This book attempts to make growth models more accessible to foresters and others interested in mixed forests, whether planted or natural. There is an increasing interest in,
 and controversy surrounding the use of mixed plantations and natural forests, and rational discussion and resolution of management options require reliable growth models linked to other
 information systems. It is my hope that this book will help researchers to build better models, and will help users to understand how the models work and thus to appreciate their strengths
 and weaknesses. During recent years, vast areas of natural forest, especially in the tropics, have been logged or converted to other uses. Well-meaning forest managers have often been
 over-optimistic in estimating forest growth and yields, and this has contributed to over-cutting in some forests. Growth models can provide objective forecasts, offering forest managers the
 information needed to maintain harvests within the sustainable capacity of the forest, and providing quantitative data for land use planners to make informed decisions on land use
 alternatives. In this way, I hope that this book will contribute to the conservation and sustainable management of natural forests in the tropics and elsewhere. This is not a "How to do it"
 manual with step-by-step instructions to build a growth model for mixed forests. Unfortunately, modelling these forests isn't that easy. There is no single "best" way to build a model for
 these forests. Rather, many approaches can be used, and the best one depends on the data available, the time and expertise available to build the model, the computing resources, and the
 inferences that are to be drawn from the model. So instead of writing a "cookbook" with one or two recipes, I review and illustrate some of the many approaches available, indicate the
 requirements of and output from each, and highlight their strengths and limitations. The book emphasizes empirical-statistical models rather than physiological-process type models, not
 because they are superior, but because they have proven utility and offer immediate benefits for forest management. A more comprehensive treatment of all the options is beyond the scope of
 this book, which is intended to serve as a ready reference manual for those building growth models for forest management. Because of my limited linguistic ability, the material covered is
 more-or-less restricted to English-language material. I have not attempted to review all the published work on growth modelling (it would be a huge task), but have tried to highlight
 examples that may be applicable to mixed forests in tropical areas. I hope that the language and terminology used in this book will be accessible to all readers, especially those for whom
 English is a second language. The glossary may help to clarify some terms, and those that have a specific technical meaning are printed in italics the first time they are used. Readers
 should consult the glossary to clarify the meaning of these words unless they are sure of the meaning. Exercises are given at the end of each chapter to reinforce points made in the
 chapter. These are simple exercises, deliberately chosen so that they can be completed quickly with pen and paper or PC and spreadsheet, but within these constraints, I have tried to keep
 them realistic. Some exercises (e.g. 9.1 and 10.3) require more specialized statistical analyses, but many commercial statistical packages (e.g. GLIM) are suitable. Where possible, these
 exercises draw on real data, but some data were simulated to create interesting exercises with few data. Whilst my approach places more responsibility on the reader to choose and develop a
 suitable modelling methodology, I hope it will help readers gain a better understanding of modelling, which should in turn lead to better models and more reliable predictions. And I hope
 that better models will provide better information, greater understanding, and better management of mixed forests.

https://espace.library.uq.edu.au/view/UQ:8211/ModellingForestG.pdf

Modelling Forest Growth and Yield: Applications to Mixed Tropical Forests

METLA Project 3306: Forest Carbon Sink and Economical Costs of Kyoto Protocol prduced a database of treewise volume- and biomass equations according to diameter at breast height and/or height includes equations for common European tree species. Equations will be used when estimating biomass expansion factors for major tree species. Database was compiled as a joint project with University of Edinburgh, Finnish Forest Research Institute and partners of COST E21 Action. The attached excel-files files extended and updated versions of those published previously in the printed publication. See the figure about a sample of compiled equations (files: appendix-A.xls and appendix-BC.xls).

/pdf/biomass-and-stem-volume-equations-for-tree-species-in-europe-8w6unw3hth.pdf

Biomass and stem volume equations for tree species in Europe.

1. Introduction.- 2. Tree Form and Stem Taper.- 3. Tree-stem Volume Equations.- 4. Tree Weight and Biomass Estimation.- 5. Quantifying Tree Crowns.- 6. Growth Functions.- 7. Evaluating Site Quality.- 8. Quantifying Stand Density.- 9. Indices of Individual-tree Competition.- 10. Modeling Forest Stand Development.- 11. Whole-stand Models for Even-aged Stands.- 12. Diameter-distribution Models for Even-aged Stands.- 13. Size-class Models for Even-aged Stands.- 14. Individual-tree Models for Even-aged Stands.- 15. Growth and Yield Models for Uneven-aged Stands.- 16. Modeling Response to Silvicultural Treatments.- 17. Modeling Wood Characteristics.- 18. Model Implementation and Evaluation.-

Modeling Forest Trees and Stands

Seeds of pearl millet were germinated on wet filter paper at temperatures up to 50 °C. In one experiment, the temperature was held at 50 °C during imbibition and was then lowered to 32 °C or 25 °C. Germination rate and the maximum fraction of seeds germinating (Gm) both decreased as the time of exposure to 50 °C increased. In contrast, exposure to 50 °C after imbibition for 8 h slowed germination but did not significantly reduce Gm. When the 'high' temperature imposed after imbibition was reduced from 50 °C to 45 °C, there was a small reduction in the rate of germination but not in Gm. The responses have implications for the optimum time of sowing in the tropics when maximum daytime soil temperature at the depth of sowing is in the range of 45-50 °C.

Time, Temperature and Germination of Pearl Millet (Pennisetum typhoides S. & H.) II. ALTERNATING TEMPERATURE

On simplifying allometric analyses of forest biomass

This paper briefly describes the underlying assumptions and inherent drawbacks of logarithmic regression models commonly used to develop biomass prediction equations. Superiority of simple nonlinear models over both the logarithmic and multiple linear regression models is discussed and demonstrated on two data sets.

Choosing Regression Models for Biomass Prediction Equations

Comparison of Methods for Assessing Spatial Distribution of Trees

This paper presents a growth analysis of a forest drainage experiment carried out over 40 years in northern Ontario. It is based on remeasurement data obtained in 1969 from 38 growth plots established following drainage in 1929 and from increment cores and sectioned trees.Results indicate that both annual tree diameter and height growth increased significantly after draining; that tree growth before draining was related to site quality only, while after draining it was related also to tree vigor and distance of water flow from the nearest ditch; that both stand diameter and height growth were related to site index, stand age, and initial stocking; and that stand basal area and volume growth were, in addition, related to a product sine function of distance of water flow from the ditch, peat moisture, decomposition, and depth.Both individual tree and stand growth responded well to draining, with younger and more vigorous trees that were growing on better-quality sites showing the greatest response. For a gi...

Analyses of a Forest Drainage Experiment in Northern Ontario. I: Growth Analysis

This paper describes the spatial pattern, expressed by Pielou's nonrandomness index, of trees within 13 sampled tracts from the major forest types of northern Ontario. Results indicate that: (a) th...

Bijan Payandeh

Papers

Choosing Regression Models for Biomass Prediction Equations

Comparison of Methods for Assessing Spatial Distribution of Trees

Analyses of a Forest Drainage Experiment in Northern Ontario. I: Growth Analysis

Spatial pattern of trees in the major forest types of northern Ontario

Relative accuracy of a new base-age invariant site index model