A methodology for enhanced flexibility of integrated assessment in agriculture

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.

[1]  Dale S. Rothman,et al.  growing pains: a conceptual framework for considering integrated assessments , 1997 .

[2]  Andrea Emilio Rizzoli,et al.  Development of a conceptual framework for integrated analysis and assessment of agricultural systems in SEAMLESS , 2006 .

[3]  F. V. Tongeren,et al.  Global models applied to agricultural and trade policies: a review and assessment , 2001 .

[4]  Jan Rotmans,et al.  An integrated model for the assessment of the greenhouse effect: The Dutch approach , 1990 .

[5]  Martin K. van Ittersum,et al.  Methodology to translate policy assessment problems into scenarios: the example of the SEAMLESS integrated framework , 2009 .

[6]  Marcel Van Oijen,et al.  On the use of specific publication criteria for papers on process-based modelling in plant science , 2002 .

[7]  Manu Sharma,et al.  A policy decision tool for integrated environmental assessment , 2005 .

[8]  Holger Meinke,et al.  From rainfall to farm incomes - Transforming advice for Australian drought policy. I. Development and testing of a bioeconomic modelling system , 2007 .

[9]  G. Brundtland,et al.  Our common future , 1987 .

[10]  Andrea Castelletti,et al.  PIP: A participatory and integrated planning procedure for decision making in water resource systems , 2004 .

[11]  G. Bellocchi,et al.  A Component-Based Framework for Simulating Agricultural Production and Externalities , 2010 .

[12]  F. Kessler.  Projections , 2020, International Encyclopedia of Human Geography.

[13]  G. Fischer,et al.  Effects of climate change on global food production under SRES emissions and socio-economic scenarios , 2004 .

[14]  Thomas R. Sinclair,et al.  Criteria for publishing papers on crop modeling , 2000 .

[15]  Rob Knapen,et al.  A goal oriented indicator framework to support integrated assessment of new policies for agri-environmental systems. , 2009 .

[16]  Floor Brouwer,et al.  Environmental and Agricultural Modelling : Integrated Approaches for Policy Impact Assessment , 2010 .

[17]  John M. Antle,et al.  The tradeoff analysis model: integrated bio-physical and economic modeling of agricultural production systems , 2004 .

[18]  M. V. Asselt,et al.  An updated scenario typology , 2003 .

[19]  Anthony J. Jakeman,et al.  Integrated assessment modelling for water resource allocation and management: A generalised conceptual framework , 2007, Environ. Model. Softw..

[20]  Andrea Emilio Rizzoli,et al.  Methodological concepts for integrated assessment of agricultural and environmental policies in SEAMLESS-IF , 2006 .

[21]  G. Fischer,et al.  Socio-economic and climate change impacts on agriculture: an integrated assessment, 1990–2080 , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[22]  Sander Janssen,et al.  A database for integrated assessment of European agricultural systems. , 2009 .

[23]  Andrea Castelletti,et al.  A participatory and integrated planning procedure for decision making in water resource systems , 2007 .

[24]  B. Eickhout,et al.  A multi-scale, multi-model approach for analyzing the future dynamics of European land use , 2008 .

[25]  Thomas Heckelei,et al.  Capturing market impacts of farm level policies: a statistical extrapolation approach using biophysical characteristics and farm resources , 2009 .

[26]  Anthony J. Jakeman,et al.  Progress in integrated assessment and modelling , 2002, Environ. Model. Softw..

[27]  M. Rounsevell,et al.  Future scenarios of European agricultural land use: I. Estimating changes in crop productivity , 2005 .

[28]  G. Finnveden,et al.  Scenario types and techniques: Towards a user's guide , 2006 .

[29]  Robert M. Argent,et al.  An overview of model integration for environmental applications--components, frameworks and semantics , 2004, Environ. Model. Softw..

[30]  Sander Janssen,et al.  Defining assessment projects and scenarios for policy support: Use of ontology in Integrated Assessment and Modelling , 2009, Environ. Model. Softw..

[31]  R. Rabbinge,et al.  Exploratory land use studies and their role in strategic policy making. , 1998 .

[32]  O. Oenema,et al.  Development and application of the integrated nitrogen model MITERRA-EUROPE , 2007 .

[33]  William D. Nordhaus,et al.  Rolling the ‘DICE’: an optimal transition path for controlling greenhouse gases , 1993 .

[34]  Carolien Kroeze,et al.  Accounting for user's uncertainty information needs in Integrated Assessment models: The case of the SEAMLESS Integrated Framework , 2009 .

[35]  Stéphane Bellon,et al.  Sustainable development indicator frameworks and initiatives , 2009 .

[36]  M. Donatelli,et al.  Integrated assessment of agricultural systems: a component - based framework for the European Union (Seamless) , 2008 .

[37]  M. Pérez-Soba,et al.  Sustainability Impact Assessment of land use policies , 2008 .

[38]  John Robinson,et al.  The problem of the future: sustainability science and scenario analysis , 2004 .

[39]  Markus Amann,et al.  The RAINS model: A tool for assessing regional emission control strategies in Europe , 1999 .

[40]  Jan Rotmans,et al.  Integrated assessment: A growing child on its way to maturity , 1996 .

[41]  Roy F. Weston,et al.  A dynamic, hierarchical approach to understanding and managing natural economic systems , 1997 .

[42]  T. D. Mitchell,et al.  Ecosystem Service Supply and Vulnerability to Global Change in Europe , 2005, Science.

[43]  Henry D. Jacoby,et al.  Integrated Global System Model for Climate Policy Assessment: Feedbacks and Sensitivity Studies , 1999 .

[44]  Marian Leimbach,et al.  A modular approach to Integrated Assessment modeling , 2005 .

[45]  T. Carter,et al.  Future scenarios of European agricultural land use: II. Projecting changes in cropland and grassland , 2005 .

[46]  Martin K. van Ittersum,et al.  Land use models in complex societal problem solving: Plug and play or networking? , 2009, Environ. Model. Softw..

[47]  P. A. Leffelaar.  On Systems Analysis and Simulation of Ecological Processes with Examples in CSMP and FORTRAN , 1993, Current Issues in Production Ecology.

[48]  Peter Nijkamp,et al.  Impact Assessment and Evaluation in Transportation Planning , 1994 .

[49]  J. Bakkes,et al.  Scenario development to explore the future of Europe's rural areas , 2006 .

[50]  William D. Nordhaus,et al.  A Regional Dynamic General-Equilibrium Model of Alternative Climate-Change Strategies , 1996 .

[51]  Hartmut Bossel,et al.  Assessing Viability and Sustainability: a Systems-based Approach for Deriving Comprehensive Indicator Sets , 2002 .

[52]  M. Vanclooster,et al.  SAFE - A hierarchical framework for assessing the sustainability of agricultural systems , 2007 .

[53]  Thomas Heckelei,et al.  Concept and explorative application of an EU-wide, regional agricultural sector model (CAPRI-Project). , 2001 .

[54]  Pete Smith,et al.  A coherent set of future land use change scenarios for Europe , 2006 .

[55]  Richard S. J. Tol,et al.  The European Forum on Integrated Environmental Assessment , 1998 .

[56]  Omar Masera,et al.  Evaluating the sustainability of complex socio-environmental systems. the MESMIS framework , 2002 .

[57]  T. Hertel.  Global Trade Analysis: Modeling and Applications , 1999 .

[58]  Richard S. J. Tol.  Integrated Assessment Modelling , 2020, Handbook on the Economics of Climate Change.

[59]  P. A. Leffelaar,et al.  Multi-scale sustainability evaluation of natural resource management systems: Quantifying indicators for different scales of analysis and their trade-offs using linear programming , 2005 .

[60]  D. A. Oudendag,et al.  Development and application of the integrated nitrogen model MITERRA-EUROPE . Task 1 Service contract "Integrated measures in agriculture to reduce ammonia emissions" , 2007 .