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56 results for “Agricultural Research”
Figure 9 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 9 BESTMAP dashboard mock-up, visualizing the impact of the policies through the policy indicators.
Figure 8 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 8 BESTMAP ABM concept. Individual farmer's decision-making is modelled using the RAA (orange elements). Each farmer agent has an internal state that is characterized by goals & needs (profit maximization, safety first, risk aversion, etc.), knowledge (education, farming strategies, beliefs about consequences of different practices, etc.), values (social norms, strength of beliefs, value of conservation or biodiversity, etc.) and assets (monetary resources, production means, information, etc.). These shape farmer's attitude towards different behaviours (e.g. pro or contra organic farming), perceived norm (social pressure or other farmer's behaviour) and perceived behavioural control (agent's capacity to perform certain behaviours). Every farmer agent can perceive the state of the farming system (e.g. yields, ESS provision), the behaviour of other farmers as well as exogenous drivers (e.g. price or policy changes). Evaluating these lead to changes in attitudes or perceived norm, which can expand or reduce its behavioural options. Each behavioural option represents an intention to perform a certain behaviour (e.g. adopting a different crop choice or setting aside land). Using a multi-objective utility function, the farmer agent will select a specific behavioural option that will change the state of its farm's fields and provide some economic benefit to the farmer. Specifically, farmer's decisions can lead to changes in cropping system, farm and field size, or in farm ownership, which allows us to analyse/model structural change.
Figure 4 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 4 BESTMAP conceptual framework for policy impact assessment modelling, combining actions at EU scale (green), and activities within a representative set of case studies (orange; demonstrated in five areas within BESTMAP). The framework combines workshops (ovals), modelling (rounded rectangles) and interviews (ovals) producing several datasets (curved rectangles) and an online interactive dashboard (barrel shape). At the EU level, BESTMAP will co-design with policy-makers and stakeholders policy scenarios which, with existing scenarios of climate change and other global events, will input into the global economic models (here DART-BIO CGE model, but also MAGNET, CAPRI etc.). Georeferenced data layers ("Case Study Base Layers"; CBL) will be collated from existing sources including the Land Parcel Identification System (LPIS), Farm Accountancy Data Network (FADN), CORINE land-use/land cover, INSPIRE Geoportal, Copernicus Land Monitoring, national/regional datasets and existing remote sensing products. This will be used to define 'prototype' Farm System Archetypes (FSAs) which help stratify and design the interview campaign in each of the case studies. BESTMAP will collect demographic, behavioural characteristics and socio-economic information using semi-structured interviews (with harmonized protocols). Ecosystem services models (here the InVEST biophysically based models, but simpler e.g. capacity matrix or more complex e.g. SWAT models can in be used in the future) will estimate ESS, environmental/climatic impacts and biodiversity provided by different farming units. The 'bundle' of ESS, geo-statistically modelled socio-economics and behavioural characteristics will add to the CBL and define the final typology of FSAs in the case study. A generic ABM template based on the RAA behavioural theory will be locally adapted by the same interviews/surveys, and driven by the outputs (prices and costs of commodities, energy etc.) from the global economic model (DART-BIO) as well as narratives and drivers arising from a national/regional workshop interpreting the EU-level policy. The change in FSAs resulting from those scenarios via the ABM will translate to change in ESS and socio-economics defining the FSA 'bundles' including their uncertainty. Those impacts at the case study level will be translated into stakeholders-defined policy indicators (e.g. of the SDGs) and visualized using a standard interactive web-based data portal policy dashboard, the use of which will be the focus of training and dissemination activities.
Figure 1 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 1 Top-level conceptual framework of BESTMAP. BESTMAP combining existing global/EU scale (green) models used by the EC, in particular Partial Equilibrium (PE) and Computable General Equilibrium (CGE) models with regional analyses (yellow) by formalizing engagement with stakeholders to define scenarios; using existing geospatial data and empirical data collection to map farming systems based on a novel concept of Farming System Archetypes (FSA) namely farms with a characteristic bundle of ESS, biodiversity, socio-economics and behavioural characteristics of decision-making agents (i.e. farmers); linking economic (typically global) large scale economic based PE/CGE to agent-based models; describing changes in FSAs's distribution, ESS, biodiversity and socio-economics in a representative sample of case study areas.
Figure 5 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 5 BESTMAP demonstration case studies: overview of CS locations in the context of Land Systems Archetypes mapped by Levers et al. 2018 at 3km resolution for Europe. No land system archetypes shown for Serbia as the mapping included EU countries only. Insets show the exact boundaries of CS areas.
Figure 2 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 2 The conceptualization of Farming System Archetypes (FSAs) in BESTMAP is an extension of land-use intensity framework from Erb et al. 2013 to include the land manager/farmer and its behavioural characteristics (based here on the RAA). FSA have a typical 'bundle' of ESS, outputs, outcomes, inputs and farmer characteristics.
Supplementary Info for manuscript "A new genome sequence resource for five invasive fruit flies of agricultural concern: Ceratitis capitata, C. quilicii, C. rosa, Zeugodacus cucurbitae and Bactrocera zonata (Diptera, Tephritidae)" - F1000 Research
Open the record for dataset details and reuse information.
Bibliographic collection: agriculture-related LCA research from Scopus database (October, 3, 2023).
<p>Bibliographic collection. Database: Scopus. Search query: The search query: TITLE-ABS-KEY (<em>agr*</em> W/3 (<em>"life cycle analysis"</em> OR <em>"life cycle assessment"</em> OR <em>"life cycle cost*"</em> OR <em>"life cycle impact"</em> OR <em>*lca</em> OR <em>*lcia</em> OR <em>lcc</em>)). Filtering: Only English, article, excl trade journals. Contains 259 articles (adjusted after screening the relevance of titles, abstracts, keywords for the research purposes).</p>
Correlation between the availability of supplementary material and raw research data in journals of the Agriculture Multidisciplinary area of WoS
Open the record for dataset details and reuse information.
Figure 6 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
Figure 6 Typical agricultural landscapes of BESTMAP demonstration case studies.
Figure 2 from: Blanco-Gutiérrez I, Esteve P, Garrido A, Gómez-Ramos A, Arce A, Zubelzu S, Díaz-Ambrona CH, Sánchez R, Calatrava J, López-Correa JM (2021) RECLAMO: Unlocking the potential of wastewater reuse for agricultural irrigation in Spain . Research Ideas and Outcomes 7: e76793. https://doi.org/10.3897/rio.7.e76793
Figure 2 GANNT Diagram.
Figure 1 from: Blanco-Gutiérrez I, Esteve P, Garrido A, Gómez-Ramos A, Arce A, Zubelzu S, Díaz-Ambrona CH, Sánchez R, Calatrava J, López-Correa JM (2021) RECLAMO: Unlocking the potential of wastewater reuse for agricultural irrigation in Spain . Research Ideas and Outcomes 7: e76793. https://doi.org/10.3897/rio.7.e76793
Figure 1 PERT Diagram. Project structure and links between WPs.
Exploring the Landscape of Controlled Environment Agriculture Research: A Systematic Scoping Review of Current Trends and Topics
<p>No description provided.</p>
Post-Typhoon Mawar UAV Orthomosaic: UOG Yigo Agricultural Research and Education Center
<p>This orthomosaic was created from 815 images taken on 02 June 2023. Images were collected at 400 ft AGL, with a DJI Zenmuse H20 mounted on a DJI Matrice 30T UAV. The mission was planned using the DJI Pilot application and post-processed using DroneDeploy. The resolution of the orthomosaic is .82in/px.</p> <p>The following aerial imagery is intended solely for the purpose of assessing damages to the natural and built environment associated with Typhoon Mawar. Typhoon Mawar's closest approach to Guam was 24 May 2023. It is strictly prohibited to distribute this imagery to any unauthorized individuals or third-party entities without prior authorization.</p> <p>The University of Guam Drone Corps (funded by NASA Guam Space Grant and NASA Guam EPSCoR) and the Western Pacific Tropical Research Center retain all rights to the aerial imagery, including but not limited to copyrights and intellectual property rights. Any unauthorized modification, reproduction, or distribution of the imagery is strictly prohibited.</p> <p>By using this data, you acknowledge and agree to abide by the terms and conditions set forth in this disclaimer.</p> <p>Privacy Disclaimer: The aerial imagery captured for damage assessment may inadvertently include images of private property and individuals. While every effort has been made to respect privacy, it is possible that identifiable information or sensitive locations may be visible in the imagery. University of Guam Drone Corps and the Western Pacific Tropical Research Center disclaim any responsibility for the unintentional collection or dissemination of such information. The recipient(s) of this imagery are urged to handle it with due care and take necessary precautions to protect the privacy rights of individuals and comply with applicable privacy laws and regulations.</p>
Post-Typhoon Mawar UAV Orthomosaic: UOG Ija Agricultural Research Station
<p>This orthomosaic was created from 644 images taken on 13 June 2023. Images were collected at 400 ft AGL, with a DJI Zenmuse H20 mounted on a DJI Matrice 30T UAV. The mission was planned using the DJI Pilot application and post-processed using DroneDeploy. The resolution of the orthomosaic is 1.4in/px.</p> <p>The following aerial imagery is intended solely for the purpose of assessing damages to the natural and built environment associated with Typhoon Mawar. Typhoon Mawar's closest approach to Guam was 24 May 2023. It is strictly prohibited to distribute this imagery to any unauthorized individuals or third-party entities without prior authorization.</p> <p>The University of Guam Drone Corps (funded by NASA Guam Space Grant and NASA Guam EPSCoR) and the Western Pacific Tropical Research Center retain all rights to the aerial imagery, including but not limited to copyrights and intellectual property rights. Any unauthorized modification, reproduction, or distribution of the imagery is strictly prohibited.</p> <p>By using this data, you acknowledge and agree to abide by the terms and conditions set forth in this disclaimer.</p> <p>Privacy Disclaimer: The aerial imagery captured for damage assessment may inadvertently include images of private property and individuals. While every effort has been made to respect privacy, it is possible that identifiable information or sensitive locations may be visible in the imagery. University of Guam Drone Corps and the Western Pacific Tropical Research Center disclaim any responsibility for the unintentional collection or dissemination of such information. The recipient(s) of this imagery are urged to handle it with due care and take necessary precautions to protect the privacy rights of individuals and comply with applicable privacy laws and regulations.</p>
Post-Typhoon Mawar UAV Orthomosaic: UOG Inarajan Agricultural Research Station
<p>This orthomosaic was created from 132 images taken on 13 June 2023. Images were collected at 400 ft AGL, with a DJI Zenmuse H20 mounted on a DJI Matrice 30T UAV. The mission was planned using the DJI Pilot application and post-processed using DroneDeploy. The resolution of the orthomosaic is .82in/px.</p> <p>The following aerial imagery is intended solely for the purpose of assessing damages to the natural and built environment associated with Typhoon Mawar. Typhoon Mawar's closest approach to Guam was 24 May 2023. It is strictly prohibited to distribute this imagery to any unauthorized individuals or third-party entities without prior authorization.</p> <p>The University of Guam Drone Corps (funded by NASA Guam Space Grant and NASA Guam EPSCoR) and the Western Pacific Tropical Research Center retain all rights to the aerial imagery, including but not limited to copyrights and intellectual property rights. Any unauthorized modification, reproduction, or distribution of the imagery is strictly prohibited.</p> <p>By using this data, you acknowledge and agree to abide by the terms and conditions set forth in this disclaimer.</p> <p>Privacy Disclaimer: The aerial imagery captured for damage assessment may inadvertently include images of private property and individuals. While every effort has been made to respect privacy, it is possible that identifiable information or sensitive locations may be visible in the imagery. University of Guam Drone Corps and the Western Pacific Tropical Research Center disclaim any responsibility for the unintentional collection or dissemination of such information. The recipient(s) of this imagery are urged to handle it with due care and take necessary precautions to protect the privacy rights of individuals and comply with applicable privacy laws and regulations.</p> <p> </p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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