David W. MacFarlane
David W. MacFarlane
Professor of Forest Measurements and Modeling, Michigan State University
Bestätigte E-Mail-Adresse bei msu.edu
Zitiert von
Zitiert von
Characteristics and distribution of potential ash tree hosts for emerald ash borer
DW MacFarlane, SP Meyer
Forest Ecology and Management 213 (1-3), 15-24, 2005
Potential availability of urban wood biomass in Michigan: Implications for energy production, carbon sequestration and sustainable forest management in the USA
DW MacFarlane
Biomass and Bioenergy 33 (4), 628-634, 2009
Population density influences assessment and application of site index
DW MacFarlane, EJ Green, HE Burkhart
Canadian Journal of Forest Research 30 (9), 1472-1475, 2000
A call to improve methods for estimating tree biomass for regional and national assessments
AR Weiskittel, DW MacFarlane, PJ Radtke, DLR Affleck, H Temesgen, ...
Journal of Forestry 113 (4), 414-424, 2015
Assessing uncertainty in a stand growth model by Bayesian synthesis
EJ Green, DW MacFarlane, HT Valentine, WE Strawderman
Forest science 45 (4), 528-538, 1999
Selecting models for capturing tree-size effects on growth resource relationships
DW MacFarlane, RK Kobe
Canadian Journal of Forest Research 36 (7), 1695-1704, 2006
Evaluating the biomass production of coppiced willow and poplar clones in Michigan, USA, over multiple rotations and different growing conditions
Z Wang, DW MacFarlane
biomass and bioenergy 46, 380-388, 2012
A hierarchical model for quantifying forest variables over large heterogeneous landscapes with uncertain forest areas
AO Finley, S Banerjee, DW MacFarlane
Journal of the American Statistical Association 106 (493), 31-48, 2011
Comparing field-and model-based standing dead tree carbon stock estimates across forests of the US
CW Woodall, GM Domke, DW Macfarlane, CM Oswalt
Forestry 85 (1), 125-133, 2012
Incorporating uncertainty into the parameters of a forest process model
DW MacFarlane, EJ Green, HT Valentine
Ecological Modelling 134 (1), 27-40, 2000
Bayesian synthesis for quantifying uncertainty in predictions from process models
EJ Green, DW MacFarlane, HT Valentine
Tree Physiology 20 (5-6), 415-419, 2000
Modeling larval malaria vector habitat locations using landscape features and cumulative precipitation measures
RS McCann, JP Messina, DW MacFarlane, MN Bayoh, JM Vulule, ...
International journal of health geographics 13 (1), 17, 2014
Regional Stem Profile Model for Cross-Border Comparisons of Harvested Red Pine (Pinus resinosa Ait.) in Ontario and Michigan
WT Zakrzewski, DW MacFarlane
Forest Science 52 (4), 468-475, 2006
Neighbour effects on tree architecture: functional trade‐offs balancing crown competitiveness with wind resistance
DW MacFarlane, B Kane
Functional Ecology 31 (8), 1624-1636, 2017
Evaluating a non-destructive method for calibrating tree biomass equations derived from tree branching architecture
DW MacFarlane, S Kuyah, R Mulia, J Dietz, C Muthuri, M Van Noordwijk
Trees 28 (3), 807-817, 2014
Modeling loblolly pine canopy dynamics for a light capture model
DW MacFarlane, EJ Green, A Brunner, RL Amateis
Forest Ecology and Management 173 (1-3), 145-168, 2003
A generalized tree component biomass model derived from principles of variable allometry
DW MacFarlane
Forest Ecology and Management 354, 43-55, 2015
Predicting survival and growth rates for individual loblolly pine trees from light capture estimates
DW MacFarlane, EJ Green, A Brunner, HE Burkhart
Canadian Journal of Forest Research 32 (11), 1970-1983, 2002
Modelling vertical allocation of tree stem and branch volume for hardwoods
NR Ver Planck, DW MacFarlane
Forestry: An International Journal of Forest Research 87 (3), 459-469, 2014
Quantifying tree and forest bark structure with a bark-fissure index
DW MacFarlane, A Luo
Canadian Journal of Forest Research 39 (10), 1859-1870, 2009
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