Scale changes and model linking methods for integrated assessment of agri-environmental systems

https://doi.org/10.1016/j.agee.2011.05.016Get rights and content

Abstract

Agricultural systems and problems of sustainability are complex, covering a range of organisational levels and spatial and temporal scales. Integrated assessment (IA) and modelling (IAM) is an attempt to capture complex multi-scale problems. Scale changes and model linking methods (referred to as scaling methods) are important in dealing with these problems but they are often not well understood.

The present study aims to analyse scaling methods used in the recently developed multi-scale IA model SEAMLESS-IF which is applied to two case studies of complex agri-environmental problems. The analysis is based on a classification of up- and down-scaling methods which is extended for the purpose of this study.

Our analysis shows that scale changes refer to different spatial, temporal and functional scales with changes in extent, resolution, and coverage rate. Accordingly, SEAMLESS-IF uses a number of different scaling methods including data extrapolation, aggregation and disaggregation, sampling, nested simulation and employs descriptive response functions and technical coefficients derived from explanatory models. Despite the satisfactory results obtained from SEAMLESS-IF, a comparative quantitative analysis of alternative scaling methods is still pending and requires further attention. Improved integration of scaling methods may also help to overcome limitations of IA models related to high data demand, complexity of models and scaling methods considered, and the accumulation of uncertainty due to the use of multiple models. In the case studies, the most challenging scaling problem refers to the appropriate consideration of the farm level as intermediate level between the field and market levels. Among the scaling methods analysed, summary models are hardly applied. This is because they are often unavailable due to limited systems understanding and because they may differ depending on the question at stake. The classification of scaling methods used has been helpful to structure this analysis.

Highlights

► Analysis of scale changes and model linking methods in integrated assessment based on a classification of scaling methods and two case studies. ► A variety of scaling methods are used to link models and data at the field, farm, regional and market levels. ► Among the scaling methods considered summary models are rarely applied. ► Classification of scaling methods offers strong support to the conceptual analysis of scaling issues in IA models.

Introduction

Agricultural systems and associated problems of sustainable development are typically complex. Addressing issues such as climate change, food and energy supply, globalisation of markets, population and economic growth, and scarcity of natural resources requires approaches that integrate relationships across disciplines, levels of organisation and scales. As for agricultural systems, levels of organisation can range (depending on the problem) from the organism (plant or animal) to the field and farm, the landscape and region up to the continental and global levels. While responses at the organism and field levels are mainly determined by biophysical relationships, responses at the farm, regional and higher levels are also affected by socio-economic, political, cultural and other factors.

Understanding of the complexity of agricultural systems and its interactions with the environment is not only of growing interest to scientists but has also important practical implications for the management and policy decision making processes for agriculture and its sustainable development.

Integrated assessment and modelling (IAM) is an attempt to account for the complexity of agricultural systems (Ewert et al., 2009). In recent years several integrated assessment (IA) models have been developed also for agricultural systems (Verboom et al., 2007, van Ittersum et al., 2008, Piorr et al., 2009, Uthes et al., 2010). However, the specific multi-scale character of such integrated socio-ecological systems is often ignored or only partially represented.

Although IA models usually comprise several sub-models representing different components (and organisational levels) of the system, the selection of these sub-models is often done ad hoc without a well formulated underlying concept of the organisational levels considered and the methods used for linking these levels and the associated scales (see Section 2.1). In fact, scale changes and methods to link scales and models in IAM have rarely been addressed systematically, which is not least due to the lack of available classifications for these methods. Only a few attempts were made to classify scaling methods for models used in natural resource management (Dalgaard et al., 2003, Ewert et al., 2006) and for dynamic vegetation models (van Oijen et al., 2009). It should be noted that the term scaling methods as used here refers to the methods used to integrate data and models at different levels and scales. It is therefore not to confuse with the concept of scaling (Enquist and Niklas, 2001, Enquist et al., 2003) which can be considered as one scaling method (see Section 2.2).

The objective of this paper is to perform a conceptual analysis of the scale changes and methods of model integration used for addressing complex integrated assessment problems in agri-environmental systems. We use the example of the recently developed integrated modelling framework SEAMLESS-IF (System for Environmental and Agricultural Modelling-Integrated Framework) (van Ittersum et al., 2008) and consider two test cases to demonstrate the scale change encountered, scaling methods applied and how these can be analysed with a classification (Ewert et al., 2006). This analysis should help to identify limits and gaps in the understanding of scaling problems and methods in IAM for agriculture.

Section snippets

Structuring complexity in agricultural systems

Agricultural systems and relationships to the environment are characterised by a large number of components and many interactions among these components. Several methods have been developed to effectively describe and analyse complexity (Seppelt et al., 2009). Among these, hierarchy theory, often used by ecologists to structure systems in different hierarchical levels (Holling, 1992, O’Neill and King, 1998), can provide a conceptual framework for structuring the analysis of agricultural systems

Conceptualizing scale changes, indicators used and model choice

As the first step we conceptualize the problems to be addressed in the test cases with respect to the levels of organisation considered, the indicators selected and the models applied. In the first example (Fig. 2), the assessed change of the external driver(s) (in this case tariff cuts due to the G20 proposal) occurs at the level of the EU but has implications on regional markets and consequently on farms across the EU. In order to simulate the effect of this change on farm behaviour,

Scale changes in IA models

This is, to the best of our knowledge, the first study where an explicit attempt was made to develop a structured view on scale changes encountered and methods used for model integration in IA models for agri-environmental systems. The importance of scale issues for impact assessment in agriculture has been addressed earlier (Dalgaard et al., 2003, Ewert et al., 2006, van Keulen, 2007, Zander et al., 2007), but a structured analysis of scale changes and linking methods has not been performed.

Conclusions

The present study provides a systematic analysis of the scaling methods used in IAM of agri-environmental systems. We found that different scale changes are encountered and model linking methods are used when dealing with assessment problems that should inform users about impacts at the field, farm and market levels. The resulting number of scaling methods used to transfer data between models and scales is large and adds to the complexity of the overall IA model structure. From the obtained

Acknowledgements

The work presented in this publication was funded by the SEAMLESS integrated project, EU 6th Framework Programme for Research Technological Development and Demonstration, Priority 1.1.6.3. Global Change and Ecosystems (European Commission, DG Research, Contract No. 010036-2). We gratefully acknowledge all SEAMLESS participants who contributed to the development of SEAMLESS-IF. The helpful comments of two reviewers are also acknowledged.

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