Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
56
datasets available to search
ShareScore release 0.9.0
Dataset results
56 results for “Agricultural management”
Long-term Agricultural Experiments: Data Management Survey
<p>Results of an online survey used to guage views of researchers within the LTE community on data management issues and knowledge. The survey was broken down in to 4 main questions and can be found at the following link - further responses are still welcome: <a href="https://forms.office.com/e/8DmapwLRr8" target="_blank" rel="noopener">https://forms.office.com/e/8DmapwLRr8</a>.</p> <ul> <li>About your role</li> <li>Data management & sharing</li> <li>Describing LTEs and their data</li> <li>Challenges for data management & sharing </li> </ul> <p>At the time of publication, 55 responses had been recieved.</p> <p>The survey was developed in response to an LTE Conference Workshop held at Rothamsted Research, UK in June 2023.</p>
Raw data for the submitted manuscript entitled "Mapping and Disposal of Irrigation Pipes for a Sustainable Management of Agricultural Plastic Waste", authors Ileana Blanco, Giuliano Vox, Fabiana Convertino, and Evelia Schettini
<p><span>The file regards the evaluation of plastic indexes and agricultural plastic waste quantities in Apulia region due to the use of irrigation pipes. The data is used to identify the critical areas for plastic waste production due to irrigation pipes.</span></p>
Data to support the publication "Impact of agricultural management on soil aggregates and associated organic carbon fractions: Analysis of long-term experiments in Europe"
<p><strong>Raw data:</strong> Experimental plot ids and information, mass distribution of all aggregate fractions after wet sieving, Sand content of each fraction to conduct the sand correction, mass distribution of all fractions after isolating the micro-aggregates held within the macroaggregates, yields per treatment, carbon content per fraction (raw data)</p> <p><strong>All data per plot: </strong>SOC content, MAOM and POM content of each fraction presented in the fractionation scheme included in the manuscript, together with the mass of the relative fractions. </p> <p> </p>
Dataset for review on innovative contracts for the promotion of biodiversity and ecosystem services in agricultural management: contract design and governance characteristics
<p>Dataset for H2020 project Contracts2.0 (GA No 818190), WP2, Task 2.1, Deliverable 2.1.</p> <p>This dataset contains the information needed to reproduce the results in:<br> Bredemeier, Birte; Herrmann, Sylvia; Sattler, Claudia; Prager, Katrin; van Bussel, Lenny, Rex, Julia (submitted): Can the greater integration of biodiversity and ecosystem services into agricultural management be achieved through innovative contract design? Submitted to Ecosystem Services.<br> <br> The Contracts2.0 project aims to develop novel contract-based approaches to incentivise farmers for the increased provision of environmental public goods alongside private goods.<br> Based on a literature review and integration of expert knowledge, we identified a comprehensive set of approaches to innovative contracts deviating from mainstream AECM contracts, and provide an overview of the variety of contracts currently being tested and experimented with. This includes different contract types like innovative Payments for Ecosystem Services (PES) approaches, value chain approaches and land tenure contracts, as well as their hybrids.</p> <p><br> We analysed 62 cases providing insights into characteristics of contract design and contract governance and the wider policy framework.</p> <p><br> The present dataset contains information on the criteria and specifications used to describe the cases studied, as well as the evaluation of each case.</p> <p><br> This information has been compiled to the best of our knowledge based on the sources available.<br> <br> For further information on the project Contracts2.0, please visit our website www.project-contracts20.eu. </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>
Farmer adaptive behavior and risk management in EU agriculture
<p>Risk and risk management are essential elements of agriculture and affect the wellbeing of farm households. Farmers react to production, market and institutional risks and challenges by taking measures on or off the farm. Such risk management measures are often costly and have implications for up- and downstream industries as well as the environment. The risk exposure of European farms is increasing. For example, climate change will increase the frequency and magnitude of extreme weather events like droughts, heatwaves and heavy rainfalls that potentially have detrimental effects on agricultural production. Thus, the adaptive capacity and risk management options in European agriculture need to be improved. Policy shall support this process. Policies are needed to support a diversity of risk management solutions and not only focus on a few solutions. Strategies to cope with risk often go beyond the level of the individual farm. Cooperation, learning and sharing of risks play a vital role in European agriculture and shall be strengthened. Thus, coordinated policies targeting beyond the individual farm and considering all the stakeholders involved in the risk management strategies are needed to ensure their effective implementation. Moreover, policies need to facilitate to take full advantage of the rapid technological progress and improved data availability (e.g. based on satellite imagery) to develop a wider set of risk management strategies.</p>
CESM2 Atmospheric CO2 without agricultural management
This dataset was created to understand the impacts of agriculture on CO2 concentrations. Two 1-degree simulations were branched from the CMIP6 "CESM2-esm-hist" simulation in 1970. The first of these turned off the explicit representation of agriculture so that all crop areas are represented as "generic" C3 crops, where crop phenology is simulated as C3 grasses and do not include irrigation or fertilization (referred to as "generic crop"). The second uses the explicit representation of agriculture but removes industrial N fertilization (referred to as "no fertilization"). To ensure that changes in CO2 fluxes were minimally impacted by model drift, each simulation equilibrated carbon fluxes in 1970 by cycling over a single year of forcing for ten years. The CESM2 simulated these alternative representations of agriculture in a CO2 emissions-forced historical scenario following the "esm-hist" experimental protocol.
Information flows around agricultural best management practices in central Pennsylvania
<p>This dataset was collected between February and April 2019, to assess the information network of agricultural Best-Management Practices (BMPs) in central Pennsylvania, a sub-region of the Chesapeake Bay watershed.</p> <p>It contains information flows (or "messages") relating to 16 specific BMPs, including:</p> <ul> <li>the BMP it relates to (e.g. riparian buffers, manure management planning, no-till, cover-cropping, etc.);</li> <li>the source and target of the information (actors);</li> <li>the kind of message (e.g. funding, regulation, technical assistance, etc.);</li> <li>the weight (strength) of messages (only for those received by farmers directly).</li> </ul> <p>Over 3900 messages/information flows were recorded, involving 57 actors.</p> <p>This data was used to conduct the study "Navigating agricultural nonpoint source pollution governance: A social network analysis of best management practices in central Pennsylvania".</p>
juan-duenas/NHESS: Soil conditioner mixtures as an agricultural management alternative to mitigate drought impacts: a proof-of-concept.
<p>The dataset and the R script have been enhanced and corrected, respectively. The main figures of the associated publication have been added in two different qualities.</p> <p>This data is associated to a paper that will appear in an special issue of the journal Natural Hazards and Earth System Sciences. https://nhess.copernicus.org/articles/special_issue1295.html</p>
Figure 2 in Generalist ground-nesting bees dominate diversity survey in intensively managed agricultural land
Figure 2. Species richness compared between sampling periods. Dark grey bars: species from the genus Andrena Fabricius (Andrenidae); light grey bars: species from the genera: Halictus Latreille, Lasioglossum Curtis (Halictidae), Osmia Panzer (Megachilidae), and Nomada Scopoli (Apidae); black bars: species from the genus Bombus Latreille (Apidae). Different letters above the dark grey bars indicate a significant statistical difference between sampling periods in total species richness of all sampled genera (F (3, 42) = 20.01, p<0.001).
Figure 1 in Generalist ground-nesting bees dominate diversity survey in intensively managed agricultural land
Figure 1. Total number of bees sampled in this study at the four different sampling periods. Dark grey bars: individuals from the genus Andrena Fabricius (Andrenidae); light grey bars: individuals from the genera: Halictus Latreille, Lasioglossum Curtis (Halictidae), Osmia Panzer (Megachilidae), and Nomada Scopoli (Apidae); black bars: individuals from the genus Bombus Latreille (Apidae). Different letters above the dark grey bars indicate a significant statistical difference between sampling periods in activity-density of individuals from all sampled genera (F (3, 42) = 18.89, p<0.001).
MADFORWATER: WP3: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task3.1: Reduction of crop water requirement and tools for irrigation management with treated WW: Subtask 3.1.1: Plant Growth Promotion (PGP) bacteria to enhance crop resistance to water stress and salinity: Subset1
<p>This dataset contains the data underlying the following publication: Mouna Mahjoubi, Simone Cappello, Yasmine Souissi, Atef Jaouani and Ameur Cherif (February 7th 2018). Microbial Bioremediation of Petroleum Hydrocarbon– Contaminated Marine Environments, Recent Insights in Petroleum Science and Engineering Mansoor Zoveidavianpoor, IntechOpen, DOI: 10.5772/intechopen.72207</p> <p> </p>
DIGITAL TOOLS FOR MONITORING AND MANAGEMENT IN AGRICULTURAL PRODUCTION
<p>The digitalization of agricultural production has led to the widespread use of mobile technologies for monitoring and managing agricultural processes. Applications and devices such as drones and satellites allow you to receive real-time data on soil conditions, humidity, plant growth, and pest threats. This data helps farmers to make quick decisions on irrigation, fertilization, and crop forecasting. Modern mobile solutions such as AgroMonitor integrate with various data sources and provide accurate information, which increases the efficiency of resource management and reduces production risks. The introduction of these technologies not only helps to increase yields but also ensures the sustainable development of agricultural operations, minimizing the impact of external factors. Thus, digital tools are becoming an important element of the modernization of the agro-industrial sector, contributing to its competitiveness at the global level.</p>
Agricultural plastic pollution reduces soil function even under best management practices
Open the record for dataset details and reuse information.
Silicon concentrations and stoichiometry in two agricultural watersheds: implications for management and downstream water quality
Agriculture alters the biogeochemical cycling of nutrients such as nitrogen (N), phosphorus (P), and silicon (Si) which contributes to the stoichiometric imbalance among these nutrients in aquatic systems. Limitation of Si relative to N and P can facilitate the growth of non-siliceous, potentially harmful, algal taxa which has severe environmental and economic impacts. Planting winter cover crops can retain N and P on the landscape, yet their effect on Si concentrations and stoichiometry is unknown. We analyzed three years of biweekly concentrations and loads of dissolved N, P, and Si from subsurface tile drains and stream water in two agricultural watersheds in northern Indiana. Intra-annual patterns in Si concentrations and stoichiometry showed that cover crop vegetation growth did not reduce in-stream Si concentrations as expected, although, compared to fallow conditions, winter cover crops increased Si:N ratios to conditions more favorable for diatom growth. To assess the risk of non-siliceous algal growth, we calculated a stoichiometric index to quantify biomass growth facilitated by excess N and P relative to Si. Index values showed a divergence between predicted algal growth and what we observed in the streams, indicating other factors influence algal community composition. The stoichiometric imbalance was more pronounced at high flows, suggesting increased risk of harmful blooms as climate change increases the frequency and intensity of precipitation in the midwestern U.S. Our data include some of the first published measurements of Si within small agricultural watersheds and provide the groundwork for understanding the role of agriculture on Si export and stoichiometry.
Derived data and code for Deines et al. 2020, Agricultural Water Management
<p>This codebase accompanies the paper:</p> <p>Deines, J.M., M.E. Schipanski, B. Golden, S.C. Zipper, S. Nozari, C. Rottler, B. Guerrero, & V. Sharda. 2020. Transitions from irrigated to dryland agriculture in the Ogallala Aquifer: Land use suitability and regional economic impacts. Agricultural Water Management 233:106061. DOI: <a href="https://doi.org/10.1016/j.agwat.2020.106061">https://doi.org/10.1016/j.agwat.2020.106061</a></p> <p>Data needed to reproduce the figures from Deines et al. 2020 can be found in the <code>data</code> folder.</p> <p>See the Readme.md or <a href="https://github.com/jdeines/Deines_etal_2020_agwat_OgallalaTransistions">https://github.com/jdeines/Deines_etal_2020_agwat_OgallalaTransistions</a> for more information.</p>
Selection of a diversionary field and other habitats by large grazing birds in a landscape managed for agriculture and wetland biodiversity
<p>Several populations of cranes, geese, and swans are thriving and increasing in modern agricultural landscapes. Abundant populations are causing conservation conflicts, as they may affect agricultural production and biodiversity negatively. </p> <p>Management strategies involving provisioning of attractive diversionary fields where birds are tolerated can be used to reduce negative impact to growing crops. To improve such strategies, knowledge of how the birds interact with the landscape and respond to current management interventions is key.</p> <p>We used GPS locations from tagged common cranes (Grus grus) and greylag geese (Anser anser) to assess how they use and select differentially managed habitats, such as diversionary fields to decrease impact on agriculture and wetlands protected for biodiversity conservation.</p> <p>Our findings show a high probability of presence of common cranes and greylag geese in the protected area and in the diversionary field, but also on arable fields, potentially causing negative impact on agricultural production and wetland biodiversity.</p> <p>We outline recommendations for how to improve the practice of diversionary fields and complementary management to reduce risk of negative impact of large grazing birds in landscapes tailored for both conservation and conventional agriculture.</p>
MADFORWATER: WP3: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task3.1: Reduction of crop water requirement and tools for irrigation management with treated WW: Subtask 3.1.1: Plant Growth Promotion (PGP) bacteria to enhance crop resistance to water stress and salinity: Subset2
<p>This dataset contains the data underlying the following publication: Hassen W, Neifar M, Cherif H, Najjari A, Chouchane H, Driouich RC, Salah A, Naili F, Mosbah A, Souissi Y, Raddadi N, Ouzari HI, Fava F and Cherif A (2018) Pseudomonas rhizophila S211, a New Plant Growth-Promoting Rhizobacterium with Potential in Pesticide-Bioremediation. Front. Microbiol. 9:34. doi: 10.3389/fmicb.2018.00034</p>
MADFORWATER: WP2: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task 2.3–Agro-industrial wastewater treatment: Subtask 2.3.1. –Treatment of olive mill wastewater (OMWW): Aerobic biological treatment in sequenced batch reactors (SBRs): Subset 1
<p>This dataset contains the data underlying the following publication: Fatma AROUS, Chadlia HAMDI, Souhir KMIHA, Nadia KHAMMASSI, Amani AYARI, Mohamed NEIFAR, Tahar MECHICHI, Atef JAOUANI (2018). Treatment of olive mill wastewater through employing sequencing batch reactor: Performance and microbial diversity assessment. 3 Biotech 2018, 8, 481. https://doi.org/10.1007/s13205-018-1486-6.</p>
Data from: Nitrification is a minor source of nitrous oxide (N2O) in an agricultural landscape and declines with increasing management intensity
<p>The long-term contribution of nitrification to nitrous oxide (N<sub>2</sub>O) emissions from terrestrial ecosystems is poorly known and thus poorly constrained in biogeochemical models. Here, using Bayesian inference to couple 25 years of <i>in situ</i> N<sub>2</sub>O flux measurements with site-specific Michaelis-Menten kinetics of nitrification-derived N<sub>2</sub>O, we test the relative importance of nitrification-derived N<sub>2</sub>O across six cropped and unmanaged ecosystems along a management intensity gradient in the U.S. Midwest. We found that the maximum potential contribution from nitrification to <i>in situ</i> N<sub>2</sub>O fluxes was 13-17% in a conventionally fertilized annual cropping system, 27-42% in a low-input cover-cropped annual cropping system, and 52-63% in perennial systems including a late successional deciduous forest. Actual values are likely to be less than 10% of these values because of low N<sub>2</sub>O yields in cultured nitrifiers (typically 0.04 to 8% of NH<sub>3</sub> oxidized) and competing sinks for available NH<sub>4</sub><sup>+</sup> <i>in situ</i>. Most nitrification-derived N<sub>2</sub>O was produced by ammonia oxidizing bacteria (AOB) rather than archaea (AOA), who appeared responsible for no more than 30% of nitrification-derived N<sub>2</sub>O production in all but one ecosystem. Although the proportion of nitrification-derived N<sub>2</sub>O production was lowest in annual cropping systems, these ecosystems nevertheless produced more nitrification-derived N<sub>2</sub>O (higher V<sub>max</sub>) than perennial and successional ecosystems. We conclude that nitrification is minor relative to other sources of N<sub>2</sub>O in all ecosystems examined.</p>
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.