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1,425 results for “Agriculture”
Soil-Adjusted Vegetation Index (SAVI) derived from 2017 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Soil-adjusted Vegetation Index (SAVI) from 2017 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as multiple individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_SAVI_CAP2017-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf and kml index maps). Javascript code used to process SAVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Lots for greening: Identification of metropolitan vacant land and its potential use for cooling and agriculture in Phoenix, Arizona, USA
This project provides the first systematic assessment of non-governmental vacant parcels for potential greening (VPPG) the Phoenix metropolitan area—land parcels that are or can be privately owned but which contain no buildings, are unpaved, have no apparent use, and are potential candidates for urban greening. To achieve the data, a new method for the identification of vacant lands was employed that combines remote sensing techniques and cadastral data and trains the computer to distinguish different forms of vacant land. The classification result proved to be an effective approach for open land identification and identified approximately 19500 ha of open land in the metro area. The model achieved an average accuracy of 90.67%. This dataset only includes VPPG and does not include other vacant land determined to be inappropriate for potential greening (developed/abandoned or impervious surface). (Overall accuracy for all classes was 87.20%).
Supplemental materials of the Castaño-Sánchez et. al. (2023) article (Agricultural Systems) containing the IFSM model input parameters not included in the main text, and the Criollo ranches survey form
CONTEXT: The southwestern United States is experiencing an increasingly warmer and drier climate that is affecting cattle production systems of the region. Adaptation strategies are needed that will not compromise environmental quality or profitability. Options include the use of desert-adapted beef cattle biotypes, such as Rarámuri Criollo cattle, and crossbreds of Criollo with more traditional British breeds. Currently, most calves raised in the Southwest are grain finished, often with irrigated crops produced in the hydrologically-threatened Ogallala Aquifer region. A viable alternative may be grass finishing with the rainfed forage of the arid and semi-arid rangeland of the Southwest or in the temperate grasslands of the Northern Plains. OBJECTIVE: Compare the environmental impacts and production costs of grain-finishing in Texas and grass-finishing in the Northern plains and the Southwest with traditional Angus cattle vs. Criollo and Criollo x Angus cattle. METHODS: Nine supply chain strategies were simulated using the Integrated Farm System Model to compare farm-gate life cycle intensities of greenhouse gas emissions (carbon footprint), fossil energy footprint, nitrogen footprint, blue water footprint and production costs using representative (appropriate soils, climate, and management) ranch and feedlot operations. RESULTS AND CONCLUSIONS: For both finishing options (grass, grain), Criollo x Angus cattle had the best environmental (3%-27% lower), and production cost (4-23% lower) outcomes followed by pure Criollo and then Angus cattle. Crossbred production combined the lower feed supplementation requirements of Criollo cows with heavier final carcasses of offspring from Angus genetics. Crossbred cattle with grass finishing in the Southwest or Northern Plains outperformed on most environmental variables as well as production costs, mostly due to reduced external input requirements (primarily feed). A downside for grass-finished crossbreds was greater carbon fo
Italy Southern Regions organic waste stream, Agricultural, Forest and Municipal Solid Waste, years 2018 and 2030
<p>Southern Italy regions agricultural residues (straw, pruning) quantification, years 2018 and 2030</p> <p>Souther Italy regions above ground annual forest increment, 2018 and 2030 technical and environmental contraints</p> <p>Souther Italy regions Municipal Solid Waste separation 2018, and 2030 minimum target EU waste Directive 2018/851</p>
Agricultural land use and livestock composition by case study of the SURE-Farm project - Input data for a dynamic nitrogen flow model
<p>Dataset used as input to the model by Pinsard et al (2021) and results published in D5.5 of the SURE-Farm project.</p>
The ecological and economic benefits of sustainable agricultural practices
<p><strong>Code and data for Mata et al.'s <em>The ecological and economic benefits of sustainable agricultural practices: Evidence from on-farm trials in broad-acre crops. </em></strong></p> <p><strong>Abstract</strong></p> <p>1. A transition to more sustainable agricultural practices is essential to mitigate the negative environmental impacts of conventional farming and to ensure long-term food security. However, widespread adoption requires robust evidence demonstrating their efficacy and economic viability.</p> <p>2. We co-designed a two-year field trial with farmers and agronomy advisors in Australia to evaluate the ecological and economic outcomes of sustainable agricultural practices for managing the redlegged earth mite, a major pest of Australian crops and pastures. We compared 'Novel' treatments – representing long-term farmer-implemented sustainable practices based on biological control – with 'Conventional' treatments, and 'Plus' treatments designed as counterfactuals to disentangle the effects of specific pest control and plant nutrient components.</p> <p>3. Redlegged earth mite densities remained below economic thresholds across all treatments and years, demonstrating effective pest control in both conventional and sustainable systems. Notably, the Novel treatment supported higher densities of beneficial arthropods, suggesting increased biological control potential.</p> <p>4. Yield and gross profit margins were generally similar across treatments, indicating that sustainable agriculture practices can maintain profitability while fostering biodiversity.</p> <p>Practical implication. Our study provides evidence that biological control and biofertiliser supplementation can be effectively used to manage agricultural pests. It also demonstrates the value of close collaboration with farmers and agronomy advisors in conducting ecological field research with real-world applications.</p>
Ecological filtering shapes the impacts of agricultural deforestation on biodiversity
<p>This dataset and associated code are for the manuscript titled "Ecological filtering shapes the impacts of agricultural deforestation on biodiversity", which is due to be published in the journal Nature Ecology & Evolution (accepted on September 20, 2023). The abstract of this manuscript is as follows:</p><p> </p><p>The biodiversity impacts of agricultural deforestation vary widely across regions. Previous efforts to explain this variation have focused exclusively on the landscape features and management regimes of agricultural systems, neglecting the potentially critical role of ecological filtering in shaping deforestation tolerance of extant species assemblages at large geographical scales via selection for functional traits. Here we provide a large-scale test of this role using a global database of species abundance ratios between matched agricultural and native forest sites that comprises 71 avian assemblages reported in 44 primary studies, and a companion database of ten functional traits for all 2,647 species involved. Using meta-analytic, phylogenetic, and multivariate methods, we show that beyond agricultural features, filtering by the extent of natural environmental variability and the severity of historical anthropogenic deforestation shapes the varying deforestation impacts across species assemblages. For assemblages under greater environmental variability – proxied by drier and more seasonal climates under greater disturbance regime – and longer deforestation histories, filtering has attenuated the negative impacts of current deforestation by selecting for functional traits linked to stronger deforestation tolerance. Our study provides a heretofore largely missing piece of knowledge in understanding and managing the biodiversity consequences of deforestation by agricultural deforestation.</p>
Land use and land cover samples for specific regions of interest in the Brazilian Cerrado agricultural belt
<p>Land use and land cover samples for specific regions of interest in the Brazilian Cerrado agricultural belt in 2019/2020. Total of samples: 957. The process to collect them is described in Chaves, M., & Sanches, I. (2023). Improving crop mapping in Brazil's Cerrado from a data cubes-derived Sentinel-2 temporal analysis. Remote Sensing Applications: Society and Environment, 32, 101014. <a href="https://www.sciencedirect.com/science/article/pii/S2352938523000964">https://www.sciencedirect.com/science/article/pii/S2352938523000964</a> and Chaves, M., Soares, A., Mataveli, G., Sánchez, A., & Sanches, I. (2023). A semi-automated workflow for LULC mapping via Sentinel-2 data cubes and spectral indices. Automation, 4(1), 94-109. <a href="https://www.mdpi.com/2673-4052/4/1/7">https://www.mdpi.com/2673-4052/4/1/7</a>.</p>
Global energy use and carbon emissions from irrigated agriculture
<p>This repository contains supporting data for: "<strong>Global energy use and carbon emissions from irrigated agriculture"</strong></p><p>Email: qinjingxiu17@mails.ucas.ac.cn and duanweili@ms.xjb.ac.cn</p><p>The dataset contains:</p><p>-Global energy consumption and CO2 emissions from irrigation . </p><p>-Global CO2 emissions from groundwater degassing . </p><p>-Energy consumption and CO2 emissions with different irrigation and pumping systems and irrigation water sources. </p><p>-Global energy consumption and CO2 under drip and sprinkler scenarios. </p><p>-Global energy consumption and CO2 under mix electricity scenarios. </p><p>-Energy units: Terajoule (TJ); CO2 emissions units: (Tonnes CO2)</p><p>-Files are uploaded in .tif raster data. </p>
Phlorest phylogeny derived from Lee & Hasegawa 2011 'Bayesian phylogenetic analysis supports an agricultural origin of Japonic languages'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Lee S, Hasegawa T (2011) Bayesian phylogenetic analysis supports an agricultural origin of Japonic languages. Proceedings of the Royal Society B: Biological Sciences, 278(1725):3662–9.</p> </blockquote>
Agricultural Mitigation Initiatives
<p>This dataset is an ongoing effort to explore the main international mitigation initiatives related to the agrarian sector. The dataset has been created by a research team based at the Geneva Graduate Institute since 2020 based on studies of the sector, conversations with experts and internet searches. The internet search has been conducted using key words, including notions such as:</p><ul><li>Climate change mitigation initiatives/projects/programs/campaigns</li><li>netzero</li><li>nature based solutions</li><li>natural capital</li><li>ecosystem services</li><li>carbon market initiatives</li><li>low carbon agriculture</li><li>afforestation initiatives</li><li>Soil carbon initiatives</li><li>Paludiculture initiatives</li><li>Land use change and climate change mitigation</li></ul><p>Findings have been narrowed down to include only initiatives directly or indirectly related to the agrarian sector. These included netzero campaigns, climate action coalitions, national mitigation programs, non-governmental projects and other mitigation initiatives. Many of the findings were interconnected. For example, a mitigation initiative could be part of a wider mitigation program or coalition. We have therefore tried to reflect these interconnections in the structure of the dataset. For each entry we also included a description with the main characteristics of the entry (coalition, campaign, program or project) the main thematic of the entry and the main actors involved. The main thematic found were:</p><ul><li>carbon sequestration in soils</li><li>advocacy for action</li><li>food systems transformations (climate smart agriculture)</li><li>Land use management</li><li>natural capital initiatives</li><li>carbon accounting standards</li><li>biodiversity conservation and restoration</li><li>technical guidelines for GHGs emission reductions in agriculture</li><li>dissemination</li><li>ecosystems' accounting</li><li>pollution and waste reduction</li></ul><p>The dataset also includes a second spreadsheet where we have started to map some of the actors of the carbon market involved in the development, certification, validation and verification of GHGs emission reductions and removals for agrarian initiatives. </p>
Inertial Dataset for Posture Recognition in Agriculture and Construction Tasks
<p>This <strong>dataset, manually labeled</strong>, contains <strong>10 hours and 40 minutes</strong> (60 Hz) of <strong>8 typical working posture classes</strong> (standing, reaching, stooping, squatting, kneeling, lifting/lowering, carrying, and others), acquired with 16 subjects in three distinct scenarios in a lab environment:</p> <ol> <li>Isolated postures or short sequences without any associated task;</li> <li>Agriculture task (bricklaying) circuit;</li> <li>Construction task (harvesting) circuit.</li> </ol> <p>Two full-body inertial motion caption systems (<strong>17</strong> Xsens MTw Awinda <strong>IMUs</strong> each, from Xsens Technologies, B.V., The Netherlands) were used, connected to, respectively:</p> <ol> <li>Xsens MT Manager, providing raw inertial data (acceleration, angular velocity, and magnetic field data - csv files);</li> <li>Xsens MVN Analyze, providing processed data (quaternions, Euler angles, position, linear velocity, acceleration, angular velocity, angular acceleration, joint angles, ergonomic angles, center of mass, and magnetic field - xlsx files).</li> </ol> <div> <p>More information about the dataset acquisition and organization is detailed in readme.pdf file. For any questions, please contact Diogo R. Martins at <a href="mailto:diogo-martins-9@live.com.pt">diogo-martins-9@live.com.pt</a> or Sara M. Cerqueira at <a href="mailto:saracerqueira1996@gmail.com">saracerqueira1996@gmail.com</a>.</p> </div>
[Dataset] Plant–pollinator in a highly intensive agricultural landscape LTSER Zone Atelier Plaine & Val de Sèvre
<p>We built bipartite networks formed by pollinators and the flowers they forage on, using data collected in the Long Term Socio-Ecological Research site "Zone Atelier Plaine & Val de Sèvre" (Bretagnolle et al. 2028). We compiled a six-year monitoring dataset of plant–pollinator interactions, sampling by sweep-nets along transects in the main crop types of this intensive agricultural plain. </p> <p>The dataset contained all the "pollinator-plant" pair observed in each crop samples.</p>
SPATIAL DIFFERENTIATION OF THE EMISSIVITY OF AGRICULTURE IN EUROPE
<p>The file contains the data used in the article: <br>DOI:10.5604/01.3001.0054.4326</p> <p>Replacements included in the file (for 2020):<br>Country<br>Item: IPCC Agriculture<br>Total emissions in tonnes<br>Emissions per hectare of agricultural land<br>Emissions per unit value of goods produced by agriculture<br>Emissions per capita</p> <p><br>Source: FAOSTAT database</p>
Kallaama: A Transcribed Speech Dataset about Agriculture in the Three Most Widely Spoken Languages in Senegal
<p>This data is transcribed speech data, in Wolof, Pulaar and Sereer.</p> <p>The recordings are about agriculture. The recorded consist of farmers, agricultural advisers, and agri-food business managers. Type of recordings comprise interactive radio programmes, focus groups, voice messages, push messages and interviews. Therefore, spontaneous speech is prevailing. Quality of audio may vary depending on the type of programme.</p> <p>Content description :</p> <ul> <li><strong>speech_dataset_wol.tar.gz:</strong> Wolof (ISO Code 639-2: wol) speech dataset contains 55 hours of transcribed speech, including almost 13 hours of validated content check by an expert. It also contains a XSAMPA lexicon (49,132 phonetised entries) and a text corpus (1,140,508 words).</li> <li><strong>speech_dataset_fuc.tar.gz:</strong> Pulaar (ISO Code 639-2: fuc) speech dataset contains nearly 32 hours of transcribed speech, including around 11 hours of validated content check by an expert. It also contains a text corpus (742,024 words).</li> <li><strong>speech_dataset_srr.tar.gz:</strong> Sereer (ISO Code 639-2: srr) speech dataset contains 38 hours of transcribed speech, including nearly 11 hours of validated content check by an expert.<br>In total, these resources provide 125 hours of transcribed speech in the 3 most widely spoken languages in Senegal, including 35 hours of checked transcriptions.</li> </ul> <p>This work is a result of the Kallaama project, funded by Lacuna Fund for 1 year, in 2023. </p> <p>See the <a title="Kallaama speech dataset" href="https://github.com/gauthelo/kallaama-speech-dataset" target="_blank" rel="noopener">GitHub repository</a> for more details about the dataset.</p>
LAMASUS NUTS-level agricultural data derived from public FADN 1989-2009 (SGM) & 2004-2020 (SO)
<p>This dataset comprises spatial and temporal agricultural data compiled from the Farm Accountancy Data Network (FADN) Public Database available at the FADN-region level and further disaggregated using Corine Land Cover (CLC) information about agricultural area. <br><br><strong>Processing to NUTS level:</strong></p> <p>CLC data layers were used to overlay what is defined as "Agricultural areas" in CLC level 1classification (#2**) with FADN and NUTS regions. The overlay allows to calculate area-weighted shares and further to allocate FADN farm weights to the NUTS level. This allows the application of weights at the NUTS granularity. Please keep in mind, this is only possible under the assumption of heterogeneous farms within the FADN region. <br><br><strong>File description:</strong></p> <p>The dataset consists of eight files, corresponding to four different levels of NUTS coding (NUTS 0-3) according to the 2016 NUTS specification and each of those for the two different sampling periods. <br>FADN data from 2004 onwards, standard results calculated for farms grouped according to EU typology of agricultural holdings based on standard output (SO). <br>FADN data from 1989 to 2009, standard results calculated for farms grouped according to EU typology of agricultural holdings based on standard gross margin (SGM). </p> <p>For each csv file, the following columns are included: <br><br>Identifier:</p> <ol> <li>NUTS Code: The unique identifier for the NUTS (2016) region</li> <li>Year: The year of the data point</li> </ol> <p>Variables:</p> <p> 3. weighting: number of farms represented<br> 4. and after: SE standard result variables, for detailed description, please have a look at the accompanied xlsx: variable_description_zenodo.xlsx</p> <p><strong>Source information:</strong></p> <p>The raw data for the public Farm Accountancy Data Network (FADN) can be accessed through the official platform using the following link: <a href="https://agridata.ec.europa.eu/extensions/FADNPublicDatabase/FADNPublicDatabase.html" target="_new">FADN Public Database</a>.</p> <p>The CLC layers for the weighting of the spatial disaggregation can be accessed via the Copernicus homepage undern the following link: <a href="https://land.copernicus.eu/en/products/corine-land-cover">https://land.copernicus.eu/en/products/corine-land-cover</a></p> <p> </p> <div> <p>This dataset has been created as part of LAMASUS Project under the scope of Deliverable 3.2 titled "Database on EU policies and payments for agriculture, forest, and other LUM related drivers ". The data is directly linked to the work described on pages 50-57, belonging to section 3.6 Public FADN Data. The full text of the deliverable can be accessed via: <a href="https://www.lamasus.eu/wp-content/uploads/LAMASUS_D3.2_policy-and-payment-database.pdf">https://www.lamasus.eu/wp-content/uploads/LAMASUS_D3.2_policy-and-payment-database.pdf.</a></p> </div>
Soil grid data for 3 agricultural fields in Italy (Soil Moisture, soil organic carbon)
<p><span>Soil data collected in an agricultural area with annual crops in Italy (west-central Lombardia Po Valley, province of Pavia). The data refers to soil properties of 320 soil samples for Soil Moisture and 120 for SOC, collected in the topsoil (around 5-10 cm), considering a regular sampling grid, within three agricultural field with different crops (spring-summer cycle) and soils type, Rice-Loamy, Sorghum-Sandy and, Maize-Clay, in a period (before the seeding of the crops), when the soil was bare, in the framework of the EJP Steropes project.</span></p> <p><span>The aim of the collected dataset was to be able to analyse the influence of soil moisture in SOC (WP2 of the STEROPES project) prediction models from remote sensing.</span></p> <p><span>Data in the form of shape file (one shapefile for each agricultural field, for oth Soil Moisture and SOC), and pictures of the soil surface in .jpg format. </span></p>
Soil characteristics and spectral reflectance data of six agricultural fields in Switzerland
<p>Soil characteristics and spectral reflectance data of six agricultural fields in Switzerland collected within the EJP Soil project STEROPES. The data in the .csv files is organized as relational database with the database schema depicted in DB_Schema.pdf. The file headers (marked with #) contain metadata.</p>
Milan (ITALY) - Urban Agriculture spatial dataset (years 2007 and 2014)
<p>The data in this dataset is a spatial inventory of <strong>urban agriculture</strong> (UA) carried out in the city of Milan (Italy). UA areas where identified with a multi-step and iterative procedure by using different web-mapping tools, especially multitemporal Google Earth images, and ancillary data such as Google Street View and Bing Maps.</p> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p> <p><strong>Disclaimer</strong></p> <p>Despite our best efforts to validate the data, some information may be incorrect.</p> <p><strong>Description of the dataset</strong></p> <p><em><strong>Typologies of UA</strong></em></p> <ul> <li><strong>Residential garden: </strong>Private parcel near single houses (e.g. backyard), villas, buildings, industrial and commercial activities, generally managed by property owners. Cultivation is diversified ranging from leafy vegetables to herbs and fruit trees. Production is intended for self-consumption and/or for hobby purposes.</li> <li><strong>Community garden: </strong>A large area subdivided into multipleplots managed individually (i.e. allotment) or collectively by a group of people. Crop production is intended for self-consumption. Land is assigned by the Municipality; several cases of land cultivated without authorization are also common.</li> <li><strong>Urban farm: </strong>Parcel managed by professional farmers with an intensive and an advanced cropping system. The cultivation can be specialized or oriented to high diversity vegetables. The production is intended for market. The mapping procedure focus on arable crops, horticulture, vineyard, olive groves and orchard.</li> <li><strong>Institutional garden: </strong>Parcel managed by institutions or organizations like schools, religious center, prisons and non-profit organizations. The production is generally intended for self-consumption and less frequently for trade. Several gardens in this category are intended for social purposes (e.g. recreation,education, etc.).</li> <li><strong>Illegal garden: </strong>Parcel isolated, cultivated without authorization organized and managed individually or by a few people. Localization occurs on unused or abandoned areas owned by public bodies or private subjects. The production is intended for self-consumption.</li> <li><strong>Nurseries: </strong>A large area subdivided into multiple plots managed for growing ornamental plants and flowers.</li> </ul> <p><em><strong>Land use typologies</strong></em></p> <ul> <li><strong>Horticulture: </strong>annual crops generally seed sown in spring or summer (tomatoes, lettuce, zucchini, cucumbers, peppers).</li> <li><strong>Vineyard: </strong>grape vines grown in order to produce wine or table grape.</li> <li><strong>Olive groves: </strong>olive trees grown in order to produce olive oil or table olives.</li> <li><strong>Orchards: </strong>mixed trees such as orange, stone fruit, pome fruit, olive trees.</li> <li><strong>Mixed crops: </strong>an area grown with a mix of horticulture crops and fruit trees, not divisible.</li> <li><strong>Nurseries: </strong>ornamental plants, trees, flowers.</li> </ul> <p><strong>Credit</strong></p> <p>Pulighe G., Lupia F. (2019) <em>Multitemporal Geospatial Evaluation of Urban Agriculture and (Non)-Sustainable Food Self-Provisioning in Milan, Italy. </em><strong>Sustainability </strong>2019, <em>11</em>(7), 1846</p> <p>https://www.mdpi.com/2071-1050/11/7/1846</p>
"Agricultural trade and its impacts on cropland use and the global loss of species habitat." - Supplementary data
<p>This dataset and code is part of the following publication:<br> Schwarzmueller, F. & Kastner, T (2022), Agricultural trade and its impact on cropland use<br> and the global loss of species' habitats. Sustainability Science, doi: 10.1007/s11625-022-01138-7<br> </p> <p>There are three zip-folders accompanying this publication:</p> <p>Code.zip contains all the R-Scripts and input files neccessary for the calculation that were written by the authors.</p> <p>Data.zip contains the FAO-input data (as dowloaded in 2021). This exact data is not available anymore from the FAOSTAT website, which is why we included it in this repository.</p> <p>TradeMatrixFeed_import_dry_matter_1986-2013.zip contains the results from the calculation as shown in the paper.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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