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    Author(s): Frank H. KochJeff PrestemonGeoffrey H. Donovan; Everett A. Hinkley; John M. Chase
    Date: 2016
    Source: Ecological Economics
    Publication Series: Scientific Journal (JRNL)
    Station: Southern Research Station
    PDF: Download Publication  (2.0 MB)


    Government agencies in the United States eradicated 10.3 million cannabis plants in 2010. Most (94%) of these plants were outdoor-grown, and 46% of those were discovered on federal lands, primarily on national forests in California, Oregon, and Washington. We developed models that reveal how drug markets, policies, and environmental conditions affect grow siting decisions. The models were built on a rational choice theoretical structure, and utilized data describing 2322 cannabis grow locations (2004-2012) and 9324 absence locations in the states' national forests. Predictor variables included cannabis market prices, law enforcement density, and socioeconomic, demographic, and environmental variables. We also used the models to construct regional maps of grow site likelihood.  Significant predictors included marijuana street price and variables associated with grow site productivity (e.g., elevation and proximity to water), production costs, and risk of discovery.  Overall, the pattern of grow site establishment on national forests is consistent with rational choice theory. In particular, growers consider cannabis prices and law enforcement when selecting sites. Ongoing adjustments in state cannabis laws could affect cultivation decisions on national forests. Any changes in cannabis policies can be reflected in our models to allow agencies to redirect interdiction resources and potentially increase discovery success.  

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    Koch, Frank H.; Prestemon, Jeffrey P.; Donovan, Geoffrey H.; Hinkley, Everett A.; Chase, John M. 2016. Predicting cannabis cultivation on national forests using a rational choice framework. Ecological Economics, Vol. 129: 11 pages.: 161-171 1 p.


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    Cannabis Illegal cultivation National forests Predictive model Marijuana

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