A qualitative multi-attribute model for economic and ecological assessment of genetically modified crops. Bohanec, M.; Messean, A.; Scatasta, S.; Angevin, F.; Griffiths, B.; Krogh, P., H.; Znidarsic, M.; and Dzeroski, S. Ecological Modelling.
abstract   bibtex   
Genetically modified (GM) crops have become a real option in modern agriculture. They offer advantages for agricultural production, but they also raise concerns about their ecological and economic impacts. Decisions about GM crops are complex and call for decision support. This paper presents a qualitative multi-attribute model for the assessment of ecological and economic impacts at a farm-level of GM and non-GM maize crops. The model is applied for one agricultural season. This is an ex-ante model developed according to DEX methodology. In this model, cropping systems are defined by four groups of features: (1) crop sub-type, (2) regional and farm-level context, (3) crop protection and crop management strategies, and (4) expected characteristics of the harvest. The impact assessment of cropping systems is based on four groups of ecological and two groups of economic indicators: biodiversity, soil biodiversity, water quality, greenhouse gasses, variable costs and production value. The evaluation of cropping systems is governed by expert-defined rules. The paper describes the structure and components of the model, and presents three practical applications of the model, assessing both hypothetical and real-life cropping systems. In an overall assessment of the ecological and economic outcomes the model ranked cropping systems in the order: organically managed > GM systems including Bt and HT traits > conventionally managed maize. The paper discusses contributions of the model to decision-making practice and highlights methodological lessons learned during its development.
@article{
 title = {A qualitative multi-attribute model for economic and ecological assessment of genetically modified crops},
 type = {article},
 keywords = {Cropping systems,Decision support,Ecology,Economy,Genetically modified crops,Multi-attribute models,Qualitative models},
 volume = {In Press, },
 websites = {http://www.sciencedirect.com/science/article/B6VBS-4S7X6MK-2/2/d6da287862e642e925a57fe6bd28e013},
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 last_modified = {2012-01-05T12:55:02.000Z},
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 source_type = {Journal Article},
 abstract = {Genetically modified (GM) crops have become a real option in modern agriculture. They offer advantages for agricultural production, but they also raise concerns about their ecological and economic impacts. Decisions about GM crops are complex and call for decision support. This paper presents a qualitative multi-attribute model for the assessment of ecological and economic impacts at a farm-level of GM and non-GM maize crops. The model is applied for one agricultural season. This is an ex-ante model developed according to DEX methodology. In this model, cropping systems are defined by four groups of features: (1) crop sub-type, (2) regional and farm-level context, (3) crop protection and crop management strategies, and (4) expected characteristics of the harvest. The impact assessment of cropping systems is based on four groups of ecological and two groups of economic indicators: biodiversity, soil biodiversity, water quality, greenhouse gasses, variable costs and production value. The evaluation of cropping systems is governed by expert-defined rules. The paper describes the structure and components of the model, and presents three practical applications of the model, assessing both hypothetical and real-life cropping systems. In an overall assessment of the ecological and economic outcomes the model ranked cropping systems in the order: organically managed > GM systems including Bt and HT traits > conventionally managed maize. The paper discusses contributions of the model to decision-making practice and highlights methodological lessons learned during its development. },
 bibtype = {article},
 author = {Bohanec, Marko and Messean, Antoine and Scatasta, Sara and Angevin, Frederique and Griffiths, Bryan and Krogh, Paul Henning and Znidarsic, Martin and Dzeroski, Saso},
 journal = {Ecological Modelling}
}
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