Knowledge is like a bird of the forest, one person alone can never catch it (Ewe proverb)

Pursuing the Bird of the Forest: Advances in Ethnographic and Participatory Methods

C. Roncoli et alClimate Forecasting and Agricultural Resources Project

University of Georgia

Photo by Christine Jost

Structure of this review

Ethnographic and participatory methods

Cognitive and cultural setting (front-end)Cultural models of climateLocal forecasting knowledgeRepresenting probabilityConstructing credibility

Decision processes and decision makers (back-end)Learning forecast responsesCharacterizing decision makersUnderstanding constraintsInstitutional environment

Conclusions

Ethnography and participation: strengths

• ‘Insider’ point of view

•Contextual understanding

•Rapport with research subjects

Ethnography and participation: weaknesses

•Reliance on key informants may introduce biases

•Public nature of participatory methods favors prominent, educated members of community

•Group-based methods obfuscate difference, dissent

•Emphasis on local context makes findings less generalizeable

•May neglect structural analysis of power relations that local to global

Cultural and cognitive context:cultural models of climate

Ziervogel and Calder 2003

•Seasonal calendar (local classification of time)

•Activity calendars (constraints, entry points)

•Significant event calendars (rainfall and yields)

Cultural and cognitive contextcultural models of climate

Moore typology of rain events, Central Plateau, Burkina Faso, CFAR project

Cultural and cognitive contextcultural models of climate

Influence diagram of farmer mental model of climate variability

Hansen et al 2004

Morgan MG, et al (2002) Risk communication: a mental model approach. Cambridge University Press

Cultural and cognitive contextlocal forecasting knowledge

Hansen et al 2004Luseno et 2003

•Key informant interviews with local experts, •Focus groups to compile inventories of indicators and their attributes•Surveys to validate the distribution across representative samples of the population. •Ranking of indicators according to levels of confidence

Cultural and cognitive contextlocal forecasting knowledge

Ziervogel and Calder 2003

Luseno et al 2003

Cultural and cognitive contextlocal forecasting knowledge

Hansen et al 2004

“relatively malleable knowledge that is finely tuned to the continually changing circumstances that define a particular locality” (De Walt, in Eakin 1999).

Cultural and cognitive contextrepresenting probability

Examples from farmers’ daily life more effective than instructional games or tools

Cultural and cognitive contextrepresenting probability

Roncoli et al 2005

Operationalizing comprehension of terciles (Phillips and Orlove 2003): a) least two outcomes are possible b) at least some possibility of the occurrence of three possible outcomes c) highest ranked outcome likely to occur but not for certain

Cultural and cognitive contextrepresenting probability

The role of media

Cultural and cognitive contextrepresenting probability

SOFITEX (cotton export company) memo to field agents, announcing that ‘meteorological predictions seem to indicate a ‘good’ rainy season

Cultural and cognitive contextconstructing credibility

Luseno et al 2003

formal education, off-farm income urban residence, access to roads appear to be associated with lower level of confidence in local predictions among Bolivian farmers and East African pastoralists (Valdivia et al 2000, Luseno et al 2003)

Cultural and cognitive contextconstructing credibility

Ziervogel 2004

… it must be recalled that at the beginning of the season farmers were afraid because of a difficult onset of the rains, some farmers had to plant five times before rains got established in July. Today we can thank God that until now we continue to receive rain and the harvest will be good this year, there is the proof (sorghum field)…

Cultural and cognitive context

constructing credibility

Decision processes and decision makerssimulating farmer responses

C. Gladwin (1989) Ethnographic decision tree modeling. Sage Publications.

Decision processes and decision makerssimulating farmer responses

Hansen et al 2004

Decision processes and decision makerssimulating farmer responses

Ziervogel 2004

Decision processes and decision makerscharacterizing decision makers

Archer 2003

Decision processes and decision makerscharacterizing decision makers

Roncoli et al (2001)livestock and assets

Finan and Nelson (2002)production of subsistence cropscash crop saleslivestock assets

Decision processes and decision makerscharacterizing decision makers

Valdivia et al 2003

Livelihood portfolios (Bolivia, Peru)

Decision processes and decision makersunderstanding constraints

Land (Vogel 2000)

Inputs (Phillips et al 2001)

Seed (Ingram et al 2002)

Farm credit (Vogel 2000, Phillips et al 2001)

Animal traction (Phillips et al 2001, Ingram et al 2002)

Information is not enough! (O’Brien et al 1999)

Decision processes and decision makersunderstanding constraints

•Zimbabwe, Phillips et al (2001) found that among 225 farmers: many considered climate forecasts to be accurate but few used them.

•In Kenya and Ethiopia, Luseno et al (2003) found that among 300 pastoralists few used local or scientific forecasts.

•In Argentina Letson et al (2001) found that more than half of those who heard the La Nina forecast of 1998/99 did not change their decisions.

•In Bolivian and Peru Valdivia and Gere (2000) found that, while most farmers were aware of the El Niño/La Niña, only a few innovators adapted their production.

•In Brazil, Lemos et al (2002) found that few farmers in northeast Brazil believed or used scientific forecasts.

Decision processes and decision makersunderstanding constraints

Roncoli et al 2005

Decision processes and decision makersInstitutional environment

Pfaff et al 2002

Decision processes and decision makersInstitutional environment

Ziervogel and Downings 2004

Sustainable livelihood approaches

Valdivia et al 2003

Ziervogel and Calder 2003

The way forward….