Simulation of ecological, social and economic factors in agricultural systems

Dent, J. B.; Edwards‐Jones, Gareth; McGregor, M. J.

doi:10.1016/0308-521x(95)00029-5

Cited by 69 publications

(32 citation statements)

References 16 publications

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“…Land change science (LCS) addresses interactions among social systems and environmental factors regarding the behaviour of households or firms as the major proximate drivers of land change (Turner et al 2007). Agricultural sciences consider change in agricultural land-use in relation to farming systems viewed as adaptive socio-technological systems driven by land condition, household behaviour, technology, socio-economic environment and public policy (Deffontaines et al 1995;Dent et al 1995;Gibon 1999;Bontkes and van Keulen 2003). We combined concepts and methods from these scientific domains to address landscape changes in the Pyrenees by means of a case study.…”

Section: Overview Of the Study Methodsmentioning

confidence: 99%

Modelling and simulating change in reforesting mountain landscapes using a social-ecological framework

et al. 2010

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Section: Overview Of the Study Methodsmentioning

confidence: 99%

Modelling and simulating change in reforesting mountain landscapes using a social-ecological framework

et al. 2010

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“…Capturing uncertainties inherent in sustainability assessment, especially ex ante 2. More realistically integrating the decision-maker's own views, since they are not necessarily expressed with formal, quantitative models (Dent et al 1995;Munda 2005); 3. Handling incomparability, incommensurability, and noncompensation between sustainability dimensions more effectively than other, more "classic" MCDA approaches frequently used to assess sustainability, such as ELEC TRE (Loyce et al 2002;Mazzetto and Bonera 2003), AHP (Shrestha et al 2004) or multi-objective optimization models (Dogliotti et al 2004;Meyer-Aurich 2005).…”

Section: Model Dissemination and Usesmentioning

confidence: 99%

Guidelines to design models assessing agricultural sustainability, based upon feedbacks from the DEXi decision support system

Craheix¹,

Bergez²,

Angevin³

et al. 2015

Agron. Sustain. Dev.

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New agricultural systems are required to satisfy societal expectations such as higher quantity and quality of agricultural products, reducing environmental impacts, and more jobs. However, identifying and implementing more suitable agricultural systems is difficult due to conflicting objectives and to the wide diversity of scientific disciplines required to solve agricultural issues. Therefore, designing models to assess the sustainability of agricultural systems requires multicriteria decision aid methods. The French agronomist community has recently developed 11 hierarchical and qualitative models to assess sustainability using the DEXi decision aid software. Here, we give guidelines to help designers to build their own specific models. First, we present the principles and applications of the DEXi software. Then, we provide guidance on the following steps of model designing: (1) initial analysis and planning of the design process, (2) selection and hierarchy of sustainability criteria, (3) indicator selection and building, (4) parameterization, (5) evaluation, and (6) model dissemination and uses. We then discuss advantages and drawbacks of this kind of modeling formalism, the role of a participatory approach, and the main properties to consider during the design process.

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“…Technical scientists thus recognised that both the bio-physical and the socioeconomic components of a farm need to be considered, highlighting the usefulness of an interdisciplinary, systemic and gender-sensitive analysis (Hart and Pinchinat, 1982;Biggs, 1985;Jiggins and Röling, 1994;Dent et al, 1995) (see Tab. I).…”

Section: Applying Systems Thinking To Farmingmentioning

confidence: 99%

Adaptiveness to enhance the sustainability of farming systems. A review

Darnhofer

Bellon²,

Dedieu³

et al. 2010

Agron. Sustain. Dev.

308

225

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-During the last decade the context in which farmers must manage their farm has changed rapidly, and often with little warning. Dramatic price swings for agricultural commodities, more stringent quality requirements, new environmental regulations, the debates surrounding genetically modified crops, extreme climatic events, the demand for energy crops, the revision of the Common Agricultural Policy and the consequences of the financial crisis all create uncertainty regarding future threats and potentials. During such turbulent times, a one-sided focus on efficient production is no longer enough. Farmers also need to be able to cope with unexpected events and to adapt to new developments. Based on a literature review, we identify three strategies that strengthen the adaptive capacity of a farm: learning through experimenting and monitoring its outcomes, ensuring a flexible farm organisation to increase the options for new activities by the farm family, and diversifying to spread risks and create buffers. Implementing these strategies enlarges the farmer's room to manoeuvre and allows identifying transition options. These options do not depend only on the farm itself, but also on the farmer's ability to mobilise external resources and to engage in collective action. Change is then no longer seen as a disturbance, but as a trigger for the reorganisation of resources, and for the renewal of the farm organisation and activities. Implementing these strategies comes at a cost, so that farmers need to tackle the inevitable trade-offs between efficiency and adaptability. However, unless farmers master this challenge they cannot ensure the sustainability of their farms.adaptive farm management / adaptive capacity / evolutionary approach / complex adaptive systems

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Simulation of ecological, social and economic factors in agricultural systems

Cited by 69 publications

References 16 publications

Modelling and simulating change in reforesting mountain landscapes using a social-ecological framework

Modelling and simulating change in reforesting mountain landscapes using a social-ecological framework

Guidelines to design models assessing agricultural sustainability, based upon feedbacks from the DEXi decision support system

Adaptiveness to enhance the sustainability of farming systems. A review

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