| 1. |
- Emberson, Lisa D., et al.
(författare)
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Ozone effects on crops and consideration in crop models
- 2018
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Ingår i: European Journal of Agronomy. - : Elsevier BV. - 1161-0301. ; 100:Special Issue: SI, s. 19-34
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Tidskriftsartikel (refereegranskat)abstract
- © 2018 The Authors We review current knowledge of the processes by which ozone will cause injury and damage to crop plants. We do this both through an understanding of the limitations to ozone uptake (i.e. ozone being transferred from some height in the atmosphere to the leaf boundary layer and subsequent uptake via the stomata) as well as through the internal plant processes that will result in the absorbed ozone dose causing damage and/or injury. We consider these processes across a range of scales by which ozone impacts plants, from cellular metabolism influencing leaf level physiology up to whole canopy and root system processes and feedbacks. We explore how these impacts affect leaf level photosynthesis and senescence (and associated carbon assimilation) as well as whole canopy resource acquisition (e.g. water and nutrients) and ultimately crop growth and yield. We consider these processes from the viewpoint of developing crop growth models capable of incorporating key ozone impact processes within modelling structures that assess crop growth under a variety of different abiotic stresses. These models would provide a dynamic assessment of the impact of ozone within the context of other key variables considered important in determining crop growth and yield. We consider the ability to achieve such modelling through an assessment of the different types of crop model currently available (e.g. empirical, radiation use efficiency, and photosynthesis based crop growth models). Finally, we show how international activities such as the AgMIP (Agricultural Modelling and Improvement Intercomparison Project) could see crop growth modellers collaborate to assess the capabilities of different crop models to simulate the effects of ozone and other stresses. The development of robust crop growth models capable of including ozone effects would substantially improve future national, regional and global risk assessments that aim to assess the role that ozone might play under future climatic conditions in limiting food supply.
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| 2. |
- Alkan Olsson, Johanna, et al.
(författare)
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A goal oriented indicator framework to support integrated assessment of new policies for agri-environmental systems
- 2009
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Ingår i: Environmental Science and Policy. - : Elsevier BV. - 1462-9011. ; 12:5, s. 562-572
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Tidskriftsartikel (refereegranskat)abstract
- The goal oriented framework (GOF) for indicators has been developed as part of a comprehensive research project developing computerised tools for integrated assessment of the effects of new policies or technologies on agricultural systems (SEAMLESS-IF). The ambition has therefore been to create an indicator framework where the environmental, economic and social dimensions of sustainable development can be related to each other in a consistent way. Integrated assessment tools rely on such frameworks to capture and visualise trade-offs (antagonisms or synergies) among indicators between and within the three dimensions of sustainable development. The specific aims of this paper are to (i) present the GOF (ii) present how the GOF can be used to select indicators within the integrated assessment framework SEAMLESS-IF and (iii) discuss the advantages and limitations with the proposed approach. We show that the GOF has several advantages. Its major rewards are its relative simplicity and the possibility to link indicators to policy goals of each dimension of sustainability and thereby facilitate the comparison of the impacts of the new policy on the different dimensions. Another important feature of the GOF is its multi-scale perspective, which will enable the comparison of effects of a new policy between scales. Yet, as typical for all indicator frameworks, the GOF has also biases either instigated by the issues the included models cover or by the stakeholders' selection of indicators. However, due to the way the GOF and its indicators are technically implemented in SEAMLESS-IF, it can easily be extended and include new indicators to increase and update its policy relevance. (C) 2009 Elsevier Ltd. All rights reserved.
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| 3. |
- Drewes, Frank, et al.
(författare)
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Bag Context Tree Grammars
- 2006
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Ingår i: Proc. 10th Intl. Conf. on Developments in Language Theory. ; , s. 226-237
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Konferensbidrag (refereegranskat)
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| 4. |
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| 5. |
- Ewert, Frank, et al.
(författare)
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A methodology for enhanced flexibility of integrated assessment in agriculture
- 2009
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Ingår i: Environmental Science and Policy. - : Elsevier BV. - 1462-9011. ; 12:5, s. 546-561
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Tidskriftsartikel (refereegranskat)abstract
- Agriculture is interrelated with the socio-economic and natural environment and faces increasingly the problem of managing its multiple functions in a sustainable way. Growing emphasis is on adequate policies that can support both agriculture and sustainable development. Integrated Assessment and Modelling (IAM) can provide insight into the potential impacts of policy changes. An increasing number of Integrated Assessment (IA) models are being developed, but these are mainly monolithic and are targeted to answer specific problems. Approaches that allow flexible IA for a range of issues and functions are scarce. Recently, a methodology for policy support in agriculture has been developed that attempts to overcome some of the limitations of earlier IA models. The proposed framework (SEAMLESS-IF) integrates relationships and processes across disciplines and scales and combines quantitative analysis with qualitative judgments and experiences. It builds on the concept of systems analysis and attempts to enable flexible coupling of models and tools. The present paper aims to describe progress in improving flexibility of IAM achieved with the methodology developed for SEAMLESS-IF. A brief literature review identifying limitations in the flexibility of IAM is followed by a description of the progress achieved with SEAMLESS-IF. Two example applications are used to illustrate relevant capabilities of SEAMLESS-IF. The examples refer to (i) the impacts on European agriculture of changes in world trade regulations and (ii) regional impacts of the EU Nitrates Directive in combination with agro-management changes. We show that improving the flexibility of IAM requires flexibility in model linking but also a generic set up of all IA steps. This includes problem and scenario definition, the selection and specification of indicators and the indicator framework, the structuring of the database, and the visualization of results. Very important is the flexibility to integrate, select and link models, data and indicators depending on the application. Technical coupling and reusability of model components is greatly improved through adequate software architecture (SEAMLESS-IF uses OpenMI). The use of ontology strongly supports conceptual consistency of model linkages. However, the scientific basis for linking models across disciplines and scales is still weak and requires specific attention in future research. We conclude that the proposed framework significantly advances flexibility in IAM and that it is a good basis to further improve integrated modelling for policy impact assessment in agriculture. (C) 2009 Elsevier Ltd. All rights reserved.
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| 6. |
- Fronzek, Stefan, et al.
(författare)
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Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change
- 2018
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Ingår i: Agricultural Systems. - : Elsevier BV. - 0308-521X. ; 159, s. 209-224
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Tidskriftsartikel (refereegranskat)abstract
- Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (-2 to +9°C) and precipitation (-50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses.The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern.The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description.Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index.Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.
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| 7. |
- Gabbert, Silke, et al.
(författare)
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Uncertainty analysis in integrated assessment: the users’ perspective. Regional Environmental Change
- 2010
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Ingår i: Regional Environmental Change. - : Springer Science and Business Media LLC. - 1436-3798 .- 1436-378X. ; 10:2, s. 131-143
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Tidskriftsartikel (refereegranskat)abstract
- Integrated Assessment (IA) models aim at providing information- and decision-support to complex problems. This paper argues that uncertainty analysis in IA models should be user-driven in order to strengthen science–policy interaction. We suggest an approach to uncertainty analysis that starts with investigating model users’ demands for uncertainty information. These demands are called “uncertainty information needs”. Identifying model users’ uncertainty information needs allows focusing the analysis on those uncertainties which users consider relevant and meaningful. As an illustrative example, we discuss the case of examining users’ uncertainty information needs in the SEAMLESS Integrated Framework (SEAMLESS-IF), an IA model chain for assessing and comparing alternative agricultural and environmental policy options. The most important user group of SEAMLESS-IF are policy experts at the European and national level. Uncertainty information needs of this user group were examined in an interactive process during the development of SEAMLESS-IF and by using a questionnaire. Results indicate that users’ information requirements differed from the uncertainty categories considered most relevant by model developers. In particular, policy experts called for addressing a broader set of uncertainty sources (e.g. model structure and technical model setup). The findings highlight that investigating users’ uncertainty information needs is an essential step towards creating confidence in an IA model and its outcomes. This alone, however, may not be sufficient for effectively implementing a user-oriented uncertainty analysis in such models. As the case study illustrates, it requires to include uncertainty analysis into user participation from the outset of the IA modelling process.
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| 8. |
- Shrine, Nick, et al.
(författare)
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New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries
- 2019
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 51:3, s. 481-493
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Tidskriftsartikel (refereegranskat)abstract
- Reduced lung function predicts mortality and is key to the diagnosis of chronic obstructive pulmonary disease (COPD). In a genome-wide association study in 400,102 individuals of European ancestry, we define 279 lung function signals, 139 of which are new. In combination, these variants strongly predict COPD in independent populations. Furthermore, the combined effect of these variants showed generalizability across smokers and never smokers, and across ancestral groups. We highlight biological pathways, known and potential drug targets for COPD and, in phenome-wide association studies, autoimmune-related and other pleiotropic effects of lung function-associated variants. This new genetic evidence has potential to improve future preventive and therapeutic strategies for COPD.
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| 9. |
- Therond, Olivier, et al.
(författare)
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Methodology to translate policy assessment problems into scenarios: the example of the SEAMLESS integrated framework
- 2009
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Ingår i: Environmental Science and Policy. - : Elsevier BV. - 1462-9011. ; 12:5, s. 619-630
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Tidskriftsartikel (refereegranskat)abstract
- Scenario-based approaches in environmental and policy assessment studies are increasingly applied within integrated assessment and modelling frameworks. The SEAMLESS project develops such an integrated framework (SEAMLESS-IF) aiming to assess, ex-ante, impacts of alternative agro-environmental policies on the sustainability of agricultural systems. A particular challenge in this context is the consistent translation of a wide range of policy questions into scenarios that a modelling framework can assess. The present work defines a methodology for scenario-development in integrated policy assessment with specific emphasis on SEAMLESS-IF. After a general overview on scenario concepts for integrated policy assessment the adopted scenario concept and its development procedure is presented. They allow building integrated scenarios capturing the range of drivers of the assessed agricultural system in a consistent way across temporal and spatial scales. Then focus is on the particular procedures to translate the policy assessment questions into scenario parameters and to implement these parameters into SEAMLESS-IF. Two examples targeted at European and regional level combining integrated assessments of policy changes and technological innovations are considered to illustrate the SEAMLESS scenario concept. We conclude that the proposed methodology to translate policy assessment problems into scenarios effectively supports integrated assessment in SEAMLESS-IF or even in other modelling frameworks. (C) 2009 Elsevier Ltd. All rights reserved.
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| 10. |
- van Ittersum, Martin, et al.
(författare)
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Integrated Assessment of Agricultural and Environmental Policies – A Modular Framework for the EU (SEAMLESS)
- 2008
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Ingår i: Agricultural Systems. - : Elsevier BV. - 0308-521X. ; 96:1-3, s. 150-165
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Tidskriftsartikel (refereegranskat)abstract
- Abstract in UndeterminedAgricultural systems continuously evolve and are forced to change as a result of a range of global and local driving forces. Agricultural technologies and agricultural, environmental and rural development policies are increasingly designed to contribute to the sustainability of agricultural systems and to enhance contributions of agricultural systems to sustainable development at large. The effectiveness and efficiency of such policies and technological developments in realizing desired contributions could be greatly enhanced if the quality of their ex-ante assessments were improved. Four key challenges and requirements to make research tools more useful for integrated assessment in the European Union were defined in interactions between scientists and the European Commission (EC), i.e., overcoming the gap between micro-macro level analysis, the bias in integrated assessments towards either economic or environmental issues, the poor re-use of models and hindrances in technical linkage of models. Tools for integrated assessment must have multi-scale capabilities and preferably be generic and flexible such that they can deal with a broad variety of policy questions. At the same time, to be useful for scientists, the framework must facilitate state-of-the-art science both on aspects of the agricultural systems and on integration. This paper presents the rationale, design and illustration of a component-based framework for agricultural systems (SEAMLESS Integrated Framework) to assess, ex-ante, agricultural and agri-environmental policies and technologies across a range of scales, from field-farm to region and European Union, as well as some global interactions. We have opted for a framework to link individual model and data components and a software infrastructure that allows a flexible (re-)use and linkage of components. The paper outlines the software infrastructure, indicators and model and data components. The illustrative example assesses effects of a trade liberalisation proposal on EU's agriculture and indicates how SEAMLESS addresses the four identified challenges for integrated assessment tools, i.e., linking micro and macro analysis, assessing economic, environmental, social and institutional indicators, (re-)using standalone model components for field, farm and market analysis and their conceptual and technical linkage.
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