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1,648 results for “farm”
Turbulent kinetic energy over large wind farms observed and simulated by the mesoscale model WRF (3.8.1)
<p>This repository contains the WRF configuration files necessary to reproduce the simulations <br> as described in Siedersleben et al. 2019 (https://doi.org/10.5194/gmd-2019-100)</p> <p>The file windturbines_GMD.txt contains the locations of <br> all windturbines implemented in the simulations. The corresponding attributes of each <br> wind turbine type is described in the wind-turbine-xx.tbl. Be aware that all windturbines use the same power and thrust coefficients only the different hub heights and rotor diameters are taken into account as described in Siedersleben et al. (2019).</p> <p>The namelist.input_nameOfSimulation files necessary to run the simulations are provided in this repository as well. You may notice that <br> there are less namelist files than simulations. The simulations not using a TKE source use the same namelists as the ones with a TKE a source. However, the WRF model needs to be recompiled using the manipolated module_wind_fitch.F (you find this file in this repository). The sensitivity studies investigating the impact of the uncertainties in the power and thrust coefficients use the namelist of the control simulation CNTRb, but with manipulated wind-turbine-x_modMin/Max.tbl wind turbine files.</p> <p>The two python files get_era5*.py can be used to retrieve the ERA5 data, driving the WRF model. <br> Note that the dates and pathes have to be adjusted in the python files. <br> After downloading the surface and model level data some postprocessing <br> is necessary as described nicely here: "http://valcap74.blogspot.com/2017/10/how-to-run-wrf-model-driven-by-era5-on.html". For this<br> purpose the simple script called postProcessERA5 (based on the blog entry mentioned above) can be used.</p>
Supply chain sustainability standards in temperate farming commodities
<p>Could companies contribute to farmland biodiversity in temperate landscapes? Most of the business efforts to enhance sustainability in agricultural supply chains have been focused on tropics. Important temperate crops like cereals and oilseeds are left largely unchecked. This results in a missed opportunity to reverse biodiversity decline in about 47% of the world's cropland. But implementation of voluntary sustainability standards to restore biodiversity in temperate and Mediterranean biomes would require doing things differently than in tropical commodities. Temperate agriculture differs both in its operations (lack of divergence between staples and cash crops, crop rotation) and landscape context (lack of agricultural expansion frontiers, complex pressures on biodiversity). Consequently, agricultural certification and similar schemes need to be linked primarily to a unit of space rather than a specific commodity. However, necessary tools such as advanced criteria, metrics and data infrastructure are lacking in temperate agriculture, and their development depends on input from conservation scientists and practitioners.</p> <p>Supporting information for<em> Supply chain sustainability standards in temperate farming commodities </em>consists of:</p> <ol> <li>Dataset for Figure 1;</li> <li>Additional information on methodology (VSSs uptake in retail industry) and sources (LEAF Marque case study, references for evidence of impact of feasible objectives for VSSs in conventional temperate agriculture).</li> </ol> <p> </p>
Supplementary data: Breeding wheat for organic farming: can the high grain protein gene Gpc-B1 help to tackle challenges in view of end-use quality?
<p>Agronomic and quality data of organic wheat (<em>Triticum aestivum</em>), mean comparisons and supplementary figures related to the publication "Breeding wheat for organic farming: can the high grain protein gene Gpc-B1 help to tackle challenges in view of end-use quality?" by Grausgruber et al. (2024) published in the Journal of Cereal Science.</p>
Decomposition data in organic, permaculture and conventional horticultural farms of Central Hungary
<p>This dataset has been produced from the PhD research of Alfréd Szilágyi supervised by Csaba Centeri and Eszter Kovács Tormáné. The study compared permaculture, organic and conventional farming systems regarding their ecosystem-service provision potential and sustainability. Multiple ecological indicators were measured in the field during the field study in 2020, and the basic datasets (soil test results; photo gallery of the studied farms with soil core sample; soil resistance and moisture; decomposition; earthworms; nematodes; soil surface fauna; pollinators; agrobiodiversity and habitat types) are uploaded in Zenodo separately to provide scientific data on permaculture systems. In this way, we hope to contribute to international efforts to evaluate the performance of agroecological agriculture alternatives. These publications also serve as supplements to the PhD thesis. For the sake of further usability of the datasets short description of the used methods is described. For further information please contact the authors.</p>
Navigating the complex policy landscape for carbon farming in The Netherlands and the EU -- Open Research Europe Extended Data-- Tables 1-6, Figures 1-2
<p>This is extended data for the article entitle 'Navigating the complex policy landscape for carbon farming in The Netherlands and the EU' submitted to Open Research Europe by Eise Spijker. </p>
Gerational issues in linking family farming production, traditional food in diet, physical activity and obesity in Pacific Islands countries and territories: the case of the Melanesian population on Lifou Island
<p>In the Melanesian culture, traditional activities are organized around family farming, although the lifestyle transition taking place over the last several decades has led to imbalances in diet and physical activity, with both leading to obesity. The aim of this interdisciplinary study was to understand the links between family farming (produced, exchanged, sold, and consumed food), diet (focused on produced, hunted, and caught food), physical activity (sedentary, light, and moderate-to-vigorous physical activity) and obesity in Melanesian Lifou Island families (parents and children). Forty families, including 142 adults and children, completed individual food frequency questionnaires, wore tri-axial accelerometers for seven continuous days, and had weight and height measured with a bio-impedance device. Qualitative and quantitative interviews were conducted at the household level concerning family farming practices and sociodemographic variables. Multinomial regression analyses and logistic regression models were used to analyze the data. Results showed that family farming production brings a modest contribution to diet and active lifestyles for the family farmers of Lifou Island. The drivers for obesity in these tribal communities were linked to diet in the adults, whereas parental socioeconomic status and moderate-to-vigorous physical activity were the main factors associated to overweight and obesity in children. These differences in lifestyle behaviors within families suggest a transition in cultural practices at the intergenerational level. Future directions should consider seasonality and a more in-depth analysis of diet including macro- and micro- nutrients to acquire more accurate information on the intergenerational transition in cultural practices and its consequences on health outcomes in the Pacific region.</p>
Data and Code from: On-farm land management strategies and production challenges in United States Organic Agricultural Systems.
<p>This repository contains data and code used in:</p> <p>Isaac Mpanga, Russel Trondstad, Jessica Guo, David LeBauer, and John Omololu, 2021. On-farm land management strategies and production challenges in United States Organic Agricultural Systems. Current Research in Environmental Sustainability.</p> <p>It provides USDA Surveys of Agricultural Production from 2008-2019 to investigate state and national trends by state in organic farm area, number, and sales, as well to evaluate national trends in on-farm land-use practices and challenges facing US organic production.</p> <p>It also includes code used to transform, visualize, and analyze the data, and derived data products - notably organic farm area and sales with values imputed to correct for redacted state level measures.</p>
Germination of crop species in response to whole-soil inoculants that originate from conventional vs organic farming systems
<p>Dataset of manuscript entitled “Germination of crop species in response to whole-soil inoculants that originate from conventional vs organic farming systems”. This manuscript includes the results of WP2 from the SOFT project (ref. 890874).</p>
Soil extracellular enzyme activity increases during the transition from conventional to organic farming
<p>Dataset of manuscript entitled “Soil extracellular enzyme activity increases during the transition from conventional to organic farming”. This manuscript includes the results of WP1 from the SOFT project (ref. 890874).</p>
Small-scale farming in drylands: New models for resilient practices of millet and sorghum cultivation - Dataset and code
<p>This repository contains the primary research data and R code used for data analysis for the article "<em>Small-scale farming in drylands: New models for resilient practices of millet and sorghum cultivation"</em> Published in the journal PLOS ONE (<a href="https://doi.org/10.1371/journal.pone.0268120">https://doi.org/10.1371/journal.pone.0268120</a>)</p> <p>N.B. To run the code unzip the folder 9-RData.zip and save it in the same working directory as the datasets</p> <p>V 2.0 changes:</p> <p>A. Datasets 1-2-3 - small formatting corrections</p> <p>B. Dataset 7 - fixing some errors in the calculations</p> <p>C. Code - minor fixes and seimplicifaction</p> <p> </p>
Modelled hydrodynamic profiles and salmon louse larval densities at Norwegian salmon farms
<p>Data compiled for use by the PreventLice web app, a decision support tool intended to help Norwegian salmon farmers avoid salmon louse infestations: <a href="https://havforskningsinstituttet.shinyapps.io/preventlice">https://havforskningsinstituttet.shinyapps.io/preventlice</a></p> <p>Each file contains the relevant data for a registered salmonid farm in Norway, identified by its locality number according to the Norwegian <a href="https://sikker.fiskeridir.no/akvakulturregisteret/web/sites">Aquaculture Registry</a>. A total of 1023 localities are included in version 1.0.0.</p> <p>The data are in long rectangular format, with each row corresponding to a single depth interval on a single date. Each row provides variables for locality number ("loc"), date ("date"), depth (m, "depth"), daily mean temperature (°C, "meanTemp"), daily mean salinity (ppt, "meanSal"), daily mean current speed (ms<sup>-1</sup>, "meanCurrSpd"), daily 95th percentile current speed (ms<sup>-1</sup>, "95PercCurrSpd"), daily salmon louse infestation pressure (copepodids m<sup>-3</sup>, "meanCopDensity"), and daily mean significant wave height (m, "SignWaveHeight").</p> <p>Temperature, salinity and current speeds are taken from the NorFjords-160 model (<a href="https://doi.org/10.1016/j.ecss.2020.107028">Dalsøren et al. 2020</a>), a finer-scale update of the NorKyst-800 model (<a href="https://doi.org/10.1007/s10236-020-01378-0">Asplin et al. 2020</a>). Wave height data are taken from the MyWaveWAM800m Norwegian coastal wave forecasting system (<a href="https://thredds.met.no/thredds/fou-hi/mywavewam800.html">Norwegian Meteorological Institute</a>). Salmon louse copepodid densities are estimated by coupling louse biology and behaviour parameters with hydrodynamic predictions from NorKyst-800 (<a href="https://doi.org/10.1371/journal.pone.0201338">Myksvoll et al. 2018</a>).</p>
Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries
<p>Summary:</p> <p>There are over 608 million farms around the world but they are not the same. We developed high spatial resolution maps telling where small and large farms were located and which crops were planted for 56 countries. We checked the reliability and have the confidence to use them for the country-level and global studies. Our maps will help more studies to easily measure how agriculture policies, water availabilities, and climate change affect small and large farms respectively.</p> <p>The code, source data, and the simultaneously farm-size- and crop-specific harvested area datasets, including the GAEZv4 crop map based dataset and SPAM2010 crop map based dataset, are open-access, free, and available, which can be found below. The resulting dataset is available in *.csv and *.nc (netCDF) for each crop and farming system. For each crop, farming system, and farm size, we provide the gridded harvested area in the coordinate Systems of EPSG:4326 - WGS 84. Gridded summaries over crops and farming systems are also available.</p> <p>-----------------------------------------------------------------------------------------------------------------------</p> <p>How to cite this dataset:</p> <p>Su, H., Willaarts, B., Luna-Gonzalez, D., Krol, M.S. and Hogeboom, R.J., 2022. Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries. <em>Earth System Science Data</em>, <em>14</em>(9), pp.4397-4418.</p> <p>-----------------------------------------------------------------------------------------------------------------------</p> <p>Update history:</p> <p>I am happy to receive any questions, comments, or potential collaboration on further dataset development. Please drop your email to Han Su (h.su@utwente.nl, han_su20@163.com)</p> <p>Version 1.03.1: Fix bugs in data format; Netcdf didn't show properly before in QGIS. Data underlying the three versions are the same.</p> <p>Version 1.02: New data summary, add Netcdf data format</p> <p>Version 1: Initial dataset for peer-review, CSV format only</p> <p>-----------------------------------------------------------------------------------------------------------------------</p> <p>Note: please cite the original publications/sources if any data source based on which this dataset was developed is reused for your own study.</p> <p>SPAM2010: </p> <p>Yu, Q., You, L., Wood-Sichra, U., Ru, Y., Joglekar, A. K. B., Fritz, S., Xiong, W., Lu, M., Wu, W., and Yang, P.: A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps, Earth System Science Data, 12, 3545-3572, 10.5194/essd-12-3545-2020, 2020.</p> <p>GAEZv4:</p> <p>FAO and IIASA: Global Agro Ecological Zones version 4 (GAEZ v4), FAO UN, Rome, Italy, 2021</p> <p>The dataset of Ricciardi et al.'s:</p> <p>Ricciardi, V., Ramankutty, N., Mehrabi, Z., Jarvis, L., and Chookolingo, B.: How much of the world's food do smallholders produce?, Global Food Security, 17, 64-72, 2018.</p> <p>The global dominant field size dataset:</p> <p>Lesiv, M., Laso Bayas, J. C., See, L., Duerauer, M., Dahlia, D., Durando, N., Hazarika, R., Kumar Sahariah, P., Vakolyuk, M., Blyshchyk, V., Bilous, A., Perez-Hoyos, A., Gengler, S., Prestele, R., Bilous, S., Akhtar, I. U. H., Singha, K., Choudhury, S. B., Chetri, T., Malek, Z., Bungnamei, K., Saikia, A., Sahariah, D., Narzary, W., Danylo, O., Sturn, T., Karner, M., McCallum, I., Schepaschenko, D., Moltchanova, E., Fraisl, D., Moorthy, I., and Fritz, S.: Estimating the global distribution of field size using crowdsourcing, Glob Chang Biol, 25, 174-186, 10.1111/gcb.14492, 2019.</p> <p>GLC-Share:</p> <p>Latham, J., Cumani, R., Rosati, I., and Bloise, M.: Global land cover share (GLC-SHARE) database beta-release version 1.0-2014, FAO, Rome, Italy, 2014.</p> <p>CAAS-IFPRI cropland extent map:</p> <p>Lu, M., Wu, W., You, L., See, L., Fritz, S., Yu, Q., Wei, Y., Chen, D., Yang, P., and Xue, B.: A cultivated planet in 2010 – Part 1: The global synergy cropland map, Earth System Science Data, 12, 1913-1928, 10.5194/essd-12-1913-2020, 2020.</p>
WEFE data collection for a pilot smallholder farm in Costa Rica
<p>In-situ and remote data collection of environmental and socioeconomic data for the planning of integrated water, energy, food, and environment systems for the case study of a smallholder farm in Costa Rica.</p>
Dataset of hair cortisol concentration in 950 finishing pigs on 20 commercial farms
<p>This dataset contains the hair cortisol concentrations of 950 finishing pigs. Pig hair was sampled as part of a study funded by the European project HealthyLivestock. Pigs were sampled in two separate batches on 20 farms (24 pigs/batch, two batches/farm. NB: 10 samples could not be analyzed at the laboratory). Farms were located in western France. A reference to the article relating to this dataset will be added when the article will be published.</p> <p>-The first sheet includes the 950 hair cortisol concentrations, distinguishing the batches and farms where pigs were sampled.</p> <p>-The second sheet includes the estimation of the average size of a pig batch on the 20 farms where hair was sampled + the estimation of the percentage of pigs sampled per batch.</p>
Aquavitae CS4Task7 Saccharina latissima and alaria longline in IMTA abalone grow out farm
<p>Preliminary production of kombu Saccharina latissima and Alaria on long lines produced under organic certification standards.</p> <p>Biomass production characterized by the number of plant per linear meter of longline, length (cm), width (cm) and weight (g) of 30 individual collected on 3 different section of the longline (S1,S2,S3).</p>
Database 2 - Second-year samplings in the CodeRe-farm pilot sites in Greece
<p>This database contains the results of the second year of AWIN assessments and general data recordings in the Greek goat pilots participating in the Code: Re-farm research project.</p>
Weather data for the period 2009 to 2022 from the Open Field location at University Farms, Case Western Reserve University
Data from the Open Field weather station at University Farms of Case Western Reserve University include observations from 2009 to 2022. University Farms is located in Hunting Valley, Ohio. From 10/20/2009 to 10/30/2014, the weather station was located at N 41.496883, W 81.436117, when it was relocated to N 41.49759, W81.43738. Data include date/time (in 15-minute intervals), wind speed, wind gust speed, wind direction, air temperature, relative humidity, solar radiation, rainfall, soil moisture, soil temperatures at 0, 2, and 5 cm soil depth, and data logger battery charge.
Weather data for the period 2009 to 2022 from the North Woodlot location at University Farms, Case Western Reserve University
Data from the North Woodlot weather station at University Farms of Case Western Reserve University include observations from 2009 to 2022. University Farms is located in Hunting Valley, Ohio. The North Woodlot weather station is at N 41 29.969, W 81 25.234. Data include Date and time (in 15-minute intervals), wind speed, wind gust speed, air temperature, relative humidity, solar radiation, soil moisture, soil temperatures at 0, 2, and 5 cm soil depth, and data logger battery charge.
Soil physical, chemical, and root data from forest stands at Thompson Farm in Durham, NH
In July 2019, quantitative soil pits were excavated alongside replicate power cores from three mature oak-pine forest stands at Thompson Farm (Durham, NH) to characterize soil physical and chemistry properties as well as root depth profiles in control and treatment plots associated with the Thompson Farm DroughtNet project, as well as in the footprint of the Ameriflux tower. Mean total sampling depth across all pits and cores was 84cm, though half of samples reached 100 cm or deeper. Chemical and isotopic analyses for each sample layer include pH, total N, total C, and stable isotope ratios of N and C. Physical data include soil mass, rock volume, bulk density, and soil texture. Oven-dried root mass in each sample layer was recorded in multiple size categories (from pits) or sorted to species (from power cores).
Year 2008, meteorological data, 15 minute intervals, from the MBL Marshview Farm weather station located in Newbury, MA
Year 2008 meteorological measurements at MBL Marshview Farm of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
ScienceDex guides
Understand access before you commit
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.