Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/217047
Authors: 
Junquera, Victoria
Meyfroidt, Patrick
Sun, Zhanli
Latthachack, Phokham
Grêt-Regamey, Adrienne
Year of Publication: 
2020
Citation: 
[Journal:] Environmental Science & Policy [Volume:] 109 [Year:] 2020 [Pages:] 103-115 [ISSN:] 1462-9011
Abstract: 
Crop booms in forest frontiers are a major contributor to land conversion and deforestation. In this study, we investigate the smallholder-driven northern Laos rubber boom in two case study areas (CSAs) with different speed and intensity of rubber expansion. We assess the relative importance of market, contextual, and behavioral factors in fostering or hindering the conversion of forest to rubber plantations. We develop a Bayesian network (BN) model of land-use change based on household surveys, expert interviews, market price data, and land use maps covering the period 2000–2017. We use regression analysis to inform the structure of the BN, compare model results, and analyze time-varying effects. The BN and regression models incorporate perceived price signals as a combination of market price and local price knowledge, and local self-reinforcing imitation dynamics as a combination of aggregate rubber conversion and imitation behavior elicited in the survey. Results show that deforestation was lower in strictly protected areas but not in forests with lesser protection status. Imitation had a large effect on rubber uptake in both areas. In the CSA that experienced the most intensive spread of rubber, price signals transmitted through social networks had a significant impact, especially throughout the stage of rapid expansion. Rubber expansion continued in both areas during periods of descending prices mainly because of increased cash availability and access to inputs. Our research sheds light on the underlying dynamics of crop booms and contributes to the understanding of agricultural expansion processes.
Subjects: 
crop boom
agricultural expansion
price signal
imitation
Bayesian network
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by-nc-nd/4.0/
Document Type: 
Article
Document Version: 
Published Version

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