Mental Models & Communication in Agriculture

A report for the Rural Industries Research and Development Corporation by Nick Abel, Helen Ross, Ann Herbert, Michelle Manning, Paul Walker & Helen Wheeler

November 1998 RIRDC Publication No 98/140 RIRDC Project No UCA-1A

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© 1998 Rural Industries Research and Development Corporation. All rights reserved. ISBN 0 642 ISSN 1440-6845 Mental Models & Communication in Agriculture Publication no 98/140 Project no.UCA-1A The views expressed and the conclusions reached in this publication are those of the author and not necessarily those of persons consulted. RIRDC shall not be responsible in any way whatsoever to any person who relies in whole or in part on the contents of this report. This publication is copyright. However, RIRDC encourages wide dissemination of its research, providing the Corporation is clearly acknowledged. For any other enquiries concerning reproduction, contact the Publications Manager on phone 02 6272 3186.

Researcher Contact Details Helen Ross Centre for Environmental Studies Australian National University ANU ACT 0200 Phone: 02 6249 2159 Fax: 02 6249 0757 Email: hross@cres.anu.edu

Nick Abel CSIRO Wildlife and Ecology PO Box 84 Lyneham ACT 2602 Phone: 02 6242 1534 Fax: 02 6241 4020 Email: N.Abel@dwe.csiro.au

RIRDC Contact Details Rural Industries Research and Development Corporation Level 1, AMA House 42 Macquarie Street BARTON ACT 2600 PO Box 4776 KINGSTON ACT 2604 Phone: 02 6272 4539 Fax: 02 6272 5877 Email: rirdc@netinfo.com.au Website: http://www.rirdc.gov.au

Published in November 1998 Printed on environmentally friendly paper by the AFFA Copy Centre

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Foreword It is thought that people use ‘mental models’ for making sense of the world. A mental model is used to translate incoming information and filter it selectively. These models differ between individuals and groups in terms of information content and structure and this difference may inhibit communication, and could be one reason for the non-adoption of technologies and management recommendations emanating from research and extension agencies. The aim of this project was to develop and test a method for eliciting, describing and comparing agricultural groups’ mental models about landscape ecological processes. The research team focused on graziers, researchers and extension officers. This report outlines the development of two methods for eliciting such mental models, and the development of a new software package (INFLUENCE) designed by Paul Walker to be used together with the commercial package VENSIM for the analysis of causal relationships. It is hoped that by better understanding each others’ mental models about the technology, and the ecological, social and economic environment for adoption, stakeholders will bridge communication gaps, which may lead to modification of the technology, proposals for modifying the decision-making environment, or pilot testing of the technology. This report, the latest addition to our diverse range of over 250 research publications, forms part of RIRDC’s Human Capital, Communications and Information Systems R&D program, which aims to enhance human capital and facilitate innovation in rural industries and communities. Peter Core

Managing Director Rural Industries Research and Development Corporation

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Acknowledgements The authors thank the Rural Industries Research and Development Corporation for sponsoring this study, and Roslyn Prinsley, our project officer, for her encouragement and support. We also thank the University of Canberra, CRES and CSIRO for their financial and intellectual contributions, and the North Australia Research Unit for assisting our communication while Michelle Manning worked from Darwin. Peter Cullen, Henry Nix, Val Brown, John Harris, David Goldney, Judy Pinn, Tony Dunn and Dennis Hodgkins have particularly encouraged us in this study. Sarah Ryan, Steve Dovers, Art Schulman, David Blacket, Frank Vanclay, Peter Cornish, John Cameron and Bob Fisher made valuable comments at various stages of the project. Denise Byrne transcribed the large number of interviews with impeccable accuracy. Warren Muller gave us much advice on the analysis of components, and carried out the logit analyses in chapter 4, and Lee Belbin conducted pattern analyses - we are indebted to them both. Advice on Principal Component Analysis was provided by Cathy Hales. Other statistical advice was provided by Wayne Robinson and George Cho. We thank Vanessa Dutton for conducting some of the interviews. We thank all those who participated in the pilot interviews for this study - Roger Attwater, Betty Bearse, Howard and Matthew Crozier, Doug and Anne Darbyshire, and Anne’s father George, David Porritt, Rainer Rehwinkel, Stacey Costello, Graham Taylor, Judith Turley and David Watson. They played an important role in the shaping of the design. Confidentiality prevents our naming the graziers, extension staff and researchers who participated in one or both of our studies - we appreciate their enormous commitment of time and interest, and hope the results prove useful to them. We appreciate equally the participation of academic, extension and grazier colleagues who participated in our two workshops. We are very grateful to Barry Griffith and the ACT Department of the Agriculture and Landcare Section of the ACT Parks and Conservation Service for allowing us the use of the Holding Paddocks. Ashley Bolton kindly let us use his property for interviews, and provided information on its history.

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Contents Acknowledgments i Executive Summary ii 1. INTRODUCTION 1 2. THE PSYCHOLOGY OF ENVIRONMENTAL INTERPRETATION 3 2.1 Environmental Cognition 3 2.2 Personal Construct Psychology 4 2.3 Mental Models 5 2.4 Synthesis 6 2.5 Cognitive or Mental Maps as Spatial Mental Models 7 3. ELICITING MENTAL MODELS 9 3.1 Aims and Background 9 3.2 The Transect Study 10 3.3 The Land Classification Study 22 3.4 Workshops 24 3.5 Costs 24 3.6 Ways of Streamlining the Transect Method 26 4. COMPARING THE CONTENTS OF MENTAL MODELS – LANDSCAPE FEATURES 27 4.1 Transect Study 27 4.2 The Land Classification Study 37 4.3 Summary of Findings from the Transect and Land Classification Studies 42 5. REPRESENTATION AND COMPARISON OF MENTAL MODELS – LANDSCAPE PROCESSES 45 5.1 Influence Diagrams 45 5.2 Summary of Findings 46 6. MENTAL MODELS AND COMMUNICATION IN AGRICULTURE AND LANDCARE 58 6.1 Knowledge, Information and Communication 58 6.2 Contribution of Mental Model Analysis to Communication 61 6.3 Bridging the Barriers 62 6.4 Mental Models and Written Communication 63 6.5 Mental Models and Sustainable Land Management 64 7. CONCLUSIONS AND RECOMMENDATIONS 65 7.1 Theory 65 7.2 Method 66 7.3 The Comparison of Mental Models 66 7.4 Communication in Agriculture and Landcare 67 7.5 Recommendations for Future Work 68 REFERENCES 69 APPENDIX - Sub-components and Properties Mentioned by Interviewees 74

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LIST OF TABLES Table 3.1 Interview Questions 16 Table 3.2 Examples of Components, Sub-components and Properties used in the ‘Components coding frame’ 17 Table 3.3 Examples of Processes and Sub-components used in the ‘ Processes coding frame’ 18 Table 3.4 Extraction of Information from the Interview Transcripts 18 Table 3.5 Examples of Indicators 19 Table 3.6 Part of a Matrix of Direct Influences (imaginary data) 21 Table 3.7 Cost of Components of the Transect Method 25 Table 3.8 Cost Components of the Land Classification Method 25 Table 4.1 Analysis of Deviance: Use of Components 32 Table 4.2 Analysis of Deviance of separate Components 33 Table 4.3 Sub-components/Properties emphasised by Groups 33 Table 4.4 Use of Indicators to prompt of support Theories 34 Table 4.5 Significance of Adjusted Residuals for Indicators 35 Table 4.6 Areas of Comfort 36 Table 4.7 Examples of Areas of Comfort shown in answers to “What other information would you require to manage this land?” 36 Table 4.8 Use of Classification Categories by Graziers and researchers 38 Table 4.9 Comparison of the use of Components and Sub-components by Graziers and Researchers 39 Table 4.10 Principal Component Analysis of Components and Sub-components used in Land Classifications 41 Table 5.1 Variables in Rank Order of Number of Direct and Indirect Linkages for the Composite Model 45

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LIST OF FIGURES Figure 3.1 Accumulation of Information in Relation to Number of Questions Asked 12 Figure 3.2 Topography, Hall 13 Figure 3.3 Topography, Abattoir Holding Paddocks 13 Figure 4.1 Frequency of mention of Components 29 Figure 4.2 Frequency of mention of Sub-components and Properties 30 Figure 4.3 Variation in Frequency of mention of Components 31 Figure 4.4 Variation in use of Classification Categories by the Graziers 39 Figure 4.5 Variation in use of Classification Categories by the Researchers 40 Figure 4.6 Standardised Factor Scores for the Graziers and Researchers 41 Figure 5.1 Influence of Soil Type - Composite of Researchers, Graziers and Extension Officers 48 Figure 5.2 Influence of Soil Type – Researchers 49 Figure 5.3 Influence of Soil Type – Graziers 50 Figure 5.4 Influence of Soil Type - Extension Officers 51 Figuure 5.5 Variables Influencing Runoff - Composite of Researchers, Graziers and Extension Officers 52 Figure 5.6 Variables Influenced by Runoff - Composite of Researchers, Graziers and Extension Officers 53 Figure 5.7 Variables Influencing Water Quality - Composite for Researchers, Graziers and Extension Officers 54 Figure 5.8 Variables Influencing Water Quality – Researchers 55 Figure 5.9 Variables Influencing Water Quality – Graziers 56 Figure 5.10 Variables Influencing Water Quality - Extension Officers 57 Figure 6.1 Information-Knowledge Transformation 58

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Executive Summary People are thought to use ‘mental models’ for making sense of the world. A mental model is used to translate incoming information and filter it selectively. A mental model has a structure, and can change over time. Mental models differ between individuals and groups in terms of information content and structure. Our premise is that these differences inhibit communication, and are one reason for the non-adoption of technologies and management recommendations emanating from research and extension agencies. The aim of this project was to develop a method for eliciting, describing and comparing the mental models of different groups involved in agriculture and land management. In fact we developed two. We used Personal Construct Psychology, mental model theory and other theory on environmental cognition as bases for developing these methods. In the first method, termed the ‘transect study’, we worked with graziers, extension officers and researchers, and focused on their models of water processes in farm landscapes. Transects were established on two pastoral properties. Eight members of each professional group were interviewed separately at sites along one or other of the transects. We found that interviewing while walking the land was a powerful way of eliciting mental models. Interviewees were asked a combination of open-ended and probing questions. Their answers were recorded verbatim, and the content analysed. This technique reduced the risk of our imposing our own preconceptions on the data. The method is highly productive although not cheap, and requires some skills to apply. It is appropriate for situations in which one requires a detailed understanding of people’s thinking. Our aim was to develop method, not draw broad conclusions about the mental models of graziers, extension officers and researchers. Our ability to generalise was limited by the small numbers and limited representativeness of interviewees. We were, however, able to infer some conclusions about graziers in the ACT, extension officers on the Southern Tablelands, and CSIRO soil and water researchers. There was great variability among interviewees in the frequency with which they mentioned landscape terms. This variability is accounted for more by differences between individuals than between the three professional groups. However, there were substantial differences between the groups in their frequency of mention of terms relating to soils and water. The graziers emphasised management, animals and atmosphere more than the other groups. They used management indicators more, and soil indicators less than other interviewees. Management was their “area of comfort”. The extension officers emphasised vegetation and people more than the other two groups. They showed no area of comfort, but were able to span a broad range of issues, in keeping with their role in agriculture. The researchers referred to topography, soils and water most frequently of the three groups. They also used soil indicators more. Soil was the area of comfort for half the researchers. The transect study also yielded understanding of mental models of land and water processes. Computer-generated influence diagrams proved to be an effective way of representing the models of each of the three professional groups. The influence diagrams could be used by groups seeking to improve communication through mutual understanding of their mental models, perhaps as an initial step in the formulation of a research program or a decision support model. Our second approach, the ‘land classification method’, compared land classification criteria used by graziers and researchers. Interviewees were paired off, each pair consisting of a researcher and a grazier. Each member of the pair was interviewed separately on the same tract of land. They were asked to classify the tract. Their boundaries were marked on

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plastic overlays over aerial photographs. Interviewees were asked to explain the criteria for their differentiations. Explanations were recorded and analysed for content. Graziers' and researchers' classification maps and criteria were different. The researchers relied heavily on soil characteristics and landscape elements. They were interested in the deeper layers of the soil and rocks. Management, soils, water and vegetation criteria played a major role in how graziers classified an area. The graziers used a broader range of indicators than the researchers. They were interested in the top few centimetres of the soil, but used vegetation as an indicator of soil properties. Differences such as those we found between the three professional groups could become barriers to communication about the adoption of technology or management practises. The psychological theories we used suggest that barriers caused by differences in mental models cannot be bridged by increasing the volume or glossiness of information. Communication is a process, not an event. It is best begun by establishing the relevance of the issue, and building social relationships. For communication to take place it is not necessary for one party to adopt the other’s model, provided there is understanding of the other person’s model. But communication need not necessarily result in a behavioural change by any party. If mental models are re-structured as a consequence of the new experience, then behavioural change may follow. The elicitation methods we developed could play a useful part in this. The main outcome of this work is two methods for eliciting mental models, and the application of a new software package (INFLUENCE) designed by Paul Walker to be used together with the commercial package VENSIM for the analysis of causal relationships. The elicitation methods are highly suited to cases where the detailed understanding of mental models is crucial to the resolution of conflict - for example, where wide discrepancies between the mental models is suspected, the gains and losses to stakeholder groups might differ widely, or the absolute gains or losses are expected to be large. Our elicitation methods would be too costly where the stakes are not so high, but INFLUENCE, used with VENSIM, is applicable to a wide range of conditions where formal elicitation of the mental models of groups is not necessary. In such cases a composite mental model representing the combined models of all stakeholders could be generated directly in a workshop using VENSIM and INFLUENCE. The required understanding of mental models would be a generated during the process of constructing the composite model. This project has developed two ways of eliciting and comparing mental models. We propose the next stage should be to apply them in a case study of non-adoption of an apparently useful technology. The aim would be the exchange and mutual comprehension of mental models among the various stakeholders, such as farmers, banks, R&D Corporations, Landcare groups, researchers and government agencies. The premise is that differences in mental models block communication among the groups. In understanding each others’ mental models about the technology, and the ecological, social and economic environment for adoption, we would expect stakeholders to bridge communication gaps. This may lead to modification of the technology, proposals for modifying the decision-making environment, or pilot testing of the technology. The work should be carried out in an economically important farming area, such as the Riverina, or the wheat-sheep belt. Output would be a manual on the method for general application, perhaps accompanied by a video.

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1. Introduction Imagine a grazier has inherited a property from his father. He was raised on the property, and learned to manage it from his father, for whom he holds great respect. He stocks sheep at the same rate as his father. Suppose also an extension officer tells our grazier that, according to departmental standards for ground cover, his land is overstocked. If the grazier is to accept this information, he must think through the management and financial implications, that is obvious. But he must also re-think his relationship with his father, and how that is affected by rejection of the old man’s advice, so the indirect effects of incorporating a new idea could be psychologically disturbing. The adoption of new ideas is not, therefore, simply a question of telling and listening, for the ‘mind set’ of the grazier inhibits the acceptability of the information. In this study we consider the mind set as a mental model (after Craik 1952), or personal construct system (after Kelly 1955). This report is about methods we have developed for eliciting and comparing people’s mental models, or personal construct systems. We hope these methods will facilitate communication among farmers, graziers, Landcare groups, extension staff, researchers, funding bodies and government agencies seeking improved land management and profitability through adoption of better practices and technology. . Farmers have often been reluctant adopters of new practices. Possible reasons are listed below: 1. farmers may not perceive the need for change; 2. the sub-culture within which the farmer lives does not accept the technology, making it

difficult for the individual to adopt (Vanclay and Lawrence 1995); 3. risk and uncertainty - the farmer may prefer to continue with current practices rather

than make investments with uncertain or risky returns; 4. the investment in knowledge required of the adopter is greater than the perceived

benefit (Vanclay and Lawrence 1995); 5. farmers have not adopted conservation practises because they bear the costs, but

benefits are received by others in the catchment, the wider community or future generations.

6. lack of assets - insufficient labour or capital to manage the new practice or technology; 7. inappropriateness - the technology or practice is unsuited to the farming system because

of its scale, incompatibility with other enterprises and so on; 8. processing, marketing and transport constraints. We are not taking a judgemental stance on adoption. There are frequently sound social, economic or technical reasons why farmers do not adopt new practices. However, this leads to the question, why were inappropriate practices or technologies generated in the first place? One reason, we suggest, is misunderstanding between farmers and graziers, and other groups. We examine the role of mental models or personal constructs in understanding and communicating about agriculture and land management. Our report focuses primarily on the first of the reasons given above for why farmers and graziers have not adopted recommendations, that is ‘perceptions’. We examine these in terms of personal

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constructs, and mental models. For most of this report we refer to these collectively as mental models. We will also deal with the second reason for non-adoption - that is, the existence of sub-cultures with different views of the world. A sub-culture is any set of people that is distinguishable from the culture in which it is set by differences in norms, values, knowledge and institutions (Mann 1983). Vanclay and Lawrence (1995) regard farmers (presumably graziers too) as members of a sub-culture. Differences between the mental models of sub-cultures may prevent understanding. There are many dimensions to problems of communication among sub-cultures - social, political, economic, ecological and technical. We do not offer personal constructs or mental models as a replacement for these, but as a missing psychological dimension for strengthening the analysis. The psychological approach is complementary, and may be a way of comprehending and estimating the importance of those other factors. We shall return to this argument in Section 6. Before that we will cover the theory (Section 2), methods (Section 3), findings and discussion (Sections 4 and 5) of our work. Section 7 contains our conclusions and recommendations. Before ending this introduction we will say what this report is and is not about. First, it is not a study of attitudes or values. Non-adoption and ‘mismanagement’ has been blamed on farmers’ ‘attitudes’. Extension effort has been aimed at changing unfavourable attitudes so that adoption and better management follow. An attitude is seen as a predisposition towards an object, which results in a person filtering information. For instance, a person attends selectively to information about a new technology, say, and accepts that technology if it is concordant with their attitudes. There have been a number of studies of farmer’s attitudes towards agricultural and land management (eg. Barr and Cary 1984; Cary, Beel and Hawkins 1986; MacLeod and Taylor 1994). Three related problems are associated with this approach. First, attitudes are often poor predictors of behaviour. Shulman and Penman (1994), and Vanclay and Lawrence (1995) point to the lack of concordance between stated attitudes to the environment, and the adoption of conservation practices. Secondly, there is evidence that experience generates behavioural change, and that attitudinal change follows, so attitudes are not a cause of behaviour (Mackay 1994). Finally, studies of attitude may not give understanding of the reasoning behind the stated attitudes (Himmelweit 1990), in particular the farmer’s interpretation of how the ecological system being managed actually functions, how function can be manipulated by management, and what the consequences might be. Ours is an attempt to understand the constructs or mental models that underlie both attitudes and behaviour. Secondly, this work is not designed to produce generalisations about the personal constructs or mental models of all graziers, all researchers, or all extension officers. Since the focus of our work was to develop and test methods of eliciting people’s mental models about landscape ecological processes, it was not possible to include a large sample within the budget and timeframe. Our approach therefore has many implications for method, but larger samples would be necessary to draw policy conclusions. Thirdly, we did not try to elicit comprehensive mental models covering all aspects of property management - technical, social, financial, political and ecological - even though Salmon (1981) demonstrated the importance of doing so. We limited our work to models of landscape water processes. This topic was sufficiently complex to develop and test our methods, but narrow enough in scope to be covered in the time our interviewees could spare. We are confident that variations on our methods will work also for a more integrated analysis of environmental and other issues in land management.

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2. The Psychology of Environmental Interpretation

Our study was conducted in two parts. One focused on how people consider farm landscape processes to work. The other concentrated on the spatial aspects of people’s thinking. This section reviews and synthesises theory relevant to both. We have drawn on three bodies of psychological theory: Personal Construct Psychology (Kelly 1955), a theory on mental models (Craik 1952), and theory on mental (or cognitive) mapping. All try to explain how people interpret their worlds. Personal Construct Psychology provides a theory to explain all thinking and behaviour. It was developed in clinical psychology to explain how people interact with one another and in certain situations. Interactions with biophysical environments have been studied far less than interpersonal interactions, but are implicit in the theory. The concept of mental models has received far less theoretical elaboration than Personal Construct Psychology, and we are unaware of applications dealing with biophysical environments. Professionals working in information technology and highly structured aspects of mental reasoning have been main users of the concept. It is becoming influential in the organisation and management of corporations (Senge and others, 1994). It holds promise for our purposes. We view mental maps as a type of mental model, although the bodies of theory developed separately. Other relevant theories are mentioned briefly. Our use of these three main bodies of theory in relation to people’s ways of understanding biophysical environments falls under the general theoretical field of environmental cognition.

2.1 Environmental Cognition

Environmental cognition refers to the ways in which people see, understand and know their environments. It includes the immediate perception of the environment through the senses, and the mental processing of this information. It includes all forms of knowledge of the environment, such as perceiving, thinking, imagining, reasoning, judging and remembering, and how these arise from experience (Moore and Golledge 1976; Hart and Moore 1973). Experience affects behaviour with respect to the environment (Moore and Golledge 1976). Since people can only come to know the environment through their senses, people react to ‘perceived’ rather than ‘actual’ environments. Cognition therefore mediates between the individual and the environment (Rapoport 1977). The ways in which a person experiences, represents and reconstructs events is influenced by his or her mental state, prior experience and dependence on the immediate environment (Moore 1976; Stea 1981). Training and experience also help to structure cognition (Moore and Golledge 1976). The term ‘environmental perception’ is sometimes used interchangeably with ‘environmental cognition’, and sometimes to refer specifically to sensory information of environments (Ittleson and others 1974). The theories reviewed next explain processes of environmental cognition.

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2.2 Personal Construct Psychology Personal Construct Psychology (after Kelly 1955) uses the analogy of each person as a scientist who proposes, tests, and constantly refines hypotheses in the light of new personal experience. Each person creates a completely individual set of mental templates which he or she tries to fit over the world she or he experiences. The fit is not always good, so the person continuously amends the templates to achieve a better fit. The templates provide a basis for the person's behaviour. While each person's template is unique, and interpreted and used in a very personal way, large parts of it can be shared through common vocabulary and understanding. Kelly calls these mental templates 'constructs', and views them as being organised hierarchically into dynamic, constantly changing 'construct systems'. Each construct is a representation of part of the universe, which the person-scientist tests for its predictive efficiency, and sometimes revises or replaces when this capacity is found wanting. Kelly considers each construct to be dichotomous, providing two related alternatives in an either-or relationship such as black-white, or a scaled relationship such as black-grey-white. Each construct has a 'range of convenience', a set of situations to which it applies. Generally the constructs with a limited range of convenience sit near the bottom of a hierarchical system of constructs, while those with the greatest range of convenience, such as 'like-dislike' or 'good-bad' sit near the top and can subsume many other more specific constructs. Kelly's fundamental postulate, a person's processes are psychologically channelised by the ways in which he anticipates events, expresses the way a person's interpretation of the world (or prediction of events) operates through this structured but flexible network of mental pathways which both facilitate and restrict a person's range of action. Kelly elaborates his theory to describe how personal systems of constructs are structured, and how and why people change them (or cling to their unworkable aspects). Constructs are created and used in relation to 'elements', the objects, people or situations which they describe. As well as having a range of convenience, a set of elements to which it applies well, a construct may have a focus of convenience, the range of matters for which it is most useful. Similarly, a construct system has ranges and foci of convenience. A construct is permeable if it is capable of admitting new situations. People's ability to change their construct systems is limited by the permeability of their constructs. A person's construct system is composed of a finite number of constructs, though of course the number of constructs may change as the person alters his or her system. People arrange their constructs hierarchically for their own convenience in predicting events. They also emphasise the 'pole' or end of the ‘either-or’ construct which offers the greatest possibilities for extending and defining their construct system, and hence their predictions and actions. They may have construct subsystems that are incompatible, particularly if their system as a whole is likely to be threatened by changing a subsystem. Kelly explains how people can understand one another, given that their personal systems of construing differ. It is not necessary for two people to construe the world in the same way, but at least one must be able to construe how the other is construing. For instance a psychiatrist need not become like his or her patient, but must recognise and understand how the patient is seeing the world. A disadvantage of the theory, or at least the ways it has been used, is over-emphasis on the structure of the construct and construct system (Mair 1988,1989,1990; Efran 1994; R. Neimeyer 1994; Vogel 1994). This is encouraged by the availability of a powerful and appealing technique, the repertory grid. There is insufficient attention paid to process aspects of a person's thinking, such as 'if you overgrazed this steep slope, given the type of soils here, and heavy rain followed a dry spell, you would get erosion'. Emphasis on

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separate constructs and the elements to which they refer leads us to break up these extended predictive descriptions into their constituent parts rather than explore them in their own right. Narrative approaches to Personal Construct Psychology (PCP) (Vogel 1994:244) have the potential to overcome this disadvantage by dealing with flows of events in meaningful sequences. Another body of theory that takes a narrative approach to the analysis of events is script or episode theory (Abelson 1976; Schank and Abelson 1977; Forgas 1981). These approaches have potential for the analysis of process aspects of thinking, and are compatible with the mental model approach, described next. 2.3 Mental Models The concept of 'mental models' (Craik 1952) comes much closer to the process-oriented thinking we wish to investigate. Like Kelly, Craik focuses on people's ability to predict certain outcomes, using reasoning based on their previous observations. This enables engineers to design consistently safe bridges, instead of building them haphazardly and waiting to see whether they collapse. His hypothesis is that this reasoning involves translating external-world processes into words, numbers or symbols, then reaching other symbols through reasoning which can then be retranslated into external-world actions such as building a bridge to a design (Craik 1952:50). The prediction required in this type of reasoning involves the mental creation of a 'model' of the physical or mechanical processes involved. Craik defines a model as 'any physical or chemical system which has a similar relation-structure to that of the process it imitates’ (1952:51). The model need not resemble the external process it imitates, so long as it works in the same way. It need not be as complex, so long as it represents the most important processes. Indeed, models are not improved by the addition of information beyond a certain point. Craik also likens mental models to analogies (1952:53). Coming closer to Kelly's formulation, he speculates that animals and people carry in their heads a small-scale model of external reality and their own possible actions within it. A person or animal is able to 'try out various alternatives, conclude which is the best of them, react to future situations before they arise, utilise the knowledge of past events in dealing with the present and future, and ... to react in a much fuller, safer and more competent manner to the emergencies which face it' (1952:61). O'Malley and Draper (1992) claim that a mental model does more than capture isolated items of information. It is a structure, it represents several different kinds of information, and it can be 'run' as a simulation. Johnson-Laird (1983) argues that different people have more or less elaborate mental models, depending on what they need to know. For instance, television repairers need more complex models of televisions than television users. The experimental literature also shows that experts and non-experts in a subject may have quite different types of model, involving conceptual changes and perhaps superior organisation of knowledge (Rouse and Morris 1986). The expert's model may be an information-gathering model that involves a system for finding out information necessary to complete a task, rather than a highly complex model holding the complete information required (O'Malley and Draper 1992). Mental models need not be technically accurate, but they must be functional for their users (Norman 1983). Among the few theoretical observations made as to how mental models are developed or structured, Norman (1983) suggests on the basis of his study of users of complex calculators, that mental models are incomplete, unstable (in that people forget their details) and do not have firm boundaries. They are unscientific in that people maintain some 'superstitious' behaviour patterns, and people's abilities to 'run' their models are severely limited.

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Although the concept of mental models focuses on construing of processes, the field is ‘stuck at an early stage of theoretical development' (Rutherford and Wilson 1992:197). There is no agreed definition as to what constitutes a mental model, beyond the central notion of a mental process enabling mental simulations (Rouse and Morris 1986; Rogers and Rutherford 1992; Rutherford and Wilson 1992; Payne 1992). There is little exploration of the ways in which people might develop and alter their mental models. Norman (1983) does suggests, though, that through continuing interaction with the external system he or she is trying to model, a person will continue to modify their mental model in order to find a workable result. We have not found literature which considers the processes involved in mental models outside some narrow experimental contexts, nor which elaborates the structure or functioning of mental models. We consider that with elaboration the concept of mental models has great potential to handle process elements which are difficult under Kelly's statement of Personal Construct Psychology, and which have yet to be elaborated by those espousing a narrative version of Personal Construct Psychology. The question is how we can combine the best of both sets of theory. 2.4 Synthesis We elaborate the concept of mental models in the following way, borrowing from Personal Construct Psychology. A mental model is a representation of the way the world, or an aspect of it, works. It is a set of related constructs, part of a person’s construct system. It may take a variety of forms, and may or may not be capable of verbal or visual representation. For instance, some individuals may think of environmental situations or processes visually, as in a mental map or a scientist's flow diagram, while others may represent similar processes in terms of non-visual 'if-then' combinations of statements. We assume that mental models share the following features with construct systems: • they help people to anticipate how physical, social, economic or other processes will

occur, and to plan their behaviour accordingly; • they are developed and amended progressively in the light of their creator's experience.

Personal background, exposure to and interest in accepting new information, and personal experimentation all play a part in shaping and reshaping a mental model;

• individual mental models differ, but can contain common aspects with those of others

and be shared through common concepts and language; • people’s mental models, or parts of them, may be of varying detail and complexity,

depending on their interests and experience; • mental models may be arranged as subsystems within larger systems. For instance

models of erosion and deposition processes may be nested within a broader conceptualisation of landscape processes;

• each model has a range of convenience, or situations to which it applies most aptly; • mental models may be more or less permeable, or capable of accepting new detail. They

may also be more or less adaptable when potentially conflicting information becomes available;

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• it is possible for mental models, or subsystems within them, to contain incompatible aspects;

• people who have similar mental models of a situation or set of processes will tend to

hold similar expectations and will act similarly; • in order to communicate effectively or cooperate with another person, one need not hold

the same outlook or mental model, but must be able to appreciate the other person's outlook or model. For instance, a scientist need not personally share the goals of a grazier, or hold the same understanding of how his or her farm landscape works, but the scientist does need to understand the grazier's goals and frame of reference.

This combination of mental models and Personal Construct Psychology has informed the development of our research method and analysis in two main ways: 1. We have taken care to develop a method which elicits our interviewees’ own concepts

of how landscape processes work, offering them common cues in real landscapes, and constraining them as little as possible in presenting their mental models.

2. Our analysis takes into account the following aspects from Personal Construct

Psychology:

• the degree of elaborateness of each person's mental model, or parts of it;

• perceived relationships between components of the landscape (akin to 'elements' in Personal Construct Psychology) and ecological processes;

• the use of hypothetical reasoning ('if-then' connections) and use of indicators to

pose or test hypotheses. 2.5 Cognitive or Mental Maps as Spatial Mental Models A third body of theory, which is used in the spatial study, is cognitive mapping (Downs and Stea 1973; Gould and White 1974), also known as mental mapping. The theory deals both with the cognitive process of building up and amending the ‘map’, and the product, the ‘mental map’. The terms ‘mental map’ and ‘cognitive map’ are used interchangeably in the literature, though in some authors’ usage cognitive maps may bring in more dimensions of cognition (eg. Golledge 1993; Kitchin 1994). ‘Mind mapping’ (Buzan 1983) is a technique for eliciting cognition of non-spatial environmental or social relationships, and should not be confused with the other terms. Mental maps are essentially spatial mental models (Brookfield 1969; Golledge 1993; Golledge and Stimson 1987; Hirtle and Heidorn 1993). They are analogous to cartographers’ maps, which vary according to their purpose and the features of interest. They may be highly complex and abstract forms, often incomplete and distorted, of the external environment (Downs and Stea 1973). A good example is the humorous maps of Australia with Tasmania grossly enlarged, or maps of New Zealand with the sizes of the islands reversed. Although the term is generally used with respect to spatial representations, some of the literature can be read to include non-spatial representations. We restrict our use of the term in this report to spatial representations, especially since the term ‘mental models’ encompasses non-spatial forms. Both two- and three-dimensional maps are common. People’s cognitive maps can also show how a place has changed over time.

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The cognitive mapping process enables people ‘to receive and code environmental information, store it in an accessible manner, and decode it in such a way as to allow spatial behaviour to take place’ (Golledge and Stimson 1987:70). People are flexible in their ability to generate cognitive maps and alter them throughout life (Downs 1976b; Hart and Moore 1973; Downs 1976a; Golledge and Timmermans 1990). People use mental maps to understand their environments, and to navigate or conduct activities within them. Studies such as Sikana’s (1994) work with Senegalese farmers’ soil classifications, and Walsh’s (1990) with Aboriginal land classifications, also rely on people’s mental mapping abilities. Among the psychological and social processes which contribute to a person’s cognitive maps (and indeed to all environmental cognition) are sensory perception, mental state, attitudes and interests, socialisation and other forms of learning, past experience, and dependence on the immediate environment. Indeed, much of the theory in environmental cognition has been developed through the study of cognitive maps. Kaplan (1973) states that a cognitive map is valuable not so much in terms of its detail as in terms of its reliability as a guide to its owner. It is a selective representation of the environment - it is not the environment itself (Kaplan and Kaplan 1981; Shaw and Woodward 1990). By the act of perceiving the environment we change or filter it as we incorporate it into our cognitive maps (Brookfield 1969; Ittleson and others 1974). How the environment is perceived determines how the cognitive map is constructed and updated (Pope and Denicolo 1991). As one develops a greater knowledge of an environment, the sophistication of the cognitive map increases (Moore and Golledge 1976; Evans 1980; Rouse and Morris 1986). Like personal construct systems, mental maps may be unstable and incomplete. In the next section we describe the methods we have developed using these bodies of theory.

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3. Eliciting Mental Models 3.1 Aims and Background Our aims were to: 1. develop methods for eliciting the ways in which people construe ecological

processes in farm environments; 2. compare graziers', research scientists' and extension officers' ways of

construing these processes; 3. examine the implications for communication among these groups. In this section we deal with the first and second aim. The third is examined in Section 6. Two of the bodies of theory reviewed in Section 2, Personal Construct Theory and cognitive mapping, are associated with well-known methods. So far there is no established method, pertinent to our field, for eliciting mental models. 3.1.1 Methods from Personal Construct Psychology - the Repertory Grid Personal Construct Psychology owes part of its popularity to the Repertory Grid, a method designed to elicit people's constructs and see how they are applied to elements (objects). There are many variants of the technique (Fransella and Bannister 1977). In the most common, a person is asked to compare a selection of elements, two or three at a time, saying (for three) 'in what way are two of these similar and different from the third'. This technique draws out a number of bi-polar constructs, one end of the scale representing the issue of similarity, the other, the aspect of difference. For our landscape theme, this could involve showing people three sites, and asking in what ways two are similar, while different from the third. (We did do this to some extent, by asking 'how is this site different from the first', see below). An answer might have been 'two have steep slopes, the other is more gradual'. The construct is 'steep versus gradual', with a range of convenience (see Section 2.2) which applies only to slopes. Once a number of constructs have been elicited, the interviewer asks the person to apply each construct to each element. Thus one could ask of every site, 'is its’ slope steep or gradual? Is it well-vegetated or degraded?' The resulting matrix of elements matched to constructs lends itself well to computer analysis. Correlation matrices can be reprocessed to show similarities among the constructs, or similarities among the elements, or to locate elements close to constructs in multi-dimensional hypothetical space. One can also examine how a person's 'grids' change over time. Using this powerful technique Personal Construct Psychology proponents have concentrated on the structure of construct systems, but have tended to neglect both content and the cognition of dynamic processes. Our view is that the Repertory Grid gives too passive an analysis of a person's system of construing. It does not enable an adequate presentation of the 'if-then' type of reasoning which is fundamental to cognition of processes (although Kelly 1955 dealt very briefly with ‘if-then’ reasoning). The 'implications grid' (Hinkle, cited in Fransella and Bannister 1977) uses 'laddering' or 'pyramiding' to trace structural relationships within a hierarchy of constructs. While we did not test for these, there is ample evidence of such relationships in our interview transcripts.

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There are less structured ways of eliciting constructs, such as extracting them from a record of conversation, or even deducing them from drawings, or people's actions. These methods do not necessarily elicit both poles of a construct, though one can ask what the contrasting pole to each construct is. In narrative approaches to Personal Construct Psychology, and also those based on Script Theory (after Schank and Abelson 1977), verbal accounts or texts are analysed for evidence of the ways in which the person construes or predicts events. We required a method which elicited information about how people construed environmental processes, and was effective in assisting people to think and talk about the landscape. It had to be equally suitable for each of the professional groups we wished to compare. Some variant on the eliciting of narratives or scripts held promise, but we needed well-chosen prompts to elicit the type of process narrative we were interested in. The result was the 'Transect Method' described below (see Section 3.2). 3.1.2 Cognitive Mapping Methods Methods of eliciting cognitive maps are less precisely defined than the methods for eliciting personal constructs. Most rely on asking people to draw a map of a familiar subject area, such as their neighbourhood, perhaps with some supporting instructions specific to the reason for the task. For instance, Sikana (1994) asked Senegalese farmers to draw soil maps, a land classification task designed to elicit indigenous knowledge and systems of classification. We used a similar technique, described in Section 3.3. It uses theory and methods from the field of cognitive mapping in addition to personal constructs and mental models. 3.2 The Transect Study This study was designed to develop a method of eliciting people’s understanding of the way in which ecological processes work in pastoral landscapes. Issues we resolved are discussed below. 3.2.1 Developing the Methods Using the landscape to elicit mental models Extensive pilot testing, twenty interviews, was necessary before the method was ready for use with our sample of graziers, extension staff and researchers. Those who participated in the pilot interviews were drawn from our range of acquaintance - graziers from other catchments, current and former mature students with extension backgrounds, and research colleagues. At first we asked pilot interviewees to walk freely from the base to the top of a hill, themselves selecting places to stop and discuss what they saw. Their selection of stopping places indicated a lot about their personal interests (such as habitat for birds), but gave no basis for comparability between interviewees. We therefore used a transect approach (Conway and McCracken 1990), which had the advantage of focusing attention upon a particular belt of country. This still left interviewees with too much freedom to focus upon a wide range of topics, and us with the problem of lack of comparability between interviewees. We therefore marked out four sites on the transect, and conducted interviews at these. Sites were selected at features which in our view had hydrological significance. We briefed our interviewees about the boundaries of the land on which we wanted their opinions. For some questions it was the ‘site’ or belt transect, about 30m wide. For others it was specific features within the site. One question concerned the sub-catchment as a whole. Interviews obtained this way were comparable.

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Home ground, or neutral territory? We considered whether to interview graziers on their own land, or on land owned by one of them. We rejected these options as too threatening to the graziers concerned, because of the risk of their feeling self conscious about any land degradation evident. Another issue was the level of pre-existing knowledge of the land - graziers would have been highly knowledgeable about their own land, whereas extension officers and researchers might have been seeing it for the first time. Perhaps it would have been a valid comparison, because in practice researchers make generalisations about categories of land, whereas graziers manage specific properties, and extension staff use both approaches. In fact we used two interview locations, at least one of which was unfamiliar to all interviewees, and allocated interviewees to one they did not know (see study areas, Section 3.2.2). We remained equivocal about this issue, though, because in the land classification study (our other method, see below) we interviewed graziers on their own land, arguing that we wished to elicit their local knowledge of a specific site. The interview locations are described in section 3.2.2. Open-ended versus more structured questions The pilot testing showed that both open-ended and structured questions had merits. Open-ended questions were best used first, so that the interviewees could express their thoughts with minimal instruction from the research team. Structured questions then made useful probes to elicit further information (see section 3.2.4 and table 1). The precise wording of questions We found we needed a process verb, 'what is happening here', to prompt a process description (see table 3.1). The alternate versions we tested, such as 'what can you see', were less effective as some people listed features - grass, bare patch - without any suggestion as to their interconnections or meanings. It would have taken additional questions to find out why these features were perceived as important. Number of questions asked We used four sites along each transect because pilot testing suggested that more than four would be unlikely to produce sufficient new information to justify the extra time. The combination of open-ended and probing questions at each site, with two ‘whole-landscape’ questions at the end of the interview, gave a total of 23 questions at each study location. We reviewed the number of questions used after data analysis, by examining the rate at which information was accumulated with each additional question. Figure 3.1, based on the analysis of half of our interviews (those conducted at one of our two research locations, see below), suggests we chose an efficient number of questions. This is because the rate of accumulation of new information was declining rapidly when we stopped. It also suggests some questions were less efficient than others in eliciting new information - the fifth and the twelfth were inefficient, while the sixth and thirteenth were efficient questions. Figure 3.1 includes some double counting because it shows the sum of terms accumulated by individuals; many of these were mentioned by more than one individual in a group. Our categories of analysis, including components and sub-components, will be explained in later sections.

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Figure 3.1 Accumulation of Information in Relation to Number of Questions Asked

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Recording interviews We needed a verbatim record of each interview, as we would not know until later how we wanted to code the information. We were also interested in the precise words people used to express their thinking. Tape recording was undesirable, because of the risk of some interviewees feeling uncomfortable, and the logistics of ensuring a clear recording in the open air, often in windy conditions (with aeroplane noise at one of our locations). It would have been impossible to keep a microphone sufficiently close for clear recording, as people walked around the sites, or turned their heads away to look in a different direction. This left us with the issue of how to achieve reliable verbatim recording through notes. Training We used some of the pilot interviews to train all members of the research team in interview techniques, and in recording. Interview techniques included developing rapport, voice inflection, body language, timing of questions, and ways of prompting people verbally and non-verbally. To increase recording speed and accuracy, the team developed its own shorthand symbols for commonly used words, and practised speed writing through dictation tests. Team members were taught not to paraphrase, and were shown their initial tendency to do so during the practice dictation sessions. In the training sessions, we resolved questions such as what notetakers would do when getting left behind in an interview. (The interviewer would observe the notetaker’s progress, and ask the interviewee to slow down or pause if the notetaker seemed under duress. The notetaker could also signal when she or he needed some moments to catch up). 3.2.2 Study Areas We chose two locations for our interviews, to provide alternatives should potential interviewees be familiar with one of the properties, and to reduce the possibility that the accounts of landscape processes we elicited might be highly specific to one location. One transect was located on a property at Hall (ACT), the other on the Abattoir Holding

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Paddocks near Queanbeyan (NSW) (Figures 3.2 and 3.3). The transects were broadly comparable, in that each included trees at the second site, and a dam controlling an erosion gully. Our division of each transect into sites differed slightly, however. Soil pits were dug at some sites, faces cleaned, and a removable cover put over. Figure 3.2. Topography, Hall

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3.2.3 Selection of Interviewees Twenty-four people were interviewed, eight from each professional group - graziers, extension staff and research scientists. The graziers were all from Woolshed Creek catchment within the ACT. We asked them to participate because they were nearby, and because we had met them and had background information on their enterprises through previous research. Working near to our base reduced time and financial costs. We were aware in choosing these graziers that they would not be typical of graziers elsewhere because of their proximity to Canberra City and opportunity for dual-income occupations. This was acceptable to us because our main purpose was to develop methods. We were aware of the need for caution in making generalisations from our small and restricted sample, of ACT graziers, extension officers on the Southern Tablelands, and CSIRO soil, water and landscape scientists. We compiled a list of extension staff working in the ACT and nearby in NSW. They were employed by the Department of Conservation and Land Management of NSW (now Land and Water Conservation), and the Agriculture and Landcare Section of the ACT Department of Environment, Land and Planning. We interviewed all but two of the local extension officers. Researchers were all employed by the Commonwealth Scientific and Industrial Research Organisation (CSIRO). One was a landscape ecologist with the Division of Wildlife and Ecology, three were hydrologists with the Division of Water Resources, and four were soil and landscape scientists with the Division of Soils. They were selected for their research specialisations, availability, and willingness. . We did not seek gender balance in our experimental design. There were too few women in any of the occupational groups and our sample size was too small to incorporate this factor. We neither sought nor avoided a particular gender mix. Of the twenty-four individuals interviewed, only three were female, two researchers, and one extension officer. All interviewees were older than thirty. All extension officers were around the age of forty. Ages of researchers ranged between thirty and sixty-five. All but one of the graziers was over forty. All researchers and extension staff had undergone formal training. The majority of researchers had a PhD. All extension officers had a degree. Two graziers had attended short courses. 3.2.4 Interview procedure and questions Prospective interviewees were phoned to request an interview. The purpose of the research was explained, and an appointment made. On the day, they were collected by the interview team, briefed during the journey and again at the location at the start of the interview. The main briefing, given on the journey to the site is given below: We believe that communications among graziers, extension staff and researchers

could be improved. This would benefit land management. We are comparing the way graziers, extension staff and researchers think about the land. We are especially interested in how you think about the movement of water over and through the land. We also want to know how you think water affects the land, and is affected by it.

Interviews are done at selected places as we move down a slope. We will stop at

each place to ask our questions. We will ask two kinds of questions - open-ended ones, and more detailed questions.

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The two principal researchers and two research assistants (an ecologist and a social scientist in each category) conducted the interviews. The interviewing team always consisted of at least one social scientist and one ecologist, who exchanged questioner and note-taking roles during the interview. At least one principal researcher was present at most of the interviews, especially all of the earlier ones, and took a second set of notes to cross-check transcripts. The interview began at the highest site on the slope (Figures 3.2, 3.3). We pointed out the extent of the site, marked by pegs, and encouraged each person to walk around and look at it. Briefing instructions were repeated, focusing the interviewee upon landscape water processes. The interview itself consisted of open-ended questions (Table 3.1), followed by two to three specific questions designed to probe the interviewees’ construing of particular ecological processes, and to provide opportunity for the interviewee to elaborate on aspects raised under the open-ended questions. Soil pits were uncovered if the site had one, but we made no comment about the pit unless asked, and the interviewee was free to use or ignore it. The interview was concluded with two ‘whole-landscape’ questions, asked at the last site in both locations. Personal information on the interviewees was collected on the return journey. The interviews took about twenty minutes per site, including movement between the sites along each transect, giving a total of 1 hour twenty minutes for most interviews. The round-trip time between collecting our interviewee and returning him or her to base was up to three hours. In the transect study we offered the graziers a small payment ($50) in recognition of time foregone. 3.2.5 Analysis of Interview Information Each interview was transcribed verbatim, and the transcript checked against back-up notes. Discrepancies were discussed between notetakers, and resolved. We have concentrated on analysing for: 1. landscape features and characteristics mentioned (‘components’); 2. the degree and level of detail with which interviewees described features and

characteristics (‘sub-components’), and the extent to which they refer to properties of landscape features;

3. landscape processes described; 4. how interviewees used observed landscape features and characteristics to prompt or

support their own hypotheses; 5. interviewees’ use of definitive versus tentative language in their explanations of

processes. 3.2.6 Content Analysis Transcripts were analysed using ‘Content Analysis’. This is a qualitative method, involving the development and use of a ‘coding frame’ to classify sections of text into topics. Four coding frames were developed: for components, sub-components and properties; landscape processes; information that would be required by the interviewee if they were to manage

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the land; and interviewees’ awareness of cause and effect connections between the land they were viewing and other places. We saw no advantage in using a computer package for the content analysis. The creation of coding categories was the most difficult task, and since different terms could refer to the same coding category, considerable discretion was required on the part of the coding staff. A content analysis package would not have reduced the labour involved here. For recording purposes, we found it convenient to tally the use of the coding categories in EXCEL spreadsheets. The coding frames and guidelines for the classification of items were developed over several days by the two principal researchers and the research assistant who was to conduct the coding (she had not been one of the interviewers, and was thus ‘fresh’ to the material). The coding team met regularly, at least weekly, to check the consistency of coding and deal with queries. Consensus decision-making was used within the coding group.

Table 3.1 Interview Questions

Open-ended questions (asked at all sites in both locations): Can you describe what is happening here?

Prompt 1: What do you see as important about this place? Prompt 2: Why do you think its like it is?

Are there any other characteristics here which affect water? Are there any other characteristics here which are affected by water? Probing questions for Location 1 (Hall): Site A (hillside, with slump): When rain falls on this place, what happens to it?

What caused this? (pointing to the slump) Site B (next to soil pit, at slope break, by saplings): How is this site different from the last?

As these young trees grow, how will they affect water at this site? This site is now lightly grazed. What would change here if it were heavily grazed?

Site C (to west of the dam: includes dam and top of gully): (Referring to the little gully above the dam) - Why did that little gully form there?

Why do you think the dam was put here? Site D (valley flats, with soil pit): How does this site differ from the FIRST site? If you planted trees here, what effect would they have on water at this site? Probing questions for Location 2 (Holding Paddocks, ACT): Site A (hillside with contour bank and soil pit): When rain falls on this place, what happens to it? Site B (in valley floor above dam): How is this site different from the last? Why do you think the dam was put here? If these trees were cut down, how would that affect water on this site?

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Stock have been removed from this land. If it were grazed heavily, what changes would you expect in the land?

Site C (gully among pines): Why do you think this gully formed here?

What would be the effect of removing these pine trees? Site D (valley flats): How does this site differ from the FIRST site we looked at?

If you planted trees here, what effect would they have on water at this site? Whole landscape questions (asked at the last site in both locations): What other information about this land would you need to manage it properly? Do you think what is happening on this land will affect other places?

Coding landscape characteristics and features - the ‘Components coding frame’ References made to characteristics and features of the landscape were divided into one of eleven categories: Vegetation, Soil, Animals, Topography, Water, Atmosphere, Geology, People, Economics, Management, and Time. More detailed references under these terms were then coded either as sub-components (parts or details of the major component) or properties of the main component (Table 3.2). The complete coding frame is in the Appendix. Table 3.2 Examples of Components, Sub-components and Properties used in the

‘Components Coding Frame’

Components Sub-components Properties Vegetation Trees, Grass, Weeds, Pasture,

Leaves, Pines, Fodder, Timber

Cover, Shade, Density, Native, Improved, Productivity, Composition

Soil Profile, Type, Topsoil, Silt, Gravel, Subsurface, Clay, A1 Horizon

Sandy, Loam, Skeletal, Dryness, Water holding capacity, Bleaching, Gleying

Topography River, Hill, Valley, Dam, Gully, Gully head, Gully walls, Slope, Foot slope

Steepness, Stability, Undulating, Active, Flatness, Size, Cleared, Treed

Water

Rainfall, Surface water, River water, Ground water, Rain, Storm, Drought

Depth, Quality, Nutrient content, Salinity, Clarity, Intensity

A separate coding sheet was filled in for each interviewee. We recorded the first occasion each interviewee mentioned any component or sub-component. Subsequent use was not recorded. However, if the interviewee mentioned, for example, a tree species or part of a tree (sub-component) or a tree’s capacity to draw water from the ground (property), this was recorded once for each new piece of information. By this method we hoped to record the content and structure of mental models, while correcting for repetition.

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Coding landscape processes - the ‘Processes coding frame’: A separate coding frame was developed for landscape processes (Table 3.3).

Table 3.3 Examples of processes and sub-processes used in the ‘Processes coding frame’ Processes Sub-processes Erosion Cover ⇑ Erosion ⇓

Slope ⇑ Erosion ⇑ Deposition Flatness ⇑ Deposition ⇑

Slope ⇑ Deposition at break of slope ⇑ Runoff Vegetation ⇑ Runoff ⇓

Soil Saturation ⇒ Runoff Infiltration Dryness ⇒ Infiltration

Stock compaction ⇑ Infiltration ⇓ Surface water processes Tree trunks channel water

Water distributes sediments etc. Subsurface water processes Slope ⇑ Subsurface flow ⇑

Soil pores ⇒ Subsurface flow Salination Infiltration ⇑ Water table ⇑ Salinisation ⇑

Subsurface flow brings salt to surface Transpiration Vegetation ⇑ Transpiration ⇑ Linkages recognised: ⇑ ‘increases’ ⇓ ‘decreases’ ⇒ ‘affects’

Extraction of information from transcripts Table 3.4 illustrates the way in which information was extracted from the transcripts for entry into coding frames. Table 3.4 Extraction of information from the interview transcripts (coding-frame categories are in square brackets) “When there's very wet [soil property - wetness] layers [soil sub-component - layering] of

soil [soil - main component] you may have a situation where you've got water [water - main component] moving [water process - movement] very quickly through quite large [topography property - size] channels [topography sub-component - channels]. In treed [topography property - treed] catchments [topography sub-component - catchments] a lot [water property - volume] of water [water - main component] moves [water process - movement] that way. I guess the surface water [water sub-component - surface water] is going to be doing some redistribution [water process - redistribution] of nutrients [water property - nutrient content], probably”.

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The ‘Information required’ coding frame All answers to the question ‘What other information about this land would you need to manage it properly?’ were sorted by major and sub-components. Examples include useful species (vegetation - trees), palatability (vegetation - grass), and noxious (vegetation - weeds); slope (topography - physical), and past management (topography - historical, coded from its context). Coding frame for catchment influences All answers to the question ‘Do you think what is happening on this land will affect other places?’ were analysed to identify cause and effect relationships - for instance, ‘weeds on this property may infest neighbouring properties’ or ‘runoff from this property may decrease river water quality’. These relationships were sorted into the components to which they referred. Analysing for the use of indicators Landscape features the interviewees used to prompt or test hypotheses about processes affecting the landscape were termed ‘indicators’. For instance, growth or greenness of a pasture may indicate adequacy of soil water, nutrients or minerals, whilst mottling may indicate soil saturation. A list was compiled of indicators used and what they indicated. A coding frame was produced, consisting of a composite list of all ‘indicator/used to indicate’ groupings. This was sorted by the most relevant component (See Table 3.5). Transcripts were then coded to record who had used each indicator, and whether it was used to suggest or test a hypothesis.

Table 3.5 Examples of Indicators Indicator Used to Indicate ... Stunted unhealthy trees Drought stress Stunted unhealthy trees Low soil moisture Growth/greenness Soil moisture Growth/greenness Soil nutrients Silt River deposition Granite Erodable soil Soil profile Soil saturation Soil type Well drained Soil type Dispersive, prone to gullying Frogs eggs on dam banks Good healthy water Earthworm casts Earthworm activity

Definite versus tentative language Answers given to the first probing question asked in each interview, ‘When rain falls on this place, what happens to it?’, were used to analyse for interviewee confidence in their explanations of landscape processes. Words such as ‘is/is not’, ‘would/would not’ were classed as definite. Words such as ‘might/might not’, ‘perhaps’ or ‘I don’t know’ were considered tentative. The ratio of definite to tentative words used in the course of an answer was used to classify each interviewee as mainly confident or otherwise in their explanations.

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Other information from the interview transcripts Notes were kept on any interesting aspects, repetition of ideas or words and similarity in the overall format, of the answers given. Particular attention was directed towards: • subject areas for which the interviewee showed most interest or concern (areas of

comfort); • time scales mentioned. References to time were grouped into arbitrary time periods,

namely; Geological (millions of years), Historical (hundreds of years), Medium term (decades), Short term (weeks to years), Days (within days of the interview) and Undefined (used for terms such as ‘the past’, ‘short period’ etc.).

Modelling mental models So far our emphasis has been on analysing information content. Our next task was to represent and compare the ways interviewees thought about landscape water processes. The challenge was to be able to show chains of causation, and networks of interactions among landscape components as interpreted by interviewees. Buzan (1983) and Wilson and Morren (1990) have used ‘mind maps’ to represent such linkages. These are hand-drawn diagrams based on conversations. Nouns are placed in circles, which are linked by arrows labelled with verbs or verb phrases explaining the nature of the linkage. Alternatively conventional flow diagrams from systems analysis can be used. Our attempt to use them stalled when the diagram for a single interviewee became so complex that interpretation was difficult. Besides, the time for drawing diagrams for all interviewees was prohibitive. We therefore resorted to computer modelling of ‘influence diagrams’ - diagrams of causal networks. Bostrom and others (1992) used influence diagrams to characterise mental models of hazardous processes. Godet (1994) has used them in ‘structural analysis’ of more complex systems (an airport, for example) for forecasting, strategic planning, scenario development, and as a means for communicating. The method involves working interactively with stakeholders. They specify the system boundaries, list important system variables, specify direct causal links, and describe the nature of the links - whether influences are strong or weak, actual or potential, positive or negative and so on. The strength of structural analysis is its ability to identify indirect relationships implicit but not obvious in the direct linkages. For example, if a affects b, and b influences c, then a has an indirect influence on c. Such causal chains can be very long in a complex system, and form complex networks of interactions - a matrix of several dozen variables can include several million interactions (Godet 1994). Networks and chains of interaction can be traced (Walker, 1997) using matrix multiplication or graph theory. Variables are then classified according to the way they inter-relate with other variables: • influential variables are those which influence many others through their multiple

linkages (direct and indirect), but are not themselves subject to many influences; • dependent variables are those which are strongly influenced, but are not influential; • relay variables link multiple influential and dependent variables. We adapted influence diagrams to the representation of mental models. We identified components by content analysis, and identified pairs of cause and effect links (Table 3.3) (for instance ‘rainfall affects runoff’). We pooled the results of each professional group -

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for instance the results of all eight graziers were combined. The components were then arrayed in matrices, one for each professional group, and another which combined the results of all 24 interviewees. Table 3.6 is a highly simplified illustration. A direct influence is identified by ‘1’ in a cell. Next, the program INFLUENCE (Walker, 1997) was used to generate indirect linkages. We then used the decision support package VENSIM (Ventana 1995) to generate ‘influence diagrams’ for each group and for a composite model representing all interviewees. These show direct and indirect influences up to the chosen chain length, which we set at six. Table 3.6 Part of a Matrix of Direct Influences (imaginary data) Variables Affected Causal variables Rainfall Runoff Infiltration Soil Moisture Etc Rainfall 1 Runoff 1 Infiltration 1 Soil Moisture 1 1 Etc We regard these influence diagrams as representing collective mental models - our ‘model’ of the mental models. By comparing selected parts of the influence diagrams between groups we can see differences in structure, complexity and logic. The composite generated by pooling all of the interviews illustrates the level of complementarity among the three groups. While we are confident that our approach is informative, it has limitations. First, our re-construction of interviewees’ mental models is itself only a model, which rests on assumptions which may not be shared by our interviewees. These include assumptions that: • landscape ecology can be modelled as a set of interacting components (eg. soil type;

rainfall) which have particular properties; • components are linked through processes, such as surface flow, evapo-transpiration and

so on; • processes are driven by cause-and-effect relationships between components; • such a system can be represented as a flow chart, or ‘influence diagram’. This aspect of our work therefore tends to impose a ‘logical-positivist’ mode (Halfpenny 1982) upon the mental models of people for whom this mode may be inappropriate. It is likely to suit minds trained in a natural science - researchers and extension officers - but its suitability for interpreting mental models of graziers is still in question. A second limitation of our approach is this: the complexity of our representation of an interviewee's mental model depends only partly on the properties of that mental model. It depends also on our skill in eliciting each person’s mental model, and the ability of each interviewee to communicate those properties to us verbally. Thus the representations we made below of their mental models may have been depleted or distorted by the process of communication. Individuals are likely to differ in their ability to communicate. So too are professional groups. Researchers and extension officers are used to communicating ideas about landscape water processes verbally. Graziers need to do this less often, and may be less skilled at it. Our representations may therefore be biased by some linguistic factors.

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A further question about the method is the extent to which interviewees’ personal mental models took account of the multiple indirect interactions among variables. Interactions were generated by computer, using the logic that if X affects Y, and Y affects Z, then X affects Z indirectly. This does not establish that the mental model of the interviewee also contained the indirect as well as the direct linkages. Thus the huge complexity of the full influence diagrams - so complex that we can only present selected pieces - may be an artefact of the method. Finally, there is the question of the meanings attributed to words by interviewees (Buzan 1983). These meanings may vary between individuals, and, depending on the context, may be used in different ways by the same individual in the same conversation. We treated this variation as a subtlety, and where necessary interpreted meanings from their context and coded accordingly. Within the limitations of this approach, interviewees were free to express their mental models in a wide variety of ways, including: • specifying the boundary of the system (spatial, time, social and so on); • the type and number of components referred to; • specifying which components are linked, and the nature of each link. Results of our structural analysis are in Section 5. 3.3 The Land Classification Study This method was developed for eliciting spatial aspects of mental models of land researchers and land managers. It is based on cognitive mapping procedures (Section 3.1.2). Five graziers were each asked about a portion of their own properties, and were paired with a researcher who commented on the same parcel of land. Each interviewee was asked to classify the site using whatever criteria he or she thought relevant. A classification map was drawn as the interview progressed. The two maps and the classification criteria for the same site were compared and contrasted. 3.3.1 Selection of Properties A number of graziers in the Woolshed Creek catchment, the same catchment which provided interviewees for the transect study, were asked for permission to inspect their properties, with a view to participation in the study. All of the properties were in close proximity to one another. Five with varied topography, soils and vegetation (Chen unpubl.), were selected. They ranged from 81 to 567 hectares. On each property a site was chosen, using existing fences or paddocks where possible. Where the use of entire paddocks was not feasible, other boundaries were delineated by natural features and these were marked, using stakes and/or marking tape. The sites varied in size between properties. They were chosen to include a range of land types which the grazier and researcher might be expected to classify as different units.

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3.3.2 Selection of Interviewees The lessees of the properties were interviewed, so that each grazier described his own land. All were men, because men were the primary managers of the properties selected. Researchers were chosen for their interest in soils and land assessment. Most had trained as soil scientists or were working in that field at the time. Soil specialists were selected because soil is regarded as a key element in determining land capability and suitability. 3.3.3 Interview Procedure A true-colour aerial photograph of each property was enlarged, attached to a clipboard and covered with a clear plastic overlay on which the site boundaries were outlined. The owners of the properties selected were recontacted by phone, the proposed field work and research was discussed, and an interview requested. Graziers were interviewed first on their own properties, followed within a short time by an interview with a researcher on that property. Each interviewee was accompanied to the site, and rapport was established through conversation on the way. Upon arrival the boundaries of the site and the format of the interview were explained. In a standardised briefing, each interviewee was asked to classify the site using whatever criteria he or she chose. All were offered the use of soil testing kits, an auger, shovel and water. The interview was conducted thereafter without specific questions. The interviewer guided the interview to the extent of focussing on the site classification but did not ask the interviewee to use any particular approach to classifying the land (see below). Subject and interviewer walked the site, the interviewer asking the subject to point out and describe areas seen as different. The interviewees’ classifications were recorded and boundaries drawn on the overlay as directed by the subject. The interviewee was asked to explain why the area had been so classified. Each interview was recorded verbatim in writing. During the interview questions were asked to clarify a point, to probe more deeply into reasons for the classification or to direct the conversation back to classifying the land if necessary. No interviewee was asked specific questions or asked to comment on a particular land feature although each was free to do so if he or she chose. Each interviewee took the time he or she needed to complete the land classification. At the conclusion of the walk each interviewee was asked some background questions on their age and education. Each grazier was also asked about property size, and how many years he had spent on the present property and on the land overall. Each was also asked how he obtained and obtains his agricultural knowledge. Each researcher was asked the number of years he or she had been involved in land assessment. Each interview took from one and a half to just over two hours. 3.3.4 Analysis of the Land Classification Study Comparison of land classifications The graziers’ and researchers’ land classifications were marked with lines and symbols on the overlays covering the enlarged aerial photographs. Variations and similarities in land category boundaries were evident from visual inspection. To assist comparison, several points were chosen on each site, to fall on a classification boundary as defined by one or

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both interviewees. The descriptions and classifications, and the criteria used to classify the land at each of these points, were then compared. Comparison of reasons for land classifications Content analysis was used to analyse the verbal explanations of personal land classifications given during the site interviews. Two transcripts, one grazier's and one researcher's, were selected randomly and a coding frame developed. The coding frame consisted of nine categories: soil, water, vegetation, landscape elements, geology, climate, history, management and tools. Water erosion was classified with water, as were dams and creeks. Landscape elements included aspect, slope and shape of the land. History included geological history and history since white settlement. Management included pasture, weed control, animal and general property management. Vegetation included trees, grass, both native and introduced, and weeds. 'Tools' referred to those tools that were used in classification, such as aerial photos, geological maps, previous scientific field research, soil analysis kits, Munsell colour charts, and soil classification methods. Other categories are self-explanatory. The samples were returned to the total set and the coding frame applied to the whole set of transcripts. 3.4 Workshops Our research design included two seminars, both held in September 1995 while the results were under analysis. The first, held exclusively with those who participated in the 24 interviews and their spouses, was designed to give the participants the first preview of our results. This was held in combination with a barbecue at the University of Canberra. The response from researchers and extension staff was positive, but despite our efforts to consult them about their preferred form of feedback, date and venue, only one grazier couple attended. We received valuable feedback and suggestions for our analysis from this workshop. The second workshop, held at the Centre for Resource and Environmental Studies, Australian National University, explained our method, previewed our results and sought advice from a selected group of peers. Participants included our funding body, academics engaged in related research, people engaged or interested in agricultural extension work, and some of the graziers who had participated in the pilot interviews. Part of this workshop was spent in group discussion of the usefulness of the research method and its results in Landcare applications, evaluated from the standpoints of graziers, extension staff and researchers. 3.5 Costs The total cost of this project, covering both methods, was $61,700. This included salaries and on-costs, as well as operating costs. A high proportion of this was invested in the development of methods, an investment not needed every time either method is applied. We are providing estimates of the time taken to carry out the two methods, and the level of skills needed, so that potential users can estimate costs for themselves, taking into account salary levels, equipment hire, travel and accommodation.

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Table 3.7 Cost Components of the Transect Method

Activity Time Comments Training of interviewers

5 hours or more

Amount of time depends on experience in the specific method. Interviewers would probably be graduates. We preferred to have a social and a natural scientist working as a pair.

Soil pits dug by backhoe

20 mins per pit, 10 for digging, 10 for re-filling

Add travel time. Number of pits depends on complexity of landscape.

Pilot interviews 1hr 20 mins each

Number of pilot interviews needed probably 5-10, depending on complexity of interviews. Two interviewers recommended per interview. Add travel time.

Main interviews 1hr 20 mins each

Number depends on purpose of interviews, variability between interviewees, number of groups, level of precision required, and other factors. Add travel time.

Interviewees time

1hr 20 mins per interview

A rate should be negotiated with interviewees. Add travel time.

Analysis The ratio of analysis time to interview time is between 10:1 and 25:1

Type and depth of analysis depends on purpose. Content analysis is laborious and requires skill and judgment, and training in most cases. If more than one person is doing it, there must be very careful standardisation. For large samples use of a computer is justified. Producing influence diagrams is not hard, provided software is available. VENSIM costs around $2000 a license. INFLUENCE is not commercially available.

Administration Time required hard to estimate.

This includes project coordination, transport and interview arrangements, financial management, and so on.

Table 3.8 Cost Components of the Land Classification Method Activity Time Comments Training of interviewers

Amount of time depends on experience in the specific method. Interviewers would probably be graduates. Discipline may be irrelevant, so long as breadth of understanding is sufficient.

Pilot interviews 1hr 30 to 2 hours each

2 or 3 should be sufficient. Add travel time.

Interviewees time

1hr 30 to 2 hours per interview

A rate should be negotiated with interviewees. Add travel time.

Analysis The ratio of analysis time to interview time is between 10:1 and 25:1

Type and depth of analysis depends on purpose. Content analysis is laborious and requires skill and judgment, and training in most cases. If more than one person is doing it, there must be very careful standardisation. For large samples use of a computer is justified.

Administration Time required hard to estimate.

This includes project coordination, transport and interview arrangements, financial management, and so on.

Both our methods provide options in cost and intensity of effort which fall between detailed qualitative approaches such as ethnography, and wide-scale quantitative approaches based on survey. In ethnographic methods, a researcher or very small team typically spends many months in intensive observation of a small group of people. At

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another extreme is surveys. These are most suitable for testing hypotheses on large samples, particularly where generalisation of findings across a wide population is required. A participant in one of our workshops likened our method to rapid rural appraisal (RRA), a suite of methods designed to provide some of the richness of ethnographic methods, in less time and with less intensity of involvement with the communities concerned. Indeed, transect walks are part of the set of RRA methods used frequently in developing countries. RRA seeks much of the richness of an ethnographic approach, without requiring years in the field. Our method is excellent for finding out how people think about landscapes, and could be varied to encompass other issues in farm and land management. It represents a very efficient way of eliciting in-depth information, which surveys are incapable of providing. It is not suitable for large samples. Where breadth of information and validation across samples representative of the population at large are the over-riding concerns, surveys remain the method of choice. 3.6 Ways of Streamlining the Transect Method While other research teams can benefit from our groundwork, as with any method they will need to customise it for their own purposes. Just as a good survey needs preliminary qualitative research in order to formulate and test the questions, this method requires choice of sites and some pre-testing of the most appropriate questions, or versions of our questions, to use in each new application. This could include the choice whether to use a structured interview format in which each interviewee is asked exactly the same questions (whether open-ended or more specific questions), or a semi-structured format such as the one we used in the land classification study. Future users of our transect method could reduce costs by customising it in the following ways: • Reduce the interview length, by reducing the number of interview sites within the

interview transect or the number of questions asked at each site; • After training, delegate the interviewing to assistants; • Avoid verbatim note-taking and transcription by recording only the content which will

be entered in the coding frame. In our case, this would mean any mention of a component, sub-component, property or process, and the cause-effect linkages or ‘if-then’ lines of reasoning involved. If other teams are interested say in the particular terminology or idiom used, this would be recorded. Depending on the skill of the field interviewers, it may be necessary to develop a coding frame from pilot interviews first.

The results of the transect and land classification studies are reported in Sections 4 and 5. Section 4 emphasises the components, sub-components and properties of components noted by our interviewees, while Section 5 represents the mental models of the three professional groups participating in our transect study in terms of ‘Influence Diagrams’.

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4. Comparing the Contents of Mental Models - Landscape Features

This section and the next are concerned with differences in the content of the mental models of graziers, extension officers and researchers. In this section, we focus on the landscape features - components, sub-components and their properties - our interviewees identified. In Section 5, we describe and analyse the landscape processes they commented on.

In both sections we try to answer these research questions:

• do our interviewees’ mental models differ in their structures, shown by the range of components, sub-components, properties of components and landscape ecological processes mentioned? This relates to Kelly’s contention that everyone’s construct system is unique, and also to his concept of range of convenience, the range of situations within which the construct system works conveniently for predicting events (1955:137);

• do the models differ in their focus of convenience, or issues for which they are most

useful (Kelly 1955:10-11, 137) ?; • to what extent do the models of individuals and groups overlap? In his commonality

corollary, Kelly proposes that to the extent that two people share constructs, their psychological processes are similar. In his sociality corollary, he proposes that to understand another person, one need not share the same constructs, but be able to construe how the other person is construing?;

• to what extent do the interviewees use hypothetical reasoning (described by Kelly as ‘if-then’ questions) and indicators to test hypotheses?;

• are there differences in the spatial mental models of researchers and graziers?. We explored the first four questions with our transect study, and the last in our land classification study. 4.1 Transect Study

Section 3.2.6 described the content analysis procedure, by which the interview transcripts were coded into a number of categories: • the major components, sub-components and properties of components described; • the major landscape ecological processes described; • examples of hypothetical reasoning, using landscape indicators; • information required to manage the land, and awareness of cause and effect relationships

between the study area and other places.

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A complete list of the components and their related sub-components and properties is given in the Appendix. The processes are listed in Section 3, Table 3.3, and outlined diagrammatically in Section 5. In the following analyses, we distinguish between two different kinds of statistical inference. In the first, we test a hypothesis based on theory. If the hypothesis is supported, we reach tentative conclusions about the populations our samples, though small, represent. In the second kind we observe a pattern in the data, make an ad hoc hypothesis, and test for its statistical significance on the same data set in which we observed the pattern. Even if the test supports the hypothesis, we cannot infer anything about our local populations of graziers, extension officers or researchers because we did not conduct the test on fresh data. All the test shows is that there is a statistically significant difference between categories in our data, which may or may not represent the populations from which it was taken. 4.1.1 Analysis of the Frequency of use of Components, Sub-components and Properties

The names of the 24 interviewees were arranged against a list of all components, sub-components and properties mentioned (Appendix). The total number of components, sub-components and properties used by each group was then calculated in two ways: 1. The sum of the frequencies of use by individuals within each of the three groups. The

total for researchers was 1071, extension officers 1046, and graziers 858. These totals included instances when the same sub-component or property was used by more than one individual in a group.

2. The second way was to count each time a component, sub-component or property was

used as one instance of use, regardless of how many individuals in the group used it. Researchers as a group used 438 different classifications, extension officers 442, and graziers 438. There is therefore no difference in the number of sub-components and properties recognised by the groups as a whole. When pairs of direct cause-effect relationships were identified in the content analysis of processes such as run-off and infiltration (see Sections 3.2.6 and 5), graziers collectively noted 85, and extension officers and researchers noted 112 each, implying a greater complexity. However, the difference may be due to the greater communication skills of extension officers and researchers.

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Figure 4.1 Frequency of Mention of Components

Topo

grap

hy

Soi

ls

Veg

etat

ion

Wat

er

Man

agem

ent

Ani

mal

s

Geo

logy

Peo

ple

Atm

osph

ere

Tim

e

Eco

nom

ics

0.0

1.0

2.0

3.0

4.0

5.0

Percent frequency of mention

Percent frequency of mention = 100 x number of terms mentioned in a component by a group/ total number of terms mentioned in all components by all interviewees

extensionresearchers

graziers

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Figure 4.2 Frequency of Mention of Sub-components and Properties

0.00 0.50 1.00 1.50 2.00 2.50 3.00

T/Topographic zones

T/Topo sub-zones 1

T/Topo Properties

T/Topo sub-zones 2

T/Aspect

Soil properties

Soil constituents

Soil types

V/Veg properties

V/Plant species

V/Type of plant

V/Plant groups

V/Plant parts

V/Plant uses

W/Water Properties

W/Water types

W/Seasons/events

W/Water sub-types

M/Activities

M/Infrastructure

M/Equipment 1

M/Grazing

M/Equipment 2

M/Animals

M/Management - misc

A/Animal groups

A/Animal types

A/Animal properties

A/Animal outputs/parts

Geology sub-comp 1

Geology sub-comp 5

Geology sub-comp 3

Geology sub-comp 2

Geology sub-comp 4

P/Types of people

P/Skills and methods

P/Impacts

P/Occupations

At/Atmos sub-comp 3

At/Atmos sub-comp 2

At/Atmos sub-comp 1

Time

Economics

Sub-

com

pone

nts

and

Prop

ertie

s

Percent frequency of mention

ExtensionistsResearchersGraziers

Percent frequency of mention = 100 x number of terms mentioned in a sub-component by a group/ total number of terms mentioned in all sub-components by all interviewees

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Figure 4.3 Variation in Frequency of Mention of Components

Eco

nom

ics

Peo

ple

Geo

logy

Atm

osph

ere

Ani

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Veg

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Man

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Tim

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Soi

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Topo

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ME

DIA

N

ME

AN

0

100

200

300

grazierresearcher

extension

Coefficient of Variation, %

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A cluster analysis was carried out on the matrix of interviewees and components, sub-components and properties. Individuals did not fall into clear groups, showing there is much overlap among the groups in their use of components and sub-components and properties. Figure 4.1 shows the mean percentage frequency of use of components. Figure 4.2 shows sub-components and properties. The base for calculating these percentages is the total number of categories used by all three groups - 742. Differences in patterns of relative emphasis on the various components, sub-components and properties are suggested. Figure 4.3 shows the coefficients of variation for each group and each component, based on an arcsin transformation of the proportional frequencies using a base of 742. There is very large variability between individuals within each group. The statistical significance of differences in the frequencies of use of categories were tested using a generalised linear model based on the binomial distribution. Analyses were carried out by Warren Muller of the CSIRO Biometrics Unit. A logit link function was specified (McCullagh and Nelder 1983), where:

n = ln[u/1-u] n = linear predictor ln = natural logarithm u = expected value

The base for calculating proportional frequencies was the combined number of sub-components and properties within each component. The algorithm weighted proportional frequencies according to the base used, giving lower weight to sub-components within a component that was mentioned infrequently. Experimental design was a nesting of interviewees and groups. Results of the first analysis are in Table 4.1. Table 4.1 Analysis of Deviance: Use of Components Probability of

statistical significance % Total Deviance

Accounted for Analysis of Deviance <0.005 66.7 Group >0.100 3.1 Individual <0.005 15.2 Component <0.005 36.8 Group. component interaction <0.005 11.6

The analysis as a whole is highly significant, and accounts for two thirds of the variability in the data. It indicates there is more difference between individuals than groups, and a highly significant difference between components. The substantial interaction between groups and components suggested it was worthwhile to analyse components individually. Time, and Economics, which had no sub-components, were not analysed. Results are in Table 4.2. The analysis of deviance for Atmosphere was not significant, and further statistics were not calculated.

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Table 4.2 Analysis of Deviance of Separate Components

Group

Individual

Sub-component

Group sub-comp interactions

Analysis of Deviance

p % p % p % p % p % Topography * 5.7 ** 23.0 *** 14.8 *** 11.8 *** 55.4 Soils *** 30.2 *** 17.8 *** 39.1 0.8 *** 87.8 Vegetation 1.2 *** 18.9 *** 42.1 4.5 *** 66.8 Water *** 20.4 8.5 *** 22.6 6.9 *** 58.5 Management * 5.1 *** 19.0 *** 31.8 *** 9.3 *** 65.2 Animals 2.7 *** 35.5 *** 21.1 6.3 *** 65.7 Geology 0.3 *** 36.1 2.1 *** 17.4 *** 56.0 People 5.8 *** 42.5 3.7 ** 9.2 *** 61.1 Atmosphere nc nc nc nc nc

Key

p = probability; % = % total deviance accounted for by analysis of deviance; nc = not calculated *** = p<0.01; ** = p<0.05; * = p<0.1; a blank cell = p>0.1

In the components Soils and Water, groups account for more of the variability than do differences between individuals. The reverse is true in other components. However, there are significant interactions between group and sub-component, (ie. sub-component/ properties) in the case of Topography, Management, Geology and People. A substantial proportion of variability is accounted for by sub-components/properties, with the exceptions of Geology, People and Atmosphere. The relative importance to each group of the sub-components/properties of Soils, Water, Topography, Management, Geology and People is summarised next, based on Figure 4.2 and the Appendix. Table 4.3 Sub-components/Properties Emphasised by Groups Group Soils Water Topography Management Geology People Graziers Equipment 1

Activities Types and details of geological structures 2

Extension officers

Types Aspect, Zones Sub-zones 1 3

Grazing, Infrastructure

Type of geological structure 4

Occupations, Skills & Methods

Researchers Types, Constituents, & Properties

Sub-types, Seasons/ events, Properties

Zones, Sub-zones 5, Properties

Animals Geological structure

Impacts, Types

1 Management sub-components 2,3 4 Geology sub-component 2 2 Geology sub-components 3, 4, 5 5 Topography sub-component 3 3 Topography sub-component 2 6 Geology sub-component 1

See Appendix for details within these classifications

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4.1.2 Use of Indicators

Analysis of our interviewees’ use of indicators while interpreting the land (Section 3.2) is a convenient way of illustrating their hypothetical reasoning. They used indicators either to prompt or support a theory (Table 4.4) Table 4.4 Use of Indicators to Prompt or Support Theories

Indicators used to prompt a theory: Prompt Theory

“There's frogs eggs on the banks... it must be good healthy water, good clear water.” “The dandelion population... suggests the pasture is not in very good condition""The nature, size and incidence of the old tree growth...

is indicative of soil quality and depth”

Indicators used to support a theory: Theory Support

“So this area could be more fertile than higher up slope....

In fact we've got some weedy species which like more fertile conditions, which could bear that out.”

“It's salinised.... hence the patchy establishment of the phalaris (grass species).”

“There's soil moisture here... evident in the growth of the sown species.” The majority of graziers used indicators to prompt a theory, while the majority of researchers used indicators to support a theory. Extension staff used both approaches. The interviewees’ ‘areas of comfort’ were also evident in their use of indicators. For example, weeds to a researcher may indicate fertile soil, whereas to a grazier they indicate poor pasture and an inadequate management regime.

Analysis of the residuals of Chi2 (Everitt 1977) was used to test the significance of differences in the frequencies with which the three groups used indicators. Table 4.5 suggests the graziers used management indicators more than expected, but paid less attention than expected to soil indicators. The researchers used soil indicators more than expected. Some interviewees were tentative in their interpretation of the land, others more definite in their conclusions. We made an ad hoc hypothesis that the researchers were taking a scientific approach (tentative), and the other two groups, with their emphasis on the need to take management action,

were being more definite. Analysis did not support our hypothesis (Chi2, p>0.1). We perceived also that the three groups used different time-scales, researchers focusing on geological and historical scales, graziers on the shorter-term. This view was not supported by analysis either (p>0.1).

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Table 4.5 Significance of Adjusted Residuals for Indicators Component Graziers Researchers ExtensionistsVegetation ns ns nsSoils p < 0.01 (-) p < 0.01 (+) nsTopography ns ns nsWater ns ns nsManagement p < 0.01 (+) ns ns

(+) means observed frequency of use of indicators greater than expected (-) means observed frequency less than expected ns = not significant, p>0.05 4.1.3 Qualitative Analysis

Two trends became evident when analysing the interview transcripts. Firstly, interviewees would approach and answer a question from a particular and often quite predictable angle. The researchers tended to address and answer the majority of questions in a theoretical manner. The graziers adopted a more practical outlook and answered as though they themselves were the owner and manager of the land in question. The extension staff exhibited both theoretical and practical lines of thinking. The type of answer given by an extension officer appeared to be determined by that individual’s perception of the question. If the question was perceived to be predominantly theoretical, the extension staff often called upon their theoretical knowledge to answer it, answering as though speaking to a group of researchers. If the question was perceived to warrant a more practical answer, extension staff would think practically, and often answered as though speaking to a group of graziers. This capacity on the part of the extension staff we interviewed appears very useful for their communication role. The second trend which became evident concerns the content and flow of the answers given. Generally, interviewees would address each question directly in the first few lines of their answer, perhaps whilst the question was still fresh in their mind. After this time, however, answers began to drift away from the question topic towards subject areas the interviewees were obviously interested in, familiar with and comfortable talking about. We called these topics the interviewee’s ‘area of comfort’. We believe it corresponds with Kelly’s view that each construct system has at least one ‘focus of convenience’ (Section 2). Areas of comfort are listed in Table 4.6. It is evident from the table that Management is an area of comfort for all graziers. Researchers either exhibited Soils as an area of comfort or showed none. Similarly, extension staff did not have an area of comfort, or exhibited a unique area of comfort. The diversity of areas of comfort in the group of the extension officers coincides with the relative evenness of their emphasis upon the whole range of sub-components and processes (Figures 4.1 and 4.2). The similarity in areas of comfort among graziers and among researchers coincides with the tendency of these two groups to specialise in their concerns.

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Table 4.6 Areas of Comfort Areas of comfort favoured

Graziers all 8 used management

Researchers none identified for 4; the other 4 used soils

Extension officers none identified for 4; 1 used soils and history, 2 used management, 1 used geology

Areas of comfort tended to be related to interviewees’ land management concerns. This was exemplified in answers given to the question, “What other information would you need to manage this land?” (Table 4.7). The researchers were predominantly concerned with conducting research for land management. The graziers were concerned with better management in order to sustain production and the extension staff were aware and empathetic of both. Table 4.7 Examples of Areas of Comfort shown in Answers to “What other

information would you need to manage this land?” Group Topic alluded to in

answer Answer quote

Researcher Computer-based modelling

“If you did this (modelling), it could be replicated in many other parts of the Southern Tablelands...we don’t have many good quantitative analyses, even of a system like this.”

Researcher Analysis “You could do some soil permeability tests...You’d want to put in some piezometers just to check where the water table...You could do an electromagnetic survey just to check that salinity wasn’t liable to be a problem in the future.”

Grazier Fence locations “The main information I’d like to have would be, would the fences stay where they are or could I shift them...now eliminate those corners, eliminate the over-grazing as far as carrying capacity goes, everyone carries that different, what some people consider 2 sheep per acre country some consider as only one, but some will run 3.”

Grazier Landuse planning “I would want to know what other soil types are available or exist on the property. For example, if there weren’t any other reasonably level areas like we’re on, I’d be thinking of planting oaks, utilise it for cropping.”

Extension Protecting land “I have two environmental aims: the first is to protect the productivity of the land, and the second is to protect the water bodies that the land drains into”.

Extension Managing land “I’d want to know his short and long term aims for the property: whether he had money to put into it, because it’s silly saying control the weeds and grow good pasture if he didn’t have the money to do it... because you got to keep him in production, you don’t want to send him bankrupt... you’re balancing what the land needs and what the grazier needs.”

This ends the presentation of findings from the transect study. They are summarised at the end of this chapter. The next sub-section is about the classification of land by graziers and researchers.

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4.2 The Land Classification Study

4.2.1 Introduction

This section explores the differences between researchers and graziers in the ways they classify land. Extension officers were excluded for logistical and budgetary reasons. The section is drawn from Herbert (1995). The method is described in Section 3.3. Five graziers were interviewed at a site on their own properties, and paired with five researchers commenting on the same sites. In contrast with the structured set of questions used in the transect study, these interviews used semi-structured questions. 4.2.2 Use of Classification Categories by Graziers and Researchers

Using content analysis (Section 3.3.4), components and sub-components and phrases used in each interview were placed in one of nine categories - soils, water, vegetation, climate, geology, landscape elements, management, history or tools. 'Tools' refers to equipment that the interviewee needed to assist in the classification. These included auger, shovel, soils testing kit, results from previous work in the area, geological maps and aerial photographs. These tools were used solely by the researchers. Graziers probably had the tool of local experience, the use of which was not obvious. Both the graziers and the researchers used soils extensively when classifying sites. However, use of components and sub-components within that category was very different (Table 4.8). The graziers discussed the soil in terms of management and productivity. They talked of it descriptively, referring to it as being on granite (incorrect), shaley or alluvial, gravelly, silty, powdery, productive, rich and other terms. No grazier used formal technical terms. The researchers investigated physical properties and classified soils into Great Soil Groups. This summarises some characteristics of soils, though not those relevant to production (Webster and Butler 1976). The researchers discussed duplex soils, and variation in soil colour because of position in the landscape. They discussed pH, structure, horizons, depth and colours. The graziers, when talking of geology, tended to focus more on exposed rocks, while the researchers talked as well of underlying geology and geological history. This appeared to have some influence on their land classification. Land management was a major factor in the classifications of graziers. The grazier who talked least of management does not live on his land, and has leased it for a relatively short period. Only one researcher talked of management to any extent and he had previously done extension-related work. In conjunction with his soil classification, he used Land Capability Classification (Sonter 1991) as a guide to appropriate land uses for the site. The graziers mentioned recent and management history, while the researchers referred to geological history, with some interest in land use history since white settlement of the area. Three graziers and three researchers felt they may be influenced by seasonal differences in how they saw and classified the land, whilst the remainder felt that their classification would not be affected.

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Table 4.8 Use of Classification Categories by Graziers and Researchers

Category Graziers Researchers

Climate talked of local climate variations; their effects on management; important in classification

barely considered it

Geology talked of surface rock mostly; how it affected management, pasture growth

discussed underlying geology; its importance in soil formation

History mostly more recent history, including management history

geological, with some interest in land use history since white settlement

Landscape elements

occasional mention of aspect and slope in relation to exposure to west and drying effects

both aspect and slope mentioned; position in landscape and shape of slope discussed

Management major influence on classification scarcely mentioned

Soils descriptive - did not classify according to any classification methods used by soil researchers; discussed in terms of productivity; important in classification

descriptive and used the Great Soil Groups as a classification device; the major classification device

Tools not used important in soil classification

Water availability of water to stock; tended to regard erosion as a thing of the past - present management practices were sufficient to prevent further problems; important in classification

talked in terms of its erosion potential; saw erosion as active; recognised early signs; expressed concern about it

Vegetation used it as an indicator of changes in soilfertility and soil depth; important in classification

not used to the same extent; usually only trees usedas indicators, if mentioned at all

4.2.3 Statistical Analysis

Percentages for each person’s use of each component were derived from the raw data in the content analysis. For example, if a person made 20 different references to components or sub-components, and two of those references were to vegetation, that person was counted as having referred to vegetation with a frequency of 10 per cent. Medians, interquartile ranges and spread in the use of the categories among the graziers (Figure 4.4) and the researchers (Figure 4.5) were compared. Figure 4.4 suggests that the graziers emphasised Management, Soils, Vegetation and Water considerably more than Climate, History, Geology and Landscape elements. They did not use terms relating to tools at all. The researchers used terms related to soils more often than they used other categories for classification (Figure 4.5). The graziers used a broader range of criteria than the researchers. Frequency counts were not normally distributed, and data were paired, so a Wilcoxon matched-pairs signed ranks test was indicated. The null hypothesis is that there was no difference in the use of various land classification categories between graziers and researchers. Results (Table 4.9)

39

showed that researchers used components and sub-components relating to Soils, Landscape elements and Tools significantly more often than graziers (P<0.1) whilst graziers used components and sub-components relating to Vegetation and Management significantly more often than researchers(P<0.1). This confidence level was acceptable, considering the small sample size and exploratory nature of the research (Sokal and Rohlf 1981). Table 4.9 Comparison of the use of Components and Sub-components by Graziers and Researchers. Wilcoxon matched-pairs signed ranks test.

Character Wilcoxon value Probability Soils T - = 15 0.06 Water T+ = 11 ns Vegetation T+ = 15 0.06 Landscape elements T - = 15 0.06 Geology T - = 10 ns Climate T+ = 13 ns History T - = 8 ns Management T+ = 15 0.06 Tools T - = 15 0.06

Confidence level p <0.1. ns = not significant. Figure 4.4. Variation in Use of Classification Categories by Graziers

Use of categories

50 interquartile range

40

% 30

20 median

10

0

clim

ate-

geol

ogy-

hist

ory-

land

scap

e el

emen

ts -

man

agem

ent-

soils

-to

ols-

vege

tatio

n-

wat

er-

40

Figure 4.5 Variation in use of Classification Categories by the Researchers

Principal Component Analysis was used to identify groups of inter-correlated variables and show if they could be represented by a smaller number of underlying factors (Diekhoff 1992). The primary purpose was data reduction. The variables were the nine components from the content analysis. Sample size was too small to draw conclusions about graziers or researchers in general, but inferences could be made about these particular groups and the sub-cultures they represented. The resulting plot for each person is considered a statistical representation of the relative importance of the various land classification criteria for that person. Collectively the plots represent the similarities and differences between the two groups. Eigenvalues represent the amount of variance accounted for by a factor. A factor with an eigenvalue greater than 1.0 explains at least as much variability in the set of original variables as is found in one of those variables (Diekhoff 1992). Three factors with eigenvalues greater than one were chosen (Table 4.10). Standardised factor scores were calculated for each individual, and presented in Figure 4.6.

Use of categories

70

60

50 median

40

% 30 interquartile range

20

10

0

clim

ate-

ge

olog

y-

hist

ory-

land

scap

e el

emen

ts

man

agem

ent-

soils

-to

ols-

vege

tatio

n-w

ater

-

41

Table 4.10 Principal Component Analysis of Components and Sub-components used in Land Classification.

Factor Characteristics Eigenvalue Variance accounted for %

Cumulative variance, %

1 soils, land, tools contrasted with vegetation, climate, management

4.5

49.6

49.6

2 history contrasted with geology

1.7 19.3 69.0

3 water, vegetation contrasted with soil

1.4

15.1

84.0

The first three factors together account for 84% of the variance in use of categories by graziers and researchers. Factor 1 (Figure 4.6) represents the contrast between Soils, Land and Tools (positive coefficients), and Vegetation, Climate and Management (negative coefficients). It accounts for approximately 50% of the variance (Table 4.10). It demonstrates the researchers' greater use of the Soils, Landscape elements and Tools categories and the graziers' emphasis on Vegetation, Climate and Management categories in classification. Factor 2 contrasts the use of History and Geological terms, which account for about 20% of the variance. There is no separation between the groups on this factor. Factor 3 shows that graziers tended to use more water and vegetation-related terms whilst researchers placed more emphasis on soils. Each circle has its value shown nearby. The diameter of the circles is approximately proportional to that value. Figure 4.6. Standardised Factor Scores for the Graziers and Researchers (Circle diameters indicate relative value at the Factor 3 level).

Factor 2

G2history R5, -0.62

R3-0.93 0.29

G1

Factor 1 soils, landscape elements, toolsvegetation, climate, management 1.96

R1, -0.71G3, 0.52 G4, 1.11

G5, -0.5 R4, 0.13

Factor 3 R2, -1.25

water and vegeation (+ive)contrasted with soils (-ive)

geology

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The graziers' representations tend to be more scattered on the figure, but the influence of Vegetation, Climate and Management is strong. Their use of Soil in classification is not highlighted in the principal components analysis as they are focusing almost equally on it and the other criteria compared at Factor 1 level, particularly Management. This is also demonstrated in Figure 4.4. Site maps (Herbert 1995) show that graziers and researchers produced different classification boundaries. Differences in outcomes could often be explained by the level of detail a person might use. At one site the grazier classified to a fine level using subtle soil changes. His classification of those areas was influenced by the rock outcrops within them but these did not appear to affect his management of the area. The scientist, on the other hand, had in this case looked at the site from a management perspective and classified one area as a single unit because of the presence of rock outcrops and erosion gullies. He was concerned that this area would be susceptible to erosion if ploughed and suggested that it should be used only for light grazing. Differences between classification maps of the same site may have been due to the difficulty of putting arbitrary boundaries around subtle soil changes. In one case a boundary the researcher identified was not distinct. The grazier examining the same site separated areas because of gradual changes in soil gravel content, slight changes in relief and vegetation changes. He, too, noted the gradational changes in soils over much of the area but chose to use different indicators as well when identifying other boundaries. 4.3 Summary of Findings from the Transect and Land Classification Studies

Transect Study

1. There was great variability among interviewees in the frequency with which they used components. For interviewees as a whole this variability is accounted for more by differences between individuals than between the three professional groups. However, there is a significant interaction effect between groups and components. Some 66.7% of total variability is accounted for by components, individuals, interactions and groups combined, in that order of importance.

2. In the components Soil and Water, groups account for more of the variability than do

differences between individuals. 3. There were significant interaction effects between groups and sub-components for

Topography, Management, Geology and People. 4. The graziers’ emphasised Management, Animals and Atmosphere more than the other two

groups. Compared with all interviewees, they used Management indicators more, and Soil indicators less than expected. They mentioned the following sub-components more frequently than the other two groups: Equipment (two coding categories), Management Activities, and the most detailed Geology sub-components. Their ‘area of comfort’ was Management.

5. The extension officers emphasised Vegetation and People more than the other two groups.

Relative to the other two groups they emphasised these sub components: Types of Water, Aspect, Topographic Zones and Sub-zones (first sub-zone category in the coding frame), Grazing Management, Management Infrastructure, Types of Geological Structure (Geology Sub-component 2), Peoples’ Occupations, Skills and Methods. They showed no particular area of comfort, but rather showed a broad spread of interest (range of convenience) across the components and sub-components, in keeping with the requirements of their work.

43

6. Of the three groups, the researchers’ referred to Topography, Soils and Water most frequently. Compared with all interviewees, they referred to soil indicators more frequently than expected. They mentioned the following sub-components more often than the other two groups: Soil Types, Soil Constituents, Soil Properties, Water Sub-types, Seasons and Atmospheric Events, Water Properties, Topographic Zones and Sub-zones, Topographic Properties, Animal Management, Geological Structure, and Types of People. For half of them no area of comfort was evident; for the other four it was Soils.

There was much in common between the groups. For the purposes of communication about land management, common recognition of landscape processes and the ways in which components, sub-components and properties feature in these processes (Section 5), is of greater interest than the components alone.

Land Classification Study

1. Graziers' and researchers' classification maps were different, as one would expect given the

differences in their criteria. 2. The researchers tended to use a more limited suite of characteristics than the graziers. They

relied more heavily on soil characteristics and landscape elements than on other criteria, such as vegetation, which may indicate changes in soil types. Within these categories they have a richer vocabulary than graziers. The use of terms relating to pH, structure, soluble salts, chemical composition, colour and formal classification systems indicates the technical character of their classification techniques.

3. The researchers used aerial photos, geological maps, previous research, soil testing kits,

shovels, augers and other tools. The graziers may have used the 'tools' of local knowledge and direct experience, but we could not confirm this.

4. The graziers may have lacked the technical expertise of the researchers when describing soils,

but still used them in classification. Soils, water and vegetation criteria were almost equally important, and management played a strong role in their classifications.

5. While all the graziers recognised changes in soil types, their descriptions were mostly of the

top few centimetres. However, their knowledge of vegetation as an indicator of changes in soil type and fertility, and their observations of vegetation change and of stock preferences for different parts of the site, suggested that they may use these, and other indicators, as surrogates for soils in land classification. The graziers use a broader range of indicators than the researchers in classifying land.

General Conclusion

In terms of communication within an Agricultural Knowledge and Information System (AKIS) or in Landcare, there were similarities and differences among the mental models of both individuals and groups. This is consistent with Personal Construct Theory, which emphasises the uniqueness of each person’s construct system, but also identifies the basis for people sharing aspects of their construing (Kelly 1955; Kalekin-Fishman and Walker 1997). There are two ways of viewing the degrees of similarity and difference among the groups’ mental models. One is that the differences could hamper information flows, while the points of similarity should enhance them. Kelly (1955) argues that similarity of construct systems aids

44

communication. The other view, also drawn from Kelly’s (1955) theory, is that people’s mental models need not be similar in order for them to communicate, but each must be capable of ‘construing the other person’s outlook (1955:95). We discuss the implications of our findings for communication in Section 6. This section has concentrated on the use of components, sub-components and properties in the mental models of our three participating groups. In the next section we explore the ways in which ecological processes feature in these mental models.

45

5. Representation and Comparison of Mental Models - Landscape Processes

Although useful, the statistical approach reported in the previous section gives only a partial sense of the structure and complexity of a mental model. In particular it does not represent how the thinker construes landscape processes. In this section we present the results of an analysis which enabled us to examine mental models of causes and effects in the landscape. 5.1 Influence Diagrams

In Section 3.2.6 we explained how direct cause-effect relationships were identified in interview transcripts (eg. Soil type affects Runoff). Examples of processes identified by our interviewees are listed in Table 3.3. The pairs of variables were grouped into sets according to the profession of the interviewee. They were also pooled to form a single set for all interviewees. A matrix of first-order effects was constructed for each of the four sets. Structural analysis of the composite matrix gave the results summarised in Table 5.1. Table 5.1 Variables in Rank Order of Number of Direct and Indirect Linkages for the Composite Model of All Three Groups

Influential Resultant Relay soil type water quality erosion slope nutrients run off contour bank sedimentation infiltration rain intensity seeds management water fertiliser soil moisture geology soil nutrients rain duration ground water soil depth land slip grazing pressure health of vegetation dam water table soil saturation salinity vegetation cover growth of vegetation soil disturbance compaction by stock rain tracks litter vegetation contour plough rain splash rooting depth season soil strength catchment area dead tree flood outcrop

46

Influence diagrams were generated (Walker, 1997) tracing causes and effects to the sixth level. Indirect effects were so numerous that a single influence diagram representing all linkages would be too complex to be useful. Influence trees for selected variables (Soil type, Runoff and Water quality) were therefore extracted from each of the four matrices, and are presented in Figures 5.1 - 5.10. Comparison of the diagrams for each group shows differences in structure, content and complexity. The composite diagrams show whether a particular influence was noted by one, two or all three groups. We remind readers that influential variables affect many others through their multiple linkages (direct and indirect), but are not themselves subject to many influences. Dependent variables are strongly influenced, but are not influential, while relay variables link multiple influential and dependent variables (Godet, 1994). Figures 5.1 to 5.4 (after page 3) are diagrams for the influential variable Soil Type. Graziers had the simplest group model of linkages involving this variable. The extension officers’ model added management and sub-surface flow elements. Researchers added a Soil Moisture sub-tree while retaining most of the variables in the other two models. Figures 5.5 and 5.6 show composite trees for the relay variable Run-off. The value of this classification of variables by structural analysis is that it may indicate where indirect management intervention in a process (intended or not) could influence many other variables. Figures 5.7-5.10 show the dependent variable Water Quality. The graziers’ model is the simplest, although it includes many variables. Extension officers added Sub-surface Flow, Infiltration, Salinity and Sedimentation. Researchers added a Soil moisture sub-tree, but dropped the emphasis on Infiltration. These variables are selected for illustration. For resolving differences between the mental models of the three groups in a resource management issue, the software used can bring any variable into focus, and show its affects and what it is affected by.

5.2 Summary of Findings

1. Comparison of the group and composite diagrams shows complementarity between the mental models of the groups. While they vary in complexity, the pathways of influence between the various sub-components, properties and landscape processes are essentially similar. This offers a useful basis for communication (see below). Among the influence diagrams reported here and those which are not, the grazier group’s mental model had fewer direct and indirect linkages than those of the extension officers and researchers. More variables do not improve the predictive capacity of a model beyond a certain point (Craik 1952). The important issue is not technical accuracy but that the model be functional for its user (Norman 1983). Since the focus of the graziers’ models appears to be management (see Section 4), we should expect their models to concentrate on linkages they find useful for that purpose.

2. The influence trees provide a simple visual representation of mental models. Since understanding the mental models of others is a pre-condition for communication (Sections 2 and 6), the diagrams appear to provide a powerful and flexible technique for groups to analyse one another’s mental models. They could provide a basis for discussion among any set of groups developing a technology, solving a management problem or grasping some new opportunity. The influence diagrams can be accessed and manipulated quickly, and any

47

variable inspected for its influential, relay or dependent linkages. Linkages can be discussed, disagreements debated, further evidence sought or new research initiated. The software packages Influence (Walker, 1997) and Vensim (Ventana 1995) allow modelling to be conducted under workshop conditions, without unwelcome technological intrusion into a social process.

In the next section we discuss the implications of our work for communication about agriculture and Landcare. (Figures 5.1 - 5.10 follow after this page. Bracketed variables indicate that that variable has already appeared elsewhere in the same diagram.).

48

Figure 5.1. Influence of Soil Type - Composite of Researchers, Graziers and Extension Officers Identified by 3/3groups Identified by 2/3 groups Identified by 1/3 groups

soil type

erosion

management

(erosion)

(run off)

(water quality)

(run off)

soil nutrients

water quality

infiltration

plant growth

(run off)

(soil moisture)

water table (salinity)

run off

(erosion)

fertilizer

ground water

health of vegetation

(infiltration)

land slip

nutrients

sedimentation (water quality)

seeds

(water quality)

soil moisture

growth of vegetation

(infiltration)

(run off)

vegetation type

(infiltration)

(soil moisture)

surface water

transpiration

sub surface flow

(erosion)

salinity

drainage line

scald

(water quality)

49

Figure 5.2. Influence of Soil Type - Researchers

soil type

erosion

management(run off)

(water quality)

(run off)

soil nutrients

water quality

infiltration(soil moisture)

water table (salinity)

run off

(erosion)

fertilizer

health of vegetation

nutrients

sedimentation (water quality)

seeds

(water quality)

soil moisture

growth of vegetation

(infiltration)

(run off)

vegetation type

(infiltration)

(soil moisture)

surface water

transpiration

sub surface flow salinity drainage line

50

Figure 5.3. Influence of Soil Type - Graziers

soil type

erosion water quality

infiltrationplant growth

water table salinity

run off

(erosion)

fertilizer

ground water

(infiltration)

nutrients

sedimentation

seeds

(water quality)

51

Figure 5.4. Influence of Soil Type - Extension Officers

soil type

erosion

management(erosion)

(run off)

(run off)

water quality

infiltration (run off)

run off

(erosion)

land slip

nutrients

sedimentation (water quality)

seeds

(water quality)

sub surface flow

(erosion)

salinityscald

(water quality)

52

Figure 5.5. Variables Influencing Runoff - Composite of Researchers, Graziers and Extension Officers (Key same as Figure 5.1).

run off

catchment areacompaction by stock

contour bankdamwindmilldead tree

drainage linesalinity

(geology)leak in dam

(sub surface flow)tree cover(trees)

water loggingwater table

(slope)

erosion

(catchment area)(compaction by stock)(contour bank)

contour plough(dam)

(dead tree)droughtfence design

flood(rain duration)(geology)

(grazing pressure)(management)

rabbits(rain duration)(rain intensity)

rain splashrooting depth

(run off)(slope)(soil depth)

(soil disturbance)(soil saturation)

soil stability(soil type)

soil water holding capacity

sub surface flow

(geology)perched water table

(slope)(soil depth)

soil porositysoil profile(soil type)

(trees)(water logging)

trackstreesvegetation cover

erosion gullygeology

grazing pressure

infiltration

(contour bank)contour furrowcracks in rock

(geology)(grazing pressure)

(litter)(rain duration)(rain intensity)

(run off)(slope)

soil compactionsoil crust

(soil depth)(soil moisture)(soil permeability)

(soil repellence)(soil strength)

(soil type)(vegetation cover)vegetation type(soil moisture)

(weather)litter

management(erosion)outcrop

rain durationrain intensityslope

soil depthdeposition(flood)(slope)

soil disturbance

soil moisture

(infiltration)rain

season(slope)

(soil depth)(soil type)(vegetation type)

soil permeabilitysoil repellencesoil saturation

soil strengthsoil typewater stock camp

vegetationweather

Parentheses around a (variable) show that it is linked to other variables in the causal tree

53

Figure 5.6. Variables Influenced by Runoff - Composite of Researchers, Graziers and Extension Officers (Key same as Figure 5.1).

run off

erosion

management

(erosion)

(run off)

(water quality)

(run off)

soil nutrients

(water quality)

fertilizer

ground water

health of vegetation

infiltration

plant growth

(run off)

soil moisture

growth of vegetation

(infiltration)

(run off)

vegetation type

water table salinity

land slip

nutrients

sedimentation (water quality)

seeds

water quality

Parentheses around a (variable) show it is linked to other variables in the causal tree.

54

Figure 5.7. Variables Influencing Water Quality - Composite for Researchers, Graziers and Extension Officers (Key same as Figure 5.1).

water quality

damwindmilldung

erosion

catchment areacompaction by stockcontour bankcontour plough(dam)dead treedroughtfence designflood(rain duration) (geology)grazing pressure(management)rabbits

rain durationrain intensityrain splashrooting depth(run off)slopesoil depthdepositionsoil disturbancesoil saturationsoil stabilitysoil typewatersoil water holding capacity(sub surface flow)tracks(trees)

vegetation cover littermanagement(erosion)

run off

(catchment area)(compaction by stock)(contour bank)(dam)(dead tree)

drainage line(salinity)(slope) (erosion)erosion gully(geology)(grazing pressure)

infiltration

(contour bank)contour furrowcracks in rock(geology)(grazing pressure)(litter)(rain duration)(rain intensity)(run off)(slope)soil compactionsoil crust(soil depth)(soil moisture)

(soil permeability)(soil repellence)(soil strength)(soil type)(vegetation cover)vegetation type(weather)(litter)(management)outcrop(rain duration)(rain intensity)(slope)(soil depth)

(soil disturbance)

soil moisture

(infiltration)rainseason(slope)(soil depth)(soil type)(vegetation type) (soil permeability)soil repellence(soil saturation)soil strength(soil type)stock camp(vegetation)

weather

salinity

geologyleak in dam

sub surface flow

(geology)perched water table(slope)(soil depth)soil porositysoil profile(soil type)(trees)(water logging) tree covertrees

water loggingwater tablehydrogeology(infiltration)(trees)

sedimentation(dam)(run off) soil permeabilityvegetation

Parentheses around a (variable) show that it is linked to other variables in the causal tree

55

Figure 5.8. Variables Influencing Water Quality - Researchers

water quality

dam

dung

erosion

catchment area

contour bank

(dam)

geology

grazing pressurerain duration

rain intensity

rain splash

(run off)slope

soil depthdeposition

soil saturation

soil stability

soil typewatersoil water holding capacity

tracks

vegetation cover

litter

management(erosion)

run off

(catchment area)

compaction by stock

(contour bank)

(dam)

drainage linesalinity(erosion)

erosion gully

(grazing pressure)

(litter)

(management)outcrop

(rain duration)

(rain intensity)

(soil depth)

soil disturbance

soil moisture

infiltration

rain

season

(soil depth)

(soil type)vegetation type

(soil permeability)

soil repellence

(soil saturation)soil strength

(soil type)

stock camp

(vegetation)

sedimentation(run off)soil permeability

vegetation

56

Figure 5.9. Variables Influencing Water Quality - Graziers

water quality

erosion

compaction by stock

contour bank

contour plough

damwindmill

fence design

geology

grazing pressure

management

rain duration

rain intensity

rooting depth

(run off)

slope

soil disturbance

soil typewater

tracks

trees

vegetation cover

run off

(compaction by stock)

(contour bank)

(dam)

(geology)

outcrop

(rain duration)

(rain intensity)

(slope)

soil saturation

(soil type)

vegetation

weather

57

Figure 5.10. Variables Influencing Water Quality - Extension Officers

water quality

erosion

contour bankcontour plough

damdrought

floodgeology

grazing pressuremanagement(erosion)

rabbitsrain durationrain intensity

rain splashrooting depth

(run off)slope

soil depthsoil disturbance

soil saturationsoil typewater

sub surface flow

(geology)(slope)

soil profile(soil type)

water loggingtracks

vegetation cover

run off

compaction by stock(contour bank)

(erosion)(geology)

(grazing pressure)

infiltration

(contour bank)cracks in rock

(geology)(rain intensity)

soil compactionsoil crust

(soil depth)soil strength

(soil type)(vegetation cover)

litter(management)(rain intensity)

(soil saturation)(soil type)

vegetation

salinity

(geology)(sub surface flow)

treeswater tablehydrogeology

sedimentation(dam)(run off)

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6. Mental Models and Communication in Agriculture and Landcare

6.1 Knowledge, Information and Communication

Sections 4 and 5 have shown that mental models can be elicited and compared, and that there are differences between individuals and groups in the content and structure of models. We now return to theory, and discuss how our findings could help improve communication. The global context of this study is the widely held view that there is a mismatch between human management of ecological systems and the intrinsic behaviour of those systems (Gundersen and others 1995). The Landcare movement is a national response to this perception. The sectoral context is the ‘agricultural and knowledge information system’, or AKIS (Roling 1990). It comprises farmers, extension officers, researchers, agricultural industries, government agencies and funding bodies. Woods and others (1993) describe information flows within the Australian AKIS. What is communicated in any such system is not knowledge, but information. According to Roling (1990), knowledge is transformed into information for communication, and on receipt is re-transformed to knowledge. Transformations occur at many nodes within an AKIS. Figure 6.1 is based on an explanation in Roling (1990). Transformations occur at each stage of the cycle. Every transformation is a potential barrier to communication.

Figure 6.1 Information-knowledge Transformation

Information on local farming system

Farmer knowledge

Research questions specified

Dissemination of information

Research findings

Observations of farmer use

Potential solutions

Recommendations for on-farm testing

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Brown (1995) describes the hierarchy of organisations and groups at local, state and federal levels in the context of communication within Landcare. Communication within this network would involve knowledge-information transformations at a multitude of nodes, with possibilities of communication barriers at each one caused by differences in mental models of individuals and groups. We argue that the mechanism for transformation is the recipient’s mental model (Section 2). According to Personal Construct Theory, recipients most readily accept those parts of the message that fit their mental models (Salmon 1981), so the message is, from the standpoint of the originator, frequently ‘distorted’, if it is not ignored altogether. This is a consequence of the way our minds work, rather than ignorance or wilfulness, for one of the functions of our mental models is to impose a structure on incoming information. If we could not do that, our minds would be overwhelmed by the amount of information received. Our sense-making models enable us to remain afloat in the sea of information. However, the same models can hamper communication between individuals or groups possessing incompatible models. Improved communication in agriculture cannot, therefore, be achieved by an increase in the volume or glossiness of information. It requires either re-structuring of mental models to achieve greater compatibility between the would-be communicators, or for each communicator to gain the ability to construe how the others are construing. Learning to construe how another is construing actually adds an extra dimension to one’s own mental models. In Salmon’s words, the “road from current management practice to required management practice is paved by reconstructions” (1981:34). Reconstruction of mental models is psychologically hazardous (Kelly 1955). Reasons are the hierarchical structure of our mental models, the inter-dependence of their parts and our psychological dependence on them (Section 2). We gave an imaginary example in Section 1 of a grazier who found it difficult to change his management practises because of his relationship with his father. Sometimes, one cannot shuffle a minor construct without risk of disturbing the whole structure. Kelly (1955) talks of the need to restructure “without letting the rain in”. Here is the fundamental reason why transitions of understanding are often jerky, sometimes painful, usually reluctant, and perhaps never made. Here is a common source of the frustration felt by conflicting stakeholders in agriculture and land management. Humans learn through experience, and because our experiences differ, we learn different things. In Section 2 we set out Kelly’s (1955) view that each of us is a scientist, generating hypotheses about our environment from our personal constructs, testing them against experience, and modifying our construct system accordingly. Learning takes place continuously (Salmon 1981). This view leads to a blurring of the distinction between experts and lay-persons. Our results show significant differences (as well as over-lap) between researchers’ and graziers’ mental models. They do not show that either one is superior. Each is developed for a purpose, and because these differ, the models are different too and barriers to communication may arise. The researchers and extension officers tend to believe that the farmers would manage the land better if they adopted more complex and comprehensive models of their properties. This view is open to question. Rouse and Morris (1986) show that to manage machines effectively, the operator need only know enough to do the job. It was shown experimentally that providing theoretical background could hamper effectiveness in some circumstances. Though we showed that the graziers’ composite model was simpler than the researchers’, it does not follow that if the graziers elaborated their models they would manage the land any more effectively. It is generally accepted in science that over-elaboration of a theory weakens its power, as Popper (1968) illustrated with his searchlight analogy. We have shown in Sections 4 and 5 that graziers simplified their landscape models by focusing on management at the expense of soils and water. Researchers simplified theirs by reducing emphasis on people and management. Each group

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needs to do this in order to control the flood of information and focus management or intellectual effort (Kelly’s focus of convenience). Communication barriers can arise when different individuals or groups have construed differently, omitting and emphasising different parts of reality, so they have insufficient overlap in their models. Salmon (1981) explained the relative simplicity of farmers’ models in terms of a theory of craftsmen. Vanclay’s and Lawrence’s (1995) view that farmers have a distinguishable sub-culture which shares information supports Salmon’s theory. He argued that although the individual farmer appears unaware of its extent or complexity, there exists a body of knowledge built and tested over time, and held by farmers as a group. This fits Kelly’s (1955) concept of the commonality corollary. However, according to Salmon, the knowledge is not held completely by any individual, but is distributed unevenly among individuals. He suggested that farmers’ general aversion to formal training courses was the result of their membership of a group which built and tested knowledge through practical experience, not the assimilation of second hand information. They preferred to tap the body of collective knowledge rather than undertake formal training. Our findings support the concept of sub-cultures with shared mental models. There are two ways of looking at this sharing. One is to see the overlap between mental models, and stress their similarity. Simpson and Wilson (1996) review literature which supports the view that institutions develop unique bodies of knowledge and beliefs which are the foundation of shared meanings and values within that institution. However, we found high variability between individuals within each group (Figure 4.3), so overlap is very far from total. Another way of considering sharing of mental models by members of a sub-culture is as distributed knowledge (Simpson and Wilson, 1996). In this case the collective mental model would differ from every individual model, and the whole would be more than the sum of the parts, as is claimed by Hutchins (1991) for some tribal groups. This is compatible with Salmon’s view of the farmer as craftsman. Researchers have sub-cultures too. Kuhn’s (1970) concept of the ‘paradigm’[ is one way of describing them. While providing a focus for research and a way of distributing knowledge among scientists, they also form barriers to communication across intellectual disciplines, and between lay persons and scientists. Our work has demonstrated substantial differences between the mental models of graziers and researchers, but also a degree of commonality. Hope of improving communication is particularly encouraged by the group mental model of the extension officers: both their range and focus of convenience spans those of the other two groups (Sections 4 and 5). As participants in one of our workshops pointed out, this is a particularly important aspect of our findings. Funding constraints are leading Australian State and Territory governments to reduce the number of extension officers, and to look to alternatives to promote understanding of agricultural systems. The main alternatives, Landcare groups, and reliance on research scientists to communicate their own findings, are both subject to the constraints we identify - how well can those involved learn to construe how others are construing? To bridge the barriers caused by differences in mental models, we need methods. Salmon suggested repertory grids as a means to “tap the pool of collective knowledge handed on from farmer to farmer” (1981: 27). Linked to computer simulations of farming decisions, it was an excellent way of simulating change over time and getting feedback from the consequences of decisions. Brett (1984) has also used repertory grids with farmers. We propose that a merging of personal construct theory with mental model theory is even better attuned to the construing of ecological and other dynamic processes. Interviewing while traversing the land is a highly effective way of eliciting mental models. Linked to the technique of influence diagrams, it provides a powerful way of understanding mental models of complex processes, therefore a potentially effective way of bridging communication barriers in the AKIS and Landcare.

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If differences in mental models can hamper communication, and if communication is helped by mutual understanding of mental models, the next question is how might our methods contribute to information flow and knowledge generation within the AKIS and Landcare?

6.2 Contribution of Mental Model Analysis to Communication

In Section 3, we presented the costs of applying our transect method, and suggested ways of customising and streamlining it for future use. We emphasised that this is a method suited to in-depth understanding of people’s mental models, and not to application over very large samples because of the high cost of a long interview and its analysis. The benefits of eliciting mental models justify application of either of our methods to explore patterns of thinking among key groups. Both land traverses and land classification are effective cues for eliciting mental models. We have shown it is possible to construct and compare group models. While these assist analysis, we must recognise that group models do not wholly represent the thinking of any particular individual. Since farm management policies are directed towards improving communication between types of people rather than particular individuals, this is not a limitation. It is feasible, quicker and cheaper to build a shared model directly, without the intervening steps, as Godet (1994) has shown. A composite model could be a fruitful basis for analysing a problem, designing a research program, or evaluating a practice or technology. For example, a group could be chosen to represent key players in a project to develop a new approach to integrated pest control - farmers, researchers and extension officers, say. They could visit relevant sites where they discuss the ecology of the pests, using the land and its plants and animals as stimuli. They would return from the field to participate in the construction of a matrix of cause and effect, from which a model of inter-relationships could be constructed. In our case we used Influence and Vensim (Section 3), which permit the construction of influence diagrams (Figures 5.1 on). They can be generated during the course of a meeting, allowing rapid feedback to the group. Rapid iteration promotes quick development of an influence diagram that is agreed upon by the whole group. In cases of disagreement about how a system works, or about the relative importance of variables, relationships between variables can be discussed, further information sought, and agreements negotiated. Disagreements point to areas where more research is needed. Influence diagrams can be used to identify areas of ignorance, and hence priorities for research. Packages like Vensim (Ventana 1995) can also be an environment for simulation and decision support models. What began as an exercise in eliciting, comparing and combining mental models could develop into a research program involving experimentation, a simulation model developed and ‘owned’ by landholders, extension officers and researchers, and perhaps a decision-support model. Our approach is not limited to analyses at the farm scale, for its flexibility makes it adaptable to various spatial, time, ecological and socio-economic scales. Management committees could work with researchers and agency representatives in the development of socio-economic and bio-physical models of catchments. Local government or regional development organisations could apply the approach in land development conflicts. There is also scope for working at state and national levels in the analysis of policies (Walker and Young 1996; Abel and Tatnell 1996). While failures of communication at these various levels may be associated with mismatches of mental models, we are not advocating the naive view that mutual exposure of mental models will on its own clear the channels. The reasons for conflict are generally rooted in clashes of

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social, economic or political interests (Mercer 1995), and resolving them will generally require negotiations and trade-offs. Our work has emphasised that what is important in communicating about research and extension is an ability to construe the constructs of others. Radical solutions are likely to require the re-structuring of mental models to accommodate the constructs of other groups. Some psychological principles for approaching this are summarised next.

6.3 Bridging the Barriers

Mackay (1994) emphasises the importance of relevance, and relationship in communication. In Sections 4 and 5 we identified substantial differences in the foci of convenience of researchers and graziers. A researcher trying to influence the mental model of a grazier would find this difficult, we predict, if she focused on soil classification. If the focus was built around management her chances would be better. The message must be about the intended listener’s circumstances, fall within their range of convenience, and preferably address a focus of convenience. But the chances of successful communication are much improved if a satisfactory relationship is established. When differences in mental models are blocking communication, the solution lies in gaining mutual understanding of the models. But people are generally reluctant to reveal their models unless a satisfactory social relationship is established. Communication in these circumstances cannot be instantaneous. Instead it requires investment of time in the establishment of relationships leading towards the sharing of mental models. The kinds of group processes we suggested above could be designed to lead from tentative and exploratory initial contacts in which there is little tangible exchange of messages, developing towards highly co-operative meetings at which there is mutual understanding of ranges and foci of convenience, therefore of overlap, complementarity and conflict among mental models. Communication has been established. The most common reason for establishing good communication is the wish to change another person’s behaviour - in adopting a new technology, or sustainable land management practice. It is widely assumed that to achieve this we must first ‘change their mind’ (Mackay 1994). Good communication does not necessarily result in one of the communicants changing their mental model. If the model is at a person’s psychological core, its integrity is defended. When we attempt to change a persons mental model through information, we often find the recipient has instead used that information selectively to reinforce it (Mackay 1994). Information which does not reinforce is rejected. In this sense, the human is a poor scientist, ignoring information which threatens a theory. Alternatively we could try confrontation. The effect of that is usually to make the person feel threatened, further reinforcing the mental model. Senge and others (1994) offer a set of practical approaches to the sharing of mental models. They include: • reflection - the conscious slowing down of our thinking processes so as to become more

aware of how we form our own mental models; • inquiry - open questioning of the assumptions behind each others’ explanations and

proposals; • advocacy (promotion of a particular mental model) balanced by inquiry; • creation of scenarios of plausible futures as a means of revealing differences and similarities

in current views of the world; • the fostering of multiple perspectives on an issue, rather than a battle for the best. In Section 1 we pointed out that we are not studying values and attitudes. A powerful reason against studying attitudes is that behaviour is not caused by attitude. When there is dissonance between our behaviour and our attitudes, it is the attitudes which tend to move into line with the behaviour (Festinger 1957 and Schein 1956, cited in Mackay 1994). According to Kelly’s view

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of the person as scientist, our mental models are built from our experience. Change in mental models comes through new experience. To change behaviour, therefore, the environment with which we interact must change (Mackay 1994). This has major implications for technology adoption, land management, and the processes of research and extension. Some are: • Action Research (Bawden, 1989) is a participative approach to the solution of local

problems. It is generally based around the needs of local stakeholders, and may involve interdisciplinary teams. Emphasis is on the definition of issues by stakeholders, local management of projects, and the generation of locally acceptable recommendations. Learning is experiential, rather than through the literature or training. Our work suggests that interaction of farmers with researchers and extension officers in the context of the farmers’ environment is likely to lead to the reconstruction of mental models of all three;

• Adaptive Management (Holling 1978) has generally involved the participation of natural

resource managers and researchers in the participative construction of simulation models, decision support systems and management strategies. Again, the interaction of the two groups and with the environment they wish to manage, offers an opportunity for reconstruction;

• when land users do not respond to information recommending technology adoption or

sustainable land management, it may be because their decision-making environment has not changed. New opportunities for technical change may have to be supported by changes in land users’ economic environment, such as tax breaks, before their behaviour changes.

6.4 Mental Models and Written Communication

Written information is a special problem. Much of the attempted communication in the AKIS and Landcare is written. There is rarely a prior attempt to establish relevance or relationships, and no active sharing of mental models. Information transmitted thus will reinforce existing mental models, or be shed. There is a sense among natural scientists we meet in Australia of rifts between themselves and lay people, despite the scientists’ interest in learning from farmers and graziers. Communication of scientific information has never been easy, but it has never been more important as we face the likelihood of very rapid environmental, social and economic changes. Examples of successful communication may hold messages for us. The Holistic Resource Management (HRM) movement is one (Savory 1988). Regarded by mainstream pasture and range scientists as non-science because of the absence of controlled experiments, it has been embraced by pastoralists in the US and elsewhere, and there is a growing interest in it in Australia. Savory’s HRM model includes highly simplified ecological theory, a set of management tools, guidelines on ecological, animal, labour and financial matters, and a learning regime. It is capped by the grazier’s goals and quality-of-life considerations. The prominence given to the grazier’s goals is likely to establish the relevance to the grazier of Savory’s message. Frequent use of highly practical examples establishes Savory as a member of the grazier’s sub-culture. Theory is cut to a minimum, and, shades of Kelly, the emphasis is on learning by doing and monitoring. There are examples of Australian literature which follow a similar pattern (eg. Campbell 1991), but it is rarely written by researchers. Most writing by researchers is for other researchers with the same mental models. As disciplines become more specialised and fragment into sub-disciplines, ranges of convenience shrink and foci become narrower, so that communication across specialisms becomes harder, and with lay people, almost impossible. If we extrapolate this trend, researchers will have an increasing amount of information to communicate to a declining audience. The trend must be changed. Researchers with important messages for policy makers, practitioners and public must learn how to make their messages relevant to specific

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audiences, establish a relationship, and use metaphors, terminology or idiom that are familiar to that audience. If the scientist objects to transforming a formal theory into a metaphor, it is worth remembering that theories about Nature are already metaphors anyway; the art of communication is to find a metaphor that is better suited to the mental model of the audience.

6.5 Mental Models and Sustainable Land Management

In this report we have not sought objective yardsticks against which to compare the accuracy of mental models. To do that would go against one of the primary messages of the report - that we each construct our own models of the world. What matters to individuals is not the accuracy of the models, but their effectiveness in controlling information and guiding our lives. Clinical psychology based on personal construct theory helps people with ineffective models of social relationships to reconstruct them, often painfully. A growing number of lay persons and scientists perceive that our cultural mental models are leading humans into an increasingly pathological relationship with our biophysical environment. While the response of scientists and environmental pressure groups is to increase the flow of supposedly alarming information about this, there have been in response only minor adjustment of economies and management practices. Gunderson and others (1995) show how institutional theories of the environment are incompatible with the biological resources those institutions are mismanaging. But increasing the flow of advisory information to recipients whose mental models are pre-disposed to shed it, is pointless. Before major institutional changes occur, there must be structural change of mental models, induced by exposure to new experiences. To paraphrase Salmon (1981:34), the road from exploitation to sustainable resource use will be paved with reconstructions.

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7. Conclusions and Recommendations The purpose of this research was to develop a method for eliciting and comparing mental models, on the assumption that it could improve the flow of information within the Agricultural Knowledge and Information System (AKIS) and Landcare. We have reached conclusions on theory, method, the comparison of mental models, and the implications of our work for communication. We also have suggestions for further work.

7.1 Theory

We propose a synthesis of personal construct theory and mental model theory. We call the outcome a mental model. We believe mental models have these attributes. • They help people to anticipate how physical, social, economic or other processes will occur,

and to plan their behaviour accordingly. • They are developed and amended progressively in the light of their creator's experience.

Personal background, exposure to and interest in accepting new information, and personal experimentation all play a part in shaping and reshaping a mental model.

• Individual mental models differ, but can contain common aspects with those of others and be

shared through common concepts and language. • People’s mental models, or parts of them, may be of varying detail and complexity,

depending on their interests and experience. • Mental models may be arranged as subsystems within larger systems. For instance models of

erosion and deposition processes may be nested within a broader conceptualisation of landscape processes.

• Each model has a range of convenience, or situations to which it applies most aptly. • Mental models may be more or less permeable, or capable of accepting new detail. They

may also be more or less adaptable when potentially conflicting information becomes available.

• It is possible for mental models, or subsystems within them, to contain incompatible aspects. • People who have similar mental models of a situation or set of processes will tend to hold

similar expectations and will act similarly. • In order to communicate effectively or cooperate with another person, one need not hold the

same outlook or mental model, but must be able to appreciate the other person's outlook or model. For instance, a scientist need not personally share the goals of a grazier, or hold the same understanding of how his or her farm landscape works, but the scientist does need to understand the grazier's goals and frame of reference.

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7.2 Method

• Walking the land is a powerful way of eliciting mental models, whether for understanding processes or spatial relationships.

• Marking aerial photographs in the field is an effective method for capturing and comparing

the land classifications of different groups. • Using transects, fixed sites and features within transects, and asking interviewees to keep to

a theme were effective ways of focusing their descriptions of their mental models, so that results were comparable.

• In terms of return to effort, the number of questions we asked in the transect study was about

right. Some questions were more efficient than others in eliciting new information. • Briefing interviewees was important for standardising their understanding of our purpose. • A combination of open-ended questions followed by probing questions was effective in

eliciting information. Choosing the right words is critical, and even open-ended questions need to be standardised.

• Verbatim recording was used to avoid imposing interviewers’ constructs. • Content analysis is skilled, expensive and slow. We saw no advantage in using a computer

package. • Influence diagrams are an effective way of representing mental models of landscape

processes, and make possible the comparison of structure and content. They may, however, build in linkages which interviewees did not actually make.

• Our methods are cost effective for the level of detail they provide. Skills are needed in the

question design, interviewing and analysis. Our methods are best applied where detailed understanding of people’s thinking is required, over small to medium-sized samples.

7.3 The Comparison of Mental Models

Transect Study

• There was great variability among interviewees in the frequency with which they used components. This variability is accounted for more by differences between individuals than between the three professional groups. However, there were substantial differences between groups in the frequency of use of sub-components of Soils and Water.

• The graziers’ emphasised Management, Animals and Atmosphere more than the other two

groups. Compared with all interviewees, they used management indicators more, and soil indicators less than expected. Their ‘area of comfort’ was Management.

• The extension officers emphasised Vegetation and People more than the other two groups.

They showed no particular area of comfort, but rather showed a broad spread of interest (range of convenience) across the components and sub-components, in keeping with the requirements of their work.

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• Of the three groups, the researchers’ referred to Topography, Soils and Water most frequently. Compared with all interviewees, they referred to soil indicators more frequently than expected. For half of them no area of comfort was evident; for the other four it was Soils.

• There were significant differences between the groups in the frequency of use of sub-

components.

• There are differences in the structure, complexity and linkages of the influence diagrams with which we represented the mental models of the three groups participating in the transect study. Extension officers and researchers had more complex models.

• None of the models was ‘complete’, and to an extent they were complementary.

Constructing a composite model is likely to be an effective way of sharing mental models as a basis for improving communication.

Land Classification Study

• The researchers tended to use a more limited suite of characteristics than the graziers when classifying land. They relied more heavily on soil characteristics and landscape elements than on other criteria, such as vegetation, which may indicate changes in soil types. Within these categories they had a richer vocabulary than graziers.

• The researchers’ use of tools-related terms during the land classification exercise was to be

expected because they use aerial photos, geological maps, previous research, soil testing kits, shovels, augers and other tools. It may be argued that graziers have used the 'tools' of local knowledge and direct experience.

• The graziers may have lacked the technical expertise of the researchers when describing

soils, but still used them, relying on other land characteristics as well to aid in classification. Management played a big role in how they saw and classified an area, but soils, water and vegetation were almost equally important to each grazier.

• While all the graziers recognised spatial variations in soils, their descriptions were mostly of

the top few centimetres. However, their knowledge of vegetation as an indicator of changes in soil type and fertility and their observations of vegetation change and of stock preferences for different parts of the site suggested that they may use these, and other indicators, as surrogates for soil depth and fertility in land classification.

• Graziers' and researchers' classification maps of the sites were different, in keeping with the

differences in the criteria used..

7.4 Communication in Agriculture and Landcare

• According to the theories we used, barriers to communication are predicted to occur when mental models are incompatible. A barrier is bridged when one party understands the mental model of the other. It is not necessary for one to adopt the other’s model.

• Theory predicts that barriers caused by differences in mental models cannot be bridged by

increasing the volume of information. This would strengthen the barrier by reinforcing a mental model.

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• Communication is a process, not an event. It is best begun by establishing relevance and social relationship. This can be difficult in the case of written communication, but this strengthens the case for learning as much as possible about one’s audience, and trying to fit the communication to their mental model. Because of the variety of mental models there must also be variety in communications.

• Communication need not necessarily result in the behavioural change which one of the

parties wanted. That, we predict, is the consequence of new experience, not new information. Providing that new experience through Action Research, Adaptive Management or a change in the policy environment may be a necessary condition for behavioural change. If mental models are re-structured as a consequence of the new experience, then behavioural change follows.

7.5 Recommendations for Future Work

In Section 3 we suggested ways in which future users could vary the transect method to their needs. For example, a useful next step might be a case study of non-adoption of an apparently useful technology. This should be carried out in an economically important farming area, such as the Riverina, or the wheat-sheep belt. Such a study could focus on facilitating the sharing of mental models among the various stakeholders involved, such as farmers, banks, R&D Corporations, Landcare groups, researchers and government agencies. The premise is that differences in mental models blocks communication among the groups. In understanding each others’ mental models about the technology, and the ecological, social and economic environment for adoption, we would expect stakeholders to incorporate versions of one another’s mental models into their own, and thus bridge communication gaps. This may lead to modification of the technology, proposals for modifying the decision-making environment, or pilot testing of the technology. Output could include a manual on the method for general application, perhaps accompanied by a video. In developing our two methods, our main intention has been to provide ways of eliciting people’s mental models of environmental processes. The methods can easily be adapted to encompass land management or other foci of convenience. We hope to see others customise these methods for their own purposes, and thus enrich Australians’ repertoire for construing how landholders and others involved in agricultural systems understand the land and its management.

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Appendix Sub-components and Properties Mentioned by Interviewees Explanation Main components are Vegetation, Soils, Animals, Topography, Water, Atmosphere, Geology, Time, People, Economics and Management. These were classified during the content analysis into the sub-components listed below. The classification is hierarchical. For example, the category “tree” (vegetation sub-component 1) can be qualified by mention of one or more parts (vegetation sub-component 2), by being a member of a particular group of plants (vegetation sub-component 3) and species (vegetation sub-component 4), by the use to which it is put (vegetation sub-component 4), and by its properties. Vegetation Sub-component 1 Type of plant

Vegetation Sub-component 2 Plant parts

Vegetation Sub-component 3 Plant groups

Vegetation Sub-component 4 Plant species

flora trees grass thistles weeds shrub suckers pasture

leaves roots flowers trunk hollows canopy understorey stump seed cone branches bark thorns needles

poplars pines eucalypts mistletoe rushes reeds sedges perennials annuals deciduous legume vegetables Rosaceae lichen moss

Black Wattle Toad Rush Saffron Thistle briar Cyprus dandelion Blakeley's gum Red Gum Yellow Box red box ti-tree willows pumpkin datura birch stipa couch [juncus] themeda barley grass rye grass poa sorrel polyanthemus pampas grass kangaroo grass subclover Phalaris Microlaena stringybark radiata rosethorns dollar gums acacias clover apple gums Patterson's Curse lucerne oats nightshade St John's wart Prickly pear Scotch thistle sweet maleen blackberries wild asparagus

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Vegetation Sub-component 5 Plant uses

Vegetation properties

Soil Sub-component 1 Soil constituents

Soil Sub-component 2 Soil types

fodder timber firewood fencing hay

cover shade population density shelter density woodiness palatability productivity rankness quality improved native introduced level of development degeneration ability to extract water ability to extract nutrients ability to hold soil tussocky health composition ability to have wet roots dry soil tolerance salt tolerance seeding capability stunted stressed size shape leaf texture age colour length of leaf water use responsiveness to water species nutrition value dominance competitiveness water content poison content nitrogen addition decay regeneration/ regrowth spindley roughness root depth senescence carbon/nitrogen ratio carbon content leaf area rows debris on fences toughness

profile/structure/layering soil type top/surface soil crust/crusting subsurface soil litter sediment nutrients silt clay gold sand material/particles microbial population inorganic matter organic matter A horizon A1 horizon A2 horizon B horizon B2 horizon C horizon

Podsolic Solodic Sodic Granitic Thixotrophic Xeric Mesic Duplex Relict Lithosol Depositional Erosional Agricultural soils Swamp soils Forest soils River soils Saprolite Chernozerm

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Soil properties

Animal Sub-component 1 Animal type

Animal Sub-component 2 Animal groups

Animal Sub-component 3 Animal outputs/parts

sandy clayey/heavy rocky/gravelly earthworms casts perched clay lenses cryptogams [?] consolidated loam skeletal water balance permeability water repellence water attraction dryness wetness depth/shallowness water logged/saturated fineness of particles salt content/salinity porosity/channels fertility/productivity texture shape of particles water holding capacity erodibility strength drainage massiveness density dispersibility nitrogen content phosphorus content potassium content calcium content carbon content Metals content pollutant content filtering capability iron/oxide content aluminium content manganese oxide sesquioxides bareness mineral content health/condition pH nodules oxidation deficiencies quality stability lightness hardness mottling colour smell bleaching tendency to leach adhesiveness melts when wet tendency to slip/slide cemented development level compaction bioturbation gleying aerobic condition consistency conductivity

microfauna herbivores native wildlife feral/pests vertebrates Parasites domestic/stock

frogs beetles ants insects koalas kangaroos fish birds ducks worms/flukes wombats rabbits foxes hares sheep cattle goats horses

frogs eggs feet manure

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Animal Properties Topographic Sub-component 1

[Zones?] Topographic Sub-component 2[Sub-zones 1?]

Topographic Sub-component 3[Sub-zones 2?]

activity bone grazing capability grazing selectiveness number/plague pregnancy productivity size smell weight

aberration airport bank coast' collapsed structures contours creek creek head dam deposition zone depression discharge zone erosion zone filtering zone flat grassland hill hollow lake microtopography minefield mountain range National park plantation/forest quarry recharge zone residential zone ridgeline riparian zone river roads saline scald sides of pihole [?] slump/land slip small debris dams soak/wet zone spring stables stream swamp/wetland terrace transmission zone treeline trench valley vegetation corridors water catchment zone water course/drainage line water

alluvial fan aspect basin blind bank blow out carbon sink contour bank/berm contour rip lines creek bank cutoff bank dam face dam head dam rim dam walls deposition mound diversion bank flood plain/river flats fox hole graded bank gully inlet level bank nutrient sink rabbit warrens river bank salt sink sediment sink slope spillway/overflow spoils stock camps stock tracks/pugs stock watering point stream bed undercut bank valley axis valley bottom valley walls vehicle tracks/ruts wombat hole

apex of alluvial fan break of slope crest of slope dam slope foot slope gully floor gully head gully walls hill slope hillside leeward side lower slope mid slope peak pedestals/columns pock marks river mouth saddle side slope tunnelling/piping upper slope

Topographic Sub-component 4 Aspect

Topographic Properties Water Sub-component 1 [Types of water?]

Water Sub-component 2 [Water sub-types?]

east northeast easterly north east north west northerly south east southerly west northwest westerly

active agricultural land cleared concave slope convex slope depth/shallowness flatness grassed habitat horizontal length low lying pockets roughness /rockiness shape size smoothness stability steepness treed undulating

dam water discharge water drinking water ground water overflow water rainfall recharge water river water runon/off water surface water

rain drops rain splash subsurface, in soil water table

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Water Sub-component 3 [Seasons/weather events?]

Water Properties and Behaviour

Atmospheric Sub-component 1[Air and climate?]

Atmospheric Sub-component 2[Air gases and seasons?]

drought flood frost storm

Properties and behaviour algal content channel flow chemical content clarity/turbidity colloid content colour convergent flow depth divergent flow duration eddy flow energy/velocity erosive power eutrophic health intensity lateral flow - subsurface level mineral content nitrate content nutrient content overland flow pattern perched pollutant content ponding pressure quality quantity/volume salinity sediment content seed content sheet flow subsurface flow supply transporting power vertical flow -subsurface

air atmosphere climate fire microclimate temperature weather

bad season carbon dioxide content dry season February good season Greenhouse gases growing season January season spring summer sunlight wet season winter

Atmospheric Sub-component 3 [Weather conditions?]

Geological Sub-component 1 [Geological structures?]

Geological Sub-component 2 [Types of geological structures?]

Geological Sub-component 3 [Further types ?]

cold conditions dry duration hot improvement of soil intensity northwesterlies prevalence westerly winds wet wind

bedrock boulders geomorphology hydrogeology lithology mantle metamorphosed rock morphometry pedologic regolith river stones rock rock bar rock plains

aeolian alluvial colluvial igneous ordovician saprolite sedimentary volcanic

basaltic diorite granite ironstone larva [lava?] limestone quartz sandstone shale slate

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Geological Sub-component 4 [Further types?]

Geological Sub-component 5 [Detail of geological structures?]

Time (main component) Economics (main component)

plagioclase orthoclase

aggregate angle bareness crystals decomposed depth exposed face fault line fracture/crack heterogeneous homogenous shape size subterranean drainage weathered

day geological historical medium short term undefined

commercial venture compensation costs education resource expensive financial status interest rates land value margins money nutrient inputs products/production returns rewards/benefits risk sale outlets tax deductions

Human Sub Component 1 Types of people

Human Sub Component 2 Skills and methods

Human Sub Component 3 Occupations

Human Sub Component 3 Impacts

bank planners bushman city people Costin developers early settlers Europeans -whites experts extension officers farmers/farming gardeners/market general public geologist graziers/grazing humans hydrogeophysicist modellers neighbours pedologists people professionals Prosser scientists soil conservationists soil scientists soil surveyor Tom Uren tourists users

analysis/tests equations equipment knowledge knowledge - animals knowledge - local area knowledge - soils knowledge - vegetation knowledge - water knowledge - winds management skill maps/photos modelling planning -goals research taste views/opinions waste disposal

Occupations agriculture agronomy horticulture

cigarette butts dust noise odour

Management Sub-component 1 [Infrastructure]

Management Sub-component 2[Equipment 1?]

Management Sub-component 3[Equipment 2?]

Management Sub-component 4[Miscellaneous?]

access roads electric fences fence corners fences gates machinery/vehicles management paddocks pasture rusting fences shelter belts water/dams wind breaks windmills yards

back hoe catchment protection chainsaw erosion control fertilizer/chemicals/herbicides fire hazards flood detention front end loader horse & dray quadricycle ripper salinity control shovel soil conservation steel pegs stock protection stock water storage/supply tractor water control

dips gypsum pumps stone work straw mulch superphosphate trickle pipes

area - km2 siting of dams

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Management Sub-component 5 [Activities?]

Management Sub-component 5 (continued)

Management Sub-component 6[Animals?]

Management Sub-component 7 [Grazing]

bituminised building clearing/removal [constr_dams] contour ploughing control cropping cultivation/sowing digging excavation [frg_dam_water] harvesting hay making maintenance ploughing pruning repairs replanting restoration

ripping shooting spelling spraying stock movement top dressing tree planting utilisation weed control

domestic feral/ pests native -kangaroos

stock rotation stocking rates