Modelling Forest Growth and Yield: Applications to Mixed Tropical Forests
Jerome K. Vanclay
Language: English
Pages: 329
ISBN: 2:00134682
Format: PDF / Kindle (mobi) / ePub
This book provides an introduction to growth modeling in mixed forests, with emphasis on the tropics. It is not intended as a "how-to" manual with step-by-step instructions, as there is no simple best way to model such forests. Rather it reviews different approaches, highlighting their strengths and limitations. It emphasizes empirical-statistical models rather than physiological-process type models, because of the proven utility of the former in forest management. Each chapter includes exercises which can be completed manually or on PC and spreadsheet. The book will serve as a reference manual for practitioners and as a text for advanced level courses in forest modeling
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And length), diameter increment and survival of all trees on a 5×30 m plot. Estimates are based on potential height growth (i.e. 68 M odelling Forest G rowth and Yield site index curves) and the corresponding open-grown crown width, adjusted for competition and tree status. Crowns are assumed to take a standard conical (spruce) or dome shape (beech). The principal variables in predicting the modifiers are the lateral crown restriction (a competitive influence zone index based on crown width),.
The model. The one stochastic component in the JABOWA 70 M odelling Forest G rowth and Yield model (Botkin et al. 1972, Botkin 1993) was the number and species of trees recruited each year. Alder et al. (1977) and Vanclay (1991d) suggested models in which all relationships derived from regression analyses contain a stochastic component. The difficulty with these approaches is that care must be taken to preserve the appropriate correlation between stochastic elements. For example, it is likely.
Plot variation and thus reduce between plot variance. Cost considerations usually dictate that temporary inventory plots (or point samples) are most efficient for resource inventory. Specialized techniques for timber cruising offer great efficiencies (see e.g. Schreuder et al. 1993), but may not provide data suitable for input to yield forecasting systems. Continuous Forest Inventory for yield control: Some systems of yield regulation monitor the forest growth and harvesting by remeasuring a.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 14 16 22 31 32 3. Size Class Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stand Table Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transition Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cohort Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Synthesis . . . . . . . . . . . . . . . . . .
Analyses just like any other explanatory variable. The multi-dimensional analogue of the binary variable is the qualitative variable, which may take a given range of integer values (i.e. 0, 1, . . ., n), and is equivalent to a set of n binary variables. If a regression package does not allow the use of qualitative variables, the same result can be obtained using n binary variables (Z) with Zi = 1 when the qualitative factor (e.g. soil type) is i, and zero otherwise. There are several factors.