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System perspectives of experts and farmers regarding the role of livelihood assets in risk perception: results from the structured mental model approach


Schoell, R; Binder, C R (2009). System perspectives of experts and farmers regarding the role of livelihood assets in risk perception: results from the structured mental model approach. Risk Analysis, 29(2):205-222.

Abstract

Pesticide application is increasing and despite extensive educational programs farmers continue to take high health and environmental risks when applying pesticides.
The structured mental model approach (SMMA) is a new method for risk perception analysis. It embeds farmers’ risk perception into their livelihood system in the elaboration of a mental model (MM). Results from its first application are presented here. The study region is Vereda la Hoya (Colombia), an area characterized by subsistence farming, high use of pesticides, and a high incidence of health problems. Our hypothesis was that subsistence farmers
were constrained by economic, environmental, and sociocultural factors, which consequently should influence their mental models.
Thirteen experts and 10 farmers were interviewed and their MMs of the extended pesticide system elicited. The interviews were open-ended with the questions structured in three parts: (i) definition and ranking of types of capital with respect to their importance for the sustainability of farmers’ livelihood; (ii) understanding the system and its dynamics; and (iii) importance of the agents in the farmers’ agent network. Following this structure, each part
of the interview was analyzed qualitatively and statistically. Our analyses showed that the mental models of farmers and experts differed significantly from each other.
By applying the SMMA, we were also able to identify reasons for the divergence of experts’ and farmers’ MMs. Of major importance are the following factors: (i) culture and
tradition; (ii) trust in the source of information; and (iii) feedback on knowledge.

Abstract

Pesticide application is increasing and despite extensive educational programs farmers continue to take high health and environmental risks when applying pesticides.
The structured mental model approach (SMMA) is a new method for risk perception analysis. It embeds farmers’ risk perception into their livelihood system in the elaboration of a mental model (MM). Results from its first application are presented here. The study region is Vereda la Hoya (Colombia), an area characterized by subsistence farming, high use of pesticides, and a high incidence of health problems. Our hypothesis was that subsistence farmers
were constrained by economic, environmental, and sociocultural factors, which consequently should influence their mental models.
Thirteen experts and 10 farmers were interviewed and their MMs of the extended pesticide system elicited. The interviews were open-ended with the questions structured in three parts: (i) definition and ranking of types of capital with respect to their importance for the sustainability of farmers’ livelihood; (ii) understanding the system and its dynamics; and (iii) importance of the agents in the farmers’ agent network. Following this structure, each part
of the interview was analyzed qualitatively and statistically. Our analyses showed that the mental models of farmers and experts differed significantly from each other.
By applying the SMMA, we were also able to identify reasons for the divergence of experts’ and farmers’ MMs. Of major importance are the following factors: (i) culture and
tradition; (ii) trust in the source of information; and (iii) feedback on knowledge.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:February 2009
Deposited On:14 Jan 2009 07:02
Last Modified:05 Apr 2016 12:39
Publisher:Wiley-Blackwell
ISSN:0272-4332
Additional Information:The attached file is a preprint (accepted version) of an article published in Risk Analysis.
Publisher DOI:https://doi.org/10.1111/j.1539-6924.2008.01153.x

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