A component-based framework for simulating agricultural production and externalities
Marcello Donatelli
(1, 2)
,
Graham Russel
(3)
,
Andrea Emilio Rizzoli
(4)
,
Marco Acutis
(5)
,
Myriam Adam
(6, 7)
,
Ioannis Athanasiadis
(4)
,
Matteo Balderacchi
(8)
,
Luca Bechini
(5)
,
Hatem Belhouchette
(7, 9)
,
Gianni Bellochi
(10)
,
Jacques-Eric J.-E. Bergez
(6, 7)
,
Marco Botta
(10)
,
Erik Braudeau
(11)
,
Simone Bregaglio
(1)
,
Laura Carlini
(1)
,
Eric Casellas
(12)
,
Florian Celette
(7)
,
Enrico Ceotto
(10)
,
Marie-Hélène Moirez-Charron
(6)
,
Roberto Confalonieri
(13)
,
Marc Corbeels
(6)
,
Luca Criscuolo
(10)
,
Pablo Cruz
(6)
,
Andrea Di Guardo
(10)
,
Domenico Ditto
(5)
,
Christian Dupraz
(7)
,
Michel M. Duru
(6)
,
Diego Fiorani
(10)
,
Antonella Gentile
(5)
,
Frank Ewert
(14)
,
Christian C. Gary
(7)
,
Ephrem Habyarimana
(10)
,
Claire Jouany
(6)
,
Kamal Kansou
(7)
,
Rob Knapen
(14)
,
Giovanni Lanza Filippi
(5)
,
Peter A Leffelaar
(14)
,
Luisa Manici
(10)
,
Guillaume Martin
(6)
,
Pierre Martin
(15)
,
Eelco Meuter
(14)
,
Nora Mugueta
(10)
,
Rachmat Mulia
(16)
,
Meine van Noordwijk
(17)
,
Roelof Oomen
(14)
,
Alexandre Rosenmund
(13)
,
Vittorio Rossi
(8)
,
Francesca Salinari
(10)
,
Ariel Serrano
(10)
,
Andrea Sorce
(10)
,
Grégoire Vincent
(18)
,
Jean Pierre J. P. Theau
(6)
,
Olivier Therond
(6)
,
Marco Trevisan
(8)
,
Patrizia Trevisiol
(5)
,
Frits K. van Evert
(14)
,
Daniel D. Wallach
(6)
,
Jacques Wery
(7, 9)
,
Arezki Zerourou
(6)
1
Agriculture Research Council
2 European Commission's Joint Research Centre
3 Edin. - University of Edinburgh
4 IDSIA - Istituto Dalle Molle di Studi sull'Intelligenza Artificiale
5 Department of Crop Science
6 AGIR - AGroécologie, Innovations, teRritoires
7 Fonctionnement et conduite des Systèmes de culture Tropicaux et Méditerranéens
8 Catholic University of the Sacred Heart
9 UMR SYSTEM - Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens
10 CRA - Agricultural Research Council
11 IRD [Réunion] - Institut de Recherche pour le Développement
12 UBIA - Unité de Biométrie et Intelligence Artificielle (ancêtre de MIAT)
13 European Commission
14 WUR - Wageningen University and Research Centre
15 UM2 - Université Montpellier 2 - Sciences et Techniques
16 World Agroforestry Centre
17 INRA - Institut National de la Recherche Agronomique
18 IRD - Institut de Recherche pour le Développement
2 European Commission's Joint Research Centre
3 Edin. - University of Edinburgh
4 IDSIA - Istituto Dalle Molle di Studi sull'Intelligenza Artificiale
5 Department of Crop Science
6 AGIR - AGroécologie, Innovations, teRritoires
7 Fonctionnement et conduite des Systèmes de culture Tropicaux et Méditerranéens
8 Catholic University of the Sacred Heart
9 UMR SYSTEM - Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens
10 CRA - Agricultural Research Council
11 IRD [Réunion] - Institut de Recherche pour le Développement
12 UBIA - Unité de Biométrie et Intelligence Artificielle (ancêtre de MIAT)
13 European Commission
14 WUR - Wageningen University and Research Centre
15 UM2 - Université Montpellier 2 - Sciences et Techniques
16 World Agroforestry Centre
17 INRA - Institut National de la Recherche Agronomique
18 IRD - Institut de Recherche pour le Développement
Marcello Donatelli
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Myriam Adam
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- ORCID : 0000-0002-8873-6762
Hatem Belhouchette
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Jacques-Eric J.-E. Bergez
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Simone Bregaglio
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Eric Casellas
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Florian Celette
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Marie-Hélène Moirez-Charron
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Christian Dupraz
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Michel M. Duru
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Christian C. Gary
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Claire Jouany
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Kamal Kansou
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Rob Knapen
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Guillaume Martin
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Meine van Noordwijk
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Vittorio Rossi
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Grégoire Vincent
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Olivier Therond
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Jacques Wery
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Résumé
Although existing simulation tools can be used to study the impact of agricultural management on production activities in specific environments, they suffer from several limitations. They are largely specialized for specific production activities: arable crops/cropping systems, grassland, orchards, agro-forestry, livestock etc. Also, they often have a restricted ability to simulate system externalities which may have a negative environmental impact. Furthermore, the structure of such systems neither allows an easy plug-in of modules for other agricultural production activities, nor the use of alternative components for simulating processes. Finally, such systems are proprietary systems of either research groups or projects which inhibits further development by third parties. The EU Sixth Framework Integrated Project SEAMLESS aims to provide a tool to integrate analyses of impacts on the key aspects of sustainability and multi-functionality, particularly in Europe. This requires evaluating agricultural production and system externalities for the most important agricultural production systems. It also requires a simulation framework which can be extended and updated by research teams, which allows a manageable transfer of research results to operational tools, and which is transparent with respect to its contents and its functionality. The Agricultural Production and Externalities Simulator (APES) is a modular simulation system aimed at meeting these requirements, and targeted at estimating the biophysical behavior of agricultural production systems in response to the interaction of weather and agro-technical management. APES is a framework which uses components that offer simulation options for different processes of relevance to agricultural production systems. Models are described in the associated help files of components, and a shared ontology is built on the web. Components like these, which are designed to be inherently re-usable, that is not targeted specifically to a given modelling framework, also represent a way to share modelling knowledge with other projects and the scientific community in general. This chapter describes the current state of APES development and presents modelling options in the system, and its software architecture.