Malafant, K.W.J. and Fordham, D.P., 1997. Integration Frameworks in Agricultural and Resource Planning and Management. Eds. R.K. Munro and L.M. Leslie, Climate Prediction for Agricultural and Resource Management: Australian Academy of Science Conference, Canberra, 6-8 May 1997, Bureau of Resource Sciences, Canberra, pp 287-304.
Integration Frameworks in Agricultural and Resource Planning and Management
K.W.J. Malafant and D.P. Fordham
Bureau of Resource Sciences,
Department of Primary Industries and Energy, Canberra, Australia
Aesthetic, economic and ecological values attached to natural resources by society, provide a complex and potentially opposing mix, complicating decisions on the planning and management of resources. "The need for integration in research, policy making and practice in environmental planning and management needs no justification here; it is, like sustainability, self-evidently desirable but at the same time intractable and elusive" (Benson, 1995). The concept of an integrated approach to planning and management of environmental, resource and sustainability issues has been accepted at the highest government levels (Agenda 21, United Nations Development Commission, 1993). In supporting this approach, there is a need to concentrate on understanding and exploring the decision processes, rather than, just an intimate knowledge of specific components or models.
Integrated modelling frameworks can be used to integrate the best and most appropriate of existing models, knowledge and data for a project. The aim of the framework approach is to determine the best options for integrating environmental, resource, ecologically sustainable management, heritage, economic and social analyses for use in planning, conservation and industry development. The development of an integration model (or system) for resource management should include the provision of real time and reliable tools, scenario modelling, and can include optimisation techniques. Credibility, transparency and consistency are valued elements of the system.
These modelling frameworks allow resource managers and policy makers to integrate and evaluate resource information and make more informed decisions. Integrated frameworks allow the future to be explored by examining the driving processes, their interrelationships and inconsistencies or tensions, rather than attempting to forecast the future by extrapolating data measured from past activities. The integration of the user with the framework provides interaction, intervention and interpretation. The exploration of alternative(s) and options must be allowed, so the policymaker can anticipate and understand risk and discover new options for action.
Integration framework systems, tools or toolkits have not been widely applied in Australia. They provide the "glue" to form complex modelling systems from many diverse sources. This paper discusses some of the concepts, applications and examples of integrated frameworks and modelling tools, which allow the integration of environmental, resource, economic and social models to provide decision makers with tools for exploring alternative options for the future.
"The complexity of the environment, the time and spatial scales involved, and the diversity of environmental effects are such that the implications of decisions affecting the environment are not always readily apparent. This complexity and the number of stakeholders involved makes it difficult to arrive at decisions which accommodate all wants and needs" (Qureshi, Greenfield, Kingham and Krol, 1995).
The concept of an integrated approach to planning and management of environmental, resource and sustainability issues has been accepted at the highest government levels (Agenda 21, United Nations Development Commission, 1993). As Benson (1995) notes, "The need for integration in research, policy making and practice in environmental planning and management needs no justification here; it is, like sustainability, self-evidently desirable but at the same time intractable and elusive". To support this approach a higher-level perspective of the system needs to be considered, concentrating on the interactions between the components, rather than a deterministic, mechanical view. This perspective is required if we are to understand the system behaviour and more importantly, make sense of it (Coveney and Highfield, 1995).
As Bradbury (1997) notes, there is no universal modelling paradigm that can be applied to all situations. This was the reason for the failure of the systems dynamic approaches of the Club of Rome models - the one tool was applied to all situations. The use of "elegant little models", does not work either (Bradbury, 1997). These are generally too limited, although the experience we gain from them may be an invaluable guide to reality (Barrow and Silk, 1983). The discussion of model space by Bradbury (1997) identifies the need for integration frameworks. These frameworks "outsource" the modelling to other systems, concentrating on the protocols and interactions for welding the various model components together.
Integrated modelling frameworks integrate the best and most appropriate of existing models, data and knowledge. The development of an integration model for resource management should include the provision of real time and reliable tools, scenario modelling and can include optimisation techniques, with credibility, transparency and consistency valued elements of the system. Integration framework systems, tools or toolkits are not well known or understood in Australia. These tools have few inherent models, but facilitate the integration of various models or analysis tools into a single coherent framework.
Such frameworks have been developed in several European countries and Australia, with the aim of aiding policy development and resource management. The most common scale of these frameworks has been regional, since this scale enables characterisation of the system with most accuracy. The tools are being used to explore large ecological and resource management issues in these countries.
A model can be defined as a "... representation of part of reality as seen by the people who wish to use that model to understand, to change, to manage and to control that part of reality" (Pidd, 1996). The important aspects of this definition are the concepts of understanding, change, management and control. No where in this definition is there an explicit mention of prediction. As Allaby (1995) notes, "we do not predict, we forecast, and there is a difference". With forecasts we are dealing with possibilities, not the inevitable single or optimum outcome generally associated with predictions. The purpose of our forecasts is not to predict the future, but to examine the possible choices open to us by exploring alternative futures (Allaby, 1995).
Integration is the organisation of organic, psychological, or social traits and tendencies into a consistent and harmonious whole.
"A scenario is a description of the current situation, of a possible or desirable future state as well as of the series of events that could lead from the current state to this future state" (Schoute, Finke, Veeneklaas and Wolfert, 1995). A scenario modelling framework combines this series of events over time and provides an insight into the impacts of this changing series on the possible future states. The important aspects of this definition are the insight into the impacts and the focus on possible future states.
Modelling is traditionally viewed as a synthesis for science inquiries, a predictive tool to extrapolate into the future and a means to identify weaknesses in information or knowledge. The emphasis in modelling has been on science advancement, while the use of modelling as a tool to investigate environmental and socioeconomic problems is a relatively new perspective. When used in scenario analysis, modelling is a way to explore, rather than predict the future (Gault et al., 1987). Scenario analysis, or the development of outlines, synopses or plots, has an advantage over expert judgements and many other planning approaches. They necessitate a systematic, coherent approach to the issues under study, and elucidate future uncertainties (Schoute et. al.,1995). Scenarios can be of considerable assistance to policy makers where there is a general consensus amongst scientists about probable outcomes, but a paucity of hard data.
Bradbury (1997) defines a 4 x 5 x 3 classification of models based on: their use - explanation, prediction, hypothesis generation and comparison; the truths they seek - empiric, analytic, synthetic, dialectic and pragmatic; and their orientation or sort - variable, event or individual. This model space is quite sparse in terms of models and modellers perusing the models, as well as having quite disjunct corners. Alternatively, we could classify the models by way of their structure: quantitative - numeric, analytic or empiric; qualitative - rule based or knowledge based; and mental - learned responses, intuition and perceptions (Malafant and Fordham, 1996). Others may classify the models by the modelling approaches they adopt: hard systems approaches generally concerned with how, not what; and soft systems approaches which provide progress through learning and insight (Whittaker, 1993; Pidd, 1996).
No matter how we define our model space, the need for integration and combination of complex modelling components and concepts still remains. In the current environment there is a need to convey complex model and analysis information, complex spatial and temporal information, and scenario or possible futures information. The need is for methods that integrate and extend our understanding of the system, rather than just a detailed knowledge of specific components, models or processes. Pure prediction is at best a thankless task, and it is more feasible and useful to focus on alternative futures and policies which can help us analyse, manage and control the system or processes we seek to understand (Simon, 1996).
A number of approaches to the issues mentioned above have been developed on the international scene in recent years. Integration of models is a major feature of almost all these approaches. Because environmental and socioeconomic systems have emergent properties which relate to the whole and not merely to the sum of the parts, modelling of the components alone does not simulate the whole system properly. Models that address specific components of the system need to be carefully integrated in order to produce realistic scenarios.
Decision Making Tools
Bradbury (1996) has conceptualised the technology envelope for decision making (Figure 1). This technology envelope clearly identifies the interactions between the different tools and technologies. The technologies combine database and information directories, spatial analysis systems, mathematical and statistical methods, decision systems and visualisation techniques to communicate the information and decisions. Decision support systems (DSS) are only one of the technologies that must be integrated.
Sprague (1980) defines three technology levels for decision making and support: DSS tools; Specific DSS and DSS generators. DSS tools are the most fundamental level of elements or software that facilitate the development of specific DSS and DSS generators. Specific DSS are applications that are tailored towards the solution of a specific problem, or a particular decision maker or group of decision makers. DSS generators are software packages which provide the capabilities to build specific DSS. However, they are still oriented towards particular areas, models or methods, for example, Executive Information Systems and financial planning (Sprague, 1980).
Alternatively, the tools that are used to provide support, information and analysis for decision making, can be classified into four broad categories (Malafant and Davey, 1996):
The models or methods that form the blocks or components of the framework can be described as: quantitative or numeric models; qualitative models and mental models. The flexibility of being able to include the numerical models of the biophysical world, the qualitative models such as rule and knowledge bases of the Artificial Intelligence world, and the mental models of learned responses, intuition and perception of the social world, should not be underestimated. As Casti (1994) notes, "Observations and real-world facts are the building blocks from which we construct our visions of reality." It is these "... abstract pictures of reality ..." (Casti, 1994) and the interactions in our system that we use to predict and explore the future states of our world.
These decision making or enhancing tools are used "... by people with different levels of technical capability, and vary in the nature and scope of the task to which they can be applied" (Sprague, 1980).
Figure 1. The Technology Envelope (Bradbury, 1996).
A framework can be used to integrate the best and most appropriate of existing models and data for a project. Not only are sources and forms of spatial and temporal data highlighted, but also where data and methods are lacking. The framework design should be flexible; as new research and/or models become available, they can be integrated into the framework. The aim of the framework approach is to determine the best options for integrating environmental, resource, ecological sustainable management, heritage, economic and social analyses for use in planning, conservation and industry development. Specific objectives of the framework concept are to develop an information or decision system which:
An important feature of the modelling approach adopted by many of these framework systems is workshopping or stakeholder analysis. This involves specialists, stakeholders or clients from a variety of disciplines, coming together to develop the framework components and linkages of the system. Development of the framework by a group of people with experience in the individual components of the system and with knowledge of local conditions ensures that the best available knowledge is exploited and that outcomes are realistic and feasible (Grayson, Blake, and Doolan, 1993). The development of these frameworks is an iterative task, with not only the design, but also the construction and component validation changing as the user explores the system behaviour. There is no final framework, rather an initial version is built and then modified and developed in response to changing user experience, learning and knowledge (Keen, 1981).
Comparing these four categories of decision making tools with those of Sprague (1980): support tools, models, methods and concepts are similar to the DSS tools; management and decision support systems to the Specific DSS and integration frameworks extend the concept of the DSS generator. Integration frameworks "outsource" the modelling work and concentrate on the methods and protocols for welding the models together, forming an harmonious coherent whole (Bradbury, 1997).
Integration Framework Systems - Some Example Toolkits
The concepts of integration in modelling frameworks, systems dynamics diagrams and software suites that implement them, are not particularly new. Systems such as STELLA, DYNAMO (Pugh, 1961) and AEAM (Grayson et. al., 1993) have been used to implement complex modelling systems. The systems dynamic diagrams developed by Forrester (1961) have provided the inspiration for the conceptual, framework or hierarchy diagrams used by many toolkits to develop the framework "infrastructure" and linkages. The example toolkits outlined below are modern incarnations of these ideas, considerably expanded, and more flexible than those of the past. The toolkits chosen represent the biases of the authors in that are they are the subset of those tools available that have been used by the authors in developing and implementing integrated modelling frameworks. For a more complete review of these toolkits and other systems see Malafant and Davey (1996).
Whatif? is an object-oriented scenario modelling package providing a structured set of tools for groups of experts to interact, express their ideas and apply concepts to achieve resolutions to debate(s) of economically and ecologically sustainable resource issues. The system uses the "design" approach to modelling (Gault, Hamilton, Hoffman and McInnis, 1987). In this approach the system is modelled by firstly identifying what components, as objects, exist in the system. Then linkages between the objects, in a functional sense, are established to form the framework. The model framework incorporates input and output objects, as well as other objects which may modify these inputs and outputs.
This approach tightly integrates the user with the models and integrated framework. The user provides novelty and change to the system by the specification of control variables over the simulation timeframe. The approach aims to explore, rather than predict the future, and is not oriented towards global optimisation or equilibrium conditions. This lack of global optimisation or equilibrium constraints can lead to results which are inconsistent or socially unacceptable - tensions. The user can then resolve these tensions by either exploring alternative scenarios or the user may exercise choice and accept scenarios where tensions still exist (Veitch, Fordham and Malafant, 1993).
The Facet Spatial Spreadsheet system is described by its manufacturers as a "complete spatial decision tool, not a GIS". It is a modelling and management decision making tool which includes a range of interactive analysis, data fusion and visualisation tools. Models can combine economic, geographic, environmental and natural resource concerns.
Facet has been used quite extensively in a number of disciplines overseas. The product was originally developed as an object oriented environment for research in digital mapping, image processing and machine vision. However, the system has been expanded to provide a multi-disciplinary environment for problem solving, being used in strategic planning, exploration, habitat modelling, coastal zone management, stock market analysis and atmospheric research (Melzer, Hawkins and Akenhead, 1993; Ayers, Murray, Prisco, Akenhead and Melzer, 1993).
The FACET system utilises sophisticated computing technologies including:
The Calyx family of products from ESSA Software in Canada are PC-based software tools that allow the examination of environmental impacts and their consequences. Two products are of interest:
Calyx has been used as a component of a Prototype Environmental Management Information System developed for the Department of Defence by BRS/NRIC and ESSA. Calyx was used to determine: the environmental impacts of a pre-defined set of activities - the movement of vehicles along tracks, roads or across country ; the set of relevant environmental variables - constraint maps developed in ARC/Info; and the remediation effort required. Rule bases relating vehicle usage to environmental impacts and remediations were incorporated into the Calyx knowledge base, the output a map indicating the impacts for vehicles.
ModelMaker uses conceptual or model diagrams to construct decision frameworks. It is primarily intended for developing simulation-type models rather than empirical, qualitative or mental models. It provides an easy to use software environment shielding the user from the "programming" aspects of modelling, although access to the underlying model equations is provided.
Analytica uses hierarchies of models to develop and manage complex interactions and relationships. As with many of these toolkits, Analytica uses a systems dynamic or conceptual diagram to establish the model components and their linkages.
Examples of the use of these toolkits include: long-term socio-economic analysis of policy decisions in Canada and the USA; forestry and multiple land-use debates in the USA, Australia and Canada; and the analysis of environmental impacts, their consequences and remediation strategies for defence training areas in Australia, the USA and Canada.
Applications of Integrated Frameworks
To provide a better understanding of the applications and flexibility of integrated frameworks, two examples of their application to agricultural and resource planning and management are discussed.
Example 1: Sustainable Irrigation Futures
In this example we describe an integrated framework for identifying the likely impact of policy and planning options on the biophysical resource base, agricultural productivity and socio-economic structure of irrigated regions in the Murray-Darling basin over the next 20 years.
In Australia, there are major concerns for the long term economic viability of some irrigated farms, particularly small farms and those engaged in traditional grazing and mixed cropping activities. Increasing costs associated with environmental restitution and water market reforms will further reduce the viability of farms with consequent impact upon local and regional communities and the environment. Urgent policy decisions must be made by resource managers and regional communities to provide a framework for the future shape of sustainable irrigated agriculture. Soundly based information on the implications of these decisions is fundamental to the development of successful programs and policies.
The major constraint to regional and central policy-making is the lack of information describing biophysical futures for irrigation districts, though there is a wealth of information describing current biophysical and socio-economic conditions of the irrigated regions. A framework is being developed to identify the likely impact of a range of factors on the biophysical, production and socioeconomic structure of irrigation regions over the next twenty years. The modelling framework, developed using the Whatif? scenario modelling toolkit, (Figure 2) links four main components: biophysical, production, enterprise and regional and national flow-on effects (Fordham and Malafant, 1995; Malafant and Fordham, 1995). This framework draws on the concepts of a prototype framework outlined by Veitch et. al. (1993).
The framework components integrate models operating at four spatial and two temporal resolutions. Spatially, models operate at fine scale grids (100m x 100m), fields (hectares), farms (10s of hectares) and regions (1000s of hectares). There are approximately 2400 cells in the finite difference grid covering a region of some 3000 hectares. The temporal scales are day and year. The component models include the numerically intensive hydrological models running on a finite difference grid at a daily time-step, linear algebra formulations of the I/O models at regional and yearly scales, and the simple linear relationships of the crop production functions.
Figure 2. The DSS framework diagram showing the four main components, linkages, feedback, outputs and data requirements.
The integration of the biophysical models with production and economic models enhances the long term decision-making process. By evaluating the model across a range of price, cost and yield scenarios based on the effects of waterlogging and soil salinity, comparisons of the impacts and sensitivities of changes on business return and level of equity on the farm together with the flow-on effects to the community can be explored.
Figure 3 provides an example of the output for a number of different scenarios under identical climatic conditions and varying irrigation regimes. The output demonstrates the effects of different irrigation schedules on the water table depth; contrasting the do-nothing scenario with reduced water availability. The impact of water table depths on soil salinity are also shown; contrasting the do-nothing scenario with reduced water availability and increasing salinity of the irrigation water.
Figure 3. Scenario output or Views from the Whatif? Irrigation Framework.
The framework is being used to consider a number of possible policy scenarios or futures: the implications of the do-nothing scenario; the impact of increasing salinity; alternative land uses; reduction in water applications; the effects of changing input costs and/or commodity prices and the impact of industry development. The aim is to develop programs and policies based on a sound understanding of local biophysical, economic and social conditions and which lead to a long term productive and sustainable irrigated agriculture.
Example 2: Multiple and Sequential Use of Forests
In this example we describe a prototype integrated framework to assist policy makers approach the resolution of multiple land-use issues of forests.
Comprehensive Regional Assessments (CRAs) are being undertaken in state forested regions leading to regional forest agreements between the states and the Commonwealth. Currently, there is no appropriate mechanism to integrate these assessments to enable the recognition and consideration of all forest values in forest use decisions. Without such integration, not all values may be considered in developing scenarios for the decision making process. Other key problems with the current non-integrated approach are: the amount of time required for agencies to give technical advice on revisions to forest use scenarios; problems with proof of transparency and consistency in their advice; and the limited scope of decision makers to interactively explore forest use options.
The integration must consider two main streams of assessment: environment and heritage; and economic and social. An integration framework should have the capacity to consider off-reserve management, resource use, industry development options and enable regional level trade-offs. Successful development of the framework will have implications for the assessment of options in terms of forest sustainability, reserve design and industry structure. The development of an integration framework for forest management should include the provision of real time and reliable tools and optimisation and scenario modelling techniques. Credibility, transparency and consistency across states will be valued elements. This approach requires the integration of quantitative, qualitative and mental models, or a mix of both hard and soft systems methodologies (Pidd, 1996).
Figure 4. The conceptual diagram for Multiple Use of Forest Framework.
For each forest use system, reserve system plus off-reserve management system, there are a number of values. Among these are biodiversity, wilderness, recreation and forestry values. Depending on the availability of information on the economic and environmental attributes of forests, various decision models may be used to explicitly compare alternative options. The decision models chosen will be related to the important issues and concerns of specified CRAs - the integration framework will need flexibility to cater for variation across CRAs.
The modelling framework, developed using the FACET spatial spreadsheet, (Figure 4) links a number of main components and model types: rule-based models for diversity - 15% of this forest community must be retained; land use allocation models based on linear programming formulations and social perceptions of the acceptability of land uses; forest growth models based on numeric and empirical information; categorical and lookup-table models for the occurrences of mineral resources and their viability; and the socio-economic models. Figure 4 also highlights another important aspect of integrated frameworks - the re-use of existing models, framework components or entire frameworks from other projects or applications.
Figure 5. Demonstration Output from the FACET Forest Framework.
Figure 5 provides an example of the typical structure of a FACET integrated framework. The figure clearly shows the spreadsheet-style nature of the "browser" or model-builder; the use of visualisation to represent information and the use of forms to allow the user to interact with the models and visualisation. In this example, the user can interact with the framework by selecting a region in the visualisation and receiving information about the current status of that region - landuse, name and suitability score. Alternatively, the user may wish to alter the landuse of the selected region and observe the effects on the suitability score and other aspects of the framework.
Decision support systems (DSS) and scenario modelling frameworks are designed to aid the evaluation and decision making process. They are not designed to replace or automate the decision process, but to aid and improve the effectiveness of the users interaction and understanding of the complex system under consideration. Ironically the more information made available on the inadequacies and assumptions in the decision making process the less contention it attracts. This transparency allows attention to be focussed on the main objective rather than individual, less important, detailed terminology and aspects which are invariably interpreted in different ways by different disciplines. Ideally "a DSS should be process independent, and user-driven or controlled" (Sprague, 1986).
Integrated or decision frameworks are important in forming coherent systems that can convey the information required to enable effective and constructive decision making and planning. They can also act as awareness raising and educational tools. The exploration of alternative futures using decision frameworks allows the policymaker to anticipate and understand risk and to discover new options for action. Scenario analysis provides a way of exploring alternative futures, and can support informed debate of the policy decisions and the resolution of their tensions. The relative merits of alternative futures and policies can be discussed as can the merits of reaching the same end point by different means.
Such modelling frameworks seek an integrated approach to address environmental, economic and social concerns. The frameworks allow the user to interact with them at any point, while their non-hierarchical structure enables the sharing of objects and model outputs, and allows component feedback. The framework treats the world as a set of changing behaviour patterns, focusing on interconnections. The economy and the environment is seen as one system with stocks, flows, feedbacks and thresholds influencing the way the system behaves (Meadows, Meadows and Randers, 1992).
The 4 x 5 x 3 classification of models outlined by Bradbury (1997) is based on their use - explanation, prediction, hypothesis generation and comparison, the truths they seek and their orientation or sort. Integrated framework techniques elevate the catchall of model use, comparison, to the level of overseer. Comparison of models for explanation, prediction and hypothesis generation can be easily achieved. This leads to a reduction in model space; hopefully distance in this space is reduced and the disjunct corners of the modelling universe start to fade.
In all cases the models used in scenario analysis must be appropriate to the objectives of policy clients. An appropriate balance must be struck between the need for models to be physically and biologically logical, feasible and realistic, and the requirements of the policymakers that often address regions or whole continents.
A higher-level perspective of the system needs to be considered, concentrating on the interactions between the components, rather than a deterministic, mechanical view, if we are to understand the system behaviour and if possible, provide an explanation of the behaviour (Coveney and Highfield, 1995). However, also a note of warning from Coveney and Highfield (1995), "Real-world complex systems do not behave with clockwork regularity and precise long-term forecasts about them are frequently moonshine."
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