A comparison between traditional ordinary least-squares regression and three methods for enforcing additivity in biomass equations using a sample of Pinus radiata trees

Develops models to estimate components, subtotals and above-ground total biomass for a Pinus radiata D.Don biomass dataset using traditional linear and nonlinear ordinary least-squares regressions, and contrasts these equations with the additive procedures of biomass estimation. Source: National Lib...

Full description

Saved in:

Bibliographic Details
Published in:	New Zealand journal of forestry science Vol. 50; no. 7; p. 1
Main Author:	Mohan, K. C
Format:	Journal Article
Language:	English
Published:	Rotoura SCION 2020
Subjects:	above-ground additive Bark Biomass Datasets Estimates Foliage Forest biomass Forest management Forestry and Wood Technology Goodness of fit Least squares Least squares method Leaves linear Mathematical models nonlinear Pinus radiata radiata pine Regression Regression analysis seemingly unrelated regression Skog och träteknik Statistical analysis Statistical tests Statistics Trees
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Develops models to estimate components, subtotals and above-ground total biomass for a Pinus radiata D.Don biomass dataset using traditional linear and nonlinear ordinary least-squares regressions, and contrasts these equations with the additive procedures of biomass estimation. Source: National Library of New Zealand Te Puna Matauranga o Aotearoa, licensed by the Department of Internal Affairs for re-use under the Creative Commons Attribution 3.0 New Zealand Licence.
Bibliography:	Includes appendix, graphs, references, tables Archived by the National Library of New Zealand Includes links to related electronic resources
ISSN:	1179-5395 0048-0134 1179-5395
DOI:	10.33494/nzjfs502020x90x