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91 results for “food systems”
European consumers ́ preference and willingness to pay for food products labelled as obtained by a circular farming system -in relation to environmental attitudes and consumption behaviours
<p>Data was collected with questionnaire-based research carried out in Belgium, Croatia, Hungary, Italy, Poland, and Spain as part of a European project. The survey questions were designed to obtain the Willingness to pay using 2 different methodologies the discrete choice experiment and the open-end choice experiment. The survey also included questions about consumers environmental attitudes, and consumption behavior (purchase, use and recycling), to identify if them have influence on preferences towards more sustainable food products. The 3 analyzed food products were pork meat, milk and bread, all of them obtained through different agricultural production systems (circular, conventional, and organic agriculture). The sample was stratified in terms of gender and age to be representative to the average population in each country. Furthermore, respondents included in this study were those that are mainly, or in part responsible for the household food shopping. The questionnaire was translated to the languages of the countries involved in the data collection and pre-launched using a pilot sample of 50 consumers in each case study country. Finally, a total of 5,362 validated questionnaires were obtained. Data was collected online using the Qualtrics market research company, and Net panel market company for Hungary from June 2021 to January 2022.</p>
Food systems Emissions shares, 1990-2019
<p><strong>Greenhouse gas emissions from agri-food systems </strong></p> <p><strong>(1990-2019)</strong></p> <p> </p> <p><strong>Overview</strong></p> <p>We present results from the <a href="https://www.fao.org/faostat/en/#data/EM">FAOSTAT emissions shares</a> database which disseminates emissions from all economic sectors and from agri-food systems by gases (CO<sub>2</sub>, CH4, N<sub>2</sub>O, F-gases and their total in CO<sub>2</sub>eq) relative to 236 countries and territories over the period 1990 – 2019.</p> <p>In 2019, global greenhouse gas emissions from all economic sectors totaled about 54 billion tonnes CO<sub>2</sub>eq (54 Gt CO<sub>2</sub>eq), emissions from agri-food systems totaled 16.5 billion tonnes (Gt CO<sub>2</sub>eq) representing 31 percent of the total anthropogenic emissions from all economic sectors.</p> <p>This dataset focuses on emissions from agri-food systems which includes data on emissions from the farm-gate, land use change and pre- and post-production. The sum of these three sectors comprises the agri-food system emissions (16.5 Gt CO<sub>2</sub>eq).</p> <p>Pre- and post-production include emissions from: fertilizers manufacturing, on-farm electricity use, food processing, food transport, food retail, food waste disposal, food household consumption and food packaging.</p> <p><strong>Data Structure</strong></p> <p>The data is structured as a tabular data with attributes: AreaName, ISO3, ItemName, ElementName, Year, Value, Unit.</p> <p><strong>Attributes (Columns)</strong></p> <p>Attributes in the data are defined as below:</p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Descriptions</strong></p> </td> </tr> <tr> <td> <p><strong>AreaName</strong></p> </td> <td> <p>characterizes all countries including world and regional aggregates</p> </td> </tr> <tr> <td> <p><strong>ISO3</strong></p> </td> <td> <p>represents three letter ISO3 country codes (not all regional aggregates have ISO3 country codes)</p> </td> </tr> <tr> <td> <p><strong>ItemName</strong></p> </td> <td> <p>represents all items covered in the data</p> </td> </tr> <tr> <td> <p><strong>ElementName</strong></p> </td> <td> <p>represents all gases covered in the data</p> </td> </tr> <tr> <td> <p><strong>Year</strong></p> </td> <td> <p>period covered by the data</p> </td> </tr> <tr> <td> <p><strong>Value</strong></p> </td> <td> <p>represents the emissions value</p> </td> </tr> <tr> <td> <p><strong>Unit</strong></p> </td> <td> <p>Unit of measurement (in this data emissions are measured in kilotonnes)</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p><strong>Files included in the dataset</strong></p> <p>We have included two files available for downloaded. The csv file contains the data in .csv format and a excel file contains data in .xlsx format.</p> <p><strong>Global warming potential (GWP)</strong></p> <p>In this data, the emissions total (CO<sub>2</sub>eq) is computed by applying the GWP values from the IPCC Fifth Assessment Report (AR5) as given below:</p> <p> </p> <table align="center"> <tbody> <tr> <td> <p> </p> </td> <td> <p><strong>Greenhouse gas</strong></p> </td> <td> <p><strong>GWPAR5 (IPCC, 2014)</strong></p> </td> </tr> <tr> <td> <p><em>Single</em></p> <p><em>gases</em></p> </td> <td> <p>N<sub>2</sub>O</p> </td> <td> <p>265</p> </td> </tr> <tr> <td> <p>CO<sub>2</sub></p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>CH<sub>4</sub></p> </td> <td> <p>28</p> </td> </tr> <tr> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p><em>F-gases </em></p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> <td> <p>HFC-23</p> </td> <td> <p>12,400</p> </td> </tr> <tr> <td> <p>HFC-32</p> </td> <td> <p>677</p> </td> </tr> <tr> <td> <p>HFC-41</p> </td> <td> <p>116</p> </td> </tr> <tr> <td> <p>HFC-125</p> </td> <td> <p>3,170</p> </td> </tr> <tr> <td> <p>HFC-134</p> </td> <td> <p>1,120</p> </td> </tr> <tr> <td> <p>HFC-134a</p> </td> <td> <p>1,300</p> </td> </tr> <tr> <td> <p>HFC-143</p> </td> <td> <p>328</p> </td> </tr> <tr> <td> <p>HFC-143a</p> </td> <td> <p>4,800</p> </td> </tr> <tr> <td> <p>HFC-152</p> </td> <td> <p>16</p> </td> </tr> <tr> <td> <p>HFC-152a</p> </td> <td> <p>138</p> </td> </tr> <tr> <td> <p>HFC-161</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>HFC-227ea</p> </td> <td> <p>3,350</p> </td> </tr> <tr> <td> <p>HFC-236cb</p> </td> <td> <p>1,210</p> </td> </tr> <tr> <td> <p>HFC-236ea</p> </td> <td> <p>1,330</p> </td> </tr> <tr> <td> <p>HFC-236fa</p> </td> <td> <p>8,060</p> </td> </tr> <tr> <td> <p>HFC-245ca</p> </td> <td> <p>716</p> </td> </tr> <tr> <td> <p>HFC-245fa</p> </td> <td> <p>858</p> </td> </tr> <tr> <td> <p>HFC-365mfc</p> </td> <td> <p>804</p> </td> </tr> <tr> <td> <p>HFC-43-10mee</p> </td> <td> <p>1,650</p> </td> </tr> <tr> <td> <p>Sulfur hexafluoride (SF<sub>6</sub>)</p> </td> <td> <p>23500</p> </td> </tr> <tr> <td> <p>Nitrogen trifluoride (NF<sub>3)</sub></p> </td> <td> <p>16,100</p> </td> </tr> <tr> <td> <p>PFC-14</p> </td> <td> <p>6,630</p> </td> </tr> <tr> <td> <p>PFC-116</p> </td> <td> <p>11,100</p> </td> </tr> <tr> <td> <p>PFC-218</p> </td> <td> <p>8,900</p> </td> </tr> <tr> <td> <p>PFC-318</p> </td> <td> <p>9,540</p> </td> </tr> <tr> <td> <p>PFC-31-10</p> </td> <td> <p>9,200</p> </td> </tr> <tr> <td> <p>PFC-41-12</p> </td> <td> <p>8,550</p> </td> </tr> <tr> <td> <p>PFC-51-14</p> </td> <td> <p>7,910</p> </td> </tr> <tr> <td> <p>PCF-91-18</p> </td> <td> <p>7,190</p> </td> </tr> <tr> <td> <p>µGWP</p> </td> <td> <p>5,195</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Data Sources</strong></p> <p>FAOSTAT climate change, emissions shares <a href="https://www.fao.org/faostat/en/#data/EM">data</a>, <a href="https://fenixservices.fao.org/faostat/static/documents/EM/cb7514en.pdf">analytical brief</a>.</p> <p>The primap-hist national historical emissions timeseries <a href="https://zenodo.org/record/5494497#.Yf18Cy8w2ic">data</a>, <a href="https://essd.copernicus.org/articles/8/571/2016/essd-8-571-2016.html">paper</a>.</p> <p> </p>
Foodscapes - A global clustering of terrestrial food production systems
<p>This repository contains the final outputs from the global <strong>Foodscape</strong> mapping exercise. In this project we aimed to identify broad homologues of foodscape classes, comparable in a minimum set of biophysical and management characteristics, would help to design possible interventions and leverage points for more sustainable agriculture.</p> <p>Provided are maps in spatial geotiff format (.tif) which can be opened in any typical GIS software such as ArcGIC or QGIS. The Resolution is 0.5' degree (WGS 84 or <em>longitude-latitude</em> projection). Both the foodscape map (<em>Foodscapes_combinedGEOTIFF_final.tif</em>) and a reclassified intensity map (<em>Foodscapes_combinedGEOTIFF_intensity.tif</em>) are provided. Accompanied with the tif files are .clr and .qml files, both of which provide layout information on colours used for each code combination in the global maps. The .qml file will be automatically loaded when opening the layer in QGIS.<br> In addition, in the Microsoft Excel sheet "<em>Foodscapes_combinedLEGEND_final.xlsx</em>" a legend with qualitative description and label of each class is provided.</p> <p>If you use this layer in any way, please cite this repository and the describing <a href="https://osf.io/puyzw/">preprint</a>.</p>
Evolution of left-right asymmetry in the sensory system and foraging behavior during adaptation to food-sparse cave environments
<p>Laterality in relation to behavior and sensory systems is found commonly in a variety of animal taxa. Despite the advantages conferred by laterality (e.g., the startle response and complex motor activities), little is known about the evolution of laterality and its plasticity in response to ecological demands. In the present study, a comparative study model, the Mexican tetra (<em>Astyanax mexicanus</em>), composed of two morphotypes, i.e., riverine surface fish and cave-dwelling cavefish, was used to address the relationship between environment and laterality. The use of a machine learning-based fish posture detection system and sensory ablation revealed that the left cranial lateral line significantly supports one type of foraging behavior, i.e., vibration attraction behavior, in one cave population. Additionally, left-right asymmetric approaches toward a vibrating rod became symmetrical after fasting in one cave population but not in the other populations. Based on these findings, we propose a model explaining how the observed sensory laterality and behavioral shift could help adaptation in terms of the tradeoff in energy gain and loss during foraging according to differences in food availability among caves.</p> <p>This repository contains all of raw videos used in this study.</p> <p>Please let us know if you have any question on these videos</p>
Diminishing returns on labor in the global marine food system: Dataset S1 and code for analysis
<p>Dataset on the number of marine fishers 1950-2015 accompanying the manuscript "Diminishing returns on labor in the global marine food system" by K. J. N. Scherrer, Y. Rousseau, L. C. L. Teh, U. R. Sumaila and E. D. Galbraith. Includes 1) script for data analysis, 2) processed fisheries labor data set, 3) separate data file with average socioeconomic indicators by country needed for analysis, 4) data documentation. </p>
Linking Processed Foods and Processing Techniques to the FoodEx2 Coding System
<p>This excel file complements the <a href="https://doi.org/10.5281/zenodo.1488652">EU database of processing factors for pesticide residues</a> by providing the mapping between processed food/feed listed in the database and the FoodEx2 coding system.</p>
Food quantity and quality modulates inducible defences in a common predator-prey system
<p><span>Zooplankton display different inducible defences against invertebrate and vertebrate predators. The response pattern to gape-limited invertebrate predators involves increased somatic growth and offspring body size but delayed maturity and reduced offspring numbers. In contrast to this general pattern, the freshwater model organism <em>Daphnia magna</em> has been reported to exhibit a different response when encountering the gape-limited tadpole shrimp <em>Triops cancriformis</em>. Under laboratory conditions, <em>D. magna</em> showed increased somatic growth, earlier maturation, and an increase in both offspring number and size. We propose here that the discrepancy between the previously observed and the theory-based response patterns against invertebrate predators is due to differences in food availability in the applied laboratory settings and assessed whether the defensive response of <em>D. magna</em> against <em>T. cancriformis</em> is modulated differently by food quantity and quality. We found a strong impact of food quantity and quality on the defence response of <em>D. magna</em> to <em>T. cancriformis</em> kairomones. The prey seem to be able to overcome trade-offs between morphological defence traits and reproductive traits, but distinctly between high food quantity and high food quality. Thereby, reproductive traits were preferred over morphological defences. Furthermore, removal of particles from the <em>T. cancriformis</em>-conditioned water caused a defence pattern in <em>D. magna</em> that was consistent with the general response pattern known from other invertebrate predators, thus explaining the described discrepancy to previous studies with <em>T. cancriformis</em>. <span> </span>Our study highlights the importance of assessing food-related effects on predator-prey interactions to understand trophic relationships and food web processes.</span></p>
Mapping the existing food systems, value chains and markets for agroecological products
<table> <tbody> <tr> <td>The food systems and markets of our focal farming systems are mapped as well as communities across the value chain, including operations from production over to food disposal after consumption, all along with the contribution of these operations to socio-economic and environmental outcomes. The datasets are the results of survey/interviews on the above topic in the CANALLS project ALLs.<span> </span></td> </tr> </tbody> </table>
Agriculture and food system scenarios with particular focus on organic and agro-ecological farming practices in the EU
<p>This is a comprehensive dataset of the agriculture and food system scenarios co-developed with stakeholders with the agricultural land use model BioBaM-GHG 2.0 and presented in Deliverable 4.2 of the H2020 project UNISECO. It includes sub-national (NUTS1/2-level) data on agricultural production and consumption, land use, greenhouse gas emissions from livestock and agricultural activities, etc. for the base year 2012 and the scenario years 2030 and 2050. The scenarios include a Business as usual case and four scenarios with focus on organic and agro-ecological farming practices in the EU, based on different storylines. Further information is available from the above-mentioned deliverable.</p> <p>A detailed model description is provided in the paper "Exploring the option space for land system futures at regional to global scales: The diagnostic agro-food, land use and greenhouse gas emission model BioBaM-GHG 2.0", in which these scenarios are also presented as an exemplary application of the model BioBaM-GHG 2.0.</p> <p>This work was funded by the ERA-NET SusAn project 101243 AnimalFuture, as well as by the European Union’s Horizon 2020 research and innovation programme and its funding of the H2020 UNISECO project under grant agreement N°773901.</p>
Fig. 3 in Scientific note Preliminary examination of food web structure of Nicola Lake (Taim Hydrological System, south Brazil) using dual C and N stable isotope analyses
Fig. 3. Trophic position estimates of fishes collected at Nicola Lake, Taim Hydrological System. # symbols denotes different individuals of the same species.
Fig. 2 in Scientific note Preliminary examination of food web structure of Nicola Lake (Taim Hydrological System, south Brazil) using dual C and N stable isotope analyses
Fig. 2. Plot of δ15N and δ13C values for plants (), mollusks () and fishes () collected at Nicola Lake, Taim Hydrological System. Sources of carbon assimilated by consumers are indicated by the relative positions of taxa on the x-axis; trophic level is indicated by relative position on the y-axis. The dashed line distinguishes between producers and consumers. # symbols denotes different individuals of the same species.
Fig. 1 in Scientific note Preliminary examination of food web structure of Nicola Lake (Taim Hydrological System, south Brazil) using dual C and N stable isotope analyses
Fig. 1. Patos-Mirim Lagoon complex (ca. 14,000 Km2) (A) in southern Brazil showing the Taim Hydrological System (B) with the ecological reserve's limits (320.4 Km2) and Nicola Lake (2.45 Km2) (C).
Dataset for "Demystifying food systems transformation: a review of the state of the field"
<p>Datasets offer the raw and analysed data used for the review article: "Demystifying food systems transformation: a review of the state of the field". This file contains the three main databases used and variations on the literature data filtering. Dataset 3 includes a breakdown of the analysis criteria adopted and reported on.</p>
Workshop - European initiatives for nutrient recycling in agri-food systems
<p>This is the video of the workshop (16 December 2019 – Vila-sana, Lleida, Spain) jointly organised by the Circular Agronomics project and the H2020 project <a href="https://www.nutri2cycle.eu/">Nutri2Cycle</a> on European initiatives for nutrient recycling in agri-food systems.</p> <p>The event showcased the various strategies that exist across Europe to recycle nutrients and that the different EU-funded projects already working to achieve this can complement one another in order to attain a common goal.</p> <p>One of the experimental sites within the Catalonia case study of the Circular Agronomics project was visited.</p>
Food fraud data based on the European Rapid Alert System for Food and Feed (RASFF)
<p>The data contains information on food fraud and was used to predict food fraud type using a Bayesian Network model. Food fraud notifications for the period 2000-2014 were downloaded from the Rapid Alert System for Food and Feed (RASFF) database. Each record contains detailed information on the kind of notification and the products and countries involved. Based on the description in each notification we added a variable "food fraud type" (i.e. six different types of food fraud). A set of 749 notifications for the years 2000-2013 was used to train a Bayesian Network model to predict food fraud type. This model was validated using the 88 notifications for the year 2014.</p> <p>Interpretation of the data and details on the performance of the BN model can be found in the research article titled “Prediction of food fraud type using data from Rapid Alert System for Food and Feed (RASFF) and Bayesian network modelling” <a href="https://doi.org/10.1016/j.foodcont.2015.09.026">https://doi.org/10.1016/j.foodcont.2015.09.026</a></p> <p> </p> <p> </p> <p><strong>Column names</strong></p> <p>year - year notification was made</p> <p>product - categorization of the different products</p> <p>notification - categorization of the notifications</p> <p>notified - country that made the notification</p> <p>origin - country where the product originated from</p> <p>fraud - classification of fraud type</p>
Data from: Effect of yeast addition on the biogas production performance of a food waste anaerobic digestion system
<p>Food waste contains numerous easily degradable components, and anaerobic digestion is prone to acidification and instability. This work aimed to investigate the effect of adding yeast on biogas production performance, when substrate is added after biogas production is reduced. The results showed that the daily biogas production increased 520 ml and 550 ml by adding 2.0% (VS) of activated yeast on the 12th and 37th day of anaerobic digestion, respectively, and the gas production was relatively stable. In the control group without yeast, the increase of gas production was significantly reduced. After the second addition of substrate and yeast, biogas production only increased 60 ml compared with that before the addition. After fermentation, the biogas production of yeast group also increased by 33.2% compared with the control group. Results of the analysis of indicators, such as volatile organic acids, alkalinity, and propionic acid, showed that the stability of the anaerobic digestion system of the yeast group was higher. Thus, the yeast group is highly likely to recover normal gas production when the biogas production is reduced, and substrate is added. The results provide a reference for experiments on the industrialisation of continuous anaerobic digestion to take tolerable measures when the organic load of the feed fluctuates dramatically.</p>
Food fraud data based on the European Rapid Alert System for Food and Feed (RASFF)
<p>The data contains information on food fraud. A total of 1634 food fraud notifications for the period 2000-2020 were downloaded from the Rapid Alert System for Food and Feed (RASFF) database. Each record contains detailed information on the kind of notification and the products and countries involved. Based on the description in each notification we added a variable "food fraud type" (i.e. six different types of food fraud). This dataset can be used to analyze food fraud, and a subset was used to train a Bayesian network model to predict food fraud type. This research article titled “Prediction of food fraud type using data from Rapid Alert System for Food and Feed (RASFF) and Bayesian network modelling” can be found here: <a href="https://doi.org/10.1016/j.foodcont.2015.09.026">https://doi.org/10.1016/j.foodcont.2015.09.026</a></p> <p> </p> <p><strong>Dataset column names</strong></p> <p>year - year notification was made</p> <p>product - categorization of the different products</p> <p>notification - categorization of the notifications</p> <p>notified - country that made the notification</p> <p>origin - country where the product originated from</p> <p>fraud - classification of fraud type</p>
The Future of Large Dams in Global Food and Energy Systems
<p>Global basin scale data on current and future demands for irrigation and hydropower, as well as available resources in terms of solar photovoltaic and existing storage.</p>
eDNA metabarcoding of avocado flowers: 'Hass' it got potential to survey arthropods in food production systems?
<p>In the face of global biodiversity declines, surveys of beneficial and antagonistic arthropod diversity as well as the ecological services that they provide are increasingly important in both natural and agro-ecosystems. Conventional survey methods used to monitor these communities often require extensive taxonomic expertise and are time-intensive, potentially limiting their application in industries such as agriculture, where arthropods often play a critical role in productivity (e.g. pollinators, pests and predators). Environmental DNA (eDNA) metabarcoding of a novel substrate, crop flowers, may offer an accurate and high throughput alternative to aid in the detection managed and unmanaged arthropod taxa (e.g. flower-visiting insects and potential pollinators). Here, we compared the arthropod communities detected with eDNA metabarcoding of flowers, from an agricultural species (<em>Persea americana </em>- 'Hass' avocado), with two conventional survey techniques; Digital Video Recording (DVR) devices and pan traps. In total, 80 eDNA flower samples, 96 hours of DVRs and 48 pan trap samples were collected. Across the three methods, 49 arthropod families were identified, of which 12 were unique to the eDNA dataset. Alpha diversity levels did not differ across the three survey methods although taxonomic composition varied significantly, with only 12% of arthropod families found to be common across all three methods. This study demonstrates that eDNA metabarcoding of flowers to detect visiting arthropods, although in a developmental stage, can complement traditional survey methods and increase the diversity of taxa detected with implications for both natural and agro-ecosystems.</p>
Data for publication: A food system transformation can enhance global health, environmental conditions and social inclusion
<p>Data related to the publication "A food system transformation can enhance global health, environmental conditions and social inclusion"</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.