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Does commercialization of a non-timber forest product reduce ecological impact? A case study of the Critically Endangered Aquilaria crassna in Lao PDR

Published online by Cambridge University Press:  15 April 2008

Anders Jensen*
Affiliation:
University of Copenhagen, Danish Centre for Forest, Landscape & Planning, Rolighedsvej 23, DK-1958 Frederiksberg C, Denmark.
Henrik Meilby
Affiliation:
University of Copenhagen, Danish Centre for Forest, Landscape & Planning, Rolighedsvej 23, DK-1958 Frederiksberg C, Denmark.
*
*University of Copenhagen, Danish Centre for Forest, Landscape & Planning, Rolighedsvej 23, DK-1958 Frederiksberg C, Denmark. E-mail anderslaos@yahoo.co.uk
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Abstract

Aquilaria crassna, a tree species on CITES Appendix II and categorized as Critically Endangered on the IUCN Red List, is the main source of the highly valuable, fragrant and resinous agarwood that is extracted in forests in South-east Asia, exported to East Asian and Arab countries, and used for a range of medicinal, aromatic and religious products. Based on interviews with local, non-local and foreign harvesters in Laos we examined the relationships between harvesters' daily net revenue from agarwood extraction, their degree of commercialization (i.e. their differential access to markets) and their ability to target harvesting towards the small fraction of trees that do contain commercial qualities and quantities of agarwood. For comparison we included data on number of trees felled during the most recent harvesting trip. The analysis showed that poor targeting ability and low degree of commercialization were associated with low daily net revenues, whereas good targeting ability and high degree of commercialization were associated with high daily net revenues. In the case of A. crassna in Laos it therefore appears that the activities of highly commercialized harvesters are less harmful to A. crassna populations than those of less specialized, local harvesters.

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Copyright
Copyright © Fauna & Flora International 2008
Figure 0

Table 1 Summary statistics for daily net revenue, degree of commercialization and targeting ability (see text for details) and number of trees felled and amount harvested on most recent trip with mean and 95% confidence intervals (in parentheses).

Figure 1

Table 2 Coefficients of correlation between daily net revenue, degree of commercialization, targeting ability and number of trees felled per man and day on most recent trip (NTF; Table 1) and ln(daily net revenue) and the reciprocal of NTF.

Figure 2

Fig. 1 Number of trees felled versus targeting ability for each of the seven degrees of commercialization (0, low ... 6, high; see text for details). Values for both variables are mean ± SE (bidirectional bars).

Figure 3

Fig. 2 Box-plot illustrating the distribution of targeting ability for each of the seven degrees of commercialization (0, low ... 6, high; see text for details). Percentiles are are 10th, 25th, 50th, 75th and 90th. Circles indicate observations below the 10th or above the 90th percentile.

Figure 4

Table 3 Linear regressions expressing logarithmic daily net revenue (ln(DNR); LAK man-1 day-1) as a function of (a) targeting ability and degree of commercialization, and (b) the reciprocal of number of trees felled (NTF) and degree of commercialization, with parameter estimates and standard errors (in parentheses).

Figure 5

Fig. 3 Studentized residuals versus predicted logarithm of daily net revenue, ln(DNR), for models (a) and (b) of Table 3. Diameter of circles is proportional to degree of commercialization; histogram gives the frequency distribution of the residuals.

Figure 6

Fig. 4 Net revenue (LAK 1,000 day-1) versus (a) targeting ability and (b) number of trees felled (man-1 day-1). Diameter of circles is proportional to degree of commercialization. Lines indicate values predicted by the regression models in Table 3 (models (a) and (b), respectively, for (a) and (b)) for each of the seven degrees of commercialization (0, low ... 6, high; see text for details). The curvature is caused by the logarithmic transformation.