| 1. |
- Bäcklund, Ann-Katrin, et al.
(författare)
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Science - policy interfaces in impact assessment procedures
- 2010
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Ingår i: Environmental and agricultural modelling: integrated approaches for policy impact assessment. - Dordrecht : Springer Netherlands. - 9789048136193 - 9789048136186 ; , s. 275-294
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Modelling tools used in impact assessment procedures can be regarded as tools for communication between science and policy. In order to create an integrated system for modelling not only the scientific components have to be in place but also the science/policy interfaces in the assessment procedures have to be identified and their social dynamics understood.To make a system like SEAMLESS Integrated Framework (SEAMLESS-IF) applicable in a European decision-making process interaction with potential users of the system is needed during different stages of development. We are here describing some of the interactive work performed to enable user involvement in the development of the framework and the learning that was triggered by this. The two cases presented are SEAMLESS User Forum with participants from the EU administration and the process of setting up assessments in test situations with regional administrations.The experience obtained from these interactions form a base for the discussion as to whether the design of SEAMLESS-IF is suited to contribute to an institutionalisation of a deliberative impact assessment process.
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| 2. |
- Lothrop Stoddard, Frederick, et al.
(författare)
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Grain legume production in Europe for food, feed and meat-substitution
- 2023
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Ingår i: Global Food Security. - 2211-9124. ; 39
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Tidskriftsartikel (refereegranskat)abstract
- Partial shifts from animal-based to plant-based proteins in human diets could reduce environmental pressure from food systems and serve human health. Grain legumes can play an important role here. They are one of the few agricultural commodities for which Europe is not nearly self-sufficient. Here, we assessed area expansion and yield increases needed for European self-sufficiency of faba bean, pea and soybean. We show that such production could use substantially less cropland (4–8%) and reduce GHG emissions (7–22% current meat production) when substituting for animal-derived food proteins. We discuss changes required in food and agricultural systems to make grain legumes competitive with cereals for farmers and how their cultivation can help to increase sustainability of European cropping systems.
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| 3. |
- 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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| 4. |
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| 5. |
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| 6. |
- 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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| 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. |
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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
(författare)
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Daily bias-corrected weather data and daily simulated growth data of maize, millet, sorghum, and wheat in the changing climate of sub-Saharan Africa
- 2024
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Ingår i: Data in Brief. - 2352-3409. ; 54
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Tidskriftsartikel (refereegranskat)abstract
- Crop models are the primary means by which agricultural scientists assess climate change impacts on crop production. Site-based and high-quality weather and climate data is essential for agronomically and physiologically sound crop simulations under historical and future climate scenarios. Here, we describe a bias-corrected dataset of daily agro-meteorological data for 109 reference weather stations distributed across key production areas of maize, millet, sorghum, and wheat in ten sub-Saharan African countries. The dataset leverages extensive ground observations from the Global Yield Gap Atlas (GYGA), an existing climate change projections dataset from the Inter-Sectoral Model Intercomparison Project (ISIMIP), and a calibrated crop simulation model (the WOrld FOod Studies -WOFOST). The weather data were bias-corrected using the delta method, which is widely used in climate change impact studies. The bias -corrected dataset encompasses daily values of maximum and minimum temperature, precipitation rate, and global radiation obtained from five models participating in the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), as well as simulated daily growth variables for the four crops. The data covers three periods: historical (1995-2014), 2030 (2020- 2039), and 2050 (2040-2059). The simulation of daily growth dynamics for maize, millet, sorghum, and wheat growth was performed using the daily weather data and the WOFOST crop model, under potential and water -limited potential conditions. The crop simulation outputs were evaluated using national agronomic expertise. The presented datasets, including the weather dataset and daily simulated crop growth outputs, hold substantial potential for further use in the investigation of future climate change impacts in sub-Saharan Africa. The daily weather data can be used as an input into other modelling frameworks for crops or other sectors (e.g., hydrology). The weather and crop growth data can provide key insights about agro-meteorological conditions and water -limited crop output to inform adaptation priorities and benchmark (gridded) crop simulations. Finally, the weather and simulated growth data can also be used for training machine learning techniques for extrapolation purposes. (c) 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY -NC -ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
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