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Agriculture, forestry and other land use are important components of a global strategy to limit climate change. Using the example of EU agriculture, this paper examines the potential contribution of technological (i.e. technical and management-based) greenhouse gas mitigation options to climate targets. The quantitative framework presented allows to analyze disparities between marginal abatement cost curves (MACCs) derived from two different commonly used approaches, and between aggregated and regional MACCs. Results highlight the importance of assessing mitigation of agricultural emissions from a multi-dimensional perspective, considering regional heterogeneity of biophysical and economic circumstances and comprising both carbon-dioxide (CO 2) and non-CO 2 emissions. Regarding the ranking of technologies in terms of mitigation potential and costs, the results underline the need to consider technologies in a combined manner, avoiding the simple aggregation of mitigation potentials by individual measures without taking their interactions into account. Focusing only on standalone MACCs can lead to an overestimation of the mitigation potential. Conversely, measures classified as relatively high cost in standalone and aggregated MACCs should not be discarded, as they can still be cost-effective in some regions. The analysis shows that there is no 'one size fits all' rule that could be followed for identifying technologies that should be implemented in all regions. From a policy perspective the results imply that farmers should be granted some flexibility to adopt a set of cost-effective mitigation options that best fits their circumstances. • This paper explores disparities between technology-specific MACCs in agriculture. • Mitigation technologies need to be considered in a combined and regional manner. • Interactions between measures affect cost-effectiveness of individual technologies. • Standalone approaches may discard options that are cost-effective for some regions. • Region-specific flexibility is required to find the most cost-effective technology mix. [ABSTRACT FROM AUTHOR]

Abstract: The ex-ante assessment of the likely impacts of policy changes and technological innovations on agriculture can provide insight into policy effects on land use and other resources and inform discussion on the desirability of such changes. Integrated assessment and modeling (IAM) is an approach that can be used for ex-ante assessment. It may combine several quantitative models representing different processes and scales into a framework for integrated assessment to allow for multi scale analysis of environmental, economic and social issues. IAM is a challenging task as models from different disciplines have a different representation of data, space and time. The aim of this paper is to describe our strategy to conceptually, semantically and technically integrate a chain of models from different domains to assess land use changes. The models that were linked are based on different modeling techniques (e.g. optimization, simulation, estimation) and operate on different time and spatial scales. The conceptual integration to ensure consistent linkage of simulated processes and scales required modelers representing the different models to clarify the data exchanged and interlinking of modeling methodologies across scales. For semantic integration, ontologies provided a way to rigorously define conceptualizations that can be easily shared between various disciplines. Finally, for technical integration, OpenMI was used and supplemented with the information from ontologies. In our case, explicitly tackling the challenge of semantic, conceptual and technical integration of models forced researchers to clarify the assumptions of their model interfaces, which helped to document the model linkage and to efficiently run models together. The linked models can now easily be used for integrated assessments of policy changes, technological innovations and societal and biophysical changes. [Copyright &y& Elsevier]