New evidence that helps to understand the northern #Laos #rubber boom from our researcher Victoria Junquera and colleagues! Get the #openaccess paper @ELSenviron here bit.ly/2zy06un and have a look at the thread for a broad overview of the results. 
Agricultural expansion is a main cause of #deforestation and sometimes occurs in the form of “#cropbooms”—very rapid and intense expansion. Rubber expanded in a boom-like fashion in northern Laos and other parts of #SoutheastAsia in the last decade.
Following earlier work (bit.ly/3dHAQ3u), we conduct a spatially-explicit analysis of rubber expansion in two study areas during 2000-2017. We analyze the role of prices, context, forest protection, and imitation with a Bayesian network model and regression analysis.
Rising international rubber market prices (green line) at the beginning of the boom were a trigger for adoption, but rubber expansion continued during periods of descending prices. Villagers’ knowledge of rubber prices (dots) progressively reflects local farm-gate prices.
The Bayesian network (BN) model includes spatially-explicit variables, a price signal, imitation, and average household income over the study period—the same variables are used in the regression analysis. BN model output shows spatially-explicit land use change.
We implement the rubber price signal as the interaction of local price knowledge and rubber market price, and imitation is implemented as the interaction of local aggregate rubber planted and self-reported imitation in adoption decisions.
The two study areas experienced two rubber adoption peaks, roughly 8 years apart. Rubber takes 7 years to mature, so the first harvests may have encouraged further expansion. Other factors (e.g., cash flow, policy triggers, investor visits) also influenced adoption decisions.
Protected forest areas reduced deforestation, but only those with the strictest protection status. In early rubber expansion years, protected areas with lesser protection status were converted to rubber plantations.
Rubber price was a trigger and a driver of rubber expansion. The trigger effect at the beginning of the rubber boom is not captured by our land-use models. Rubber price had a significant effect during the first expansion phase in the study area that experienced a rubber boom.
Price signals are complex and comprise information about past, present, and expected future prices. The origin of price information also affects adoption decisions. Our models only capture part of this complexity.
Imitation was strongest during and after periods of intense growth but not in the early stages, indicating that early adopters are more prone to make knowledge-based decisions while late adopters are more likely to follow the example of others.
unroll @threadreaderapp
