Simulation models in farming systems Research: Potential and challenges

Giuseppe Feola; Claudia Sattler; Ali Kerem Saysel

doi:10.1007/978-94-007-4503-2_13

Simulation models in farming systems Research: Potential and challenges

Giuseppe Feola^*, Claudia Sattler, Ali Kerem Saysel

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

Abstract

Integrated simulation models can be useful tools in farming system research. This chapter reviews three commonly used approaches, i.e. linear programming, system dynamics and agent-based models. Applications of each approach are presented and strengths and drawbacks discussed. We argue that, despite some challenges, mainly related to the integration of different approaches, model validation and the representation of human agents, integrated simulation models contribute important insights to the analysis of farming systems. They help unravelling the complex and dynamic interactions and feedbacks among bio-physical, socio-economic, and institutional components across scales and levels in farming systems. In addition, they can provide a platform for integrative research, and can support transdisciplinary research by functioning as learning platforms in participatory processes.

Original language	English
Title of host publication	Farming Systems Research into the 21st Century: The New Dynamic
Editors	Ika Darnhofer, David Gibbon, Benoît Dedieu
Publisher	Springer
Pages	281-306
Number of pages	26
ISBN (Electronic)	9789400745032
ISBN (Print)	9789400745025
DOIs	https://doi.org/10.1007/978-94-007-4503-2_13
Publication status	Published - 2012
Externally published	Yes

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10.1007/978-94-007-4503-2_13

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title = "Simulation models in farming systems Research: Potential and challenges",

abstract = "Integrated simulation models can be useful tools in farming system research. This chapter reviews three commonly used approaches, i.e. linear programming, system dynamics and agent-based models. Applications of each approach are presented and strengths and drawbacks discussed. We argue that, despite some challenges, mainly related to the integration of different approaches, model validation and the representation of human agents, integrated simulation models contribute important insights to the analysis of farming systems. They help unravelling the complex and dynamic interactions and feedbacks among bio-physical, socio-economic, and institutional components across scales and levels in farming systems. In addition, they can provide a platform for integrative research, and can support transdisciplinary research by functioning as learning platforms in participatory processes.",

author = "Giuseppe Feola and Claudia Sattler and {Kerem Saysel}, Ali",

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pages = "281--306",

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booktitle = "Farming Systems Research into the 21st Century: The New Dynamic",

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Simulation models in farming systems Research: Potential and challenges. / Feola, Giuseppe; Sattler, Claudia; Kerem Saysel, Ali.
Farming Systems Research into the 21st Century: The New Dynamic. ed. / Ika Darnhofer; David Gibbon; Benoît Dedieu. Springer, 2012. p. 281-306.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

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AU - Feola, Giuseppe

AU - Sattler, Claudia

AU - Kerem Saysel, Ali

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AB - Integrated simulation models can be useful tools in farming system research. This chapter reviews three commonly used approaches, i.e. linear programming, system dynamics and agent-based models. Applications of each approach are presented and strengths and drawbacks discussed. We argue that, despite some challenges, mainly related to the integration of different approaches, model validation and the representation of human agents, integrated simulation models contribute important insights to the analysis of farming systems. They help unravelling the complex and dynamic interactions and feedbacks among bio-physical, socio-economic, and institutional components across scales and levels in farming systems. In addition, they can provide a platform for integrative research, and can support transdisciplinary research by functioning as learning platforms in participatory processes.

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A2 - Gibbon, David

A2 - Dedieu, Benoît

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Simulation models in farming systems Research: Potential and challenges

Abstract

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