Participatory decision support for agricultural management. A case study from Sri Lanka

Agricultural Systems 76 (2003) 457–482 www.elsevier.com/locate/agsy

Participatory decision support for agricultural management. A case study from Sri Lanka J.D. Caina,*, K. Jinapalab, I.W. Makinb, P.G. Somaratnab, B.R. Ariyaratnab, L.R. Pererab a

Centre for Ecology and Hydrology Wallingford, Wallingford, Oxon OX10 8BB, UK b International Water Management Institute, PO Box 2075, Colombo, Sri Lanka Accepted 10 November 2001

Abstract Agricultural policy makers were helped to construct and use a decision support system (DSS) to identify problems and assess potential solutions for a river basin in Sri Lanka. Through building the DSS themselves, policy makers should reach better decisions. The main aim of the study was to test whether this could be done using a tool called a Bayesian network (BN) which is accessible to non-specialists and able to provide a generic, flexible framework for the construction of DSS. Results from a workshop indicated that the approach showed promise, providing a common framework for discussion and allowing policy makers to structure complex systems from a multi-disciplinary perspective. The need for a multidisciplinary perspective was clearly demonstrated. The study also suggested improvements to the ways in which BNs can be used in practice. Further workshops with farmers highlighted the importance of involving them in the planning process and suggested more effective ways of doing this while using BNs. # 2002 Published by Elsevier Science Ltd. Keywords: Agricultural management; Agricultural policy; Integrated management; Decision support system; Bayesian network; Stakeholder participation; Sri Lanka

1. Introduction As livelihoods in many developing countries are highly dependent on agricultural production, management of that production is an important focus of government * Corresponding author. Fax: +44-1491-692424. E-mail address: [email protected] (J.D. Cain). 0308-521X/02/$ - see front matter # 2002 Published by Elsevier Science Ltd. PII: S0308-521X(02)00006-9

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policy, both at national and local levels. In general, policy makers aim to boost food production while considering the demands of other sectors, such as industry. In addition, they will be concerned about the distribution of the benefits and the sustainability of agricultural systems as well as the wider, yet related, issues of health and education. It is now accepted that the best way of developing management strategies to address these challenges, is to view the agricultural system as an integrated whole (Zander and Kachele, 1999; Reenberg and PaarupLaursen, 1997; Ikerd, 1993). Moreover, as any management strategy requires public support for its successful implementation (Bland, 1999), it is important that such strategies are formulated with the participation of all important stakeholders (Frost, 2000; Mosley, 1996). Neither integrated analysis nor stakeholder participation are easy to achieve. In the first case, an integrated analysis must consider the interrelations between the biophysical, social and economic parts of the holistic agricultural system (Herrero et al., 1999; Oberle and Keeney, 1991). The inherent complexity of all environmental systems makes this analysis a difficult process. In the second case, meaningful participation requires effective two-way communication between ‘‘experts’’ and ‘‘laypeople’’ who can often find it difficult to understand each other. Decision support systems (DSS) can help address both of these difficulties. They can offer a framework within which complex systems can be represented in a structured way, allowing them to be more easily understood and helping to draw out additional information and new insights (Marakas, 1998; Browne et al., 1997). Additionally, they can provide tools to help analyse the links between different system components (Cain et al., 1999a; Plant, 1993; Henderson-Sellers, 1991). They can also help improve communication between stakeholders by providing a common means for all parties to express their ideas (Cain et al., 1999b; Walters, 1997). As the utility of these benefits is recognised, DSS are increasingly being developed to support agricultural management (e.g. Lewis and Tzilivakis, 2000; Sengupta et al., 2000). Studies show, however, that actual use of DSS by agricultural decision makers themselves is often limited (Cox, 1996; Foale et al., 1997; Greer et al., 1994). Lynch et al. (2000) suggest that this may be due to the lack of involvement of end users in the design and construction of DSS. Compounding this problem, Turban and Aronson (1998) note that a DSS designed in isolation from end users leads to a transfer of power from the user to the designer which can subsequently bias decisions made with it. Clearly it is important to involve end users in design if a DSS is to be useful and, therefore, used. However, as noted above, if DSS are to achieve their full potential they must also encourage the involvement of other stakeholders. For this to happen, DSS must be designed to be accessible, transparent and credible to people who may be unfamiliar with computer technology (Urs et al., 1999). A more general problem can also be identified—the complexity of environmental systems noted above, not only makes analysis difficult but also makes each analysis almost unique. DSS have been developed to support specific decisions (e.g. concerning land use) about particular types of agricultural system (e.g. arable cropping systems in the USA). However, the huge scope for decision making within agricultural management, combined with the variety of system types, means that few DSS are

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applicable outside of a fairly narrow scope. Clearly, the need to invest significant time and money in developing a DSS which may have only limited applicability will limit their widespread use. One way to overcome these three problems is to develop a generic framework for the construction and use of a DSS which is flexible and easy to understand. In this way, policy makers themselves can be facilitated to design and build their own DSS, making them more relevant to their needs and avoiding the power transfer problem. Moreover, a flexible system can be more easily adapted to meet specific needs, and one which is easily understood is more accessible to other people. This paper reports a study to develop a DSS to help manage the agricultural system in the Deduru Oya river basin in Sri Lanka, using a tool called a Bayesian network. The main aim of the study was to investigate whether Bayesian networks, together with approaches to help people use them, could provide the generic framework envisaged above. Recognising the importance of participation, the study also looked at the best ways of involving other stakeholders in the construction of the DSS so that their understanding of the system could be recognised and, potentially, incorporated into the DSS.

2. Methodology 2.1. Description of Deduru Oya river basin The Deduru Oya flows 140 km from central Sri Lanka to the west coast, reaching the sea at Chilaw (7.34
N, 79.48
E) through a basin area of 2623 km2. The basin is subject to both the south-west monsoon prevailing from April to September (known as Yala cultivation season) and the north-east monsoon prevailing from October to March (known as Maha cultivation season). The population of the basin is just under 1 million people, with 10% of these living in one of the two main towns. Twenty percent of these people are employed in either the public or private sector with the rest earning a living from farming or fishing. Sixty-three percent of families have a monthly income less than Rs. 1000 ($US 13.60) and only 2% of the population have access to pipe borne water. The rest rely on shallow wells for their domestic water supplies, often at distances of 2–3 km. Land use is fairly evenly divided between plantation crops (29% of total area— mostly coconut), paddy (15%) and other irrigated field crops (12%). Twenty-four percent of the land is uncultivated and only 2% is forested. During Maha, 36,200 ha are cultivated under irrigation although this falls to 17,300 ha during Yala. Irrigation water is supplied by three major tanks (reservoirs with command areas > 200 ha), 3559 minor tanks (reservoirs with command areas < 40 ha), 37 anicuts (diversions from watercourses) and 2100 agrowells extracting around 60,000 m3 in total each day. Other agricultural activities include poultry farming, livestock production and shrimp farming at the river mouth. There are also several small industries dealing with food processing, metal quarrying, saw milling, rice milling and cement production.

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Institutions responsible for agriculture in the basin include the Agrarian Services Department, the Irrigation Department, the Provincial Department of Agriculture, the Agricultural Development Authority, the Department of Forestry and the National Water Supply and Drainage Board. Co-ordination of activities is attempted by the Office of the Divisional Secretary in each division. 2.2. Bayesian networks as a decision support tool DSS are commonly considered to consist of a number of components: a data management system, a model management system, a knowledge engine, a user interface and a user (Marakas, 1998). Such a list suggests a highly technical, computer-based system requiring specialists to build it, if not to use it. In this study we tried to shift this emphasis from technology to the user in an attempt to provide a more generic and flexible tool which could be used by non-specialists. Data and model management systems were viewed as practices already in use by policy makers, external to the DSS, which were capable of feeding information into the knowledge engine. These practices were rarely computer based but worked and were understood by the policy makers who could, therefore, gauge the quality of the information provided by them. The Bayesian network was considered able to provide a knowledge engine which could be used by non-specialists directly (i.e. with no user interface). Bayesian networks (BNs) were originally developed as a formal means of choosing optimal decision strategies under uncertainty (Varis, 1997). For this study, they were chosen from among other decision support approaches (see Cain, 2001, for a comparison with other approaches) as they can analyse complex systems while offering two important features which help make them more accessible to non-specialists. Firstly, they are based on a flow diagram representing cause and effect relationships in a way which is easily understood. Secondly, their use of Bayesian probability theory allows subjective data elicited from stakeholders to be used together with more objective data (Draper, 1999). Consequently, stakeholders can construct BNs which represent their different perspectives and facilitate discussion of contentious issues so that conclusions can be reached which are acceptable to all parties. In this way, the management options best able to address the problems faced by stakeholders can be uncovered (Cain et al., 1999a). A further advantage is that BNs explicitly deal with the uncertainty. This is particularly valuable when analysing environmental systems, particularly those involving humans, as such systems are inherently highly uncertain. This uncertainty arises from a poor understanding of the processes driving these systems but also from the often limited availability of information about the system and from errors in that information. Clearly, if decisions are to be made about such systems it is important that they be made with a full understanding of the uncertainties involved. Bayesian networks are composed of three elements: 1. A set of nodes representing system variables, each with a finite set of mutually exclusive states. These variables can represent physical, social, economic or institutional factors.

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2. A set of links representing causal relationships between these nodes. 3. A set of probabilities, one for each node, specifying the belief that a node will be in a particular state given the states of those nodes which affect it directly (its parents). These are called conditional probability tables (CPTs) and can be used to express how the relationships between the nodes operate. Elements 1 and 2 together form a BN flow diagram (or, more formally, a directed acyclic graph) while the addition of Element 3 creates a fully functioning BN. An example is shown in Fig. 1 (the equal probability distributions across the states of each node merely indicate that the BN is not yet fully functional). The structure of this BN flow diagram encodes the perception that agricultural production is primarily determined by the land available for cultivation and the agricultural water supply. This, in turn, is affected by the amount of water stored by a dam (‘‘Dam storage’’) whose construction is being considered (‘‘Construct dam ?’’) and the flow downstream of that dam (‘‘Downstream flow’’). The other relationships represented by the BN flow diagram can be read from it in a similar way. Underlying each node in the BN (and not shown in Fig. 1) are the CPTs. Table 1 shows the CPT describing the relationships between river flow (the child node) and forest cover and rainfall (the two parent nodes). Once all the CPTs have been completed in a similar way, the BN can be compiled and used for analysis. In general terms, this is performed by altering the states of some nodes while observing the effect this has on others. As the BN is a network, the impact of changing any variable is transmitted right through the network in accordance with the relationships expressed by the CPTs. Changes in any node simply arise from the combined effect of changes in all the nodes linked to it either directly or indirectly. (In formal terms, the BN encodes a joint probability distribution over all the nodes. Every time the state of a node changes, the joint distribution is updated through the iterative application of Bayes’ theorem1. Further details are given by Jensen, 1996.) Changes in the BN are observed as changes in the chance that a node is in a particular state. Due to the uncertainty in the CPTs, it is rare for a node to definitely be in one state or another and it is far more common for probability distributions across all the states of a node to be observed. The description above highlights how Bayesian networks offer one of the main advantages of DSS use. The complexity of an environmental system is broken down into individual interactions between nodes, which are described by the BN CPTs. The BN will then combine these relationships in a mathematically logical way to allow an integrated analysis to be done on the system as a whole. Even if they were

1

Bayes’ theorum can be written as: PðBjAÞ ¼

PðAjBÞPðBÞ PðAÞ

where P(A) is the probability of event A occurring and P(B|A) is the probability of an event B occurring given that the event A has already occurred.

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Fig. 1. BN produced by government organisation group 1.

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Table 1 Conditional probability table for river flow node in Fig. 1 Forest cover

Good Good Bad Bad

Rainfall

Good Bad Good Bad

River flow Good

Acceptable

Bad

0.60 0.00 0.40 0.00

0.40 0.10 0.60 0.00

0.00 0.90 0.00 1.00

Read as, for example, ‘‘if forest cover is good and rainfall is good, then there is a 6 in 10 chance that river will be good and a 4 in 10 chance it will be acceptable’’.

able, users need not hold all the relevant features of the problem in their minds at one time as long as they have been included at some point in the BN. A second advantage to using a DSS lies in its ability to provide a common means of communication between specialists and non-specialists. Bayesian networks are particularly suited to this role in two respects. Firstly, the BN flow diagram provides a simple means of expressing concepts which can be used and understood by most people. This is partly because the structure is logical and transparent and partly because the BN uses natural language to label the elements of that structure (the nodes and the states). Secondly, while the relationships between nodes must be described numerically (the numbers in the CPTs), this is done in a way which can be easily understood by non-mathematicians. Nevertheless, the data used to complete the CPTs must be as precise as possible. The necessary data can be measured directly, output by specialised models or specified by expert judgements. Each of these approaches has strengths and weaknesses. For example, while measuring data directly may lead to the most objectively accurate results, collecting sufficient data is often prohibitively expensive or even impossible. In such cases, analyses can be performed to identify which objective data are most valuable in terms of contributing to a more accurate decision. In this way costs can be minimised. Whatever approach is taken to data collection, there will always be a degree of subjectivity. Any integrated analysis must consider human interaction, which controls the system as much as physical features such as rainfall. For example, it may be important to assess how farming practice will change with improved extension services. This can be done by questioning farmers as to how they will behave given such a change but, clearly, these responses will be subjective. A fully functioning BN must, therefore, be used with care. The user must be aware that the BN is not making objective predictions about the future behaviour of the agricultural system. Rather, it shows the logical consequences of linking the user’s understanding about parts of the system into an integrated whole. In this way, a BN facilitates a more complete understanding of the system. The conclusion that a BN is more suited to improving understanding (rather than making predictions) guides the way in which a BN should be used. Bayesian networks can be developed to automatically identify mathematically optimal decisions through the addition of decision variables and utility functions which express a users

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preference for all possible outcomes (Batchelor and Cain, 1999). However, this study intentionally avoids this approach as it is believed that this encourages users to accept the ‘‘answer’’ provided by the BN without considering where it came from or how it should be modified to account for factors which may not be included in the BN (political considerations, for example). Consequently, decision variables are not made explicit (although they are implicitly identified with the ‘‘solution’’ variables suggested by stakeholders—see Section 2.3) and the users are left to consider their preferences for the possible outcomes without external support. A good decision is one which is informed by all the information available and is consistent with the decision maker’s belief about the nature of the system. BNs are able to facilitate this process. Good decisions should also be consistent with decision makers’ preferences for each possible alternative outcome. However, although BNs can support this too, it was thought to be inappropriate in the context of this study. 2.3. Use of BNs with Deduru Oya stakeholders The DSS study was part of a wider investigation into the options for strengthening the institutions associated with water management in the basin. As it was the most important user of water by far, the agricultural sector was singled out when designing the DSS study and this also encouraged a focus on poor farmers. This was considered to be important as these are the stakeholders most likely to be left out of the planning process. Three workshops were held with different stakeholder groups. The first workshop was attended by representatives of all government organisations involved with agriculture in the basin while the remaining two were attended by different farmers from the head and tail of the basin respectively (see Table 2). Each workshop was designed to meet the practical objectives of the wider investigation as well as to answer the research questions of the DSS study. The practical objectives of the wider study were defined as: 1. identifying the agricultural problems in the Deduru Oya basin, 2. investigating the impact of possible solutions to these problems, and 3. identifying which of these solutions are most effective at solving the problems. These objectives were presented to the stakeholders at the start of the workshops. The main aim of the study, however, was to use Bayesian networks to help stakeholders address these objectives and so answer the principal research questions: 1. Can policy makers be facilitated to build and use a DSS using Bayesian networks? If so, what is the best way to do this? 2. In what ways can Bayesian networks help a planning process? 3. What are the most appropriate and effective ways of involving stakeholders in a planning process making use of Bayesian networks?

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Table 2 Description of workshop participants Workshop 1

Workshop 2

Workshop 3

Government organisations involved in water management, in four groups:

Farmers at the head of the basin, including:

Farmers at the tail of the basin, including:

Group 1

Farmers from major irrigation schemes Farmers from minor irrigation schemes Highland farmers Lift irrigation farmers

Farmers from minor irrigation schemes Highland farmers Lift irrigation farmers Tube well farmer

Livestock farmers

Shrimp farmer

Department of Irrigation Department of Forestry National Water Supply and Drainage Board Group 2 Department of Public Administration Pradeshiya Sabawas Group 3 The Department of Agriculture Agricultural Development Authority Group 4 Department of Agrarian Services

The government workshop addressed research questions 1 and 2 while the farmer workshops addressed research question 3 alone (the practical objectives were addressed by all three workshops). The research questions were answered by an assessment of the ways in which the method helped the participants address the practical objectives. This assessment was carried out, by both the research team and the participants, in terms of the logic and comprehensiveness of the outputs, as well as the ease with which they were produced. These criteria were chosen to reflect the advantages of DSS discussed in the introduction. 2.3.1. Government workshop This workshop was held on 24 September 1999 at Wariyapola Training Centre and lasted for 6 h. Thirty participants attended, representing the following government organisations:     

The The The The The

Irrigation Department Department of Agrarian Services Department of Agriculture and Agricultural Development Authority Department of Forestry National Water Supply and Drainage Board

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 The Department of Public Administration  representatives from Pradeshiya Sabawas (local councils) The participants were formed into four groups (see Table 2). Each was guided by a set of instructions and facilitated independently. Although only the facilitator of group 1 had substantial experience with Bayesian networks, the other facilitators had received some training in their development and use. Groups were asked to identify problems related to agriculture in the basin and to discuss potential solutions together with the ways in which these might be mediated to impact on the problems and the wider agricultural system. Moreover, they were asked to structure their discussions in such a way so that a BN capturing their viewpoint could be constructed. To this end, the task was divided into seven steps: 1. Identify the problems. Express these problems as objects (nodes). For example, if the problem is ‘‘poor agricultural production’’ the object of the problem is ‘‘agricultural production’’. 2. Give each of these ‘‘problem’’ objects values representing the current state of the object and a desirable future state. Define these states precisely. For example, if ‘‘agricultural production’’ is currently in a ‘‘poor’’ state it might be desirable for it to be in a ‘‘good’’ state in the future. ‘‘Poor’’ could be defined as ‘‘an average of 1 tonne per hectare’’ and ‘‘good’’ as ‘‘an average of 6 tonnes per hectare’’. 3. Discuss potential solutions to the problems. Express these solutions as objects. 4. Give each of these ‘‘solution’’ objects values representing the current state of the object and a desirable future state. Define these states precisely. 5. Discuss how each of these solutions will impact on the problems and how these impacts might be mediated. Express any intermediate factors as objects and assign values as above. Construct a diagram showing the ‘‘cause and effect’’ relationships between all these objects, using boxes and arrows. 6. Discuss any other things which the proposed solutions will affect other than the problems identified. Assign values to these and include them in the diagram. 7. Evaluate the likely degree of impact of each solution in numeric terms. Although software is available to support the construction of Bayesian networks, it is not essential. For example, the cause and effect diagram referred to in step 5 is essentially a BN flow diagram but it was decided prior to the workshop that it would be easier to construct these on paper. However, as one of the groups had made good progress during the workshop, the opportunity was taken to test the ease with which they could use the software directly. At the end of the workshop, each group was encouraged to present their cause and effect diagram to the other groups to facilitate wider discussion. Following the presentation and the closing of the meeting, the participants were asked to fill out an evaluation questionnaire.

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2.4. Farmer workshops The first workshop was held on 1 October 1999 at Kobeigane Agrarian Service Centre for farmers at the head of the basin. Nine people, out of the twelve invited, attended. The second was held on 2 October 1999 at Bingiriya Agrarian Service Centre for farmers at the tail of the catchment. Eleven people out of the twelve invited attended. At both workshops, participants were invited by field staff from the Department of Agrarian Services to be representative of the range of farmers and farming activities in the area (see Table 2). Each workshop lasted for around 4.5 h and was facilitated by social science specialists from the International Water Management Institute in Colombo. Facilitating stakeholders to construct BNs directly (as had been done with the policy makers) was felt to be inappropriate. Instead, it was decided to use a semistructured discussion to elicit the information necessary for the facilitators to construct a BN flow diagram. To ensure that the outputs were comparable, questions were asked similar to those used with the policy makers: 1. What are the problems with water resources in the basin? 2. What are the solutions to those problems? 3. Are there any negative impacts that would arise from the proposed solutions? Additional questions were also asked by the facilitators to clarify a number of details and ensure that the BN flow diagram created would represent the perceptions expressed by the farmers at each workshop. On completion of this process, notes taken during the workshop were used to develop the BN flow diagram. This was then compared to the BNs constructed by the policy makers to see if similarities and differences in viewpoints could be identified. Additionally, one of the BN flow diagrams was shown to the Deputy Director of the Irrigation Department to gauge his reaction to it as a policy maker.

3. Results 3.1. Practical objectives 3.1.1. Results from government workshop The problems and possible solutions identified by each group are shown in Table 3. Comparing the different groups, it can be seen that groups 1 and 2 have taken a wider perspective than groups 3 and 4. For example, group 3 chose factors which affect agricultural production as problems while group 1 selected agricultural production directly. This difference in perspective is, perhaps, not surprising given the composition of the different groups. As a whole, the current problems can be summarised as:  yield from agricultural production (including agricultural water supply)  processing and marketing of agricultural produce

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Table 3 Problems and solutions as identified by groups 1–4 at the government workshop Group 2b

Group 3c

Group 4d

Problems 1. Agricultural production

1. Agricultural water supply

1. Soil erosion

2. Damage to Deduru Oya 3. Domestic water supply

2. Lack of organic fertiliser use 3. Lack of and poor access to processing and marketing facilities 4. Lack of soil and water conservation practices 5. Poor farmer organisation 6. Low yields 7. Non-availability of effective and improved seeds

1. Water shortage in minor tank systems 2. Marketing problems 3. Ineffective and inefficient farmer organisations 4. Inability to add value to agricultural produce

2. Domestic water supply 3. Relocation of population as a result of dam construction 4. Cost of constructing a dam

Solutions (related problems in parentheses) 1. Construct a dam (1, 2) 1. Construct a reservoir (1, 2) 2. Restrict sand mining (2) 3. Use the sea-sand aquifer (2)

4. Increase forest cover (1,2)

2. Construct a lift irrigation scheme (1) 3. Standardisation of sand mining regulations (2) 4. Provide Department of Public Administration with adequate powers (2) 5. Provide assistance to dig shallow wells (3) 6. Construct water supply schemes for towns (3)

1. Establish soil conservation demonstrations (1) 2. Provide incentives for soil conservation (1) 3. Improve natural drainage (1)

4. Train farmers in use of organic fertilizers (2) 5. Introduce new farming technologies (2) 6. Teach farmers to produce their own seed (7)

1. Construct a dam (1) 2. Strengthen farmer organisations (1) 3. Provide training to develop knowledge and skills for self management (3, 4) 4. Provide infrastructure for marketing (2) 5. Build awareness and knowledge (2, 4) 6. Provide financial capital (2)

(Table continued on next page)

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Group 1a

Group 1a

Group 2b

Group 3c 7. Establish adequate number of nurseries (7) 8. Provide domestic food processing equipment (3) 9. Training on post-harvesting technologies (3) 10. Provide credit facilities (3) 11. Improve monitoring (5) 12. Train farmers in management skills (5)

a b c d

Department of Irrigation, Department of Forestry, National Water Supply and Drainage Board. Department of Public Administration, Pradeshiya Sabawas. The Department of Agriculture, Agricultural Development Authority. Department of Arabian Services.

Group 4d

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Table 3 (continued)

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 soil erosion (including that caused by sand mining, the practice of digging sand from the river bed to provide building materials)  domestic water supply This excludes the potential problems identified by group 1 related to their proposed construction of a dam. The cause and effect diagrams allowed the identification of a small number of solutions which affected more than one problem. By far the most important of these was the construction of a dam. The groups believed that this would have a positive impact on agricultural production and domestic water supply and a negative impact on soil erosion. With the exception of group 1, however, it was impossible to gauge the relative impact of any of these solutions, as there had not been time to elicit this information (as directed in step 7 of Section 2). For group 1, the completed Bayesian network was used by the group themselves to investigate the relative impact of each of their proposed solutions on their identified problems. This was done by changing the state of each of the solution nodes in turn and observing the resultant changes in the problem nodes. One such change is shown in Fig. 2. The numbers next to the state names for each node show the probability that that node is in that particular state, while the bars are simply a graphical representation of these. It can be seen that, given the current state of the basin, water supply and agricultural production are largely expected to be bad. The group, however, believed that there is a significant chance that the situation will not always be so gloomy, particularly in the case of agricultural production where there is a 44.5% chance that it will turn out to be good. In the opinion of the group, however, constructing a dam greatly improves the chances that problems can be solved. For example, the chance that agricultural production will be good becomes greater than the chance that it will be bad. The full

Fig. 2. (a) Current state of Deduru Oya basin as perceived by government group 1. (b) Impact of dam construction on Deduru Oya water resource problems.

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analysis revealed that the group believed that constructing the dam was by far the most beneficial solution for the basin. Not only did it bring improvements on its own but it also made other solutions more effective (e.g. increasing forest cover). The analysis also highlighted a number of conclusions which surprised the group, in that they contradicted their ideas prior to the workshop. These conclusions were:  afforestation would not have any impact on agricultural water supply without dam construction  without careful planning, the benefits in production gained from dam construction would be offset by the loss of agricultural land  the benefits to be gained from attempts to limit sand mining may not justify the resources required to achieve control  constructing the dam would achieve significantly more than all other interventions combined. 3.1.2. Results from farmer workshops Table 4 lists the problems and solutions identified by the farmers. As with the government groups, there was a variation in perspective although, this time, the variation occurred within each group. The problems identified as being least important (e.g. no grazing lands for cattle) were generally factors contributing to problems identified as being more important (e.g. agricultural productivity). As a whole, the main problems may be summarised as:    

yield from agricultural production (including agricultural water supply) soil erosion (including that caused by sand mining) domestic water supply (including groundwater depletion) lack of community co-operation

3.1.3. Comparison of policy maker and farmer perspectives Comparing the farmer BN flow diagrams with the cause and effect diagrams produced by the groups at the government workshop, showed that perceptions of problems and solutions are largely shared. Perhaps unsurprisingly, both the farmers and the government organisations perceived agricultural production and drinking water supply to be their priorities. In contrast, however, farmers did not appear to consider poor marketing to be a major constraint on their livelihoods. Other common features include an emphasis on farmer organisations, concerns with sand mining and river bank erosion, the potential of afforestation to improve water supplies and the problems arising from tank siltation. A notable difference is that water management in major irrigation schemes is only considered to be a minor issue from the government perspective while it appeared to be a major issue for the farmers at the head of the basin. Furthermore, while all farmers were concerned about damage being caused to land adjacent to rivers which is reserved to prevent erosion, the government organisations did not appear to perceive this as a problem.

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Table 4 Problems and solutions as identified by two farmer groups Farmers at the head of the basin Problems (in order of importance) 1. Water shortages for agricultural production 2. Unregulated sand mining 3. Low agricultural productivity 4. Shortage of drinking water 5. Lack of community co-operation 6. Lack of water management efforts 7. Silting of tanks 8. Acquisition of farming inputs 9. Marketing 10. Soil erosion 11. Soil salinity 12. Lack of new agricultural technologies 13. No grazing lands for cattle 14. Pollution due to excessive use of agro-chemicals 15. Damage to reserved lands 16. Salinity in drinking water Solutions (related problems in parentheses) 1. Construct a lift irrigation scheme (1, 3) 2. Construct a dam (1, 3) 3. Control sand mining (2) 4. Provide agricultural inputs at a fair cost (3) 5. Introduce new technologies (3, 10) 6. Improved irrigation management (3, 6) 7. Construct drinking wells (4) 8. Create awareness of the benefits of community co-operation (5) 9. Reward active members of the community (5) 10. Rehabilitate irrigation schemes (6) 11. Implement proper maintenance (6) 12. Hand over schemes to farmer control (6) 13. De-silt tanks (7) 14. Implement soil conservation (7) 15. Conserve reserved lands (7, 14)

Framers at the tail of the basin 1. Low agricultural productivity 2. Water shortages for agricultural production 3. Unregulated sand mining 4. Groundwater depletion 5. Soil water intrusion 6. Lack of irrigation systems 7. Damage to reserved lands 8. Pollution for fishing

1. Restore ancient irrigation systems (1, 2, 6) 2. Organise farmers to fight for their rights (1) 3. Raise awareness of new technologies (1) 4. Reforestation around reservoirs (2) 5. Rehabilitate natural drainage courses (2) 6. Control salt water intrusion (2, 5) 7. Construct a dam (2) 8. Control sand mining (3, 4)

3.2. Research questions 3.2.1. Results from government workshop2 Due to the limited time available for the government workshop, only Group 1 were able to finish the whole process. Their completed Bayesian network is shown in Fig. 1. All the other groups, however, reached Step 6 and produced ‘‘cause and effect’’ diagrams expressing the relationships between the problems they had 2

Quotations included in brackets have been taken directly from the assessment questionnaire.

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identified and the potential solutions they suggested. An example, from the Department of Agrarian Services (group 2), is shown in Fig. 3. The figure indicates a problem common to all groups in that some elements of the cause and effect diagram are not entirely logical. In Fig. 3, for example, it would probably be more logical for ‘‘Improvement of funds’’ to be dependent on ‘‘Membership increases’’ rather than directly on ‘‘Training and awareness’’. In fact, group 2 spent more time on steps 5 and 6 than other groups and, possibly as a consequence, their diagram has less problems in this respect. However, it was clear that none of the groups were immediately comfortable with expressing their logic diagrammatically (‘‘this is a systematic approach but may be difficult to some people’’). For example, discussions as to the appropriate direction of an arrow were common. More positively, however, none of the groups had difficulties in capturing their ideas in terms of variables (nodes) and assigning quantitative states to those variables. The approach appeared to work well as a means of drawing out information. Not only were many possible solutions identified (Table 3) but the facilitators noted that further possibilities had been ‘‘uncovered’’ through consideration of the cause and effect diagrams. In the case of group 2, for example, the potential impact of ‘‘Training and awareness’’ on ‘‘Water shortage in minor irrigation’’ was not noted until the ‘‘Efficient water measurement’’ box was included in their diagram. However, the small number of similar ‘‘multi-impact’’ solutions proposed by the groups, suggested that this had not happened to a great degree (see Table 3). Perhaps more importantly, although the collected outputs of the groups could be considered to be comprehensive, no one group alone considered all parts of the agricultural system. The presentation of the cause and effect diagrams at the end of the workshop did not generate significant discussion. This was thought to be due to a number of reasons (including the participants’ desire to go home) but provided no indication as to whether the diagrams were useful to the participants as a means of communicating their perspective on the problem. None of them mentioned this issue in their responses to the questionnaire. However, following the workshop, the research team did find them a useful tool to compare and combine the ideas produced by each group. This was because the diagrams helped avoid the team misinterpreting the stakeholder ideas as they showed the underlying thinking of each group to a greater degree than a simple list of problems and solutions. Group 1 appeared to find it fairly straightforward to use the BN software to develop and analyse a fully functioning network (‘‘[it was easy] to feed over solutions etc. to the computer’’). As with the other groups, the structure was not entirely logical but this did not appear to be particularly important to the group who were able to use it to reach the conclusions reported above with minimal facilitation. It was interesting to note that the group did not draw their conclusions based solely on the probability distributions across the states of the problem nodes. Instead, they looked at how all the nodes changed in response to implementing various solutions and tried to understand why the changes they observed were taking place. This was (at least partly) a response to their acknowledgment that the numbers they had entered into the CPTs were good guesses at best (‘‘[it was useful] but real data needs to be used’’). Consequently, they decided that the BN outputs could only be used as

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Fig. 3. Cause and effect diagram produced during the government workshop by representatives from the Department of Agrarian Services.

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a guide and that its outputs needed to be verified against their professional experience and understanding. Nevertheless, they all agreed that it was a useful tool in that it encouraged them to think about how the agricultural system operates as a whole (‘‘[the approach] guided us to make systematic decisions, see the results and rethink and so improve our decision making ability’’). The representatives of the Irrigation Department requested further training in the use of the tool so they could apply it to other management problems. In general, the response to the questionnaires was positive with 14 out of the 16 questionnaires returned suggesting that the approach was useful. However, a number of participants felt that more time was needed with at least one suggesting that this was unacceptable (‘‘this is time consuming’’). From group 1, there was an almost unanimous request for objective data with which to fill in the CPTs (‘‘[Should] collect all relevant data and then analyse to get more reliable outcomes’’, ‘‘spend more time on collecting relevant statistics’’). Concerning ease of use, 13 out of the 16 questionnaires returned said it had been easy to use and this was supported by a number of comments (‘‘very good because it has followed a very simple method’’, ‘‘the system is well guided and easy to reach an end’’). This was surprising given that only seven of the participants’ had used any sort of decision support approach before. However, there were a few qualifications to this (‘‘it will be more popular when people get to understand about the decision making system’’). Moreover, a number of other comments suggested that the instructions had not been easy to follow (‘‘simplify the format’’, ‘‘some more examples’’, ‘‘more detail needed’’) and that the facilitation could have been improved (‘‘facilitators should be trained very well—all of them should be on one track’’). There was a feeling that differences in facilitation led to greater progress being made by group 1. Participants in all groups perceived clear benefits from working as a group (‘‘the group study method was useful’’). 3.2.2. Results from farmer workshops The approach to the farmer workshops proved to be an effective means of encouraging them to discuss the issues surrounding agriculture in the basin. This was largely due to skilled facilitation. As can be seen from Table 4, a large number of problems and solutions were identified and these were discussed in some depth by all participants. At the end of the workshop, the farmers thanked the facilitators for providing them with an opportunity to express their opinions and asked for reassurance that their ideas would be forwarded to the responsible authority. From the detailed notes produced, it was straightforward for the facilitators to develop a BN flow diagram with some confidence that it properly represented the perceptions of the farmers. Although it was impossible to develop this into a fully functioning BN (as no numbers had been elicited for the CPTs), the research team found the farmer flow diagram useful in that it facilitated a comparison between farmer and policy maker perspectives, in much the same way as the cause and effect diagrams had allowed a comparison of the perspectives of government groups. However, due to the amount of information elicited from the farmers, the diagram was quite complicated. This proved to be a problem for the Deputy Director of the

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Irrigation Department who did not find it helpful in understanding farmer perspectives on the basin.

4. Discussion 4.1. On practical objectives While neither the problems identified at each workshop nor the solutions were a great surprise, the general consensus among all the groups gives some confidence that no important factors had been overlooked. Some conclusions may be drawn, however, from highlighting the differences which are present. In particular, the difference in perception between government and farmer groups over marketing may suggest that the farmers are unaware of the benefits of improved marketing. Therefore, if the benefits perceived by the government organisations are to be obtained, then farmer training must be provided (indeed, this was recognised by the government group 4 who include it as one of their solutions). Moreover, this casts some doubt on whether the government can achieve the level of farmer involvement necessary for improved marketing to succeed. Although encouragement can be taken from the farmers’ interest in improving community co-operation, further consultation is probably important. There are further notable differences over irrigation management and damage caused to land reservations. In these cases, policy makers are appearing to overlook what are serious concerns for the farmers. Whether these concerns are fully justified or not, policy makers should investigate these issues further and inform farmers of their findings. While such comparisons are interesting, the main results from this study arise from the use of the fully functioning BN produced by group 1 (as far as the practical objectives are concerned). The surprise expressed by the group at the results, arose, at least in part, from the integrated analysis used with the group who had not previously considered the impact of factors outside their own discipline (this will be discussed further in Section 4.2). However, it should be noted (and was by the group themselves) that these results are wholly based on subjective assessments made by the group which are unmodified by objective, measured, data. As such, the value of collecting more objective information with which to improve any subjective estimates should be carefully considered. Another potential problem, is that the simple specification of states used here may contribute to misleading results. Again, careful consideration should be given to how states can be defined most appropriately. 4.2. On research questions The results of the workshop suggest that policy makers can be facilitated to build a DSS using Bayesian networks. Only group 1 were able to produce a completed BN but the progress of the other groups was largely halted by the lack of time. Furthermore, most participants seemed to find the method straightforward. Although

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the imperfect logic of the diagrams produced is a cause for concern, it is likely that this could be rectified given more time and better facilitation. The apparent lack of logic may arise from a misunderstanding on the part of the facilitators and this should be discussed with the group as part of the process. The experience of this study suggests that the cause and effect/flow diagrams are a useful tool in doing this as they provide a common means by which ideas can be clearly expressed (see also Browne et al., 1997). In any case, the production of BN flow diagrams needs to be an iterative process so as to ensure that they represent the perspective of the group accurately. This is crucial if they are to be used to communicate and record that perspective. The arguments above suggest that more time is needed to implement the approach properly. This is a possible constraint to using the approach as it will rarely be possible to get a large number of people to attend a workshop for longer than a day. To overcome this problem, it may be more productive to form a core group of policy makers who can devote a substantial amount of time to it. This group should be taught to use the method without the need for constant facilitation, so they can proceed at their own pace (although facilitation should still be available to answer specific technical queries). Clearly, this would require a major commitment on the part of those involved who would need to be convinced of the benefits of taking this approach. In our case study, the response of the policy makers to the workshop suggests that many did see potential benefits and would be willing to use the approach further. Forming a core group, however, presents two further challenges. While a small group will be more able to spend the time necessary to construct a DSS, it will be less representative than a larger one and will generate fewer ideas. The conclusions reached by group 1 clearly show the benefits of encouraging different disciplines to analyse the agricultural system together. This was particularly true of the Forestry Department, who were forced to conclude that their favoured solution was only effective following construction of a dam by the Irrigation Department. However, Table 3 shows that, in spite of their apparent multi-disciplinarity, group 1 overlooked a number of important issues, in particular, the processing and marketing of agricultural produce. Therefore, it might be concluded that the group was too small and needed further members from other disciplines. This need not increase the size of the group excessively and so it is not necessarily a methodological problem. However, care should be taken to ensure that the core group is properly representative of all the policy makers involved without becoming unwieldy. It should also be noted that, although no problems were encountered in this case study, political considerations, rather than more objective criteria, determine group composition. One of the advantages of DSS use is to draw out as much information about a system as possible. The approaches used at the workshops may be judged moderately successful at doing this from the number of problems and solutions identified. However, while it is believed that this was partly a consequence of basing discussions around flow diagrams, it was largely a result of the number of people at the workshop. Clearly, a core group, no matter how representative, would be detrimental in this respect. To address this, members of the core group should be

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encouraged to meet regularly with their departmental colleagues who are not directly involved with the planning process. At these meetings the progress of the core group can be reported and further ideas elicited from those present (possibly using the flow diagrams as a focus). This information could then be fed back into the deliberations of the core group. As discussed above, if policy makers are to build and use a DSS, then the composition and functioning of the group doing so is an important issue. However, if this issue can be addressed successfully, our study shows that Bayesian networks are an effective way of linking disciplinary perspectives. The results obtained from group 1 demonstrate this most clearly. In the first instance, constructing the BN flow diagram as a group highlighted particular issues which needed further examination, such as the interaction between forestry and dam storage capacity. Subsequently, the input of data into the CPTs allowed the consequences for such interactions to be quantified and conclusions reached. This demonstrated the results to be gained from an integrated, inter-disciplinary analysis, although this analysis was limited by the group composition, as discussed above. With the addition of further disciplines to the group, a fully integrated analysis could be performed which would enhance the conclusions reached by allowing, for example, the effectiveness of marketing interventions to be gauged relative to the (largely) engineering solutions considered by group 1. Although the results from the fully functioning BN produced by the group were based on subjective inputs, given more time, these inputs could have been generated from more objective sources, such as the records of the Irrigation and Forestry Departments and the computer modelling tools already being used by them. Importantly, however, even though the inputs were subjective, the common framework provided by the CPT allowed the whole group to discuss what the inputs should be and so reach a consensus. Aside from encouraging an integrated analysis, Bayesian networks helped the policy makers to distinguish principal problems from symptoms of these problems. Once again, this was due to the use of a flow diagram to break representations of the system down to sub-components. For example, group 1 originally identified ‘‘sand mining’’ as a major problem in the basin. Their subsequent analysis showed that they only perceived this to be a problem in that it affected domestic water supplies. Whereas their original management strategy may have included measures aimed at reducing sand mining, this new conclusion allowed them to consider whether these measures were really the most efficient way of improving domestic water availability (in fact, further analysis with the fully functioning BN revealed that they probably were not). Lack of time was not quite so much of an issue for the farmer workshops, largely as a consequence of the limited objectives set for them. However, although the BN flow diagrams produced were useful for comparing stakeholder perspectives (at least for the research team) they were not validated by the farmers and did not allow any quantitative analysis as they lacked any input to the CPTs. A comparison of the experiences at all three workshops suggests that both validating the flow diagrams and eliciting information for the CPTs would be difficult with the farmer groups we worked with in this case study.

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However, this is not to say that it would be impossible. The farmer group could be presented with a structured set of questions at a second workshop, following construction of a BN flow diagram from the first. These questions would check that the group agreed with every relationship expressed by the BN. For example, if Fig. 1 was the BN diagram produced from the first workshop, one question would be: ‘‘What are the main factors that you think affect river flow?’’. It would be hoped that the answers would include ‘‘forest cover’’ and ‘‘rainfall’’, although the group would also be free to add further factors. Questions specifically targeted at eliciting the required data for the CPTs could then be prepared for a third workshop, following approaches suggested by Anderson (1998). These approaches are currently being tested in a further study. While such an approach may produce a fully functional BN which properly represents a farmer perspective, it will obviously take significant time to do so. Given this, it is not entirely clear that such time would be well spent. This study suggests that a properly validated BN flow diagram representing farmer perspectives would be useful in that it allows those perspectives to be more fully developed by the farmers and better understood by policy makers. However, the response of the deputy Director of the Irrigation Department to the BN flow diagram implies that if this benefit is to be fully realised, such diagrams need to be kept as simple as possible. The utility of encouraging farmers to quantify the relationships between variables (by filling in the CPTs) is even less clear, although it may be useful when those relationships relate directly to farmer experience and cannot be quantified in more objective ways. For example, a relationship expressing the degree of farmer take up of a marketing intervention would probably be best quantified by eliciting a response directly from farmers. However, a relationship expressing the change in runoff with afforestation would probably be best quantified through scientific measurement and modelling. Ultimately, the answers to such considerations depend on how policy makers intend to use a Bayesian network. Even if time were available to develop the BN flow diagrams further, our study indicates that farmers may not always be willing to be involved in this process, due to other important demands on their time. The fact that both our farmer groups asked for reassurance that the workshops would lead to some tangible outcome suggests that it is important that there is a link between such consultations and genuine changes on the ground and that this is explained. This is generally acknowledged to be an important rule of stakeholder participation (see Environment Agency, 1998, for example). The surprising level of agreement between the two farmer groups implies that both groups share the same problems and also implies that each group was sufficiently representative. The differences between the groups lay in detail rather than substance. The greater detail provided by the group from the head of the basin may indicate that they place more emphasis on certain issues than the tail-end farmers or, simply, that the dynamics of their group may have led them to consider it in greater depth. This question should be resolved through further consultation but it again raises issues of group composition. The groups in this study were selected to include a range of farmers in the hope that this would provide a properly holistic perspective

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and this appears to have worked. Alternatively, more detailed results might have been obtained if the groups had been composed of stakeholders with a single water use (i.e. groups of irrigators as distinct from groups of livestock farmers). In practical terms, however, selection of such groups would not be straightforward as few people have only a single farming activity and, in any case, an increased level of detail may be undesirable.

5. Conclusions The study provided some evidence that policy makers were able to use Bayesian networks to develop a DSS to help make decisions about the agricultural management of a river basin in Sri Lanka. One group of policy makers was able to develop a fully functioning BN, while all others developed the elements of a BN flow diagram on paper. These other groups were unable to progress further due to lack of time. The differences between the groups were thought largely to be a result of differences in facilitation. Bayesian networks were perceived to have helped the planning process in a number of ways. Principally, they provided a common framework for discussion and allowed policy makers to structure complex systems from a multi-disciplinary perspective. The need for a multi-disciplinary perspective was clearly demonstrated. Additionally, the outputs (either fully functioning BNs or flow diagrams) were useful in recording and, subsequently, communicating the perspectives of the policy makers. There was some evidence that BNs had helped draw out additional information but this was not conclusive. Given these results, Bayesian networks can be said to show significant promise as a generic and flexible framework which can be used for the construction and use of DSS by policy makers themselves. Indeed, interest was expressed by government group 1 in developing their network further and using it to support policy decisions (unfortunately, funding limitations meant that it was impossible to pursue this). However, it is recommended that the method employed to do this should be altered from that used here. In particular, it is expected that better results will be obtained if a core group of policy makers is formed which is sufficiently representative of all disciplines (government departments) involved. This group should be trained in the use of the Bayesian networks and encouraged to take sufficient time to develop a BN iteratively and in full consultation with colleagues outside of the group. Future work will test this new approach. Aside from the policy makers, other stakeholders (in this case, farmers) were found to be well informed about problems associated with agricultural management and keen to suggest potential solutions. Consequently, it can be concluded that it is crucial to involve them in the planning process. The best means of doing this in a process using Bayesian networks is less clear. Given the limited objectives set by the study in this respect, limited success was achieved. The study was able to elicit enough information from farmers to build a BN flow diagram and this did help the research team compare farmer perspectives to those of the policy makers. However,

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the diagram proved too complex for it to be easily understood by the policy makers. More importantly, the farmers neither had an opportunity to validate the flow diagram nor quantify the relationships between system components it represented. Although this seriously limited the value of the tool as a means of facilitating farmer involvement, it was not thought impossible to address these difficulties. Further work is investigating ways of doing this. As well as the areas mentioned above, future work should evaluate the use of subjective data (i.e. stakeholder perception or expert opinion) in combination with objective (i.e. measured) data. In this study, policy makers clearly felt that better management decisions could have been made using the objective data that were available (only subjective data were used due to time constraints). In addition, the value of explicitly recognising uncertainty in management decision making needs to be explored.

Acknowledgements The authors would like to acknowledge the support of the Asian Development Bank and the UK Department for International Development under their Knowledge and Research programme. The software used to support the development of the BNs in this study is produced by Norsys Corporation, Canada and Hugin Expert A/S, Denmark, who provided a generous discount on the license fee. The profile of Deduru Oya was provided by Engineering Consultancy Ltd of Sri Lanka. We would also like to thank the anonymous reviewers for providing excellent comments which improved the paper as a whole.

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