Perceptions, Networks, Knowledge and Climate
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Perceptions, Networks, Knowledge and Climate Jere L. Gilles Department of Rural Sociology, University of Missouri. Segundo Seminario Internacional de Investigación SANREM CRSP: Cambios globales y su efecto sobre los sistemas agropecuarios de la zona andina. La Paz, 28-29 junio, 2007. Goals.

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Perceptions, Networks, Knowledge and ClimateJere L. GillesDepartment of Rural Sociology, University of Missouri

Segundo Seminario Internacional de Investigación SANREM CRSP: Cambios globales y su efecto sobre los sistemas agropecuarios de la zona andina. La Paz, 28-29 junio, 2007


Goals
Goals

  • Perceptions and the scientific enterprise

  • The relationship between networks, perceptions, action and forecasts

  • The gap between scientific and local knowledge

  • A research plan to for addressing this gap


Perceptions
Perceptions

  • The word perception has a bad reputation in some circles

  • Suggests something that is based more on opinion than fact

  • Science is entirely based on perception

  • The Thomas Theorem—what is believed has real consequences but… what is real may or may not.


Where do the perceptions that influence our decisions come from
Where do the perceptions that influence our decisions come from

  • Empirical experiences of ourselves and others

  • Our networks

    • The connections we have to other people who we trust

    • Can be through publications but the most are through personal contacts



The case of forecast networks
The case of forecast networks from

  • Little overlap in networks used by forecasters and those used by producers

    • Ex. Interviews in 3 altiplano communities showed only 12% used mass media forecasts

    • Only 7% used technicians and extension


Factors leading to disconnect hypotheses
Factors leading to disconnect fromHypotheses

  • Effectiveness of scientific forecasts not as apparent as those of local forecasts.

  • Lack of congruence between products of scientific forecasters and producer needs

  • Producers cannot use forecasts in current form


Bridging knowledge systems
Bridging Knowledge Systems from

  • Practitioners organize knowledge differently than scientists

    • This creates barriers to the development of appropriate technology

    • Makes it difficult to adopt appropriate technology


Local and scientific knowledge
Local and Scientific Knowledge from

  • Both are empirical

  • Local knowledge is contextual—what works best in my situation

  • Scientific knowledge views context as problematic (seeks knowledge that is independent of time and space)


Local climate forecasting in the altiplano
Local Climate Forecasting in the Altiplano from

  • Forecasting is an important risk management tool (when, where, what to plant)

  • Farmers use a variety of indicators to make predictions –abiotic and biotic

    • Although many understand indicators people rely on a few local experts.


Cuadro 1. RESUMEN CALENDARIO DE PRONOSTICOS CLIMATICOS from

(Cantón San José)

Indicador MESES JUNIO JULIO

Plantas Thola

Animales Tiqui tiqui, Tiqui tiqui

Tuku

 Abioticos Q’ana Q’ana

Nevada Vientos

Vientos


Local forecasts linked to actions
Local forecasts linked to actions from

  • PRONÓSTICOS Y ALTERNATIVAS DE SIEMBRA

  • Pronóstico Precipitación, Pronóstico Temperatura, Modo de Siembra Alternativa Precipitación alta (lluvia en abundancia Sembrar en partes altas y suelos secos Precipitación baja (poca lluvia o sequía)Sembrar en partes bajas y suelos húmedos. Bajas temperaturas (fuertes heladas)Sembrar en lomas. No sembrar en hoyadas.


Perceptions and forecasting
Perceptions and Forecasting from

  • There may be less confidence in both scientific and local systems

    • Reports about global warning and increased dryness seem to be contradicted by flooding

    • There appears to be less confidence in local indicators even has there is increased evidence of their validity.


Challenges
Challenges from

  • To build linkages between scientific and local forecast experts

    • This can enhance use of forecasts AND

    • Provide valuable information on local scale that can help scientific forecasts

      Linkages may provide basis for a early warning system


How to meet challenge
How to meet challenge from

  • Validate traditional and scientific methods through participatory research

  • Train locals in the observation and use of indicators

  • Use collaborative learning to improve the development and communication of forecasts


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