Knowledge Elicitation Tools (KnETs) Sukaina Bharwani, (School of Geography and the Environment, University of Oxford / Stockholm Environment Institute), Michael D. Fischer (Centre for Social Anthropology and Computing), Thomas E. Downing (SEI), Gina Ziervogel (University of Cape Town)

Key features Formalisation of field data using these innovative methods has been started in 2 sites so far (East Kent, UK and Limpopo Province, South Africa). These innovative methods for knowledge engineering allow the construction of rules and heuristics used by stakeholders to: a) provide new questions and insights in the data collected and/or b) to broach the realm of tacit knowledge, or that which people find hard to articulate, which difficult to elicit by other methods c) include in an agent based model d) to provide a means to integrated qualitative and quantitative models in a way which has failed in the past due to the failure to understand the fundamental differences between them.

Knowledge engineering Sub stages involved in the process Knowledge elicitation can be a big bottleneck in the research process KnETs are tools which can automate parts of this process

Rapid prototyping Interactive questionnaire Identify salient aspects of knowledge domain Social, environmental and economic Goal – crop and strategic adaptation

Rule induction program Rule induction algorithm creates rules based on data from questionnaire

Learning program Stakeholders participate in pruning and refining resulting decision trees using a ‘learning’ program

Funding Body: Tyndall Centre for Climate Change Research School of Geography and the Environment, Oxford University Stockholm Environment Institute, Oxford, UK Climate Outlooks and Agent- Based Simulation of Adaptation in Africa (CLOUD)

Overview Case study: Mangondi –Community garden and individual profiles –Adaptation strategies –Ability to adapt to climate variability will improve ability to adapt to climate change Agent-based social simulation model Climate scenarios (Seasonal to decadal) Farmer adaptation (Based on fieldwork) Crop models (FAO CropWat)

CLOUD... Perfect vs. imperfect farmer decision-responses Analysis of adaptive capacity Variable skill levels of the forecasts Potential strategies not currently used (e.g. experimentation with a market crop)

Fieldwork Survey Interviews using KnETs methods – salient features Forecast use Irrigation reliability Market demand (More likely to trust dry forecast than wet as marketing farmers) Options – crop and strategic adaptation Perceptions are often different from reality and anchored in the memory of past extremes

Crop Model Code integrated into the pilot model Increased time steps for full cropping calendar Four growing stages Important variables:  available water holding capacity of the soil  crop coefficients  monthly precipitation  monthly potential evapotranspiration  WRSI

Adaptive capacity Strategies: Change crops (e.g. poor opting for market variety) Change planting date of existing crop Increase/decrease area Crops: Maize - subsistence crop kept for 2 years in storage Butternut - seasonal crop for Christmas market Cabbage - market crop It appears that wealthier farmers adapt more to the market (more cautious of trusting the forecast) and poorer farmers adapt more to climate signals (forecast helps to support their choices since irrigation is unreliable)

Markets Income Poor farmer sells from the garden Average farmer sells at the local market Market demand and price Higher under dry conditions

Access to water Irrigation and distance from pump is important Visible use of drought resistant planting regimes based on experiences of access to water Perceptions of water allocation are not homogeneous Possibility of planting the same crop on several plots to market more strategically. However, planting rows of multiple crops spreads their risk and they recognise it as a ‘safer’ strategy. Could WEAP be used to compare the water usage of these two options?

Social networks Different agent types and degree of heterogeneity? - Poor and better-off farmers Which agents are influential and what is the flow of information? - Poor will be influenced by all peers, but average farmers will on be influenced by other better-off farmers

Model…

Experiments Experiment A: Seasonal forecasts are not used but the community is still impacted on by climate: control experiment – No memory, no forecast. Agents assuming this year will be like last year. Experiment B: With seasonal forecasts (how vulnerable are farmers to climate change when using seasonal forecasts, compared to A?) –Forecast, no memory = Use of forecast with no biased perception. Experiment C: With climate change but not seasonal forecast: climate changes and agents learn through experience of changing climate. –Memory, no forecast. Agents assuming this year will be based on last 5 years. Experiment D: With climate change but with seasonal forecasts. –Forecast and memory.

Computers in the field!