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21 results for “agricultural policy”
MAGIC Deliverable 5.5 - Datasets - Report on the Quality Check of the Robustness of the Narrative behind the Common Agricultural Policy (CAP)
<p>This repository contains datasets used in the production of figures contained in MAGIC Deliverable 5.5</p> <p>Matthews K.B., Blackstock K.L., Waylen K.A., Juarez-Bourke A., Miller D.G., Wardell-Johnson D.H., Rivington M. (2018) Report on the Quality Check of the Robustness of the Narrative behind the Common Agricultural Policy (CAP).</p> <p>MAGIC (H2020-GA 689669) Project Deliverable 5.5 - 29th November 2018</p> <p><a href="https://magic-nexus.eu/documents/d55-report-narratives-behind-cap">MAGIC Deliverable 5.5</a></p>
Sensitivity of the global agricultural sector to changes in climate policy - EU countries compared to the rest of the world
<p>The files contain data from the FAOSTAT database used in the article: DOI:10.2478/oszn-2023-0012</p> <p>File content:<br>Agricultural emissions data for the period 1961-2020<br>Population data for 1950-2020<br>Production value from agriculture for the period 1961-2020<br>Agricultural area for the period 1961-2020</p> <p>The layout of the tables and the description of the columns is the same as the FAOSTAT database methodology</p>
A systems analysis of sustainability impacts of agricultural policies in India - R script and supporting files
<p>This includes : </p><ul><li>two R scripts used to implement the model (residue_burning. R) and generate results (residue_burning_calls.R)</li><li>three supporting .csv files used in the R scripts above </li></ul><p> </p>
CS16: Policy for sustainable development ( development of "Croatian Case Study - Legumes: their potential role in Croatian agricultural production")
<p>(i)The main aim is to develop a document that will gave an overview of current situation regarding legumes consumption and production in Croatia and also to develope a reccomendations for policy makers. <br> (ii) Purpose of this Case Study and the whole project implementation is to raise awareness about consumption and production of legumes both for human consumption and animal feed.<br> (iii) April 2019.- September 2019. <br> (iv) Species - N.A. <br> (v) PIRED facilities<br> (vi) Following different methodological tools was applied like: desk research, analysis of available quantitative historical data, interviews with key regional retailers and company managers, focus groups, benchmarking, expert analysis and expert drafting<br> (vii) detection of number of producers, utilized areas, processing infrastructure and development of recommendations for policy makers and local decision makers. <br> (viii) in progress<br> (ix) This document will have significant impact on policy, mainly on local level ( for local decision makers) because developed recommendations will gave an directions, guideline and examples of good practice.</p>
CS16: Policy for sustainable development ( development of "Croatian Case Study - Legumes: their potential role in Croatian agricultural production")
<p>(i)The main aim is to develop a document that will gave an overview of current situation regarding legumes consumption and production in Croatia and also to develop recommendations for policy makers. <br> (ii) Purpose of this Case Study and the whole project implementation is to raise awareness about consumption and production of legumes both for human consumption and animal feed.<br> (iii) April 2019.- September 2019. <br> (iv) Species - N.A. <br> (v) PIRED facilities</p>
Full list of signatories to: Pe'er et al. "Action needed for the EU Common Agricultural Policy to address sustainability challenges" (Preprint version)
<p>This is the list of signatories to the Scientists' Statement on the EU's Common Agricultural Policy:</p> <p>"Action needed for the EU Common Agricultural Policy to address sustainability challenges" (Pe'er et al. 2019).</p> <p>The preprint version (DOI: 10.5281/zenodo.3666258) has been made available online between 4.11.2019 and 19.2.2020.</p> <p>Signatories were requested to read the full statement prior to adding their name in its support; and to provide a proof that they are scientists.</p> <p>A final version of the paper has been published in March 2020 in the journal People and Nature.</p> <p> </p>
Data from: A critical analysis of the potential for EU Common Agricultural Policy measures to support wild pollinators on farmland
<p>1. Agricultural intensification and associated loss of high-quality habitats are key drivers of insect pollinator declines. With the aim of decreasing the environmental impact of agriculture, the 2014 EU Common Agricultural Policy (CAP) defined a set of habitat and landscape features (Ecological Focus Areas: EFAs) farmers could select from as a requirement to receive basic farm payments. To inform the post-2020 CAP, we performed a European-scale evaluation to determine how different EFA options vary in their potential to support insect pollinators under standard and pollinator-friendly management, as well as the extent of farmer uptake.</p> <p> 2. A structured Delphi elicitation process engaged 22 experts from 18 European countries to evaluate EFAs options. By considering life cycle requirements of key pollinating <i>taxa</i> (i.e. bumble bees, solitary bees and hoverflies), each option was evaluated for its potential to provide forage, bee nesting sites and hoverfly larval resources.</p> <p>3. EFA options varied substantially in the resources they were perceived to provide and their effectiveness varied geographically and temporally. For example, field margins provide relatively good forage throughout the season in Southern and Eastern Europe but lacked early-season forage in Northern and Western Europe. Under standard management, no single EFA option achieved high scores across resource categories and a scarcity of late season forage was perceived.</p> <p>4. Experts identified substantial opportunities to improve habitat quality by adopting pollinator-friendly management. Improving management alone was, however, unlikely to ensure that all pollinator resource requirements were met. Our analyses suggest that a combination of poor management, differences in the inherent pollinator habitat quality and uptake bias towards catch crops and nitrogen-fixing crops severely limit the potential of EFAs to support pollinators in European agricultural landscapes.</p> <p>5. <i>Policy Implications.</i> To conserve pollinators and help protect pollination services, our study highlights the need to create a variety of interconnected, well-managed habitats that complement each other in the resources they offer<i>. </i>To achieve this the CAP post-2020 should take a holistic view to implementation that integrates the different delivery vehicles aimed at protecting biodiversity (e.g. enhanced conditionality, eco-schemes and Agri-Environment and Climate Measures). To improve habitat quality we recommend an effective monitoring framework with target-orientated indicators and to facilitate the spatial targeting of options collaboration between land managers should be incentivised.</p>
A systems analysis of sustainability impacts of agricultural policies in India - Supporting Information
<p>This Supporting Information provides additional details about the HTE framework applied to study sustainability challenges and interventions in the rice-wheat cropping system of Punjab, India. It includes: </p><p>Supp. Info Word document with: </p><ul><li>Text S1-S2 (Table S1-S3) on detailed quantitative model set-up and model validation and sensitivity analysis results</li><li>Text S3-S4 (Table S4-S5) on methods used to evaluate the impacts of interventions on interactions, specifying direct (structural) and indirect (quantitative) changes as well as sustainability benefits using the inclusive wealth approach.</li><li>Text S5 on expert interviews conducted to inform choice of policy options analyzed in this work </li></ul><p> </p><p>Supp. Info Excel spreadsheet Data Set S1 with the following tables:</p><p>Data Table SD1: List of system components and their attributes</p><p>Data Table SD2: Detailed interaction matrix between system components</p><p>Data Table SD3: Attributes of crops and residues: crop production, protein content and residue generation</p><p>Data Table SD4: Attributes of crops: use of agricultural inputs for crop production </p><p>Data Table SD5: Attributes of technical components: Emission factors and GWP</p><p>Data Table SD6: Values of system components' attributes at t=1 (year=2019)</p><p>Data Tables SD7-SD14: Detailed quantitative impacts of interventions on sustainability metrics</p>
Data from: A critical analysis of the potential for EU Common Agricultural Policy measures to support wild pollinators on farmland
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Data from: Using beta diversity to inform agricultural policies and conservation actions on Mediterranean farmland
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The potential of fallow management to promote steppe bird conservation within the next EU Common Agricultural Policy reform: Distance sampling dataset on farmland bird community
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Figure 3 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 3 The Land Systems Archetypes mapped by Levers et al. (2018) for one of BESTMAP case study areas in Catalonia. Land Systems Archetypes were derived at 3km resolution for Europe based on CORINE land cover, downscaled ecological and economic data. BESTMAP aims to map Farming System Archetypes (FSAs) at the farm level.
Figure 7 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 7 BESTMAP workflow to map FSAs, defined as having a characteristic bundle of ESS (and dis-services e.g. pollution), biodiversity, socio-economic and behavioural characteristics. Geospatial datasets (the Case Study Base Layer) will be used to approximate these FSAs (the "Proto-FSAs" clusters) in the first step; followed by interviews, field data collection and modelling to assign values on ESS, biodiversity, socio-economic aspects and behaviour for a stratified sample of proto-FSAs farms. The proto-FSAs will likely share "socio-economic-environmental" space and will be split/merged to minimize (but not eliminate) that overlap by changing the clustering in Case Study Base Layer space, defining the final FSAs and their spatial distribution.
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.
Manuscript code & data: Foster et al. (2020) "Satellite-based monitoring of irrigation water use: assessing measurement errors and their implications for agricultural water management policy"
<p>Data to reproduce results presented in Foster, T., Mieno, T. and Brozovic, N. (2020). <em>Satellite-based monitoring of irrigation water use: assessing measurement errors and their implications for agricultural water management policy.</em> Water Resources Research. In Review. Files provide include:</p> <ul> <li>"ReviewMetadata_FosterWRR_2020.xlsx" - data needed to reproduce meta-analysis presented in the paper</li> <li>"MeasurementErrorCode_FosterWRR_2020.m" and "Foster2018_ProductionFunction.mat" - Matlab code and data needed to reproduce welfare loss analysis presented in the paper.</li> </ul>
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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.