop yields. J. Appl. Entomol. 147: 931-940. <a href="https://doi.org/10.1111/jen.13188">https://doi.org/10.1111/jen.13188</a></p> <p>Please cite the above article if you use any of the included data or code.</p> <p>Files are described in README.md.</p>
Supplementary data for: "The demographic history of the wild crop relative Brachypodium distachyon is shaped by distinct past and present ecological niches"
<p>Supplementary data to https://doi.org/10.1101/2023.06.01.543285</p>
Data for article Agricultural input shocks affect crop yields more in the high-yielding areas of the world
<p>This repository contains the agricultural input data of 12 crops used in the article analysis. The agricultural inputs used are (unit in parentheses):</p> <ul> <li>nitrogen (kg/ha)</li> <li>phosphorus (kg/ha)</li> <li>potassium (kg/ha)</li> <li>machinery (1000 metric horsepower cv)</li> <li>herbicides (original kg/ha, for analysis rescaled by dividing with 97.5th percentile)</li> <li>fungicides (original kg/ha, for analysis rescaled by dividing with 97.5th percentile)</li> <li>insecticides (original kg/ha, for analysis rescaled by dividing with 97.5th percentile)</li> <li>other pesticides (original kg/ha, for analysis rescaled by dividing with 97.5th percentile)</li> <li>soil organic carbon (t/ha)</li> <li>soil phosphorus (mg/kg(</li> <li>soil nitrogen (t/ha)</li> <li>non-mineral nitrogen (kg/ha)</li> <li>non-mineral phosphorus (kg/ha)</li> <li>irrigation (% of area under irrigation)</li> <li>agricultural workers (persons)</li> </ul> <p><br> This repository also includes the analysis results of yields after agricultural input shocks. This data is in<br> rasters named e.g. wheat_phosphorus_shock_75.tif, meaning that this file contains the yield data (t/ha) for wheat after a 75% shock in phosphorus input. Rasters are provided for the following:</p> <ul> <li>yields after 25%, 50% or 75% nitrogen shock</li> <li>yields after 25%, 50% or 75% phosphorus shock</li> <li>yields after 25%, 50% or 75% potassium shock</li> <li>yields after 25%, 50% or 75% machinery shock</li> <li>yields after 25%, 50% or 75% pesticide shock</li> <li>yields after 25%, 50% or 75% shock in all fertilizers (fertilizer shock)</li> <li>yields after 25%, 50% or 75% shock in all inputs</li> </ul> <p>In addition, the observed crop yield (t/ha) used to construct the model is provided. Other yield rasters are:</p> <ul> <li>modelled baseline yield (t/ha), used to calculate the yield changes in shock scenarios</li> <li>modelled zero input yield (t/ha), calculated as a scenario with zero fertilizers, machinery and pesticide inputs</li> </ul> <p>Please see the article for references on the datasets.</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)
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.