Journal
TREE PHYSIOLOGY
Volume 23, Issue 17, Pages 1153-1170Publisher
OXFORD UNIV PRESS
DOI: 10.1093/treephys/23.17.1153
Keywords
biometry; canopy structure; fish-eye lens; Populus deltoides x Populus nigra; Populus trichocarpa; seasonal dynamics; short-rotation woody crop
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A multi-scale biometric methodology for describing the architecture of fast-growing short-rotation woody crops is used to describe 2-year-old poplar clones during the second rotation. To allow for expressions of genetic variability observed within this species (i.e., growth potential, leaf morphology, coppice and canopy structure), the method has been applied to two clones: Ghoy (Gho) (Populus deltoides Bartr. ex Marsh. x Populus nigra L.) and Trichobel (Tri) (Populus trichocarpa Torr. & A. Gray x Populus trichocarpa). The method operates at the stool level and describes the plant as a collection of components (shoots and branches) described as a collection of metameric elements, themselves defined as a collection of elementary units (internode, petiole, leaf blade). Branching and connection between the plant units (i.e., plant topology) and their spatial location, orientation, size and shape (i.e., plant geometry) describe the plant architecture. The methodology has been used to describe the plant architecture of 15 selected stools per clone over a 5-month period. On individual stools, shoots have been selected from three classes (small, medium and large) spanning the diameter distribution range. Using a multi-scale approach, empirical allometric relationships were used to parameterize elementary units of the plant, topological relationships and geometry (e.g., distribution of shoot diameters on stool, shoot attributes from shoot diameter). The empirical functions form the basis of the 3-D Coppice Poplar Canopy Architecture model (3-D CPCA), which recreates the architecture and canopy structure of fast-growing coppice crops at the plot scale. Model outputs are assessed through visual and quantitative comparisons between actual photographs of the coppice canopy and simulated images. Overall, results indicate a good predictive ability of the 3-D CPCA model.
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