#  Executive summary

This deliverable presents the Data Management Plan (DMP) on open access data
handling by SUPREMA. Here, Open access (OA) refers to the practice of
providing on-line access to scientific information that is free of charge to
the end-user and reusable. ‘Scientific’ refers to all academic disciplines. In
the context of research & innovation, ‘scientific information’ can mean: (i)
peer-reviewed scientific research articles (published in scholarly journal) or
(ii) research data (data underlying publications, curated and raw data) (see
also: European Commission, 2017).

For SUPREMA, the DMP is defined as ‘the development, execution and supervision
of plans, policies, programmes and practices that control, protect, deliver
and enhance the value of data and information assets’ obtained.

This report describes the management of the research data collected and
generated during the project, and after it is completed. This also includes
data to be generated, methodologies and standards, data privacy/openness, and
preservation measures.

Changes with respect to the DoA

No changes with respect to the DoA

Dissemination and uptake

The deliverable is publicly available. SUPREMA does not necessarily opening up
all research data. In a sense, the document explains which of the research
data generated and/or collected will be made open.

Short Summary of results

DataM is the European Commission’s data portal of agro-economic modelling. It
contains the outcomes of research activities, and is operated by the Joint
Research Centre (JRC) of the European Commission. DataM, including the web
portal but also the Information System, will be used to release model runs
that are considered for open access release. A baseline comparison and
harmonization action will be addressed for all models represented in SUPREMA.
A medium-term (until 2030) assessment of European agricultural policy
alternatives will cover CAPRI, IFM-CAP and AGMEMOD-MITERRA Europe. Finally,
SUPREMA will also use different modelling tools for the long-term (until 2050)
assessment of climate change goals, using GLOBIOM and MAGNET as leading
models. Compared to the first version of the data management plan (submitted
in Month 6, June 2018), update of the data management plan is updated,
according to the current scenario in Month 12 (December 2018). A new section 4
is added.

Evidence of accomplishment

The deliverable itself can act as the evidence of accomplishment.

##  Glossary / Acronyms  
  
<table>  
<tr>  
<th>

AGMEMOD

</th>  
<th>

AGRICULTURE MEMBERSTATES MODELLING

</th> </tr>  
<tr>  
<td>

AGMIP

</td>  
<td>

AGRICULTURAL MODEL INTERCOMPARISON AND IMPROVEMENT PROJECT

</td> </tr>  
<tr>  
<td>

BI

</td>  
<td>

BUSINESS INTELLIGENCE

</td> </tr>  
<tr>  
<td>

CA

</td>  
<td>

CONSORTIUM AGREEMENT

</td> </tr>  
<tr>  
<td>

CAPRI

</td>  
<td>

COMMON AGRICULTURAL POLICY REGIONALISED IMPACT MODELLING SYSTEM

</td> </tr>  
<tr>  
<td>

CSV

</td>  
<td>

COMMA SEPARATED VALUES

</td> </tr>  
<tr>  
<td>

DBA

</td>  
<td>

DATABASE ADMINISTRATOR

</td> </tr>  
<tr>  
<td>

DG

</td>  
<td>

DIRECTORATE GENERAL

</td> </tr>  
<tr>  
<td>

DG AGRI

</td>  
<td>

DIRECTORATE GENERAL FOR AGRICULTURE AND RURAL DEVELOPMENT

</td> </tr>  
<tr>  
<td>

DG COMM

</td>  
<td>

DIRECTORATE GENERAL FOR COMMUNICATION

</td> </tr>  
<tr>  
<td>

DMP

</td>  
<td>

DATA MANAGEMENT PLAN

</td> </tr>  
<tr>  
<td>

EUROCARE

</td>  
<td>

EUROPEAN CENTRE FOR AGRICULTURAL, ENVIRONMENTAL AND REGIONAL RESEARCH

</td> </tr>  
<tr>  
<td>

GA

</td>  
<td>

GRANT AGREEMENT

</td> </tr>  
<tr>  
<td>

GDP

</td>  
<td>

GROSS DOMESTIC PRODUCT

</td> </tr>  
<tr>  
<td>

GLOBIOM

</td>  
<td>

GLOBAL BIOSPHERE MANAGEMENT MODEL

</td> </tr>  
<tr>  
<td>

IFM-CAP

</td>  
<td>

INDIVIDUAL FARM MODEL FOR COMMON AGRICULTURAL POLICY

</td> </tr>  
<tr>  
<td>

IIASA

</td>  
<td>

INTERNATIONAL INSTITUTE FOR APPLIED SYSTEMS ANALYSIS

</td> </tr>  
<tr>  
<td>

IPR

</td>  
<td>

INTELLECTUAL PROPERTY RIGHTS

</td> </tr>  
<tr>  
<td>

JRC

</td>  
<td>

JOINT RESEARCH CENTRE

</td> </tr>  
<tr>  
<td>

LCA

</td>  
<td>

LIFE CYCLE ANALYSIS

</td> </tr>  
<tr>  
<td>

LULUCF

</td>  
<td>

LAND USE, LAND-USE CHANGE, AND FORESTRY

</td> </tr>  
<tr>  
<td>

MAGNET

</td>  
<td>

MODULAR APPLIED GENERAL EQUILIBRIUM TOOL

</td> </tr>  
<tr>  
<td>

NUTS

</td>  
<td>

NOMENCLATURE OF TERRITORIAL UNITS FOR STATISTICS

</td> </tr>  
<tr>  
<td>

OA

</td>  
<td>

OPEN ACCESS

</td> </tr>  
<tr>  
<td>

SUPREMA

</td>  
<td>

SUPPORT FOR POLICY RELEVANT MODELLING OF AGRICULTURE

</td> </tr>  
<tr>  
<td>

UAA

</td>  
<td>

UTILIZED AGRICULTURAL AREA

</td> </tr>  
<tr>  
<td>

WR

</td>  
<td>

WAGENINGEN RESEARCH

</td> </tr> </table>

#  1 Introduction

##  1.1 Structure of the document

Section 1.2 will outline the need for a Data Management Plan in SUPREMA, and
proposes the use of DataM for the release of public datasets that is traceable
through the publication of related meta-data in the JRC Data catalogue and in
major European open data portals. Section 2 will present the DataM Information
System, including the data management tool, and the software used. Moreover,
the DataM portal is presented, its governance and architecture, as well as
data privacy considerations. Section 3 will summarise the steps for open
release of scenarios from CAPRI, GLOBIOM, MAGNET, AGMEMOD, MITERRA-EUROPE and
IFM-CAP, including IPR. Main conclusions are presented in Section 4 of the
report.

1.2 Why is a Data Management Plan needed?

SUPREMA participates in the Open Access and the Open Research Data Pilot of
the European Research Council (ERC). From 2017 all H2020 projects will
participate in a pilot project to make the underlying data related to project
outputs openly available for use by other researchers, innovative industries
and citizens  ( _https://www.openaire.eu/what-is-the-open-research-data-pilot_
)  . According to the Open Research Data Pilot, ‘Open data is data that is
free to access, reuse, repurpose, and redistribute. The Open Research Data
Pilot aims to make the research data generated by Horizon 2020 projects
accessible with as few restrictions as possible, while at the same time
protecting sensitive data from inappropriate access.’  (
_htps://www.openaire.eu/what-is-the-open-research-data-pilot_ )  . Data will
be released in open formats, with proper documentation to support their use in
other research. After the project completion, and if there is no objection by
any of the project partners and use anonymization is preserved, the data are
foreseen to be published in an Open Data portal (for example in
_http://opendata.europa.eu_ )  for future research.

The DMP specifies the implementation of the pilot for: data generated and
collected, standards in use, workflow to make data accessible for use, reuse
and verification by the community, and definition of a strategy of curation
and preservation of the data. Therefore, we refer to the SUPREMA Grant
Agreement (GA), Article 29.3 on “Open Access to research data”:

Regarding the digital research data generated in the action (‘data’), the
beneficiaries must:

  1. deposit in a research data repository and take measures to make it possible for third parties to access, mine, exploit, reproduce and disseminate — free of charge for any user — the following: 

     * the data, including associated metadata, needed to validate the results presented in scientific publications as soon as possible; 

  

     * other data, including associated metadata, as specified and within the deadlines laid down in the 'data management plan'. 

  

  

  2. provide information — via the repository — about tools and instruments at the disposal of the beneficiaries and necessary for validating the results (and — where possible — provide the tools and instruments themselves). 

  

  

This does not change the obligation to protect results in Article 27, the
confidentiality obligations in Article 36, the security obligations in Article
37 or the obligations to protect personal data in Article 39, all of which
still apply.

As an exception, the beneficiaries do not have to ensure open access to
specific parts of their research data if the achievement of the action's main
objective, as described in Annex 1, would be jeopardised by making those
specific parts of the research data openly accessible. In this case, the data
management plan must contain the reasons for not giving access.

The data management policy described in this document reflects the current
state of consortium agreement on data management. Data will be stored in a
database developed by JRC (DataM). Project participants will have secured web
access to the databases, which will have been automatically checked for
consistency, homogeneity and completeness. After project completion, and in
case of no objection by project partners, DataM dataset can be open to the
public for future research always consistent with exploitation and
Intellectual Property Rights (IPR) requirements. DataM public datasets will be
made traceable through the publication of related meta-data in the JRC Data
catalogue  ( _http://data.jrc.ec.europa.eu_ )  and in major European open data
portals (EU open data portal,  _http://data.europa.eu_ and European Data
Portal,  _https://www.europeandataportal.eu_ )  .

Figure 1 - DataM and open data

#  2 DataM

DataM is publicly known as a web site (  _https://datam.jrc.ec.europa.eu_ )  :
it is the European Commission's data portal of agro-economic modelling. DataM
contains model's data and estimates about the economics of agriculture and of
the sustainable resources. By definition, DataM does not deal with official
statistical data. DataM contents are the outcomes of research activities.
Indeed, DataM is operated by JRC, the Joint Research Centre of the European
Commission. Data is presented both in terms of raw CSV datasets, ready to
download, and in the form of advanced interactive dashboards or interactive
infographics that allow the self-analysis of data.

##  2.1 The DataM Information System

Internally in JRC, and in the context of SUPREMA, the term DataM does not
refer only to the web portal but to the Information System in broader terms.
The DataM Information System includes also a "data management tool" and a
"Business Intelligence tool".

Figure 2 - DataM Information System

###  2.1.1 Life-cycle of scientific data and DataM

We can consider the life cycle of scientific data as composed by three main
phases: construction, analysis and dissemination. The DataM Information System
deals principally with the last two.

Figure

3

\-

Life

\-

cycle of data

Actually the processes of strict construction of the data is arbitrary and
depends on the individual scientific activities. Typical DataM outcomes are
the result of modelling activities (i.e. GAMS software processing). However
DataM contains also results of data processing, following a scientific
methodology, over data coming from other sources. The following table (Table
1) lists the origin of DataM contents at time of writing.  
  
<table>  
<tr>  
<th>

#

</th>  
<th>

DataM Content

</th>  
<th>

Raw datasets

</th>  
<th>

Interactive Dashboard

</th>  
<th>

Interactive infographics

</th>  
<th>

Origin

</th> </tr>  
<tr>  
<td>

1

</td>  
<td>

AGMIP - Agricultural model intercomparison and improvement project - Phase 1

</td>  
<td>

Yes

</td>  
<td>

Yes

</td>  
<td>

No

</td>  
<td>

Modelling (CAPRI, IMAGE, GLOBIOM, MAGNET, MAgPIE)

</td> </tr>  
<tr>  
<td>

2

</td>  
<td>

AGMIP – Food insecurity and global climate change mitigation policy

</td>  
<td>

Yes (coming soon)

</td>  
<td>

No

</td>  
<td>

No

</td>  
<td>

Modelling (AIM, CAPRI, EPPA, ENVISAGE, FARM,

GLOBIOM, GCAM, GTEM, IMPACT, MAGPIE, MAGNET)

</td> </tr>  
<tr>  
<td>

3

</td>  
<td>

ASGTS_KENYA

</td>  
<td>

No

</td>  
<td>

No

</td>  
<td>

Yes (coming soon)

</td>  
<td>

Modelling (CGE)

</td> </tr>  
<tr>  
<td>

4

</td>  
<td>

DG AGRI-JRC - Production, trade and apparent use

</td>  
<td>

Yes

</td>  
<td>

Yes

</td>  
<td>

No

</td>  
<td>

Application of DG AGRI experts' coefficients and DG

AGRI/JRC methodolgoy to combine COMEXT data with DG AGRI data (Short Term
Outlook)

</td> </tr>  
<tr>  
<td>

5

</td>  
<td>

FOODSECURE

</td>  
<td>

No

</td>  
<td>

No

</td>  
<td>

Yes

</td>  
<td>

Modelling (IMAGE, GLOBIOM, MAGNET)

</td> </tr>  
<tr>  
<td>

6

</td>  
<td>

FTA

</td>  
<td>

No

</td>  
<td>

No

</td>  
<td>

Yes

</td>  
<td>

Modelling (AGLINK-COSIMO, MAGNET)

</td> </tr>  
<tr>  
<td>

7

</td>  
<td>

JRC - AgCLIM50 - Phase 1

</td>  
<td>

Yes

</td>  
<td>

Yes

</td>  
<td>

No

</td>  
<td>

Modelling (CAPRI, IMAGE, GLOBIOM, MAGNET, MAgPIE)

</td> </tr>  
<tr>  
<td>

8

</td>  
<td>

JRC - Bioeconomics

</td>  
<td>

Yes

</td>  
<td>

No

</td>  
<td>

Yes

</td>  
<td>

Application of calculations over publicly available data (mainly EUROSTAT
COMEXT) as from JRC methodology based on coefficients provided by NOVA
institute

</td> </tr>  
<tr>  
<td>

9

</td>  
<td>

JRC - Biomass estimates

</td>  
<td>

Yes

</td>  
<td>

No

</td>  
<td>

No

</td>  
<td>

Application of calculations over publicly available data

(mainly FAO Prodstat) as from JRC/NOVA methodology

</td> </tr>  
<tr>  
<td>

10

</td>  
<td>

JRC - Biomass uses and flows

</td>  
<td>

Yes (coming soon)

</td>  
<td>

Yes

</td>  
<td>

No

</td>  
<td>

Complex integration of multi-source data following a JRC methodology

</td> </tr>  
<tr>  
<td>

11

</td>  
<td>

JRC - BioSAMs for the EU Member States - 2010

</td>  
<td>

Yes

</td>  
<td>

Yes

</td>  
<td>

No

</td>  
<td>

Elaboration of the SAM by CGE team at JRC-Economics of agriculture department

</td> </tr>  
<tr>  
<td>

12

</td>  
<td>

JRC - Matrice de comptabilité sociale - Kenya - 2014

</td>  
<td>

Yes

</td>  
<td>

Yes

</td>  
<td>

No

</td>  
<td>

Elaboration of the SAM by CGE team at JRC-Economics of agriculture department

</td> </tr>  
<tr>  
<td>

13

</td>  
<td>

JRC - Matrice de comptabilité sociale - Sénégal - 2014

</td>  
<td>

Yes

</td>  
<td>

Yes

</td>  
<td>

No

</td>  
<td>

Elaboration of the SAM by CGE team at JRC-Economics of agriculture department

</td> </tr>  
<tr>  
<td>

14

</td>  
<td>

JRC - Social accounting matrix - Kenya - 2014

</td>  
<td>

Yes

</td>  
<td>

Yes

</td>  
<td>

No

</td>  
<td>

Elaboration of the SAM by CGE team at JRC-Economics of agriculture department

</td> </tr>  
<tr>  
<td>

15

</td>  
<td>

JRC - Social accounting matrix - Senegal - 2014

</td>  
<td>

Yes

</td>  
<td>

Yes

</td>  
<td>

No

</td>  
<td>

Elaboration of the SAM by CGE team at JRC-Economics of agriculture department

</td> </tr>  
<tr>  
<td>

16

</td>  
<td>

SCENAR2030

</td>  
<td>

Yes

</td>  
<td>

Yes

</td>  
<td>

Yes

</td>  
<td>

Modelling (CAPRI, IFM-CAP, MAGNET)

</td> </tr>  
<tr>  
<td>

17

</td>  
<td>

DEVELOPING COUNTRIES' FICHES

</td>  
<td>

No

</td>  
<td>

No

</td>  
<td>

Yes (coming soon)

</td>  
<td>

Integration of data of various sources

</td> </tr> </table>

Table 1 - DataM contents (June 2018)

For those contents whose source is not modelling, data construction is based
on ad-hoc techniques. In these cases, the typical technologies in use are: (i)
the python language (data extraction from file or web sources, and data
crunching); and (ii) database tools such as Oracle and SQL-lite (data
crunching in SQL or PL/SQL).

DataM Information System is used for the final integration of data into
consolidated datasets, and for the post-construction part. DataM can have also
an impact in the construction phase since DataM datasets can work as input for
further elaborations.

###  2.1.2 The DataM data management tool

With "data management tool" we mean a software layer above the data base
management system that factorizes common needs for the management of
(scientific) datasets.

The DataM data management tool allows:

  * loading data from external sources. 

  

  * storing data in standard format (time-series oriented star-diagram with one unique measure and arbitrary dimensions; time and "indicator" are mandatory dimensions) 

  

  * avoiding "data manipulation" operations (Creating, modifying, dropping tables and indexes) / no need of DBA support 

  

  * managing dictionaries of common reference data – allowing harmonization of data by: 

    * mapping different nomenclatures o converting units of measurement o aligning different granularities o aligning different taxonomies 

  

  

  * Managing data versioning 

  

  * Managing standard meta data for all contents o Description 

    * Contact point o Geographical coverage  1  o Time coverage o Copyright o Update frequency 

  

    * Domain 

  

    * Keyword (tags for search operations in open data portals) o Contributors: 

      * Name, Surname 

  

      * Email 

  

      * ORCID o Distributions: 

  

      * Link for bulk download of raw data 

  

      * Link for interactive data download 

  

      * Link to interactive dashboard or infographic o Related publications / methodology documents 

  

      * Title 

  

      * Authors 

  

      * DOI 

  

  

  

Figure 4 - Data management tool

The data management tool of DataM is inspired to the time-series-oriented,
reference data dictionaries and star-diagram data structures managed by the
formerly used "PROGNOZ platform" software (principles that are common to most
data warehouse technologies), while integrating the advanced management of
standard meta-data.

The current data management tool of DataM does not include features for:

  * Extracting data from arbitrary sources and transforming it in a structure suitable for the load in the database. Python routines are developed at this purpose. 

  

  * Workflow management for the collaborative construction of datasets by distributed working groups. 

  

  * Management of big data 

  

  * Management of geo-spatial data 

  

  * Management of arbitrary data structures that do not fit into a star-diagram 

  

  * Management of multilingualism 

  

###  2.1.3 The DataM BI tool: Qlik-Sense

Qlik-Sense is a modern software for business intelligence (BI). It is
considered one of the top leaders on the BI market.  2  In use in many
Directorate General, is a standard de-facto in the European Commission.
Business intelligence (BI) comprises the strategies and technologies used by
enterprises for the data analysis of business information. BI technologies
provide historical, current and predictive views of business operations.
Common functions of business intelligence technologies include reporting,
online analytical processing, analytics, data mining, process mining, complex
event processing, business performance management, benchmarking, text mining,
predictive analytics and prescriptive analytics.

JRC is adopting the BI for scientific purposes, in particular, in DataM BI is
used for:

  * Analysing the constructed datasets: 

    * Comparison with other sources o Outliers detection 

  

    * Check of the respect of "business rules" 

  

  

  * Exchanging results in proper way within the scientific circuit or with other stake-holders (i.e.: policy DG's) 

  

  * Disseminating results on the web allowing user to make self-analysis of data: 

    * interactive dashboards = dashboards with interrelated charts/tables/maps with scarce or absent narrative 

  

    * Interactive infographics = web pages with much narrative and embedded Qlik contents. 

  

  

###  2.1.4 The DataM portal

The DataM portal (  _https://datam.jrc.ec.europa.eu_ )  is an official part of
the European Commission web presence  3  ; it has been recently approved by DG
COMM and refactored following EC web standards and is inspired to the
principles of the knowledge-for-policy platform  4  being developed by JRC. It
offers the following functionalities:

  * Search contents by specifying keywords 

  

  * Download of raw data: 

    * Bulk download, zip file with CSV with data, citation text, legal notice and all metadata o Interactive data download by filtering the parts of interest of the dataset o Obtaining automatically the citation text, for correctly citing the dataset in scientific publications 

  

    * Visualizing the meta-data o Visualizing the copyright notice 

  

    * Accessing the related publications / methodology documents 

  

  

  * Accessing the Qlik interactive dashboards and infographics 

  

  * API for automatic synchronization of the meta-data with the JRC data catalogue (and subsequent dissemination to EU open data portal and European Data Portal). 

  

  * API for transmitting data on-demand to other computer systems 

  

###  2.1.5 DataM governance

At time of writing, DataM is developed and managed by a team of four IT
professionals / data scientists working under the JRC department for Economics
of Agriculture within the directorate for Sustainable Resources. It is powered
by JRC IT, in particular by the staff (and on machines) of the JRC Seville
site.

###  2.1.6 Architecture of the data base

The data base of DataM underlying the data management tool is implemented in
Oracle. Each dataset is basically implemented in 2 tables, one table where
each record identifies a time-series, and a table where each record is a data-
point (time-series identifier, time key, value). Reference data is organized
in specific tables.

The strength of this model is the plasticity: it suits to host almost all
typical datasets of our domain with a simple, common, structure.

###  2.1.7 2018 plan for DataM

Activities on DataM in 2018 will focus on:

  * Implementation of a Qlik-based system for the data quality check of model outcomes. At time of writing, works are in progress for the check of CAPRI outcomes 

  

  * Implementation of a data connector for the direct integration of GAMS and Qlik (GAMS2QLIK project, in progress) 

  

  * Refactoring of the data management tool (not in progress, the project is under inception and could encompass 2018 and 2019) 

  

###  2.1.8 Data privacy

DataM is mainly thought for public dissemination of open data, which means
that most of functionalities do not require a login. However, by logging in,
authorised users can access restricted contents that require specific access
rights. Restrictions normally apply to contents still under study, and this
can be the case for the SUPREMA project: contents under preparation can be
shared within the SUPREMA community by accessing the restricted area of DataM.

The username and password are obtained through the European Commission's user
authentication service (EU Login).

For other users, an EU Login account can be obtained through a simple
registration procedure:

Click on "Log In" on the top-right corner of the screen Click on "Create an
account" and follow the instructions.

#  3 Open access release of scenarios

##  3.1 List of variables by model

A list of variables is defined for each of the models that are considered for
open access release. A baseline comparison and harmonization action will be
addressed for all models represented in SUPREMA (Task 3.1 – Inter-model
baseline comparison and harmonization). In addition, a medium-term (until 203)
assessment of European agricultural policy alternatives will cover CAPRI, IFM-
CAP, and AGMEMOD-MITERRA Europe (Task 3.2 – Using SUPREMA for a medium-term
assessment of European agricultural policy alternatives). Finally SUPREMA will
also use the different modelling tools for the longterm (until 2050)
assessment of climate change goals (Task 3.3), using GLOBIOM and MAGNET as
leading models. All scenarios are available in Month 28 of the project, and
will be released open access soon afterwards.

###  3.1.1 CAPRI (Common Agricultural Policy Regionalised Impact Modelling
System)

The CAPRI database and scenario output covers a multitude of very diverse
variables that may be grouped in various ways. Table 1 indicates three major
groups:

  1. Key physical technological information covers production related, input related, market balance related variables in shades of brown 

  

  2. Economic variables are various prices, price elements and values (price times quantities) in shades of blue 

  

  3. Derived indicators for the environment and food security in shades of green. 

  

  

Some of these will be available from other modelling systems as well and
therefore of interest for the wider research community and presentation in
Data-M. Other parts of the CAPRI database and model output are only useful for
CAPRI experts as they require a thorough understanding of CAPRI accounting
rules and definitions. While the CAPRI database and also SUPREMA model outputs
will be fully available for download upon request from the CAPRI versioning
system, any obligation to provide technical explanations and advice on the
detailed information cannot be accepted but interested individuals are invited
to attend the CAPRI training sessions where such technical information is
provided to the attendants. In the process of filling the database for
presentation via Data-M it will be decided which variables are presented there
and should be displayed in an transparent manner.

Table 1. Main Elements of the CAPRI database and scenario output  
  
<table>  
<tr>  
<th>

</th>  
<th>

**Activities (only EU)**

</th>  
<th>

**LULUCF**

**(only**

**EU)**

</th>  
<th>

**Nutrient balances (only EU)**

</th>  
<th>

**Farm- and market balances**

</th>  
<th>

**Area use and yields**

</th>  
<th>

**GHG component**

</th>  
<th>

**Prices and**

**tariffs**

</th>  
<th>

**Value information**

</th> </tr>  
<tr>  
<td>

**Outputs**

</td>  
<td>

Output coefficients

</td>  
<td>

</td>  
<td>

</td>  
<td>

Production, seed and feed use, other internal use, losses, stock changes,
total and bilateral exports and imports, human consumption, processing,

TRQs

</td>  
<td>

Crop areas and yields by product

</td>  
<td>

GHG

effects per product

</td>  
<td>

Unit value prices from the EAA with and without subsidies

and taxes,

tariffs

applied and bindings

</td>  
<td>

Value of outputs with or without subsidies and taxes linked to

production, consumer expenditure, contributions of products to welfare

indicators,

PSE components, premiums per product

</td> </tr>  
<tr>  
<td>

**Inputs and mitigation technologies**

</td>  
<td>

Input coefficients and

implementation shares

</td>  
<td>

</td>  
<td>

Balance components by NPK

</td>  
<td>

Purchases, internal deliveries

</td>  
<td>

</td>  
<td>

</td>  
<td>

Unit value prices from the EAA with and without subsidies and taxes

</td>  
<td>

Value of inputs with or without subsidies

and taxes

link to input use

</td> </tr>  
<tr>  
<td>

**GHG components**

</td>  
<td>

GHG effects per activity

</td>  
<td>

GHG

effects per area type

</td>  
<td>

</td>  
<td>

Total GHG effects from activities

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

**Human**

**nutrients**

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

Nutrient consumption per capita

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

**Income indicators**

</td>  
<td>

Revenues, costs, Gross Value Added, premiums

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

Total revenues, costs, gross value added, subsidies, taxes,

premium

ceilings and ceiling use, PSE and welfare components

</td> </tr>  
<tr>  
<td>

**Activity levels and totals**

</td>  
<td>

Hectares, slaughtered heads or herd sizes

</td>  
<td>

Hectares

</td>  
<td>

</td>  
<td>

</td>  
<td>

Total agricultural area

</td>  
<td>

Total GHG effects from products

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

**Secondary products**

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

Marketable production, losses, stock changes, exports and

imports, human consumption, processing

</td>  
<td>

</td>  
<td>

</td>  
<td>

Consumer

prices, market prices, import prices

</td>  
<td>

consumer expenditure, contributions of products to welfare indicators,

</td> </tr> </table>

###  3.1.2 GLOBIOM (Global Biosphere Management Model)

GLOBIOM could provide the standard AgMIP (Agricultural Model Intercomparison
and Improvement Project) reporting used also by some other models (e.g. CAPRI,
MAGNET), which covers a comprehensive set of economic and environmental
indicators. In addition, GLOBIOM can also provide more detailed reporting for
specific topics of interest i.e. biomass use & climate change mitigation. An
overview of the main elements of the GLOBIOM database and scenario output is
presented in Tables below.

Table 3. Main Elements of the GLOBIOM database and scenario output  
  
<table>  
<tr>  
<th>

</th>  
<th>

**Activities**

</th>  
<th>

**Nutrient balances**

</th>  
<th>

**Market balances**

</th>  
<th>

**Area use and yields**

</th>  
<th>

**GHG**

**sector**

</th>  
<th>

**Prices and**

**tariffs**

</th>  
<th>

**Value information**

</th> </tr>  
<tr>  
<td>

**Outputs**

</td>  
<td>

Outputs for agriculture and forestry related activities

</td>  
<td>

Fertilizer use

</td>  
<td>

Production, feed use,

other uses, human consumption, processing, exports and imports for agriculture
and forestry

</td>  
<td>

Land use and land use change, crop areas, pasture, productivities

</td>  
<td>

GHG

effects for

CH4, N2O, and CO2 emissions

from AFOLU

</td>  
<td>

Unit value prices

</td>  
<td>

Value of outputs

linked to production, contributions of products to welfare indicators,

</td> </tr>  
<tr>  
<td>

**Inputs**

</td>  
<td>

Input coefficients for different activities

</td>  
<td>

Nitrogen balance components

</td>  
<td>

</td>  
<td>

Land cover information, crop areas and yields

</td>  
<td>

Emission factors

</td>  
<td>

Macroeconomic drivers (population, GDP,

technological change), carbon prices

</td>  
<td>

</td> </tr>  
<tr>  
<td>

**GHG components**

</td>  
<td>

GHG effects per activity

</td>  
<td>

Synthetic fertilizer use,

manure applied and

dropped to soils

</td>  
<td>

GHG

emissions associated to agricultural and forestry production

</td>  
<td>

Deforestation and other land use changes, dedicated energy plantations

</td>  
<td>

GHG

emissions associated

to crop- and

livestock, forestry and land use changes

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

**Human**

**nutrients**

</td>  
<td>

Consumption per capita by product

</td>  
<td>

</td>  
<td>

Total human consumption, consumption per capita

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

**Income indicators**

</td>  
<td>

Value of outputs linked to production, contributions of products to welfare
indicators

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

Value of outputs

linked to production, contributions of products to welfare indicators

</td> </tr> </table>

GLOBIOM will propose using the AgMIP reporting template, which has been used
for model comparison in the past. So, it is a format in which all models need
to deliver output data.  
  
<table>  
<tr>  
<th>

**Indicator**

</th>  
<th>

**Variable**

</th>  
<th>

**Description**

</th>  
<th>

**Unit**

</th> </tr>  
<tr>  
<td>

_**Economic** _

</td>  
<td>

POPT

</td>  
<td>

Total population

</td>  
<td>

million

</td> </tr>  
<tr>  
<td>

</td>  
<td>

GDPT

</td>  
<td>

Total GDP (MER)

</td>  
<td>

bn USD

</td> </tr>  
<tr>  
<td>

</td>  
<td>

XPRP

</td>  
<td>

Producer price/input price

</td>  
<td>

USD/t

</td> </tr>  
<tr>  
<td>

</td>  
<td>

CONS

</td>  
<td>

Domestic use

</td>  
<td>

1000 t

</td> </tr>  
<tr>  
<td>

</td>  
<td>

FOOD

</td>  
<td>

Food use

</td>  
<td>

1000 t

</td> </tr>  
<tr>  
<td>

</td>  
<td>

FEED

</td>  
<td>

Feed use

</td>  
<td>

1000 t

</td> </tr>  
<tr>  
<td>

</td>  
<td>

OTHU

</td>  
<td>

Other use

</td>  
<td>

1000 t

</td> </tr>  
<tr>  
<td>

</td>  
<td>

NETT

</td>  
<td>

Net trade

</td>  
<td>

1000 t

</td> </tr>  
<tr>  
<td>

</td>  
<td>

IMPO

</td>  
<td>

Imports

</td>  
<td>

1000 t

</td> </tr>  
<tr>  
<td>

</td>  
<td>

EXPO

</td>  
<td>

Exports

</td>  
<td>

1000 t

</td> </tr>  
<tr>  
<td>

</td>  
<td>

CALO

</td>  
<td>

p.c. calorie availability

</td>  
<td>

kcal/cap/d

</td> </tr>  
<tr>  
<td>

_**Production** _

</td>  
<td>

PROD

</td>  
<td>

Production

</td>  
<td>

1000 t

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

AREA

</td>  
<td>

Area harvested

</td>  
<td>

1000 ha

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

YEXO

</td>  
<td>

Exogenous crop yield

</td>  
<td>

t/ha

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

LYXO

</td>  
<td>

Exogenous livestock yield trend

</td>  
<td>

kg prt/ha

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

YILD

</td>  
<td>

Crop yield

</td>  
<td>

t/ha

</td> </tr>  
<tr>  
<td>

</td>  
<td>

LYLD

</td>  
<td>

Livestock yield (endogenous)

</td>  
<td>

kg prt/ha

</td> </tr>  
<tr>  
<td>

_**Environment** _

</td>  
<td>

LAND

</td>  
<td>

Land cover information (cropland, grassland, forestry, other natural
vegetation)

</td>  
<td>

1000 ha

</td> </tr>  
<tr>  
<td>

</td>  
<td>

EMIS

</td>  
<td>

Total GHG emissions from AFOLU

</td>  
<td>

MtCO2e

</td> </tr>  
<tr>  
<td>

</td>  
<td>

ECO2

</td>  
<td>

Total CO2 emissions from land use changes

</td>  
<td>

MtCO2e

</td> </tr>  
<tr>  
<td>

</td>  
<td>

ECH4

</td>  
<td>

Total CH4 emissions from crop- and livestock production

</td>  
<td>

MtCO2e

</td> </tr>  
<tr>  
<td>

</td>  
<td>

EN2O

</td>  
<td>

Total N2O emissions from crop- and livestock production

</td>  
<td>

MtCO2e

</td> </tr>  
<tr>  
<td>

</td>  
<td>

FRTN

</td>  
<td>

Fertiliser N

</td>  
<td>

1000 t

</td> </tr>  
<tr>  
<td>

</td>  
<td>

WATR

</td>  
<td>

Water for irrigation

</td>  
<td>

km3

</td> </tr> </table>

3.1.3 MAGNET (Modular Applied General Equilibrium Tool)

MAGNET will propose using the AgMIP reporting template, which has been used
for model comparison in the past. So, it is a format in which all models need
to deliver output data. MAGNET projection periods include: 2011-2020;
2020-2030; 2030-2040; and 2040-2050.

**Reporting sectors**

_**Code Description** _  
  
<table>  
<tr>  
<th>

RIC

</th>  
<th>

Rice (paddy equivalent)

</th> </tr>  
<tr>  
<td>

WHT

</td>  
<td>

Wheat

</td> </tr>  
<tr>  
<td>

CGR

</td>  
<td>

Other cereal grains

</td> </tr>  
<tr>  
<td>

OSD

</td>  
<td>

Oilseeds (raw equivalent)

</td> </tr>  
<tr>  
<td>

SGC

</td>  
<td>

Sugar crops (raw equivalent)

</td> </tr>  
<tr>  
<td>

VFN

</td>  
<td>

Vegetables, fruits, nuts (incl. roots and tubers)

</td> </tr>  
<tr>  
<td>

PFB

</td>  
<td>

Plant based fibres

</td> </tr>  
<tr>  
<td>

ECP

</td>  
<td>

Energy crops

</td> </tr>  
<tr>  
<td>

OCR

</td>  
<td>

Other crops

</td> </tr>  
<tr>  
<td>

RUM

</td>  
<td>

Ruminant meats

</td> </tr>  
<tr>  
<td>

NRM

</td>  
<td>

Non ruminant meats

</td> </tr>  
<tr>  
<td>

DRY

</td>  
<td>

Dairy (raw milk equivalent)

</td> </tr>  
<tr>  
<td>

OAP

</td>  
<td>

Other animal products (wool, honey)

</td> </tr>  
<tr>  
<td>

GRS

</td>  
<td>

Grass

</td> </tr>  
<tr>  
<td>

OFD

</td>  
<td>

Other feed products

</td> </tr>  
<tr>  
<td>

FSH

</td>  
<td>

Fish

</td> </tr>  
<tr>  
<td>

FOR

</td>  
<td>

Forestry products

</td> </tr> </table>

**Sectors subcategories (same variables as parents)**  
  
<table>  
<tr>  
<th>

VFN|VEG

VFN|FRU

VFN|NUT

NRM|PRK

NRM|PTM

NRM|EGG

</th> </tr> </table>

Vegetables

Fruits

Nuts

Pork meat

Poultry meat

Poultry eggs  
  
<table>  
<tr>  
<th>

NRM|ONR

</th> </tr> </table>

Other non-ruminant

**Sectors aggregates**

CRP All crops

LSP Livestock products

AGR All agricultural products

TOT Total (full economy, population, GDP, calories)  
  
<table>  
<tr>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<td>

**LAND variable items**

</td>  
<td>

</td> </tr>  
<tr>  
<td>

CRP

</td>  
<td>

Cropland _(including energy crops_ )

</td> </tr>  
<tr>  
<td>

GRS

</td>  
<td>

Grassland

</td> </tr>  
<tr>  
<td>

ONV

</td>  
<td>

Other natural land

</td> </tr>  
<tr>  
<td>

FOR

</td>  
<td>

Managed and primary forest

</td> </tr>  
<tr>  
<td>

NLD

</td>  
<td>

Non arable land (desert, built-up areas…)

</td> </tr>  
<tr>  
<td>

**LAND aggregates/subitems**

</td>  
<td>

</td> </tr>  
<tr>  
<td>

AGR

</td>  
<td>

Cropland + grassland

</td> </tr>  
<tr>  
<td>

ECP

**Production factors and intermediates**

</td>  
<td>

Energy crops (included in cropland)

</td> </tr>  
<tr>  
<td>

LAB

</td>  
<td>

Labor

</td> </tr>  
<tr>  
<td>

CAP

</td>  
<td>

Capital

</td> </tr>  
<tr>  
<td>

FRT

</td>  
<td>

Fertiliser

</td> </tr>  
<tr>  
<td>

OIL

</td>  
<td>

Fossil fuel

</td> </tr>  
<tr>  
<td>

**GHG emissions sources**

</td>  
<td>

</td> </tr> </table>  
<table>  
<tr>  
<th>

ENT

MMG

RCC

SFR

MAS

MGR

CRS

ORS

BSV

BCR

LAD

LFS

LOT

CFT

CMG

RMG

COT

</th> </tr> </table>

Enteric Fermentation

Manure Management

Rice Cultivation

Synthetic Fertilizers

Manure applied to Soils

Manure left on Pasture

Crop Residues

Cultivation of Organic Soils

Burning - Savanna

Burning - Crop Residues

**GHG mitigation**

**technologies**

Livestock anaerobic digesters

Livestock feed supplements

Livestock other

Crop improved fertilization

Improved cropping management

Crop improved rice cultivation

Crop other

3.1.4 AGMEMOD (Agriculture Memberstates Modelling)

Economic indicators

  * real GDP (index) 

  

  * production costs (€/ha; €/kg) 

  

  * returns (€/ha) 

  

  * prices (€/100 kg) 

  

  * yields/crop (ton/ha) 

  

  * yields/animal (kg/animal) 

  

  * production (1,000 ton) 

  

  * food use (1,000 ton) 

  

  * feed use (1,000 ton) 

  

  * seed use (1,000 ton) 

  

  * consumption/capita (kg/head) 

  

  * industrial use (1,000 ton) 

  

  * exports (1,000 ton) 

  

  * imports (1,000 ton) – area harvested (ha) 

  

  * self-sufficiency rate (ratio) 

  

  * net trade (1,000 ton) 

  

Social indicators

  * population (million inhabitants) 

  

Resources/inputs

  * land (ha) 

  

  * herd size (animals) 

  

Policy support

  * budgetary enveloppes/ceilings (1,000 €) 

  

  * coupled payments (1,000 €) 

  

  * support reaction price (€/kg) 

  

###  3.1.5 MITERRA-Europe

MITERRA-Europe, an environmental impact assessment model for agriculture, will
provide results on a range of environmental indicators, including:

  * Greenhouse gas emissions (CO  2  , N  2  O and CH  4  ) 

  

  * Changes in soil organic carbon 

  

  * NH  3  emissions 

  

  * Nutrient balances (N and P) 

  

  * Nitrogen leaching and runoff 

  

  * NO  3  concentration groundwater 

  

  * Critical load exceedance 

  

  * Soil erosion 

  

  * Soil metal balances (Cadmium, Chrome, Copper, Zinc, Lead and Nickel) 

  

  * (maybe a land use based biodiversity indicator) 

  

These results will be made available at country and regional (NUTS2 level) and
will be expressed per ha UAA, as well as total for the region or country. In
addition, the greenhouse gas emissions, and Nitrogen indicators can also be
provided per agricultural product, based on an LCA (life cycle analysis)
approach.

In respect to the new EU climate policies, the GHG results will also be
presented per sector (Agriculture and LULUCF) at national level following the
accounting rules.

Regarding Open Access, all resulting output indicators as used for baseline
and scenario tasks, will be provided as Open Access. Almost all of the input
data is derived from public data sources and can be considered therefore as
Open Access. The model itself is, is not open access, as it is a research
model and lacks the proper user interface and manuals to be used by others.

###  3.1.6 IFM-CAP (Individual Farm Model for Common Agricultural Policy)

The following variables are available for IFM-CAP but only at regional/MS/EU
and farm type levels. Individual data cannot be made public:

_Agronomic/structural indicators:_

  * Land allocation/crop area (ha) 

  

  * Herd size/animal number (heads) 

  

  * Livestock density (LU/ha) 

  

  * Share of arable land in Utilized Agricultural Area 

  

  * Share of grassland in Utilized Agricultural Area 

  

  * Land use change (ha) 

  

  * Agricultural production (Tons) 

  

  * Intermediate Input use (Tons) _Economic indicators:_

  

  * Agricultural output (€) 

  

  * CAP first pillar subsidies (€) 

  

  * CAP second pillar subsidies (€) 

  

  * Intermediate input costs (€) 

  

  * Variable costs (€) 

  

  * Total costs (€) 

  

  * Gross farm income (€) 

  

  * Net Farm Income (€) _Environmental indicators:_

  

  * Biodiversity index (index) 

  

  * Soil erosion (tonnes) 

  

##  3.2 Intellectual Property Rights (IPR)

Intellectual Property Rights (IPR) will receive special attention from the
beginning. All rules regarding management of knowledge and IPR will be
governed by the Consortium Agreement (CA). SUPREMA was based on DESCA
(Consortium Agreement Model) H2020 model for the Consortium Agreement (CA).
SUPREMA will adhere to the rules laid down in Annex II of the Grant Agreement.
The CA will address background and foreground knowledge, ownership, protected
third party components of the products, and protection, use and dissemination
of results and access rights.

The following principles will be applied:

  * Pre-existing know how: Each Contractor is and remains the sole owner of its IPR over its pre-existing know-how. The Contractors will identify and list the pre-existing know-how over which they may grant access rights for the project. The Contractors agree that the access rights to the pre-existing know-how needed for carrying out their own work under the project shall be granted on a royalty-free basis. 

  

  * Ownership and protection of knowledge: The ownership of the knowledge developed within the project will be governed by an open source license. 

  

  * Open data: Data and results obtained during the project that are based on open publicsector data will be made available free of charge. 

  

The procedures for the dissemination, protection and exploitation of
intellectual property rights (IPR) are clearly covered in the Consortium
Agreement (in Section 6: Governance Structure, Sub-section 6.2.4: Veto
rights). The intention has been to balance the requirements necessary to
protect such intellectual property and the foreseen dissemination objectives.
IPR will be applied according to the rules of the employer under the
applicable European and national laws and regulations.

#  4 Update of the data management plan

During the project review of SUPREMA (Meeting on 4th March 2019) the Data
Management Plan (D4.3) is qualified as generic. This makes sense as an initial
version (submitted June 2018, M6 of SUPREMA), and this version could now be
updated with the initial set of variables that will be released open access.
Table 4, Table 5 and Table 6 presents the current scenario of data released in
Open Access.

Table 4 maps the AGMEMOD and MITERA models and set of indicators (existing and
new) that could be released open access. When an indicator is captured by a
model it is highlighted in green (e.g. AGMEMOD, MITERRA), and if it is not
captured it is highlighted in red (e.g. AGMEMOD, MITERRA).

Table 4.5 Mapping AGEMEMOD and MITERRA to SUPREMA variables, indicators and
sectors  
  
<table>  
<tr>  
<th>

Variables

</th>  
<th>

Unit

</th>  
<th>

Captured by models (yes, no)

</th>  
<th>

Comments

</th> </tr>  
<tr>  
<td>

Prices and (farm) Income variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Real producer price/input price

</td>  
<td>

USD/t

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animal products; member states (MS) level

</td> </tr>  
<tr>  
<td>

Real export price

</td>  
<td>

USD/t

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Livestock input costs (incl feed costs, other costs)

</td>  
<td>

euro/ton

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Farm sector income (gross income: sector returns /- intermediate costs)

</td>  
<td>

th euro/farm

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Area and yield variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Area harvested

</td>  
<td>

1000 ha

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops; MS

</td> </tr>  
<tr>  
<td>

Land cover

</td>  
<td>

1000 ha

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Total land; MS

</td> </tr>  
<tr>  
<td>

Crop yield

</td>  
<td>

dm t/ha, fm t/ha

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops; MS

</td> </tr>  
<tr>  
<td>

Exogenous crop yield

</td>  
<td>

dm t/ha, fm t/ha

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops; MS

</td> </tr>  
<tr>  
<td>

Livestock yield (endogenous)

</td>  
<td>

kg prt/ha

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Milk, meat; MS

</td> </tr>  
<tr>  
<td>

Exogenous livestock yield trend

</td>  
<td>

kg prt/ha

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Milk, meat; MS

</td> </tr>  
<tr>  
<td>

Feed conversion efficiency (endogenous)

</td>  
<td>

kg prt/kg prt

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Ruminants; MS

</td> </tr>  
<tr>  
<td>

Feed conversion efficiency trend

</td>  
<td>

kg prt/kg prt

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Market variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Food use

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animal products; MS

</td> </tr>  
<tr>  
<td>

Feed use

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops, MS

</td> </tr>  
<tr>  
<td>

Feed use

</td>  
<td>

1000 t prt

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops, MS

</td> </tr>  
<tr>  
<td>

Other use (seed /industrial use, losses)

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops, MS

</td> </tr>  
<tr>  
<td>

Imports

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animal products; MS

</td> </tr>  
<tr>  
<td>

Exports

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animal products; MS

</td> </tr>  
<tr>  
<td>

Production

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animal products; MS

</td> </tr>  
<tr>  
<td>

Domestic use (total use = food+feed+other

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animal products; MS

</td> </tr>  
<tr>  
<td>

Net trade

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animal products; MS

</td> </tr>  
<tr>  
<td>

Feed use ruminant meat

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Animals; MS

</td> </tr>  
<tr>  
<td>

Feed use ruminant meat

</td>  
<td>

1000 t prt

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Animals; MS

</td> </tr>  
<tr>  
<td>

Feed use dairy

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Animals; MS

</td> </tr>  
<tr>  
<td>

Feed use dairy

</td>  
<td>

1000 t prt

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Animals; MS

</td> </tr>  
<tr>  
<td>

Feed fish sector

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Fish families; MS

</td> </tr>  
<tr>  
<td>

Seed use

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops, MS

</td> </tr>  
<tr>  
<td>

Other industrial use

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops, MS

</td> </tr>  
<tr>  
<td>

Biodiesel/bioethanol for industrial use

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops, MS

</td> </tr>  
<tr>  
<td>

Stocks

</td>  
<td>

1000 t/1000 h

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops, MS

</td> </tr> </table>  
<table>  
<tr>  
<th>

Slaughterings

</th>  
<th>

1000 h

</th>  
<th>

AGMEMOD; MITERRA

</th>  
<th>

Animals; MS

</th> </tr>  
<tr>  
<td>

Self-sufficiency rate (production/domestic use)

</td>  
<td>

Ratio

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animal products; MS

</td> </tr>  
<tr>  
<td>

Food security _(need for good definition)_

</td>  
<td>

_Index_

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Environmental variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Fertiliser N

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Water for irrigation

</td>  
<td>

km3

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Total GHG emissions

</td>  
<td>

MtCO2e

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Total CO2 emissions

</td>  
<td>

MtCO2e

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Total CH4 emissions

</td>  
<td>

MtCO2e

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Total N2O emissions

</td>  
<td>

MtCO2e

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Total NH3 emissions

</td>  
<td>

1000 kg N

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Total N leaching and runoff

</td>  
<td>

1000 kg N

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

NO3 concentration groundwater

</td>  
<td>

mg NO3/liter

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Soil organic carbon balance

</td>  
<td>

kg C/ha/year

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Biodiversity change

</td>  
<td>

% ch/yr

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Total crops; MS

</td> </tr>  
<tr>  
<td>

Soil erosion _(need for good definition)_

</td>  
<td>

Index

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Total crops; MS

</td> </tr>  
<tr>  
<td>

Technological innovation variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Technical mitigation options – Production

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Technical mitigation options – Emissions

</td>  
<td>

MtCO2e

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Technical mitigation options - CO2

</td>  
<td>

MtCO2e

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Technical mitigation options - CH4

</td>  
<td>

MtCO2e

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Technical mitigation options - N2O

</td>  
<td>

MtCO2e

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Internet of things/digitalisation _(need for good definition)_

</td>  
<td>

_Index_

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Nitrification inhibitors _(need for good definition)_

</td>  
<td>

_Index_

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Precision/smart farming _(need for good definition)_

</td>  
<td>

_Index_

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Macroeconomic variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Total population

</td>  
<td>

Million

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Total country; MS

</td> </tr>  
<tr>  
<td>

Total GDP (MER)

</td>  
<td>

bn USD 2005 MER

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Total country; MS

</td> </tr>  
<tr>  
<td>

World prices

</td>  
<td>

usd/1000 kg

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

GDP deflator (national inflation rate in yr t, compared to base year)

</td>  
<td>

index (2015=100)

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Total country; MS

</td> </tr>  
<tr>  
<td>

Exchange rate

</td>  
<td>

euro/dollar

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Total country; MS

</td> </tr>  
<tr>  
<td>

CAP policy variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Ecological focus area

</td>  
<td>

%

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Total country; MS

</td> </tr>  
<tr>  
<td>

Budgetary national envelope

</td>  
<td>

thsd euro

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Total country; MS

</td> </tr>  
<tr>  
<td>

Voluntary coupled payments

</td>  
<td>

thsd euro

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Young farmers payments

</td>  
<td>

thsd euro

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Greening payments

</td>  
<td>

thsd euro

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Price support (from envelope)

</td>  
<td>

euro/100 kg

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Environmental policy variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Carbon tax level

</td>  
<td>

USD/tCO2e

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Climate policy targets

</td>  
<td>

MtCO2e

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Total country; MS

</td> </tr>  
<tr>  
<td>

Trade policy variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Tariff rate quotas

</td>  
<td>

_1000 t_

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Consumer preference variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Qualitative variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Soil quality _(need for good definition)_

</td>  
<td>

Index

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Crops; MS

</td> </tr>  
<tr>  
<td>

Water quality _(need for good definition)_

</td>  
<td>

Index

</td>  
<td>

AGMEMOD; MITERRA

</td>  
<td>

Total country; MS

</td> </tr> </table>

Table 5 maps the MAGNET, AGMEMOD and GLOBIOM models and set of indicators
(existing and new) that could be released open access. When an indicator is
captured by a model it is highlighted in green (e.g. MAGNET, GLOBIOM, CAPRI),
and if it is not captured it is highlighted in red (e.g. MAGNET, GLOBIOM,
CAPRI) (Table

5).

Table 5 Mapping AGEMEMOD and GLOBIOM to SUPREMA variables, indicators and
sectors  
  
<table>  
<tr>  
<th>

Variables

</th>  
<th>

Unit

</th>  
<th>

Captured by models (yes, no)

</th>  
<th>

Comments

</th> </tr>  
<tr>  
<td>

Prices and (farm) Income variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Real producer price/input price

</td>  
<td>

USD/t

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animal products; member states (MS) level

</td> </tr>  
<tr>  
<td>

Real export price

</td>  
<td>

USD/t

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Livestock input costs (incl feed costs, other costs)

</td>  
<td>

euro/ton

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Farm sector income (gross income: sector returns -/- intermediate costs)

</td>  
<td>

th euro/farm

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Area and yield variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Area harvested

</td>  
<td>

1000 ha

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops; MS

</td> </tr>  
<tr>  
<td>

Area harvested – rainfed

</td>  
<td>

1000 ha

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Area harvested – irrigated

</td>  
<td>

1000 ha

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Land cover

</td>  
<td>

1000 ha

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Total land; MS

</td> </tr>  
<tr>  
<td>

Crop yield

</td>  
<td>

dm t/ha, fm t/ha

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops; MS

</td> </tr>  
<tr>  
<td>

Crop yield – rainfed

</td>  
<td>

dm t/ha, fm t/ha

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Crop yield – irrigated

</td>  
<td>

dm t/ha, fm t/ha

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Exogenous crop yield

</td>  
<td>

dm t/ha, fm t/ha

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

Crops; MS

</td> </tr>  
<tr>  
<td>

Climate change shifter on crop yield

</td>  
<td>

%

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Livestock yield (endogenous)

</td>  
<td>

kg prt/ha

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Milk, meat; MS

</td> </tr>  
<tr>  
<td>

Exogenous livestock yield trend

</td>  
<td>

kg prt/ha

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Milk, meat; MS

</td> </tr>  
<tr>  
<td>

Feed conversion efficiency (endogenous)

</td>  
<td>

kg prt/kg prt

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

Ruminants; MS

</td> </tr>  
<tr>  
<td>

Feed conversion efficiency trend

</td>  
<td>

kg prt/kg prt

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Market variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Food use

</td>  
<td>

1000 t

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animal products;

MS

</td> </tr>  
<tr>  
<td>

Feed use

</td>  
<td>

1000 t

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops, MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Feed use

</td>  
<td>

1000 t prt

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops, MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Other use (seed /industrial use, losses)

</td>  
<td>

1000 t

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops, MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Imports

</td>  
<td>

1000 t

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animal products; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Exports

</td>  
<td>

1000 t

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animal products; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Production

</td>  
<td>

1000 t

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animal products; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Domestic use (total use = food+feed+other

</td>  
<td>

1000 t

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animal products; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Net trade

</td>  
<td>

1000 t

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animal products; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Feed use ruminant meat

</td>  
<td>

1000 t

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Animals; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Feed use ruminant meat

</td>  
<td>

1000 t prt

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

Animals; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Feed use dairy

</td>  
<td>

1000 t

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Animals; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Feed use dairy

</td>  
<td>

1000 t prt

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

Animals; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Feed fish sector

</td>  
<td>

1000 t

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

MAGNET in usd

</td> </tr>  
<tr>  
<td>

Feed fish sector

</td>  
<td>

1000 t prt

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

MAGNET in usd

</td> </tr> </table>  
<table>  
<tr>  
<th>

Seed use

</th>  
<th>

1000 t

</th>  
<th>

MAGNET; GLOBIOM; CAPRI

</th>  
<th>

Crops, MS; MAGNET in usd

</th> </tr>  
<tr>  
<td>

Other industrial use

</td>  
<td>

1000 t

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

Crops, MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Biodiesel/bioethanol for industrial use

</td>  
<td>

1000 t

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

Crops, MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Stocks

</td>  
<td>

1000 t/1000 h

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

Crops, MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Slaughtering

</td>  
<td>

1000 h

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

Animals; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Self-sufficiency rate (production/domestic use)

</td>  
<td>

Ratio

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

Crops and animal products;

MS

</td> </tr>  
<tr>  
<td>

Food security _(need for good definition)_

</td>  
<td>

_Index_

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

MAGNET in usd

</td> </tr>  
<tr>  
<td>

Environmental variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Fertiliser N

</td>  
<td>

1000 t

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Water for irrigation

</td>  
<td>

km3

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Total GHG emissions

</td>  
<td>

MtCO2e

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Total CO2 emissions

</td>  
<td>

MtCO2e

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Total CH4 emissions

</td>  
<td>

MtCO2e

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Total N2O emissions

</td>  
<td>

MtCO2e

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Total NH3 emissions

</td>  
<td>

1000 kg N

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Total N leaching and runoff

</td>  
<td>

1000 kg N

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

NO3 concentration groundwater

</td>  
<td>

mg NO3/liter

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Soil organic carbon balance

</td>  
<td>

kg C/ha/year

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Energy use

</td>  
<td>

_PJ_

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Water use

</td>  
<td>

_1000 m3_

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Weather volatility/climate change _(need for good definition)_

</td>  
<td>

_Index_

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Technological innovation variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Technical mitigation options – Production

</td>  
<td>

1000 t

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Technical mitigation options – Emissions

</td>  
<td>

MtCO2e

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Technical mitigation options - CO2

</td>  
<td>

MtCO2e

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Technical mitigation options - CH4

</td>  
<td>

MtCO2e

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Technical mitigation options - N2O

</td>  
<td>

MtCO2e

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Internet of things/digitalisation _(need for good definition)_

</td>  
<td>

_Index_

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Nitrification inhibitors _(need for good definition)_

</td>  
<td>

_Index_

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Precision/smart farming _(need for good definition)_

</td>  
<td>

_Index_

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Macroeconomic variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Total population

</td>  
<td>

Million

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

Total country; MS

</td> </tr>  
<tr>  
<td>

Total GDP (MER)

</td>  
<td>

bn USD 2005 MER

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

Total country; MS

</td> </tr>  
<tr>  
<td>

World prices

</td>  
<td>

usd/1000 kg

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

GDP deflator (national inflation rate in yr t, compared to base year)

</td>  
<td>

index (2015=100)

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

Total country; MS

</td> </tr>  
<tr>  
<td>

Exchange rate

</td>  
<td>

euro/dollar

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

Total country; MS

</td> </tr>  
<tr>  
<td>

Labour productivity (labour units/turnover)

</td>  
<td>

Lab units/turnover

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Employment

</td>  
<td>

Million

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

</td> </tr>  
<tr>  
<td>

CAP policy variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Ecological focus area

</td>  
<td>

%

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

Total country; MS

</td> </tr>  
<tr>  
<td>

Budgetary national envelope

</td>  
<td>

thsd euro

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

Total country; MS

</td> </tr>  
<tr>  
<td>

Voluntary coupled payments

</td>  
<td>

thsd euro

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Young farmers payments

</td>  
<td>

thsd euro

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Greening payments

</td>  
<td>

thsd euro

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Price support (from envelope)

</td>  
<td>

euro/100 kg

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Environmental policy variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Carbon tax level

</td>  
<td>

USD/tCO2e

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Climate policy targets

</td>  
<td>

MtCO2e

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Total country; MS

</td> </tr>  
<tr>  
<td>

Energy policy targets

</td>  
<td>

_PJ_

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Positive/negative externalities

</td>  
<td>

euro/100 kg

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Trade policy variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Tariff rate quotas

</td>  
<td>

_1000 t_

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Consumer preference variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

p.c. calory availability

</td>  
<td>

kcal/cap/d

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Regional food products

</td>  
<td>

_% in total products_

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Qualitative variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Soil quality _(need for good definition)_

</td>  
<td>

Index

</td>  
<td>

MAGNET; GLOBIOM; CAPRI

</td>  
<td>

Crops; MS

</td> </tr>  
<tr>  
<td>

Water quality _(need for good definition)_

</td>  
<td>

Index

</td>  
<td>

MAGNET; GLOBIOM;CAPRI

</td>  
<td>

Total country; MS

</td> </tr> </table>

Table 6 maps the AGMEMOD and MAGNET models and set of indicators (existing and
new) that could be released open access. Table 6 maps the models. When an
indicator is captured by a model it is highlighted in green (e.g. AGMEMOD,
MAGNET), and if it is not captured it is highlighted in red (e.g. AGMEMOD,
MAGNET).

Table 6 Mapping AGEMEMOD and MAGNET to SUPREMA variables and indicators and
sectors  
  
<table>  
<tr>  
<th>

Variables

</th>  
<th>

Unit

</th>  
<th>

Captured by models (yes, no)

</th>  
<th>

Comments

</th> </tr>  
<tr>  
<td>

Prices and (farm) Income variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Real producer price/input price

</td>  
<td>

USD/t

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animal products; MS and world regional level;

</td> </tr>  
<tr>  
<td>

Real export price

</td>  
<td>

USD/t

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Livestock input costs (incl feed costs, other costs)

</td>  
<td>

euro/ton

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Farm sector income (gross income: sector returns /- intermediate costs)

</td>  
<td>

th euro/farm

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Area and yield variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Area harvested

</td>  
<td>

1000 ha

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops; MS

</td> </tr>  
<tr>  
<td>

Land cover

</td>  
<td>

1000 ha

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Total land; MS

</td> </tr>  
<tr>  
<td>

Crop yield

</td>  
<td>

dm t/ha, fm t/ha

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops; MS

</td> </tr>  
<tr>  
<td>

Exogenous crop yield

</td>  
<td>

dm t/ha, fm t/ha

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops; MS

</td> </tr>  
<tr>  
<td>

Climate change shifter on crop yield

</td>  
<td>

%

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Livestock yield (endogenous)

</td>  
<td>

kg prt/ha

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Milk, meat; MS

</td> </tr>  
<tr>  
<td>

Exogenous livestock yield trend

</td>  
<td>

kg prt/ha

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Milk, meat; MS

</td> </tr>  
<tr>  
<td>

Market variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Food use

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animal products; MS;

</td> </tr>  
<tr>  
<td>

Feed use

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops, MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Other use (seed /industrial use, losses)

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops, MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Imports

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animal products; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Exports

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animal products; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Production

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animal products; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Domestic use (total use = food+feed+other

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animal products; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Net trade

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animal products; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Feed use ruminant meat

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animal products; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Feed use dairy

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animal products; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Feed fish sector

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animal products; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Seed use

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animal products; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Other industrial use

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animal products; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Biodiesel/bioethanol for industrial use

</td>  
<td>

1000 t

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animal products; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Stocks

</td>  
<td>

1000 t/1000 h

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animal products; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Slaughtering

</td>  
<td>

1000 h

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animal products; MS; MAGNET in usd

</td> </tr>  
<tr>  
<td>

Self-sufficiency rate (production/domestic use)

</td>  
<td>

Ratio

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animal products; MS

</td> </tr>  
<tr>  
<td>

Environmental variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Total GHG emissions

</td>  
<td>

MtCO2e

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Total CO2 emissions

</td>  
<td>

MtCO2e

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Total CH4 emissions

</td>  
<td>

MtCO2e

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Total N2O emissions

</td>  
<td>

MtCO2e

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Total NH3 emissions

</td>  
<td>

1000 kg N

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Macroeconomic variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Total population

</td>  
<td>

Million

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Total country; MS

</td> </tr>  
<tr>  
<td>

Total GDP (MER)

</td>  
<td>

bn USD 2005 MER

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Total country; MS

</td> </tr>  
<tr>  
<td>

World prices

</td>  
<td>

usd/1000 kg

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

GDP deflator (national inflation rate in yr t, compared to base year)

</td>  
<td>

index (2015=100)

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Total country; MS

</td> </tr>  
<tr>  
<td>

Exchange rate

</td>  
<td>

euro/dollar

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Total country; MS

</td> </tr>  
<tr>  
<td>

Labour productivity (labour units/turnover)

</td>  
<td>

Lab

units/turnover

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Employment

</td>  
<td>

Million

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

</td> </tr>  
<tr>  
<td>

CAP policy variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Ecological focus area

</td>  
<td>

%

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Total country; MS

</td> </tr>  
<tr>  
<td>

Budgetary national envelope

</td>  
<td>

thsd euro

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Total country; MS

</td> </tr>  
<tr>  
<td>

Voluntary coupled payments

</td>  
<td>

thsd euro

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Young farmers payments

</td>  
<td>

thsd euro

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Greening payments

</td>  
<td>

thsd euro

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Price support (from envelope)

</td>  
<td>

euro/100 kg

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Environmental policy variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Carbon tax level

</td>  
<td>

USD/tCO2e

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Trade policy variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Tariff rate quotas

</td>  
<td>

_1000 t_

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

Crops and animals, MS

</td> </tr>  
<tr>  
<td>

Non-tariff measures (nr of different measures imposed on agric products)

</td>  
<td>

_number/agr product_

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Consumer preference variables

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td> </tr>  
<tr>  
<td>

p.c. calory availability

</td>  
<td>

kcal/cap/d

</td>  
<td>

AGMEMOD; MAGNET

</td>  
<td>

</td> </tr> </table>

#  5 Conclusions

The AGMIP reporting template has been accepted as a base for the economic
models and will ensure some minimal comparability of information provided
across the economic models. The current deliverable is a “plan”, therefore it
may be expected that the process initiated with the data comparisons and
presentation of selected data by the SUPREMA models will trigger some further
harmonisation and standardisation that will go beyond the AGMIP template.

DataM is the European Commission’s data portal of agro-economic modelling. It
contains the outcomes of research activities, and is operated by the Joint
Research Centre (JRC) of the European Commission. DataM, including the web
portal but also the Information System, will be used to release model runs
that are considered for open access release. A baseline comparison and
harmonization action will be addressed for all models represented in SUPREMA.
A medium-term (until 2030) assessment of European agricultural policy
alternatives will cover CAPRI, IFM-CAP and AGMEMOD-MITERRA Europe. Finally,
SUPREMA will also use different modelling tools for the long-term (until 2050)
assessment of climate change goals, using GLOBIOM and MAGNET as leading
models.