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Bayesian Networks for the management of greenhouse gas emissions in the British agricultural sector

<22>This paper describes a Bayesian Network model that includes a comprehensive realization of the IPCC guidelines for Greenhouse Gas estimation. Its main advantage over the Monte-Carlo Simulation approaches more traditionally used in environmental modeling is its capability to support both forward and backward inference. Consequently, the same model can be used in both assessment of expected emissions, and policy-making for emission reduction. It has generated widespread interest in the agro-food industry. Discussions are ongoing with land management consultants Smiths-Gore, ADAS, Unilever and Aberdeen University about productising the model. Perez-Miñana has been recruited by the Basque Research Centre for Climate Change.