Publication Date:
1987-11-01
Description:
Logistic regression analysis can be used to estimate the probability of a binary event. In forestry, its use largely has been limited to predicting the probability of mortality of individual trees. However, the potential for broader application in forest growth and yield modelling has largely been overlooked. A logistic model to predict the probability that a tree will attain a specified future diameter can be produced by establishing a series of growth "success" criteria. Given the initial diameter distribution of a forest stand, a future diameter distribution and stand characteristics can be estimated probabilistically by estimating the proportion of stems in each diameter class of the distribution which attains a specified future diameter (the "success" criterion) and the proportion which fails to achieve at least zero growth (i.e., mortality). Using permanent plot data, such a logistic model was calibrated and validated for an oak–hickory forest in southeastern Missouri. Validation indicated that the model performs satisfactorily (estimates are unbiased) for individual trees over a 5-year prediction period, and for stand characteristics over 5-, 10-, 15-, and 20-year prediction periods though precision suffers as prediction period lengthens.
Print ISSN:
0045-5067
Electronic ISSN:
1208-6037
Topics:
Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
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