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698 results for “soybean”
ECOBREED WP4 soybean data related to Randelovic et al. (2020)
<p>Data related to the publication of Randelovic et al. (2020) Agronomy 10, 1108. doi: 10.3390/agronomy10081108. Data include the following files: (a) Excel file with two sheets (2018 & 2019) including trial information (plot allocation) and number of plants per square meter; (b) RGB image of soybean trial 2018 taken at V4 stage (four unfolded trifoliolate leaves); (c) RGB image of soybean trial 2018 taken at R3 stage (beginning pod); (d) RGB image of soybean trial 2019 taken at V4 stage; (e) RGB image of soybean trial 2019 taken at R3 stage. [Growth stages according to Fehr WR, Caviness CE (1977) Stages of soybean development. Iowa State Univ. Cooperative Ext. Serv., Spec. Rep. 80.]</p>
Biorenewable soybean oil-based photocurable resin
<p>This data set corresponds to the analyses carried out in the following article: Bodor, M.; Lasagabáster-Latorre, A.; Arias-Ferreiro, G.; Dopico-García, M.S.; Abad, M.-J. Improving the 3D Printability and Mechanical Performance of Biorenewable Soybean Oil-Based Photocurable Resins. <br>Polymers 2024, 16, 977. https://doi.org/10.3390/polym16070977</p>
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>
Soybean data for paper: "Increase of simultaneous soybean failures due to climate change"
<p>Input and Output data used in the paper: ""Increase of simultaneous soybean failures due to climate change""</p> <p>Upon use of part of this dataset, please cite authors and paper related.</p> <p>Input:</p> <p>Observed soybean data obtained from official authorities pre-processed and regularised at 0.5 x 0.5 spatial resolution:</p> soy_yield_1975_2016_05x05_1prc.nc 43.6 MB soy_yield_arg_1974_2019_05x05.nc 44.6 MB soy_yields_US_all_1975_2020_05x05.nc 47.7 MB soybean_harvest_area_calculated_americas_hg.nc soybean_yields_america_detrended_1978_2016.nc 78.8 MB <p> </p> <p>Outputs:</p> <p>Hybrid model outputs for soybean yield from 2015-2100l with trends at 0.5 x 0.5 spatial resolution:</p> hybrid_trend_gfdl-esm4_ssp126_default_yield_soybea ... 14.6 MB hybrid_trend_gfdl-esm4_ssp585_default_yield_soybea ... 14.6 MB hybrid_trend_ipsl-cm6a-lr_ssp126_default_yield_soy ... 14.6 MB hybrid_trend_ipsl-cm6a-lr_ssp585_default_yield_soy ... 14.6 MB hybrid_trend_ukesm1-0-ll_ssp126_default_yield_soyb ... 14.6 MB hybrid_trend_ukesm1-0-ll_ssp585_default_yield_soyb ... 14.6 MB <p>Hybrid model outputs for soybean yield from 2015-2100 without trends at 0.5 x 0.5 spatial resolution:</p> hybrid_gfdl-esm4_ssp126_default_yield_soybean_2015 ... 7.3 MB hybrid_gfdl-esm4_ssp585_default_yield_soybean_2015 ... 7.3 MB hybrid_ipsl-cm6a-lr_ssp126_default_yield_soybean_2 ... 7.3 MB hybrid_ipsl-cm6a-lr_ssp585_default_yield_soybean_2 ... 7.3 MB hybrid_ukesm1-0-ll_ssp126_default_yield_soybean_20 ... 7.3 MB hybrid_ukesm1-0-ll_ssp585_default_yield_soybean_20 ... 7.3 MB
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL soybean simulations
<p>This data set contains output data from simulations with the model LPJmL for soybean as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</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>
2023 HYDRAS Proof of concept experiment with soybean genotypes
<h2>Description</h2> <p>This data sets contains metadata and data of the <strong>2023_POC experiment in the HYDRAS facility</strong>.</p> <p> The 2023 proof-of-concept study in hydras tested all standard field-phenotyping measurement types available in the infrastructure with 3 contrasting varieties of soybean + a control and a drought treatment using rain-out shelters. Start date: 23/5/2023, End dat: 4/10/2023. Location: Melle, Belgium. </p> <p>Data set contains: UAV sensor data, Electrical Resistivity Tomography data, soil point sensor data (water content, water potential and temperature), weather data, yield data and associated experimental information. </p> <h2>Content</h2> <ul> <li>2023_POC_metadata.xlsx contains all information about the experiment (goal, design, sensors, treatments, biological material, ...) and about the associated data files.</li> <li>Subfolder SPATIAL_INFO contains all spatial information about the experimental layout (location of field, plots, sensors, transects, ...) in GEOJSON files</li> <li>Other subfolders contain the actual data from various sources as .csv files. </li> </ul>
Activity of antioxidant enzymes and lipid peroxidation of soybean plants treated with five Diaporthe species
<p>Absorbance data from spectrophotometric measurements of catalase, reduced glutathion, lipid peroxidation and superoxide-dismutase of soybean cv. Sava plants infected with five <em>Diaporthe</em> species (i.e. <em>D. aspalathi</em>, <em>D. caulivora</em>, <em>D. eres</em>, <em>D. gulyae</em>, <em>D. longicolla</em>).</p> <p>Supplementary data to the publication Petrovic et al. (2023) The biochemical response of soybean cultivars infected by <em>Diaporthe</em> species complex. Plants 12, 2896. https://doi.org/10.3390/plants12162896</p>
Soybean yield projections in Europe under historical (1981-2010) and future climate (2050-2059 and 2090-2099 for RCP4.5 and RCP8.5)
<p><strong>General information</strong></p> <p>This dataset contains soybean yield projections in Europe under historical (1981-2010) and future climate with moderate (RCP 4.5) to intense (RCP 8.5) warming, up to the 2050s and 2090s time horizons. The data has been generated by <em>Guilpart et al. (2022) Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change, Nature Food. </em>All details can be found in this paper. A brief summary is provided below.</p> <p><strong>Summary of soybean yield projections methodology</strong></p> <p>Yield projections have been performed using data-driven relationships between climate and soybean yield derived from machine-learning (Random Forest). The Random Forest model was trained using (i) the the global dataset of historical yields updated version (Iizumi et al. 2014a), which includes grid-wise soybean yields worldwide with the grid size of 1.125 degree over 1981-2010, and (ii) the global retrospective meteorological forcing dataset tailored for agricultural application (GRASP, Iizumi et al. 2014b), which covers the period 1961–2010 at the same spatial resolution as yield data, i.e. a grid size of 1.125 degree. Time-detrended soybean yield data was related (using Random Forest) to 35 climate variables defined at a monthly time step over the seven months of the soybean growing season, plus the fraction of irrigated area, i.e. a total of 36 variables. The 35 climate variables are monthly mean daily minimum and maximum temperatures (<em>Tmin</em> and <em>Tmax</em>, degree Celsius), monthly total precipitation (<em>rain</em>, mm month<sup>-1</sup>), monthly mean daily total solar radiation (<em>solar</em>, MJ m<sup>-2</sup> day<sup>-1</sup>), monthly mean air vapor pressure (VP, hPa). The fitted model showed high R² (higher than 0.9) and low RMSE (0.35 t ha<sup>-1</sup>) between observed and predicted yields based on cross-validation.</p> <p>Then, soybean yield projections under historical over whole Europe have been performed using the GRASP climate data, and yield projections under future climate have been performed using 16 climate change scenarios consisting of bias-corrected data of eight Global Circulation Models (GCM; GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, MRI-CGCM3, and NorESM1-M, used in the Coupled Model Intercomparison phase 5 (CMIP5) and two Representative Concentration Pathways (RCPs; 4.5 and 8.5 W m<sup>-2</sup>). Soybean growing season used for projections is April to October. All projections assumed irrigated fraction equals to zero. Projections are shown only on agricultural area (cropland plus pasture), in the year 2000. Soybean yield is expressed in tons per hectare.</p> <p><strong>Files description</strong></p> <ul> <li><em>RF_soybean_historical_GRASP_median_1981_2010.nc</em> : random forest projections of soybean yield in Europe for the historical (1981-2010) period using GRASP climate data. This file contains the median yield (in tons per hectare) over 1981-2010.</li> <li><em>RF_soybean_rcp45_median_2050_2059.nc : </em>random forest projections of soybean yield in Europe for the 2050-2059 time period under RCP4.5. This file contains the median yield (in tons per hectare) over 2050-2059 and the 8 GCMs.</li> <li><em>RF_soybean_rcp45_median_2090_2099.nc : </em>random forest projections of soybean yield in Europe for the 2090-2099 time period under RCP4.5. This file contains the median yield (in tons per hectare) over 2090-2099 and the 8 GCMs.</li> <li><em>RF_soybean_rcp85_median_2050_2059.nc : </em>random forest projections of soybean yield in Europe for the 2050-2059 time period under RCP8.5. This file contains the median yield (in tons per hectare) over 2050-2059 and the 8 GCMs.</li> <li><em>RF_soybean_rcp85_median_2090_2099.nc : </em>random forest projections of soybean yield in Europe for the 2090-2099 time period under RCP8.5. This file contains the median yield (in tons per hectare) over 2090-2099 and the 8 GCMs.</li> </ul> <p><strong>References</strong></p> <p>Guilpart N. <em>et al.</em> (2022)<strong> </strong>Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change, <em>Nature Food</em>.</p> <p>Iizumi T. <em>et al.</em> (2014a) Historical changes in global yields: Major cereal and legume crops from 1982 to 2006. <em>Glob. Ecol. Biogeogr.</em> 23, 346–357.</p> <p>Iizumi T. <em>et al</em>. (2014b). A meteorological forcing data set for global crop modeling: Development, evaluation, and intercomparison. <em>J. Geophys. Res. Atmos. Res.</em> 119, 363–384.</p>
Cultivation practices in soybean production at a glance
<p>This video contains information about cultivation practices in soybean production. It was made within the scope of the Legumes Translated Horizon 2020 project. It is available in Serbian with subtitles in English, German, Hungarian, Italian, Romanian, Russian and Serbian.</p>
Bio-priming of soybean with Bradyrhizobium japonicum and Bacillus megaterium
<p>The data represent the impact of single and co-inoculation with<em> Bradyrhizobium japonicum</em> and <em>Bacillus megaterium</em> on seed germination and initial seedling growth of two soybean cultivars, under optimal and stressful conditions. Three laboratory tests,<em> i.e</em>., germination test, cold test, and accelerated aging test, were performed in order to evaluate seed quality and viability in relation to the applied bacterial treatments.</p> <p>The data are related to the publication of Miljakovic et al. (2022); doi: 10.3390/plants11151927</p>
Soil Carbon Dynamics in Soybean Cropland and Forests in Mato Grosso, Brazil
<p>These files contain the carbon content, radiocarbon, and stable isotope data for soils collected to 2 m deep in forest and soybean cropland in Mato Grosso, Brazil. </p>
Soybean dependence on biotic pollination decreases with latitude - Data and Computer code
<p>Release of Datasets and R scripts needed to reproduce the analyses and figures published in the article <em>'Soybean dependence on biotic pollination decreases with latitude'</em>, published in Agriculture, Ecosystems & Environment, Volume 347, 1 May 2023, 108376. <a href="https://doi.org/10.1016/j.agee.2023.108376">https://doi.org/10.1016/j.agee.2023.108376</a></p> <p><strong>Highlights</strong></p> <ul> <li>In the absence of pollinators, soybean yield decreases between 0 and ~50%.</li> <li>Variation in pollinator dependence (PD) was found to be structured latitudinally.</li> <li>PD decreases at high latitudes due to an apparently higher incidence of autogamy.</li> <li>Temperature and photoperiod could play an important role in determining PD.</li> <li>Changes in cleistogamy and androsterility might explain the reported trends.</li> </ul> <p><strong>Abstract</strong></p> <p>Identifying large-scale patterns of variation in pollinator dependence (PD) in crops is important from both basic and applied perspectives. Evidence from wild plants indicates that this variation can be structured latitudinally. Individuals from populations at high latitudes may be more selfed and less dependent on pollinators due to higher environmental instability and overall lower temperatures, environmental conditions that may affect pollinator availability. However, whether this pattern is similarly present in crops remains unknown. Soybean (Glycine max), one of the most important crops globally, is partially self-pollinated and autogamous, exhibiting large variation in the extent of PD (from a 0 to ~50% decrease in yield in the absence of animal pollination). We examined latitudinal variation in soybean's PD using data from 28 independent studies distributed along a wide latitudinal gradient (4-43 degrees). We estimated PD by comparing yields between open pollinated and pollinator-excluded plants. In the absence of pollinators, soybean yield was found to decrease by an average of ~30%. However, PD decreases abruptly at high latitudes, suggesting a relative increase in autogamous seed production. Pollinator supplementation does not seem to increase seed production at any latitude. We propose that latitudinal variation in PD in soybean may be driven by temperature and photoperiod affecting the expression of cleistogamy and androsterility. Therefore, an adaptive mating response to an unpredictable pollinator environment apparently common in wild plants can also be imprinted in highly domesticated and genetically-modified crops.</p> <p><strong>Content</strong></p> <p>The dataset consists of two files</p> <p>1 - <a href="https://github.com/NERC-CEH/Soybean-dependence-on-biotic-pollination-decreases-with-latitude/blob/main/%5Bdata%5D%20Cunha%20et%20al.%20MS_soybean.xlsx">[data] Cunha et al. MS_soybean.xlsx</a> is an excel file with two sheets, <strong>data</strong> and <strong>data_map</strong>. These sheets contain the data used in the models defined in the R script <a href="https://github.com/NERC-CEH/Soybean-dependence-on-biotic-pollination-decreases-with-latitude/blob/main/%5BR%20script%5D%20Cunha%20et%20al.%20MS_soybean.R">[R script] Cunha et al. MS_soybean.R</a>.</p> <ul> <li> <p>1.1 The <strong>data</strong> sheet contains the variables:</p> <ul> <li>Value = log_ratios</li> <li>Lat = latitude in decimal degrees</li> <li>Variable = yield component</li> <li>Treatment = treatment type for comparing pollinator dependence</li> <li>Reference_Data_owner = study ID where the data was obtained</li> <li>Site = site within the study where each field experiment was performed</li> </ul> </li> <li> <p>1.2 The <strong>data_map</strong> sheet contains information used for plotting the geographical distribution of the used studies:</p> <ul> <li>Reference_Data_owner = study ID where the data was obtained</li> <li>Country = country where the study was performed</li> <li>Province = province where the study was performed</li> <li>Locality/Farm = locality where the study was performed</li> <li>Lat = latitude in decimal degrees</li> <li>Long = longitude in decimal degrees</li> </ul> </li> </ul> <p>2 - <a href="https://github.com/NERC-CEH/Soybean-dependence-on-biotic-pollination-decreases-with-latitude/blob/main/%5Bdata%5D%20Cunha%20et%20al.%20MS_soybean%20%5Bdate_photoperiod%5D.csv">[data] Cunha et al. MS_soybean [date_photoperiod].csv</a> is a comma-separated file that contains the information used in the R script <a href="https://github.com/NERC-CEH/Soybean-dependence-on-biotic-pollination-decreases-with-latitude/blob/main/%5BR%20script%5D%20Cunha%20et%20al.%20AGEE%20-%20gee_temp_ts_extract.R">[R script] Cunha et al. AGEE - gee_temp_ts_extract.R</a> and produces Figure S2.</p> <ul> <li> <p>2.1 The dataset contains the following variables:</p> <ul> <li>study_ID = study ID number where the data was obtained</li> <li>study_ref = study ID where the data was obtained</li> <li>latitude = latitude in decimal degrees</li> <li>longitude = longitude in decimal degrees</li> <li>date1 = date of the sowing or flowering when the experiment was done</li> <li>date2 = a second date, when available, of the sowing or flowering when the experiment was done</li> <li>event = if the date was related to the sowing of seeds or flowering of soybean.</li> </ul> </li> </ul>
Dataset for "On the variability of the leaf relative uptake rate of carbonyl sulfide compared to carbon dioxide: insights from a paired field study with two soybean varieties"
<p>Data of measurements and model output of the publication "On the variability of the leaf relative uptake rate of carbonyl sulfide compared to carbon dioxide: insights from a paired field study with two soybean varieties". NO DOI YET</p> <p>The data consists of micrometeorological data, COS,CO<sub>2</sub> and H2O flux measurements and resistances of two soybean cultivars at an agricultural field in Italy.</p> <p>For additional information, please contact: <a href="mailto:felix.spielmann@uibk.ac.at">Felix.Spielmann@uibk.ac.at</a> or <a href="mailto:Georg.Wohlfahrt@uibk.ac.at">Georg.Wohlfahrt@uibk.ac.at</a>.</p>
Study of Leaf Wilt in Soybean Plants
<p>This dataset was produced in collaboration with the Crop and Soil Science Department of North Carolina State University and the United States Department of Agriculture (USDA). It comprises of 1892 rgb images of plots of soybean fields. The images represent soybean plants having 5 different levels of wilting, each image being assigned a value between 0 and 4 by expert annotators. 0 represents leaves with least wilting while 4 represents the most wilted leaves.</p> <p>The full_data.zip file consists of all images in the dataset. The annotation file dataAnns_goodFiles.csv has all image IDs and their corresponding annotations ranging from 0-4. The last column Annotation has all labels stored from 0-4 for the corresponding image. The treatment_camera column in the csv file refers to the plot number associated with each image. This number is included in the full image id as well. The first part of the number denotes the camera ID while the second part identifies a particular plot ID. For example, 3-106 indicates camera 3, plot 106. The remaining numbers in the image ID denote the date and time when the particular image was captured. For example image 3-106_12_08_2019_06_30_40.jpg denotes camera 3, plot 106 and was taken on 12/08/2019 at 06 hours, 30 mins and 40 seconds.</p>
Water chemistry data including nitrate stable isotopes sampled from zero-tension lysimeters in an Iowa corn-soybean field in 2017 and 2018
These data were used in the manuscript titled "Mechanisms underlying episodic nitrate and phosphorus leaching from poorly drained agricultural soils" published in the Journal of Environmental Quality. We measured nitrate, ammonium, and phosphate concentrations in zero-tension lysimeters installed along a topographic gradient in a corn and soybean field in north-central Iowa, USA, during 2017 and 2018. We measured nitrate stable isotope compositions in a subset of lysimeter samples. Concentrations of nitrate, ammonium, and ferrous and ferric iron were measured in periodic soil extractions co-located with the lysimeters.
Machine Demonstration: mechanical weed control in soybeans
<p>This video was provided by the EU funded project Legumes Translated, which supports the production and use of grain legumes in Europe. On the project website <a href="https://www.youtube.com/redirect?v=Bm_5JluTpc0&event=video_description&redir_token=Sf_vP1RmyENuQtEbJbo_0YXcxpN8MTU3ODQxNTk2OUAxNTc4MzI5NTY5&q=https%3A%2F%2Fwww.legumestranslated.eu">https://www.legumestranslated.eu</a> you will find more information and practical guidelines on the production and use of grain legumes. More Info: «Mechanical weed control in organic soy cultivation - how and when to use which machine?» <a href="https://www.youtube.com/redirect?v=Bm_5JluTpc0&event=video_description&redir_token=Sf_vP1RmyENuQtEbJbo_0YXcxpN8MTU3ODQxNTk2OUAxNTc4MzI5NTY5&q=https%3A%2F%2Fwww.bioattualita.ch%2Fcoltura%2Fa">https://www.bioattualita.ch/coltura/a</a>... Weed control is one of the main factors of economic success in organic soybean production. This video presents the following machines for mechanical weed control: 1. Weeding between and in the rows MATER Macc Unica-F Einböck Chopstar Garford Robocrop Schmotzer 2. Machines working row-independent Treffler TS 620/3M Einböck Aerostar-Rotation Carre Rotanet</p>
ChinaSoyArea10m: a dataset of soybean planting areas with a spatial resolution of 10 m across China from 2017 to 2021
<p>This dataset provides 10m-resolution maps of soybean planting areas in China during 2017-2021.</p><p>*** The data file is in ".tif" format</p><p>*** Temporal Resolution: Annually</p><p>*** Temporal coverage: 2017-2021</p><p>*** Pixel size: 10 m</p><p>*** Projection information: EPSG: 4326</p><p>The map boundary employed in this database does not imply the expression of any opinion whatsoever on the part of us concerning the legal status of any country, territory, city or area or its authorities, or concerning the delimitation of its frontiers or boundaries.</p>
NortheastChinaSoybeanYield20m: an annual soybean yield dataset at 20 m in Northeast China from 2019 to 2023
<p>Accurate monitoring of crop yield is important for ensuring food security. Current yield estimation methods, such as machine learning models or the assimilation of remotely sensed biophysical variables into crop growth models, depend heavily on ground observations and involve significant computational costs. To solve these problems, a hybrid framework coupling the World Food Studies Simulation Model (WOFOST) and the Gated Recurrent Unit model (GRU) was proposed for soybean yield estimation in Northeast China from 2019 to 2023.</p> <p>This dataset provides 20 m annual soybena yield in Northeast China from 2019 to 2023.</p> <p>*** The data file is in “.tif" format</p> <p>*** Temporal Resolution: annually</p> <p>*** Temporal coverage: 2019-2023</p> <p>*** Pixel size: 20 m</p> <p>*** Projection information: EPSG: 4326</p>
Supplementary Data - Using landscape genomics to infer genomic regions involved in environmental adaptation of soybean genebank accessions
<p><strong>File: 50K_GenotypesEU_raw_UHOH_SoySNP50K.csv.tgz </strong></p> <p>Genotyping data of SoySNP50k SNP array of 170 European soybean varieties.</p> <p>The array includes 51.955 SNP markers.</p> <p>Genotypes of each variety are in columns and each row is a SNP marker. Naming of markers follows the annotation of the soybean genome.</p> <p><strong>File: EUvarieties_infos.csv </strong></p> <p>Description of European varieties</p> <p>Contains variety name, country of origin, EU region and maturity group assignment.</p> <p> </p> <p><strong>File: Supplementary_Data_Haupt_Schmid.xlsx</strong></p> <p>Additional data derived from data analysis. Description of data contained within file (Worksheet "Summary")</p> <p> </p>
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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.