DISEÑO DE LA SECUENCIA DE ENSEÑANZA – APRENDIZAJE (SEA)

The project leader, project manager and economist each raised the difficulties in acquiring accurate interview data as a potential source o f uncertainty with bias

implications for the DSS content. The economist remarked that “when we do surveys - the data we get we can’t rely on for sure... even when we speak the same language, when we double check the data, it doesn’t add up”. These sentiments were echoed by the project manager who implied that some villagers deliberately distorted their economic worth when interviewed: “Its not easy to get true income figures. For example, when you see the Hmong - they have 4WD, quite rich - but they don’t give you the real figure. For production data, you need to systematically monitor to get true figures - interviewing may not give true figures”. Meanwhile, the project leader

suggested that data disparities were due to the ignorance o f villagers as to scientific measures:

“Instead of just asking farmers how much yield he gets, you should do a real trial so you get the data with your own eye. With research before, we have proved that just asking farmers for the data is useless - they do not understand the units we use. For example, they do not understand 1 kg - they understand 1 bundle instead. So you shouldn’t just interview, you should stay there for a season and observe to see with your own eye”.

Two alternate communicative interpretations emerge. One possible inference is that farmers are unreliable research informants, and that to manage distortions, researchers should conduct their own experiments and observations as triangulation for interviews. Alternately, the inference may be that survey instruments are unreliable because o f difficulties in asking yield questions in terms the farmers are familiar with.

The biophysical leader suggested that uncertainties would mainly derive from the economic component, both because the economics model would not incorporate all significant factors and because o f the dynamic nature o f the decision-making environment:

“I think problems for uncertainties come from the economics model - when we think about the price, we haven’t thought about the transportation cost. There are many middlemen who get highland products and they put the price lower than the standard. And they have a trend that if a particular product is successful one year, every farmer will change to that crop the next year. So it will fluctuate much more then biophysical data... You need to make corrections about the economic model - more crop varieties, changes in labour... From the beginning, you need to have in mind how to update the decision methodology”.

He also worried that the sociocultural component would introduce bias because their research would not capture enough different hill tribes to be representative: “They should do at least five tribes, maybe”42. Finally, he raised the choice o f biophysical models as a possible source o f bias, although he was confident that this bias would improve over time:

“Uncertainties will be about the decision - which model to use. When you do, for example, rainfall-runoff modeling you will provide different results when you calibrate the model. And in the future, when you have more data, the model will improve in the long-term. Assumptions are about rainfall patterns, effects of terrain on water availability”.

The biophysical leader’s concerns about inadequate representativeness and the capability o f the models in the DSS to cope with a changing decision-making environment were echoed by the economist:

“I don’t know for the modelling, if we do optimisation, I don’t know if that optimisation is real because we can’t incorporate everything in the model... If we try to use optimisation and we try to incorporate constraints, we may not be able to find those figures or even find the relationships. For example, if we want sedimentation constraint, we may not have that figure... [And] we have to assume homogeneous, but on highlands, we have many types of farmers and I don’t think the model represents that...

42 The subcatchments chosen had only two tribes strongly represented, and a small population o f a third tribe.

And from the model we plan to use, we will rely on just one year’s data, from the interviews. At the moment, we don’t consider the time trend. And the situation may change for example if they do mining in the highlands, they may run out of minerals and stop - or if some tourism starts, or if there are crops in the future that are more profitable that we didn’t consider when we built the model, and population, migration, climatic things may change.”

To manage these potential biases, she suggested comparing a range o f models and undertaking sensitivity analysis.

The anthropologist raised a potential bias in terms o f absence o f knowledge if only information from a single discipline was incorporated in the DSS, or if only information derived from scientists was incorporated in the DSS, excluding pertinent local

knowledge:

“If policymakers have one way or idea, if they get information only biophysical or only social, this leads to misunderstandings in constructing policy for highlands. I’m interested in the DSS if the DSS should have many, many information including social, biophysical and economic and opinions of the villagers, of the stakeholders”.

To illustrate how a villager’s perspective may diverge from a scientist’s, the

anthropologist described how villagers had explained to him why they disagreed with scientific advice about pest control:

“I believe in both science and the position of the people to construct information... The science cannot know everything about the environment, the villagers have knowledge about dealing with the environment in forest and land and water - their environment is part of their culture. In the past two or three days, I went to Mae Chaem. I interview the villagers who are growing rice with shifting cultivation and animals - ask them about different methods they use. They use the method to share the benefit between humans and animals to protect rice - in the ricefield, they have many bird to eat their rice. He argued with new green revolution method proposed by the government to use gun or net to kill birds to increase economic profit. He thought it was wrong. The birds just eat some rice - man and birds can live together”.

Thus, the assumption by government scientists o f the mutual exclusivity o f birds and maximum econom ic profit is contrasted with a symbiotic construing o f man and nature. Note that this particular government strategy shares similarities with the broader

government contention that Thailand’s ecological and economic security would be enhanced if villagers were removed from the highlands.

The project manager emphasised the potential for socially embedded bias in terms o f variables considered in option analysis:

“If I am a forester - the forest is mine - 1 should do whatever I want to do to develop forestry fully - 1 should use all factors I can to develop forests - but not think of others. This used to be the story of past development - the intermix between factors has not been there - they only think of forests. I think of myself - not of you - this is the bias. For successful development, you need full knowledge of factors”.

The preceding quote alludes to the history o f contentious and fragmented decision­ making in the highlands, and implies an embracing o f an integrated approach in terms o f both holistic analysis and equitable decision-making.

Also highlighting the potential for socially embedded bias, the DSS leader cautioned that the people directly involved in development o f the DSS “are technocrat people so their ideas and perceptions will affect the output heavily. Because the people in the social group will have less participation in the system, I think their preferences will be less expressed in the system”. When asked to elaborate as to how technocrat ideas would influence the DSS, he responded that the DSS would most likely end up as “something very rigid according to the theory or the quantitative modelling; input to output”.