Modeling the Distribution of Marketable Timber Products of Private Teak (Tectona grandis L.f.) Plantations

dc.contributor.authorFONTON, HOUÉDOUGBÉ NOËL
dc.contributor.authorATINDOGBE, GILBERT
dc.contributor.authorAKOSSOU, Arcadius
dc.contributor.authorMISSANON, Brice
dc.contributor.authorFANDOHAN, ADANDE BELARMAIN
dc.contributor.authorLEJEUNE, Philippe
dc.date.accessioned2026-06-02T16:06:57Z
dc.date.available2026-06-02T16:06:57Z
dc.date.issued2013
dc.description.abstractManagement of marketable products of private plantations will not be sustainable without class girth be-ing identifiable readily. Modeling marketable products is a key to obtain good fitness between observed and theoretical girth distribution. We determine the best parameter recovery method with the Weibull function for two sylvicultural regimes (coppice and high forest). Data on stand variables were collected from 1101 sample plots. The three Weibull function parameters were estimated with three parameters re-covery methods: the maximum likelihood method, the method of moments and the method of percentiles. Stepwise regression and the simultaneously re-estimated parameter using the Seemingly Unrelated Re-gression Estimation were applied to model each parameter. The results indicated that the three methods successfully predicted girth size distributions within the sample stands. The method of moments was the best one with lowest values of Reynolds error index and Kolmogorov-Smirnov statistic however the syl-vicultural regimes. The Weibull parameter distribution model developed for each of the two sylvicultural regimes was quite reliable.
dc.identifier.otherBECDB-2727
dc.identifier.urihttps://dspace.uac.bj/handle/123456789/2747
dc.language.isofr
dc.relation.ispartofOpen Journal of Forestry
dc.subjectWeibull
dc.subjectParameter Recovery Method
dc.subjectReynolds Index
dc.subjectSylvicultural Regime
dc.subjectPoles
dc.subjectLogs
dc.titleModeling the Distribution of Marketable Timber Products of Private Teak (Tectona grandis L.f.) Plantations
dc.typeArticle

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