DATASET

SUPREMA - SUpport for Policy RElevant Modelling of Agriculture

Collection: DATAM : Data/Modelling platform of resource economics 

Description

Impact assessments for agriculture are partly based on projections delivered by models. Sectoral policies are becoming more and more interrelated. Hence, there is a need to improve the capacity of current models, connect them or redesign them to deliver on an increasing variety of policy objectives, and to explore future directions for agricultural modelling in Europe.

SUPREMA (SUpport for Policy RElevant Modelling of Agriculture) is a project that has received funding from the European Union’s Horizon 2020 research and innovation programme (under grant agreement No 773499 SUPREMA) and that came to address this challenge by proposing a meta-platform that supports modelling groups linked already through various other platforms and networks.

SUPREMA should help close the gaps between expectations of policy makers and the actual capacity of models to deliver relevant policy analysis. The SUPREMA model family includes a set of ‘core models’ that are already used in support of key European impact assessments in agriculture, trade, climate and bioenergy policies.

One of the work-packages of the project ("Testing the SUPREMA model family") had the objective of testing the SUPREMA model family comparing model outcomes of three applications, including: (i) harmonize baseline assumptions and to the extent possible align baseline projections across models in the platform, and (ii) showcase the potential of the models in the meta-platform to respond to the upcoming and existing policy needs by means of two exploratory policy scenarios.

This open dataset includes 3 components: 1 - (Baseline scenario) - the harmonized baselines (for 2030 and 2050). Please note that the baseline projections do not take into account the 2020 and possible future effects of the SARS-CoV-2 pandemic 2 - (Agricultural policy scenario) - medium-term horizon scenarios aiming comparing different models and/or model combinations, that have a large degree of ‘similarity’ such as joined indicator variables, i.e.: AGMEMOD-MITERRA (combined) modelling tool and the CAPRI model. The main focus was comparing model results in both agronomic and biophysical domains. Two variants of the agricultural policy scenario have been simulated and compared: (i) a CAP greening scenario; and (ii) a sustainable diet scenario. Both scenarios are hypothetical but have been chosen in such a way that the can provide insights in future policy issues as: (i) a further greening of the CAP fits in the policy implementation space as it is included in the ongoing policy reform of the CAP after 2020; and (ii) as increasing consumer awareness about healthy diets and their relation to meat consumption, as well as the footprint/climate consequences are highly relevant with respect to the Green Deal roadmap (December 2019) and the Farm to Fork Strategy (May 2020) documents that have been recently published. 3 - (Climate change mitigation scenario) - scenarios that quantifies the GHG mitigation potential of the EU’s agricultural sector and domestic and global impacts of the EU policy, conditional on different levels of GHG mitigation efforts in the rest of the world. These are obtained through the SUPREMA models CAPRI, GLOBIOM and MAGNET and include scenarios where the EU only takes ambitious unilateral climate action up to scenario where the 1.5 C target is pursued globally

SUPREMA has been coordinated by Wageningen Research with the participation of EuroCARE, Thünen Institute, Swedish University of Agricultural Sciences (SLU), European Commission Joint Research Centre (JRC) and Research Executive Agency (REA), International Institute for Applied Systems Analysis (IIASA) and Universidad Politécnica de Madrid (UPM).

Contact

Email
jrc-datam (at) ec.europa.eu

Contributors

How to cite

Blanco Fonseca, María; Bogonos, Mariia; Caivano, Arnaldo; Castro Malet, Javier; Ciaian, Pavel; Depperman, Andre; Frank, Stefan; González Martínez, Ana Rosa; Jongeneel, Roel; Havlik, Petr; Kremmydas, Dimitrios; Lesschen, Jan Peter; Pérez Domínguez, Ignacio; Petsakos, Athanasios; Tabeau, Andrzej; Valin, Hugo; Witzke, Peter; van Dijk, Michiel; van Leeuwen, Myrna; van Meijl, Hans (2020): SUPREMA - SUpport for Policy RElevant Modelling of Agriculture. European Commission, Joint Research Centre (JRC) [Dataset] doi: 10.2905/D6EF74C6-BA91-4E37-827E-D0854FBE85DD PID: http://data.europa.eu/89h/d6ef74c6-ba91-4e37-827e-d0854fbe85dd

Keywords

CAPRI AGMEMOD climate change agriculture farm to fork CAP GHG CAP reform GLOBIOM IFM-CAP green deal MAGNET SUPREMA sustainable diets MITERRA MITERRA-EUROPE

Data access

SUPREMA - Baseline scenario
Download 
  • Dataset - bulk download - zip file with CSV inside

SUPREMA - Agricultural policy scenario
Download 
  • Dataset - bulk download - zip file with CSV inside

SUPREMA - Climate change mitigation scenario
Download 
  • Dataset - bulk download - zip file with CSV inside

SUPREMA - Agricultural policy scenario
URL 
  • Dataset - Interactive download - CSV format

SUPREMA - Climate change mitigation scenario
URL 
  • Dataset - Interactive download - CSV format

SUPREMA - Baseline scenario
URL 
  • Dataset - Interactive download - CSV format

Publications

Publication
SUPREMA - Deliverable 3.3 - Analysis of climate change mitigation scenarios
URL 
Publication
SUPREMA - Deliverable 3.1 - Inter-model baseline harmonization and comparison
URL 
Publication
SUPREMA - Deliverable 3.2 - Agricultural policy scenario description and divergence analysis
URL 

Geographic areas

World

Temporal coverage

From date To date
2000-01-01 2050-12-31

Additional information

Published by
European Commission, Joint Research Centre
Created date
2021-04-21
Modified date
2021-04-21
Issued date
2020-07-22
Landing page
https://datam.jrc.ec.europa.eu/ 
Language(s)
English
Data theme(s)
Agriculture, fisheries, forestry and food, Environment
Update frequency
unknown
Identifier
http://data.europa.eu/89h/d6ef74c6-ba91-4e37-827e-d0854fbe85dd
Popularity
13 Mar 2024: 1 visits