9.1 ­‐ S ELECCIÓN Y T RANSFORMACIÓN DE D ATOS

Typologies for knowledge intensive reasoning tasks are discussed in the cognitive psychology and knowledge engineering literatures. Several problem solving tasks have been identified: assessment of the situation, classification, diagnosis, retrodiction (calculation of past values), modelling, monitoring, design (where there are many alternative courses of action), configuration (where a specific solution has to be developed), prediction, and scheduling (Schreiber et al., 2000). All of these have to be handled as necessary by farm management consultants:

• Assessing the state of the farm, based on benchmarking, comparative analysis, observations etc.

• Classifying farms by climate, soil type etc.

• Diagnosing the cause of a problem

• Estimating previous pasture growth (retrodiction)

• Modelling the financial situation of the farmer, performing a financial analysis

• Monitoring the state of the farm over a period of time

• Considering several straightforward options

• Constructing a specific solution to handle an unusual problem

• Forecasting prices for the future

• Planning and scheduling activities to ensure the solution is viable.

Classification and simple diagnosis which both involve pattern matching are relatively straightforward involving feature matching. Klein (1998) incorporated the concept of the story building into the RPD which now handles diagnosis when the nature of the situation is unclear. If the data collected does not allow a similar experience to come readily to mind, further action may be taken. One way to remove the uncertainty is by the construction of a plausible explanation of what is going on (Pennington and Hastie, 1993; Kaempf et al., 1996).

Benjamins and Jansweijer (1994) raised the important issue of “cover” in diagnosis which is only possible when the requisite knowledge is available. The proposed cause of a problem can be consistent with some observations at one end of the spectrum or cover all the observations at the other end.

Potential hypotheses are generated by the consultants after an initial data collection and assessment of the situation (based on benchmarking and comparative analysis). Further information may be collected, confirming or eliminating hypotheses by matching actual with expected values. The situation is complicated when there are several causes of a problem, for example the high empty rate is due to poor nutrition caused by low soil fertility and poor heat detection. It might be necessary when establishing a causal chain in farm management consulting to work systematically through system models to establish an explanation (Benjamins and Jansweijer, 1994). Since the farm management consultants defer the identification of causes and the proposal of solutions until they feel that they have as much information as possible, they endeavor to be at the cover end of the spectrum. They do not appear to employ the story building strategy described by Klein (1998) and Kaempf et al., (1996).

With regard to solution generation, the farmer’s goals and weaknesses, their financial situation, the resources of the farm, and legal requirements all have to be taken into account. Farmer preferences also have to be considered. When the situation is straightforward there may several alternatives. Some of these will be selected and compared to check their viability (the design problem solving task). Multiple options are often produced in advisory situations to give the client some choice with regard to the solution because they will implement it (Shanteau, 2001). When the situation is more complicated a solution might have to be tailored to the situation. The solution to the same problem on two different farms might be completely different. Occasionally, it is not even possible to select an option from the set of possibilities and a unique solution has to be developed (the construction problem solving task), based on domain principles and knowledge of the client’s circumstances.

Possible solutions may be proposed after mentally checking that they are viable (Klein and Crandall’s mental simulation (1995). The farmer who has to implement the solution determines which options will be considered, possibly combining aspects of the alternatives proposed. Subsequently, the option or options chosen by the client are then worked through formally (by developing budgets). In this respect, the process followed is similar to that described in normative models of decision making and avoids the problem of overly optimistic forecasts that can occur when depending on mental simulation alone (Yates, 2001). The alternatives are compared explicitly in terms of their future consequences (Lipshitz, 1993). The solution selected is not necessarily optimal but must satisfy the client. Two consultants may suggest different solutions to the same problem but this is to be expected according to Shanteau (2001) and does not reflect on their competence. Klein (2009) observed that decision makers need to look both to the past (for situation diagnosis using story modelling) and the future (mentally simulating the likely success of a solution). The farm management consultants achieve this in a very sophisticated fashion. They are usually conscious of the fact that they visit the farm at one point in time and if necessary set out to try and recreate what happens at other times. Past events (history of the situation) often have to be taken into account to understand what is happening and to determine if there is an action feedback loop (Crozier and Ranyard, 1997; Orasanu and Connolly, 1993). Extensive information about the previous as well as the current state has to be collected. Farm management consultants might need to work out what happened on the farm in spring (number of stock on the farm three months ago). This process of estimation involves calculating the values for data points for example the pasture growth in spring. It goes beyond story building and is more like system reconstruction. It is useful in problem identification, diagnosis and solution evaluation.

Predictions are also made of what is likely to happen on the farm in the future, given various solution scenarios. The consultants are always acting in an uncertain environment where many variables have to be estimated based on informed sources of information (for example the buying or selling price of stock). They may look a few months, a year and several years ahead. Consultants also need to ensure that they can access several reliable sources of information (accountants, stock agents, lawyers, farmers, meat companies, banks, newspapers, professional organisations and the Ministry of Primary Industries). They can then make their predictions taking into account the situation of the farm with regard to climate, soil type, etc.

Managing uncertainty is important (Klein et al., 2003). It relates to the problem of working in a situation which is information rich and where forecasts have to be made. During a visit the consultant has to ensure that the data collected is as reliable as possible and that any predictions need to be well-grounded in research. Various informal approaches to risk are followed. Managing information overload and checking of inferences based on display data are critical. The risk to reputation is high if ill-thought out proposals are put forward. Involving the client with the detail of a solution helps reduce this risk.

Farm management consultants have to able to think logically in order to be able to carry out so many different problem solving tasks in such a complex environment. Strong analytic abilities are needed, for instance, to diagnose the cause of complex problems. Farm management consultants have to be able to analyse accounts and understand key farm financial benchmarks. Whilst software is available to help prepare the financial analyses, being able to identify the key indicators is very important for pinpointing problem areas. Interpreting figures is an essential component of the logical thinking required by the consultants.

Analytic skills needed to be taught in situ according to Heuer (1999) who believes that “Thinking analytically is a skill like carpentry or driving a car. It can be taught, it can be learned, and it can improve with practice. But like many other skills, such as riding a bike, it is not learned by sitting in a classroom and being told how to do it. Analysts learn by doing.”