Évaluation visuelle des arbres feuillus sur pied et valeur ... · Alexis Achim, qui a toujours eu confiance en moi et qui a été le premier à me donner la chance de poursuivre

Évaluation visuelle des arbres feuillus sur pied et

valeur des produits transformés

Thèse

Filip Havreljuk

Doctorat en sciences forestières

Philosophiae doctor (Ph.D.)

Québec, Canada

© Filip Havreljuk, 2015

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Résumé

Les forêts feuillues tempérées du sud du Québec ont une grande importance économique, car

elles sont la principale source d’approvisionnement des industries des produits d'apparence

en bois. Toutefois, la difficulté de relier l’apparence externe d’un arbre à la qualité interne de

son bois engendre des incertitudes liées à l’approvisionnement, puisque la qualité des bois

sélectionnés pour la récolte peut ne pas correspondre aux besoins réels des usines de

transformation. L’objectif principal de ce projet était d’améliorer les prévisions des

caractéristiques des approvisionnements de bois feuillu en reliant l’évaluation de la qualité

des arbres sur pied à la composition du panier de produits transformés et à sa valeur

monétaire. Un des facteurs internes qui affecte la valeur des sciages d’érable à sucre (Acer

saccharum Marsh.) et de bouleau jaune (Betula alleghaniensis Britt.) est la présence d’une

zone de couleur brun-rougeâtre au centre de la tige, appelée coloration de cœur. Un

échantillonnage dans 12 localisations de la zone tempérée du sud du Québec a montré que

les différences régionales de la proportion radiale de la zone colorée chez ces deux espèces

étaient principalement attribuables à des facteurs liés au développement des arbres, tels que

l’âge et les accroissements autour de la zone colorée. Une partie de la variabilité chez l’érable

à sucre était aussi associée à la température minimale annuelle d'une localisation. Par ailleurs,

l’étude de 64 érables à sucre et 32 bouleaux jaunes abattus, tronçonnés et sciés en planches

a mis en évidence le fait que parmi tous les types de défaut qui doivent être pris en

considération lors du marquage des arbres, les signes visibles d’infection fongique et les

fentes avaient la plus grande influence négative sur la valeur des deux espèces. L’analyse des

sciages a montré que la proportion des meilleurs grades augmentait avec la longueur et le

diamètre des billes, ce qui fait qu’elle était plus élevée dans le bas de l’arbre. Les billes

présentant une grande zone colorée ont produit davantage de bois de moindre valeur. Dans

leur ensemble, ces résultats permettent d’établir des liens entre le classement visuel des arbres

sur pied et la qualité de produits transformés permettant une meilleure prise de décisions liée

à l’approvisionnement en bois feuillu.

v

Abstract

Temperate deciduous forests of southern Quebec are of great economic importance because

they are the main supply source of the appearance wood products industries. However, the

difficulty of linking the external characteristics of a tree to the internal quality of its wood

creates supply-related uncertainties, since the quality of selected trees for harvest may not

correspond to the real needs of these processing industries. The main objective of this study

was to improve the supply forecasts of hardwood processing industries by linking the quality

assessment of standing trees to their products assortment and their monetary value. One of

the most important internal factors affecting the value of sugar maple (Acer saccharum

Marsh.) and yellow birch (Betula alleghaniensis Britt.) lumber is the presence of a reddish-

brown colored area in the center of the stem called red heartwood. Samples from 12 locations

throughout the temperate zone in southern Quebec showed that regional differences in the

radial proportion of the colored area in both species were mainly due to factors related to tree

development, such as age and radial growth around the colored area. Part of the variability

in sugar maple was also associated with the annual minimum temperature of a sampling

location. In addition, the study of 64 sugar maple and 32 yellow birch trees that were

harvested, bucked into logs and processed into lumber showed that among all defect types

that need to be considered for tree marking, visible evidence of fungal infections and cracks

had the largest negative influence on value in both species. The analysis of the lumber

products assortment showed that the proportion of the best grades increased with the length

and the diameter of the logs, so that it was higher at the bottom of the stem. Logs with a large

red heartwood area produced more wood of lesser value. Overall, these results link the visual

assessment of standing trees to the quality and value of processed products to allow better

decision making in the hardwoods supply chain.

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Table des matières

Résumé .................................................................................................................................. iii

Abstract ................................................................................................................................... v

Table des matières ............................................................................................................... vii

Liste des tableaux ................................................................................................................... ix

Liste des figures ..................................................................................................................... xi

Remerciements .................................................................................................................... xiii

Avant-propos ..................................................................................................................... xvii

Introduction générale .............................................................................................................. 1

Démarche méthodologique .............................................................................................. 6

Chapitre 1 Regional variation in the proportion of red heartwood in sugar maple and

yellow birch ......................................................................................................................... 9

Abstract .......................................................................................................................... 10

Résumé .......................................................................................................................... 11

Introduction ................................................................................................................... 12

Materials and methods ................................................................................................... 14

Results ........................................................................................................................... 21

Discussion ...................................................................................................................... 26

Conclusion ..................................................................................................................... 30

Acknowledgments ......................................................................................................... 31

Chapitre 2 Integrating standing value estimations into tree marking guidelines to meet

wood supply objectives ..................................................................................................... 33

Abstract .......................................................................................................................... 34

Résumé .......................................................................................................................... 35

Introduction ................................................................................................................... 36

Material and methods .................................................................................................... 38

Results ........................................................................................................................... 47

Discussion ...................................................................................................................... 54

Conclusion ..................................................................................................................... 57

Acknowledgements ....................................................................................................... 58

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Chapitre 3 Predicting lumber grade occurrence and volume recovery in sugar maple and

yellow birch logs .............................................................................................................. 59

Abstract ......................................................................................................................... 60

Résumé .......................................................................................................................... 61

Introduction ................................................................................................................... 62

Material and methods .................................................................................................... 64

Results ........................................................................................................................... 73

Discussion ..................................................................................................................... 83

Conclusion .................................................................................................................... 87

Acknowledgements ....................................................................................................... 87

Conclusion générale ............................................................................................................. 89

Bibliographie ........................................................................................................................ 95

Annexe 1 : Données utilisées pour le chapitre 1 ................................................................ 109

Annexe 2 : Données utilisées pour les chapitres 2 et 3 ...................................................... 113

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Liste des tableaux

Table 1.1 Summary of sampling locations used in the study. .............................................. 15

Table 1.2 Mean sample tree characteristics. SD is the standard deviation. .......................... 16

Table 1.3 List of explanatory variables screened in the modeling process. ......................... 20

Table 1.4 Comparison of the multiple linear regression models for sugar maple red

heartwood proportion (RHP). ............................................................................................... 24

Table 1.5 Parameter estimates and their standard errors (±SE) for the final models. .......... 25

Table 1.6 Comparison of the multiple linear regression models for yellow birch red

heartwood proportion (RHP). ............................................................................................... 26

Table 2.1 Mean sample tree characteristics for the sugar maple and yellow birch data. ..... 41

Table 2.2 Lumber grade distribution among the sawn boards. ............................................. 44

Table 2.3 Distribution of NHLA grades among tree quality classes. ................................... 44

Table 2.4 Mean sugar maple and yellow birch lumber values from 2008 to 2012. ............. 45

Table 2.5 Proportion of study trees (%) affected by a given defect category. ...................... 50

Table 2.6 Parameter estimates (± SE) and p-values for the model including DBH, fungal

infections, and cracks given by eq. (3). ................................................................................ 50

Table 2.7 Comparison of the linearized models for predicting the value per unit volume

(VAL) of each stem. ............................................................................................................. 52

Table 3.1 Mean log characteristics for the sugar maple and yellow birch data. ................... 65

Table 3.2 Lumber grade distribution among the sawn boards. ............................................. 67

Table 3.3 Mean sugar maple and yellow birch lumber values from 2008 to 2012. ............. 72

Table 3.4 Proportion of NHLA lumber grades and colors among log grades. ..................... 73

Table 3.5 List of models predicting the VRG of sugar maple and yellow birch. .................. 74

Table 3.6 Parameter estimates (and standard errors) for the best model to predicting VRG

(model 8). .............................................................................................................................. 76

Table 3.7 List of models predicting the VRC of sugar maple. .............................................. 78

Table 3.8 List of models predicting the VRC of yellow birch. ............................................. 79

Table 3.9 Parameter estimates (and standar errors) for the best model to predicting VRC of

sugar maple (model 4) and yellow birch (9). ........................................................................ 81

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Liste des figures

Figure 1.1 Location of the sampling regions across Québec. ............................................... 14

Figure 1.2 Illustration of the red heartwood separation procedure performed using ImageJ.

(A) Initial image. (B) The resulting image after applying the threshold function. (C) The

final image after the noise from the threshold function was removed. ................................. 17

Figure 1.3 Mean red heartwood proportion (RHP) for sugar maple and yellow birch in the

12 sampling regions. Two study sites are included in each region and eight trees were

sampled from each site. Error bars represent standard errors. .............................................. 21

Figure 1.4 Mean red heartwood proportion (RHP) for sugar maple and yellow birch across

bioclimatic subdomains. Eastern Balsam fir – Yellow birch subdomain (n = 92), Western

Balsam fir – Yellow birch subdomain (n = 64), Eastern Sugar maple – Yellow birch

subdomain (n = 92), and Western Sugar maple – Yellow birch subdomain (n = 128). Error

bars represent standard errors. .............................................................................................. 22

Figure 1.5 Number of discoloured wood rings as a function of the total number of rings at

1.3 m. .................................................................................................................................... 23

Figure 1.6 Model-averaged predictions and unconditional 95% confidence intervals for the

best-fit model parameters for sugar maple. ........................................................................... 24


best-fit model parameters for yellow birch. .......................................................................... 26

Figure 2.1 Location of the study areas. ................................................................................. 38

Figure 2.2 Predicted VAL (US$·m−3) in relation to vigor classification. Trees are classified

by harvest priorities: moribund trees (M), surviving trees (S), growing trees to be conserved

(C), and reserve stock trees (R) (Boulet 2007). .................................................................... 48

Figure 2.3 Predicted VAL (US$·m−3) in relation to quality classification. The four grades

(A, B, C, and D) are used to describe the potential for sawlog production, with grade A

being the highest and grade D the lowest (i.e., trees with no sawlog potential) (Monger

1991). .................................................................................................................................... 49

Figure 2.4 Predicted VAL (US$·m−3) of sugar maple and yellow birch in relation to the

presence of the main tree defects. ......................................................................................... 51

Figure 2.5 Predicted VAL (US$·m−3) in relation with sound wood depth for quality

classification (Monger 1991). ............................................................................................... 53

Figure 2.6 Predicted VAL (US$·m−3) in relation with sound wood depth for main defects.

.............................................................................................................................................. 53

Figure 3.1 Observed (bars) versus predicted (points) frequencies of the lumber volume

recovery of lumber grades (VRG). ........................................................................................ 68

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recovery of the lumber colors (VRC). ................................................................................... 69

Figure 3.3 Predicted lumber volume recovery for each lumber grade (VRG) plotted against

the small-end diameter of the log (cm) of the best model (model 8). Lines represent loess

smoothing functions with standard error through the predictions. ....................................... 75

Figure 3.4 Predicted lumber volume recovery for each color category (VRC) plotted against

the covariates of the best model for sugar maple (model 4). Lines represent loess smoothing

functions with standard error through the predictions. ........................................................ 80

Figure 3.5 Predicted lumber volume recovery for each color category (VRC) plotted against

the covariates of the best model for yellow birch (model 9). Lines represent loess

smoothing functions with standard error through the predictions. ....................................... 80

Figure 3.6 Predicted value recovery against log net volume established from the predicted

VRG (model 8). Lines represent linear smoothing functions with standard error plotted

against the observed value recovery. .................................................................................... 83

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Remerciements

Ce doctorat n’aurait pas été possible sans la contribution financière du Fonds de recherche

du Québec – Nature et technologies (FRQNT) qui m’a accordé une bourse et qui a financé

ce projet de recherche. Je tiens à remercier cet organisme pour sa confiance et son appui.

Ce fut un privilège de pouvoir travailler avec mon directeur de recherche et véritable mentor,

Alexis Achim, qui a toujours eu confiance en moi et qui a été le premier à me donner la

chance de poursuivre mes études graduées sur un sujet qui me passionne. Il a toujours été

disponible pour m’aider à avancer dans mon projet et faire de moi un meilleur chercheur.

J’aimerais aussi remercier mon codirecteur de recherche, David Pothier, pour sa

disponibilité, ses commentaires constructifs et sa rigueur qui ont permis de mener à terme ce

projet. Merci Alexis et David pour l’opportunité que vous m’avez donnée, par votre

encadrement et votre support dans mes travaux, autant du point de vue logistique, financier

et scientifique, que moral avec votre sens d’humour!

J’aimerais aussi remercier du fond du cœur tous mes assistantes et assistants de terrain et de

laboratoire : Amélie Denoncourt, Élisabeth Dubé, Frauke Lenz, Marine Bouvier, Julia

Maman, Stéphanie Cloutier, Marie-Pier Arsenault et Louis Gauthier. La motivation, l’énergie

et la bonne volonté que vous avez mise dans votre travail m’ont aidé à mener à terme ce

projet. J’ai eu beaucoup de plaisir à travailler avec vous. Merci également à mes collègues et

amis qui m’ont aidé au cours du projet : Julie Barrette, Simon Delisle-Boulianne, Emmanuel

Duchateau, Louis-Vincent Gagné, Normand Paradis et Charles Ward.

Un grand merci aussi aux personnes suivantes pour leur soutien scientifique à travers mon

doctorat: David Auty (analyses statistiques et révisions linguistiques des textes), Marc J.

Mazerolle (conseils en statistiques et programmation), Ann Delwaide (aide en

dendrochronologie), Rémi St-Amant et Jacques Régnière (aide avec BioSIM et l’analyse des

données météorologiques), Jean McDonald (conseils en transformation des feuillus), Martine

Lapointe et Jean-Philippe Gagnon (aide sur le terrain) et S.Y. Zhang (pour ses idées

originales qui ont permis d’orienter le projet de recherche).

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Un projet de cette envergure n’aurait pas été possible sans la participation de nombreux

partenaires industriels qui ont collaboré aux différents volets du projet de recherche

permettant ainsi d’augmenter la portée de cette étude. J’aimerais remercier Steve Bédard,

François Guillemette ainsi que leur équipe de la Direction de la recherche forestière du

Ministère des Forêts, de la Faune et des Parcs du Québec pour leur appui et leur collaboration

au projet de recherche. De plus, mes sincères remerciements vont au Centre de recherche sur

les matériaux renouvelables (CRMR), à la Coopérative Forestière des Hautes-Laurentides

(CFHL), à l’École de foresterie et de technologie du bois de Duchesnay, au Groupement

forestier de Portneuf, au Ministère des Forêts, de la Faune et des Parcs (MFFP), à la Station

touristique de Duchesnay (SÉPAQ) et à FPInnovations. Merci infiniment à toutes les

personnes de ces organismes qui ont collaboré de près ou de loin à cette étude.

Merci au Centre d’étude de la forêt (CEF) pour le financement des formations de

perfectionnement et des conférences auxquelles j’ai participé tout au long de mon projet de

recherche. Merci à Malcolm Cecil-Cockwell et John Caspersen pour leur accueil à

l’Université de Toronto et à la forêt de Haliburton lors de l’été 2011. Ce séjour fut très

agréable et formateur.

J’aimerais aussi remercier les membres de mon comité de thèse qui ont accepté d’examiner

ce document : Julie Cool, Ph.D. (The University of British Columbia), Myriam Drouin, Ph.D.

(FPInnovations) et Robert Beauregard, Ph.D. (Université Laval).

Je tiens à remercier particulièrement mes trois collègues de bureau et biologistes préférés,

Kaysandra Waldron, Emmanuel Duchateau et Sébastien Lavoie. Que ce soit pour parler du

travail, boire une bière ou socialiser, j’ai adoré vous côtoyer et vous avez rendu mes études

plus agréables.

Merci à tous mes autres amis avec qui j’ai pu passer de bons moments tout au long de mes

études : Anne Allard-Duchêne, Juliette Boiffin, Geneviève Bourgeois, Robin Colette, Joanie

Couture, Roxane Hamel St-Laurent, Étienne Hubert-Legault, Antoine Marcoux, Édith

Lachance, Claude Lefrançois, Pierre-Étienne Messier, Sacha Nandlall, Caroline Plante,

Mylène Savard, Annie-Claude Taillon, Valérie Packwood-Vignet et Célia Ventura-Giroux.

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Enfin, je veux remercier mes parents et ma famille qui m’ont toujours appuyé dans mon

cheminement. Leur support inconditionnel est à la source de ma réussite.

Je garde un excellent souvenir de ces dernières années et je remercie du fond du cœur tous

ceux et celles qui y ont contribué. Merci!

xvii

Avant-propos

Insertion d’articles

La présente thèse est composée de trois chapitres rédigés en anglais et présentés sous forme

d’articles scientifiques dont je suis le premier auteur. En tant que candidat au doctorat, j'ai

effectué une revue de littérature sur le sujet de recherche, établi les objectifs et les hypothèses

de recherche, planifié et réalisé l'échantillonnage sur le terrain, réalisé les analyses

statistiques et l’interprétation des résultats et rédigé l’ensemble de la thèse et des articles

scientifiques qui y sont rattachés.

Chapitre 1

Havreljuk, F., Achim, A. and Pothier, D. 2013. Regional variation in the proportion of red

heartwood in sugar maple and yellow birch. Can. J. For. Res. 43(3), 278–287.

doi:10.1139/cjfr-2012-0479

Chapitre 2

Havreljuk, F., Achim, A., Auty, D., Bédard, S. and Pothier, D. 2014. Integrating standing

value estimations into tree marking guidelines to meet wood supply objectives. Can. J.

For. Res. 44(7), 750–759. doi: 10.1139/cjfr-2013-0407

Chapitre 3

Havreljuk, F., Achim, A. and Pothier, D. Predicting lumber grade occurrence and volume

recovery in sugar maple and yellow birch logs. L’article sera soumis sous peu.

Les données brutes ayant servi à la réalisation de cette thèse ont été ajoutées sous forme de

sommaires en annexe du document.

Coauteurs des chapitres

La rédaction de cette thèse de doctorat a été encadrée par Alexis Achim et David Pothier,

mon directeur et mon codirecteur de thèse, respectivement. Ils sont les coauteurs de tous les

chapitres puisqu’ils ont supervisé les travaux de recherche, m’ont conseillé et ont bonifié les

xviii

articles scientifiques. David Auty est le coauteur du second chapitre puisqu’il a participé aux

analyses statistiques et à la révision grammaticale du texte. Steve Bédard est également

coauteur du second chapitre car une partie de l’étude a pu être réalisée dans un dispositif de

recherche du Ministère des Forêts, de la Faune et des Parcs du Québec sous sa responsabilité.

Il a également apporté ses révisions au manuscrit.

Alexis Achim : Département des sciences du bois et de la forêt, Université Laval, 2405

rue de la Terrasse, Québec, Québec, Canada. G1V 0A6.

Courriel : [email protected]

David Pothier : Département des sciences du bois et de la forêt, Université Laval, 2405

rue de la Terrasse, Québec, Québec, Canada. G1V 0A6.


David Auty : School of Forestry, Northern Arizona University, 200 East Pine Knoll

Drive, PO Box: 15018, Flagstaff, Arizona, USA. AZ 86011.


Steve Bédard : Direction de la recherche forestière, Ministère des Forêts, de la Faune et

des Parcs du Québec, 2700 rue Einstein, Québec, Québec, Canada. G1P 3W8.


Diffusion des résultats

Il est à noter que les résultats présentés dans cette thèse ont été diffusés lors des conférences

suivantes :

Havreljuk, F., Achim, A., Auty, D., Bédard, S. and Pothier, D. 2014. Integrating standing

value estimations into tree marking guidelines to meet wood supply objectives. 7è

Congrès Est du Canada et États-Unis d’Amérique en sciences forestières (eCANUSA).

Rimouski, Québec. 16-18 octobre 2014.

mailto:[email protected]




xix

Havreljuk, F., Achim, A. and Pothier, D. 2013. Variation régionale de la coloration de

cœur chez l’érable à sucre et le bouleau jaune. Colloque du CEF (Centre d’étude de la

forêt). Montebello, Québec, Canada. 24 avril 2013.

Havreljuk, F., Achim, A. and Pothier, D. 2012. Variation régionale de la coloration de

cœur chez l’érable à sucre et le bouleau jaune. Colloque III (FOR-8001). Université

Laval, Québec. 27 novembre 2012.

Havreljuk, F., Achim, A. and Pothier, D. 2012. Distribution régionale de la coloration

de cœur chez l’érable à sucre et le bouleau jaune. Colloque facultaire FFGG, Université

Laval, Québec. 15 novembre 2012.

Havreljuk, F., Achim, A. and Pothier, D. 2011. Peut-on ramener des considérations pour

la qualité du bois dans nos stratégies sylvicoles en forêt feuillue ? Journée du CRB

(Centre de recherche sur le bois), Université Laval, Québec, Canada. 25 novembre 2011.

Havreljuk, F., Achim, A. and Pothier, D. 2011. Integrating Standing Value Estimations

into Québec's Tree Marking System for Hardwoods. International Scientific Conference

on Hardwood Processing (ISCHP 3), Virginia Tech, Blacksburg, VA, USA.

http://woodproducts.sbio.vt.edu/ischp2011/index.php. October 17, 2011.


de cœur de l’érable à sucre. Journée du CRB (Centre de recherche sur le bois), Université

Laval, Québec, Canada. 26 novembre 2010.


de cœur de l’érable à sucre. Colloque du CEF (Centre d’étude de la forêt), Orford,

Québec, Canada. 14 mars 2010.

1

Introduction générale

Les forêts feuillues du sud du Québec ont une grande importance économique en raison de

leurs utilisations multiples, de la valeur de leurs bois et de la proximité des usines de

transformation et des marchés. Les principales espèces utilisées par l’industrie des feuillus

durs sont l’érable à sucre (Acer saccharum Marsh.) et le bouleau jaune (Betula alleghaniensis

Britt.) (MRNFPQ et CRIQ 2003; MRNFQ et CRIQ 2007). Entre 2008 et 2012, la

consommation totale de feuillus durs par les secteurs de transformation primaire du

déroulage, du sciage, des panneaux, des pâtes et papiers et du bois de chauffage était en

moyenne de 6 461 000 m³ (MRNQ 2013). Toutefois, cette industrie a dû composer avec un

contexte économique défavorable, ce qui a contribué à ralentir les activités de ce secteur ces

dernières années. D’ailleurs, durant cette période, la récolte des feuillus dans les forêts

publiques et privées du Québec ne s’est élevée qu’au tiers de la possibilité forestière totale

(CIFQ 2008, 2012).

Outre les problèmes associés au contexte économique, ceux liés à l’approvisionnement des

entreprises œuvrant dans le domaine des feuillus durs pourraient aussi être responsables de

la diminution de la récolte du bois. Depuis plusieurs années déjà, l’industrie des feuillus durs

fait face à la rareté des sciages de qualité supérieure des bois traditionnellement utilisés,

comme l’érable à sucre et le bouleau jaune (CRIQ 2002; MRNFPQ et CRIQ 2003; MRNFQ

et CRIQ 2007, 2008). À la fin des années 1980, les coupes à diamètre limite, qui ont eu pour

effet de dégrader le massif forestier et de réduire la qualité des bois sur pied (Metzger et

Tubbs 1971; Robitaille et Roberge 1981; Nyland 1992; Bédard et Majcen 2001; Bureau du

Forestier en chef 2012), ont été remplacées par les coupes de jardinage. En permettant de

régénérer et d'éduquer le peuplement tout en récoltant un certain volume de bois, la coupe de

jardinage vise à maintenir, voire améliorer, la productivité des forêts feuillues en prélevant

les arbres en perdition qui mourront avant la prochaine intervention (Arbogast 1957; Majcen

et al. 1990; Nyland 1998). Par conséquent, les taux d’accroissement et de survie, ainsi que la

qualité des arbres du peuplement résiduel dépendent de la stratégie de sélection des tiges

destinées à la récolte. Pour cette raison, il importe de pouvoir relier l’évaluation visuelle des

arbres effectuée lors des inventaires forestiers à la répartition du volume de bois par type

2

d’usine et à la qualité des produits transformés. Cette tâche est toutefois complexifiée par la

difficulté de relier l’apparence externe d’un arbre à la qualité interne de son bois.

Chez l’érable à sucre et le bouleau jaune, l’apparence visuelle du bois est une variable

déterminante de sa qualité puisqu’il sert surtout à la fabrication de meubles ou de parquets

(CRIQ 2002). La coloration de cœur est une des caractéristiques du bois de ces espèces qui

peut affecter leur qualité pour de tels usages. Elle fait référence à la modification uniforme

ou irrégulière de la couleur originale du bois d’une espèce donnée vers des teintes rougeâtres

ou brunâtres (Campbell et Davidson 1941; Shigo 1967). Techniquement, la coloration de

cœur est uniquement un critère visuel et esthétique n’affectant pas les propriétés mécaniques

de l’arbre (Shmulsky and Jones 2011). Par contre, dépendamment des tendances du marché,

des modes et des traditions, elle peut avoir un impact important sur la valeur du bois d’érable

à sucre et de bouleau jaune. Cette caractéristique est généralement considérée comme

indésirable puisqu’elle diminue la valeur des produits (Erickson et al. 1992; Hardwood

Market Report 2011).

Chez l’érable à sucre et le bouleau jaune, la coloration de cœur est d’origine traumatique

(Shigo 1966; Shigo 1967; Davidson et Lortie 1969; Shigo et Hillis 1973; Boulet 2007;

Belleville et al. 2011; Drouin et al. 2009), contrairement à certaines autres espèces pour

lesquelles un processus de coloration relié à la formation de duramen est en place. Chez les

deux espèces ciblées par ce projet, elle serait le résultat d’une série de processus liés aux

blessures, à l’oxydation des tissus et à l’action des microorganismes dans le bois (Shigo 1966;

Shigo 1967; Shigo et Larson 1969; Shigo et Hillis 1973; Solomon et Shigo 1976; Basham

1991). Les blessures, qu’elles soient causées par les branches mortes, les animaux ou

l’exploitation forestière, constitueraient une porte d’entrée potentielle pour la coloration en

exposant le bois du tronc aux conditions atmosphériques externes. Cette hypothèse est

appuyée par des études récentes qui ont établi des liens étroits entre les défauts externes

(nombre de branches mortes, nœuds, blessures et fourches) et la présence de coloration du

cœur (Knoke 2003; Wernsdorfer et al. 2005; Belleville et al. 2011). La réaction initiale d’un

arbre à la suite d’une blessure serait seulement liée à des processus chimiques impliquant,

d’une part, la production des composés phénoliques et, d’autre part, l’oxydation du bois

causée par l’entrée d’air dans l’arbre. Par la suite, les bactéries et les champignons non-

3

hyménomycètes peuvent infecter la blessure et amplifier le processus de coloration

(Campbell et Davidson 1941; Shigo 1966; Shigo et Hillis 1973; Sorz et Hietz 2008). Dans

certains cas, la coloration de cœur peut être suivie par un envahissement des champignons

hyménomycètes qui causent une dégradation du bois (Shigo 1966; Shigo 1967; Shigo et

Hillis 1973; Basham 1991).

Les études menées depuis une quarantaine d’années sur les feuillus durs de l’est de

l’Amérique du Nord et en Europe ont permis de mieux comprendre le développement de la

coloration de cœur à l’échelle de l’arbre. La coloration est influencée par l’âge, la sévérité et

la taille de la blessure (Shigo 1966), ainsi que par le diamètre, la hauteur et l’âge des arbres

(Campbell et Davidson 1941; Knoke 2003; Wernsdorfer et al. 2006; Giroud et al. 2008;

Drouin et al. 2009). La vigueur des arbres semble aussi jouer un rôle déterminant (Shigo et

Hillis 1973; Drouin et al. 2009; Baral et al. 2013). La coloration du bois est un processus lent

dont le développement prend des semaines (Sorz et Hietz 2008). Le bois qui est formé à la

suite d’une blessure ne sera généralement pas coloré, la coloration se limitant plutôt aux tissus

formés avant la blessure et se propageant vers l’intérieur de l’arbre (Solomon et Shigo 1976;

Basham 1991). Le développement de la coloration est plus rapide selon l’axe longitudinal

que l’axe radial de la tige, ce qui crée typiquement une colonne de coloration dans le tronc

(Campbell et Davidson 1941; Shigo et Larson 1969). Elle suit rarement une disposition

uniforme dans la tige, étant donné qu’elle est produite par la fusion de plusieurs colonnes de

coloration créées à des moments différents et selon diverses proportions (Campbell et

Davidson 1941; Shigo 1966). Toutefois, des études récentes ont permis de montrer que le

diamètre de la zone colorée diminue en montant dans l’arbre et prend un aspect généralement

fusiforme (Hallaksela et Niemisto 1998; Wernsdorfer et al. 2006; Giroud et al. 2008;

Belleville et al. 2011).

Malgré les nombreuses avancées dans la compréhension des facteurs affectant le

développement de la coloration à l’échelle de l’arbre, nous sommes toujours confrontés aux

incertitudes face aux facteurs qui influencent sa présence à une échelle plus vaste. Bien

qu’elle ait un impact majeur sur la qualité des approvisionnements, la distribution de la

coloration chez l’érable à sucre et le bouleau jaune entre les différentes régions du Québec

demeure peu documentée. Nous ne sommes donc pas en mesure de déterminer si le processus

4

de coloration de cœur peut être lié à des facteurs caractérisant les échelles du peuplement ou

de la région. Pourtant, des éléments nous font penser que l’influence du milieu et des

conditions de croissance pourraient effectivement avoir un effet important sur son

développement. En effet, plusieurs intervenants du milieu forestier prétendent que la

coloration de cœur chez l’érable à sucre et le bouleau jaune varie selon la provenance des

bois. Certains documents en font vaguement mention (Davidson et Lortie 1969; Boulet 2007;

Wiedenbeck et al. 2004), même si l’effet régional de la coloration n’a pas été observé chez

l’érable à sucre (Yanai et al. 2009). Quelques rares études (Climent et al. 2002) ont aussi

montré que le climat et le milieu de croissance pouvaient influencer le développement de la

coloration chez les espèces où un vrai processus de formation du duramen était en place.

Cependant, à notre connaissance, aucune étude précise n’a permis de démystifier le rôle joué

par le milieu de croissance dans la coloration de cœur de l’érable à sucre et du bouleau jaune.

En plus du manque de connaissances sur la distribution de la coloration de cœur des

principales espèces feuillues au Québec, l’industrie forestière fait aussi face aux incertitudes

liées à l’approvisionnement en bois feuillu qui découlent directement du système actuel de

marquage de tiges pour la récolte. Même si plusieurs variantes de la coupe de jardinage ont

vu le jour depuis son instauration en 1987, le mode de récolte est principalement axé vers le

jardinage par pied d’arbre ou par petit groupe d’arbres (Majcen et Richard 1992; Bédard et

Majcen 2001). Cette façon de faire vise à imiter le régime de perturbations naturelles des

forêts feuillues du Nord-Est de l’Amérique du Nord, produisant surtout une mortalité à

l’échelle de l’arbre ou du groupe d’arbres (Lorimer et Frelich 1994; Seymour et al. 2002).

Ainsi, depuis l’utilisation de normes de marquage associées au jardinage et aux autres types

de coupe partielle, les industries s’approvisionnant sur terres publiques n’ont pas accès à

l’ensemble des arbres d’un peuplement. Ces règles, jumelées à l’état des peuplements,

déterminent donc la qualité des approvisionnements pour les différents produits de

transformation envisagés (déroulage, sciage, pâte). Ainsi, même si un volume de bois est

alloué à une usine, son approvisionnement n’est pas assuré puisque la qualité des bois

marqués peut ne pas correspondre aux besoins réels de cette usine. Pour assurer

l’approvisionnement des usines de transformation de bois feuillu, le défi ne consiste donc

pas seulement à garantir un volume de bois récoltable, mais bien à déterminer un volume

correspondant à la qualité nécessaire à leur production rentable. Pour ce faire, on doit être en

5

mesure de relier le classement visuel des arbres sur pied effectué lors des inventaires

forestiers et la répartition du volume de bois par type d’usine et, idéalement, par classe de

qualité de produits transformés.

Depuis l’instauration des coupes de jardinage, le système de classement visuel utilisé pour

sélectionner les arbres destinés à la récolte a subi de nombreux changements. Jusqu’au milieu

des années 2000, c’est un système de marquage hybride (I-II-III-IV), tenant compte de la

vigueur et de la qualité des arbres, qui fût appliqué (Majcen et al. 1990). Le jardinage basé

sur le marquage de tiges selon ce système a fait ses preuves à l’échelle expérimentale (Majcen

et Richard 1992; Majcen 1996; Bédard et Majcen 2001; Coulombe et al. 2004; Majcen et al.

2006). Par contre, son application à l’échelle industrielle n’a pas permis d’atteindre les

objectifs visés. Coulombe et al. (2004) ont souligné que les pratiques de marquage de

l’industrie de transformation étaient non conformes aux principes de la coupe de jardinage

visant à améliorer la productivité des forêts et à augmenter la production en bois d’œuvre de

feuillus durs. Dans leur suivi des effets réels des coupes de jardinage dans les forêts publiques

du Québec de 1995 à 1998, Bédard et Brassard (2002) ont mis en évidence le fait que

l’accroissement net des peuplements jardinés dans les dispositifs de recherche du Ministère

des Forêts, de la Faune et des Parcs du Québec (MFFPQ) était plus de deux fois supérieur à

celui enregistré dans les peuplements jardinés dans le cours normal des opérations des

entreprises forestières. Ces difficultés d’application à l’échelle industrielle ont été associées

au choix inadéquat des arbres à prélever. Selon plusieurs intervenants, le système de sélection

de Majcen et al. (1990) laissait trop de latitude et d’interprétation aux marteleurs. Face à cette

problématique et aux recommandations de la Commission d'étude sur la gestion de la forêt

publique québécoise (Coulombe et al. 2004), le MFFPQ a raffiné le système de classification

des arbres feuillus du Québec.

La nouvelle classification est issue d’un système basé sur des connaissances pathologiques

appliquées surtout pour déterminer la vigueur des arbres (Boulet 2007). Quatre priorités de

récolte (M-S-C-R) guident l’ordre de sélection des tiges en fonction de défauts

pathologiques, morphologiques ou mécaniques précis. Ces défauts sont reliés à une

évaluation de la probabilité de mortalité d’un arbre avant la prochaine rotation (Boulet 2007).

La classification priorise la récolte des arbres mourants et dont la survie semble être

6

compromise (classes M et S). Ainsi, ce système vise la restauration des forêts feuillues, mais

il ne considère pas directement les variables déterminant la qualité des arbres. Fortin et al.

(2009b) ont déterminé que la capacité du système de classification MSCR à établir la

répartition du volume des arbres par classe de qualité est globalement faible.

Conséquemment, l’instauration de ce système est venue exacerber le problème

d’approvisionnement décrit plus haut. La situation pourrait être améliorée en jumelant des

classes de qualité au système MSCR (Fortin et al. 2009b). Des efforts ont été consentis en ce

sens avec l’intégration de classes de qualité additionnelles (classes O et P) (MRNFQ 2006),

mais le gain en précision apportée par ces nouvelles classes n’a jamais été quantifié. Dans

une perspective d’amélioration des prévisions d’approvisionnements, l’apport de nouvelles

variables prédictives, à l’échelle de l’arbre et du peuplement, devrait aussi être évalué.

Afin d’atteindre le plein potentiel de mise en valeur des forêts feuillues, le système de

marquage doit permettre l’atteinte de l’objectif sylvicole, par le prélèvement des arbres

dépérissants, mais aussi celui d’approvisionnement qui affecte la viabilité des usines de

transformation. Une part des éléments déterminant la qualité du bois des feuillus durs est

reliée à des facteurs morphologiques facilement évaluables par des systèmes de classification

visuelle. D’autres facteurs, comme la coloration de cœur, sont reliés à des facteurs internes.

Il importe, dans un premier temps, de mieux comprendre les facteurs faisant varier la qualité

des approvisionnements et, dans un second temps, d’inclure ces derniers dans nos systèmes

guidant la sélection des tiges.

Démarche méthodologique

L’objectif général de ce projet de recherche était d’améliorer les prévisions

d’approvisionnement des usines de transformation de bois feuillu à partir d’une meilleure

évaluation de la qualité des arbres feuillus sur pied. L’approche proposée par cette étude vise

à préciser la répartition géographique de la coloration de cœur tout en établissant des liens

étroits entre l’évaluation visuelle des arbres sur pied, leur vigueur et la valeur des produits

transformés en usine. L’ensemble du projet de recherche porte sur l’étude de deux principales

espèces feuillues du Québec, l’érable à sucre et le bouleau jaune.

7

Le premier chapitre de la thèse porte sur la distribution régionale de la coloration de cœur de

l’érable à sucre et du bouleau jaune. Douze localisations couvrant l'ensemble de la forêt

tempérée du sud du Québec ont été échantillonnées afin de quantifier la variation de la

proportion de la coloration de cœur à l’échelle de la province.

L’objectif du second chapitre était d’améliorer les directives de martelage, présentement

axées uniquement sur la vigueur, en déterminant les principales variables qui affectent la

valeur monétaire de l’érable à sucre et du bouleau jaune sur pied. Afin d’identifier les arbres

moribonds pouvant produire au moins une bille de sciage, un mesurage détaillé, incluant une

caractérisation de tous les défauts externes, a été effectué sur 96 arbres répartis dans deux

stations. Ce même échantillonnage a également servi à réaliser le troisième volet de cette

thèse.

Le troisième chapitre de la thèse vise à décrire le lien entre les caractéristiques des billes

destinées au sciage et les planches produites. Plus précisément, le but de ce volet était de bâtir

un modèle de prévision de l’occurrence et du volume des grades et des catégories de couleur

des planches sciées en fonction des caractéristiques des billes d’érable à sucre et de bouleau

jaune. Les résultats de ce volet permettent de caractériser le panier de produits découlant du

sciage et peuvent servir à établir la valeur monétaire des bois.

Afin de prendre en considération les principales variables liées à la valeur des arbres feuillus

sur pied, les aspects de qualité de l’érable à sucre et du bouleau jaune, traités dans cette thèse,

sont abordés à deux échelles. Le premier volet aborde la distribution de la coloration de cœur

à l’échelle régionale. Les chapitres deux et trois, pour leur part, sont axés sur une évaluation

visuelle aux échelles des arbres et des billes, en établissant des liens étroits avec la valeur et

la qualité des produits transformés en usine. Le dernier chapitre permet également de faire le

lien avec les deux premiers volets de la thèse, en caractérisant l’effet de la coloration de cœur

sur le panier de produits et, ultimement, sur la valeur monétaire des arbres. Ainsi, les

ajustements proposés fourniront un outil d’aide à la décision au MFFPQ lui permettant

d’attribuer, avec plus de précision, des volumes de bois aux différents types d’usines de

transformation (déroulage, sciage, pâte). De plus, ces résultats permettront de mieux

caractériser les peuplements forestiers lors de l’éventuelle vente aux enchères de leur bois.

9

Chapitre 1

Regional variation in the proportion of red heartwood in

sugar maple and yellow birch1

1 Version intégrale d’un article publié / Intergral version of a published paper:

Havreljuk, F., Achim, A. and Pothier, D. 2013. Regional variation in the proportion of red heartwood in sugar

maple and yellow birch. Can. J. For. Res. 43(3), 278–287. doi:10.1139/cjfr-2012-0479

10

Abstract

Stems of sugar maple (Acer saccharum Marsh.) and yellow birch (Betula alleghaniensis

Britt.) trees often contain a column of discoloured wood known as red heartwood, which

reduces lumber value. To quantify the regional-scale variation in red heartwood, 192 trees of

each species were sampled in 12 locations across the temperate forest zone of southern

Québec, Canada. Large regional variation in the radial proportion of red heartwood (RHP) at

breast height (1.3 m) was observed in both species. Statistical modeling showed that such

variation was mainly attributable to factors related to tree development. Cambial age had a

strong positive effect on RHP in both species, suggesting that the occurrence of red

heartwood ultimately might be unavoidable. There was also a positive effect of ring area

increment at the limit of the discoloured zone. In the case of sugar maple, there was an added

effect of the trend in ring area increments observed in the same zone, with a negative trend

being generally indicative of a larger RHP. Further variability in this species was also

associated with the annual minimum temperature of the sampling locations. The models

developed for each species explained around 60% of the variance in RHP and could be used

to improve forest management and wood procurement decisions.

Keywords: sugar maple, yellow birch, red heartwood, wood discoloration, appearance wood

products, regional climate.

11

Résumé

Les tiges d'érable à sucre (Acer saccharum Marsh.) et de bouleau jaune (Betula

alleghaniensis Britt.) contiennent fréquemment une colonne de bois brun-rougeâtre, appelée

coloration de cœur, qui réduit la valeur des produits du bois. Afin d'en quantifier la variation

à l'échelle régionale, 192 arbres de chaque espèce ont été échantillonnés dans 12 localisations

couvrant l'ensemble de la forêt tempérée du sud du Québec, Canada. Une variation régionale

élevée de la proportion radiale de la zone colorée (PRC) à hauteur de poitrine (1,3 m) a été

observée chez les deux espèces. La modélisation statistique a révélé que cette variation était

principalement attribuable à des facteurs liés au développement des arbres. L'âge cambial

avait un effet positif sur la PRC des deux espèces, ce qui indique que l'occurrence de la

coloration de cœur serait ultimement inévitable. Un effet positif de la superficie des anneaux

de croissance à la limite de la zone colorée a aussi été observé. Dans le cas de l'érable à sucre,

il y avait un effet additionnel de la tendance de l’accroissement en superficie des anneaux

dans la même zone, les tendances négatives étant généralement indicatrices d'une PRC plus

élevée. Une partie de la variabilité chez cette espèce était aussi associée à la température

minimale annuelle d'une localisation. Les modèles mis au point pour chaque espèce

expliquent près de 60 % de la variance et pourront être utilisés pour améliorer les décisions

liées à l'aménagement forestier et aux approvisionnements en bois.

Mots-clés : érable à sucre, bouleau jaune, cœur rouge, coloration du bois, produits

d’apparence, climat régional.

12

Introduction

Sugar maple (Acer saccharum Marsh.) and yellow birch (Betula alleghaniensis Britt.) are

dominant components of the North American temperate deciduous forests. Both tree species

are commercially important, especially as raw material for appearance wood products such

as furniture, flooring, cabinets, and interior finishing (CRIQ 2002). Accordingly, the wood

quality and market value of sugar maple and yellow birch depend on the visual characteristics

of sawn boards.

Red heartwood is one of the most important appearance criteria for such products. Also

referred to as red heart, heartwood discolouration, staining heart, traumatic heartwood, false

heartwood, pathological heartwood, and, mistakenly, as mineral stain, it consists of a uniform

or irregular change of the original wood colour to a darker reddish-brown colour (Campbell

and Davidson 1941; Shigo 1966, 1967; Hillis 1987; Basham 1991; Erickson et al. 1992;

Hallaksela and Niemisto 1998; Drouin et al. 2009). Although the presence of uniform red

heartwood is desirable for some end-uses, the simultaneous presence of light-coloured

sapwood and red heartwood is generally considered unappealing by consumers. As a result,

heartwood discolouration significantly reduces lumber value (Erickson et al. 1992;

Hardwood Market Report 2011).

Whereas coloured heartwood in some other species, like oak and cherry, is part of a normal

aging process (Hillis 1987), heartwood discolouration in sugar maple and yellow birch is

believed to originate from trauma (Shigo 1966; Davidson and Lortie 1969; Shigo and Hillis

1973). Injuries to the stem may induce wood oxidation and microbial activity, which can

result in discolouration (Shigo 1967; Solomon and Shigo 1976). Red heartwood does not

affect wood mechanical properties (Shmulsky and Jones 2011), but could be followed by an

invasion of hymenomycete fungi that cause wood decay (Basham 1991).

To date, research has focused on the development and within-tree distribution of red

heartwood. Some studies have found a relationship between the proportion of red heartwood

and tree diameter, height, and age (Campbell and Davidson 1941; Knoke 2003; Wernsdorfer

et al. 2006; Giroud et al. 2008; Drouin et al. 2009). It can also be related to the severity and

13

size of bole injuries induced by logging (Shigo 1966), further supporting the trauma

hypothesis. Recent studies on European beech (Fagus sylvatica L.) and paper birch (Betula

papyrifera Marsh.) provided more evidence by demonstrating direct relationships between

external defects (e.g., number of dead branches, knots, injuries, and forks) and the occurrence

and extent of red heartwood (Knoke 2003; Wernsdorfer et al. 2005; Belleville et al. 2011).

Little scientific attention has been paid to the factors that may explain regional-scale

variation, despite the potential benefits that might accrue to members of the wood products

supply chain, where these variations are understood. Some stand-level variation in red

heartwood occurrence has been reported for several hardwood species (Davidson and Lortie

1969; Wiedenbeck et al. 2004). Soil fertility might be responsible for site-to-site differences,

although this was not found to be the case in a study conducted in the northern United States

(Yanai et al. 2009). Regional differences might also be attributable to variation in radial

growth rate, which is related to the size of defects and the time to wound closure (Solomon

and Blum 1977; Vasiliauskas 2001). Alternatively, regional differences in red heartwood

development might originate from climatic factors, which are known to regulate tree growth

and physiological processes (Pither 2003; Klos et al. 2009). Some observations suggest that

the proportion of red heartwood generally increases in colder climates (Wiedenbeck et al.

2004). This could be related to the increased occurrence of external stem defects towards the

northern limit of the sugar maple distribution range (Burton et al. 2008). For example, defects

such as frost cracks are frequently accompanied by discoloured and decayed wood (Shigo

1966, 1967; Davidson and Lortie 1969).

The reasons for initiating this study were first to describe the variation in red heartwood

proportion at a regional scale and second to attempt to explain the observed differences. Our

underlying hypothesis was that, if regional variation in red heartwood proportion actually

exists, it can be explained by differences in tree age and radial growth rate alone. Rejection

of this hypothesis would imply an additional influence of other factors, such as stand-level

variables (e.g., basal area, number of stems per hectare, etc.). Red heartwood was found to

propagate longitudinally along the annual rings forming a column of discoloured wood

(Hallaksela and Niemisto 1998; Wernsdorfer et al. 2005; Giroud et al. 2008). The diameter

of the heartwood zone has also been positively related to the number and size of dead branch

14

scars (Solomon and Shigo 1976; Belleville et al. 2011), which are factors closely related to

tree age and radial growth rate.

Materials and methods

Study sites and field measurements

Twelve sampling regions were selected across the temperate forest zone of southern Québec,

Canada (Figure 1.1). The sites were all located on public land, within the sugar maple –

yellow birch or the balsam fir (Abies balsamea [L.] Mill.) – yellow birch bioclimatic domains

(Saucier et al. 1998) (Table 1.1).

Figure 1.1 Location of the sampling regions across Québec.

15

Table 1.1 Summary of sampling locations used in the study.

Region

ID Name Bioclimatic domain

Bioclimatic

subdomain

Ecological

region

Elevation

range (m)

1 Témiscamingue Sugar maple – yellow birch West 3a 325-385

2 Rapides-des-Joachims Sugar maple – yellow birch West 3a 245-315

3 Réservoir Cabonga Balsam fir– yellow birch West 4b 395-405

4 Outaouais Sugar maple – yellow birch West 3b 325-410

5 Ste-Véronique Sugar maple – yellow birch West 3b 345-370

6 Mauricie Sugar maple – yellow birch East 3c 275-345

7 Portneuf Balsam fir– yellow birch West 4c 375-495

8 Duchesnay Balsam fir– yellow birch East 4d 225-295

9 Lac Mégantic Sugar maple – yellow birch East 3d 545-590

10 Montmagny Sugar maple – yellow birch East 3d 360-380

11 Charlevoix Balsam fir– yellow birch East 4d 295-345

12 Squatec Balsam fir– yellow birch East 4f 365-415

We restricted the choice to uneven-aged stands with a main canopy dominated by mature

sugar maple and (or) yellow birch trees established on glacial tills deeper than 50 cm, and

characterized by mesic conditions (average fertility and drainage). Recently harvested sites

(<10 years) and those with severe past biotic and abiotic disturbances were rejected from the

sampling. Once these criteria were defined, we collaborated with local authorities to create a

bank of potential sampling sites in each region using forest cover maps. A random list of

potential sites was then generated and these were either rejected or retained based on field

validations of the criteria, until two study sites were selected for each species in each region.

Sampling of both species was possible at eight sites that met the selection criteria. Stand

structure and composition were measured using variable-radius plots. Two plots were

established for sampling sites covering less than one hectare and one additional plot was

established for each additional hectare.

To minimize variability between trees and focus on regional variation, the sampling was

limited to trees with a diameter between 23.1 and 33.0 cm at breast height (DBH, 1.3 m above

the ground). This range corresponds to the small sawlog timber category of the hardwood

tree grading system (Hanks 1976; Monger 1991). To limit the variability associated with

factors other than regional climate, site properties, and tree growth, only vigorous trees were

selected using the tree-marking criteria of Boulet (2007). To facilitate the sampling of

undamaged cores, trees with pathogens and severe wounds were avoided, since these are

16

often associated with stem decay. Trees were also rejected if they had an abnormal number

of dead branches or other obvious external initiation points for red heartwood (Belleville et

al. 2011) on the lower 5 m of the stem. The sampling was also limited to trees with a dominant

and codominant position in the main canopy. Following these criteria, eight sample trees per

species per site were randomly selected for sampling.

On each sample tree, DBH and total tree height were recorded (Table 1.2) and then an

increment core sample was taken at breast height. To control for any potential effects of trees

growing on an incline, cores were consistently sampled from the uphill side of each tree. The

core samples provided an assessment of red heartwood and radial growth rate in the butt log,

which is the most valuable part of the tree (Fortin et al. 2009b). In addition, maximum

heartwood area is located around breast height (Hillis 1987; Giroud et al. 2008).

Table 1.2 Mean sample tree characteristics. SD is the standard deviation.

Sugar maple Yellow birch

Variable Mean ± SD Range Mean ± SD Range

Dbh (cm) 28.3 ± 2.8 23.1 – 33.0 28.0 ± 2.7 23.1 – 33.0

Height (m) 20.6 ± 2.2 15.1 – 26.1 19.7 ± 2.1 14.6 – 26.4

Age (years) 93 ± 22 57 – 155 83 ± 23 31 – 153

Sample core processing

All cores were air-dried and progressively sanded down to allow a clear identification of

growth rings. A total of 192 sugar maple and 192 yellow birch cores were processed and

scanned at high resolution using an optical scanner (Epson GT-15000, Japan).

The distinction between discoloured heartwood and light-coloured wood was in most cases

abrupt but was harder to detect in some samples owing to a gradual transition. To avoid

subjectivity, the limit between discoloured and noncoloured wood was determined in a two-

step method using the image-processing software ImageJ (Abramoff et al. 2004). First, a

threshold function was executed to divide the scanned image into either features of interest

(i.e., red heartwood and sapwood) or background. An iterative threshold selection procedure

was applied (Ridler and Calvard 1978). In the second step, the noise generated by the

17

threshold method was removed to obtain a clear boundary between red heartwood and

sapwood area (Figure 1.2).

Figure 1.2 Illustration of the red heartwood separation procedure performed using ImageJ.

(A) Initial image. (B) The resulting image after applying the threshold function. (C) The final

image after the noise from the threshold function was removed.

All samples were processed individually using the same procedure. They were calibrated and

corrected for any artifacts and (or) breakages, to ensure that accurate lengths were used in

subsequent analyses. Some samples were soaked in water to improve the contrast between

the discoloured and noncoloured wood. The total radial core length (from pith to bark) and

the length of discoloured wood were measured on each sample.

Tree-ring increments were measured using the semiautomatic image analysis system

WinDendro (Guay et al. 1992). To ensure accuracy, samples were first analyzed under a

magnifying glass and the less discernible rings were marked with a pencil. Annual ring width

increments were converted into ring area increments using the method described by LeBlanc

(1996). Following previous studies (Shigo 1966, Hallaksela and Niemisto 1998; Wernsdorfer

et al. 2005), red heartwood was assumed to be symmetrically distributed around the pith, and,

therefore, the radial measurements on each core were converted into area values that assumed

circularity of the stems.

Climatic characterization

The climatic data used in our analyses were generated by BioSIM (Régnière et al. 2012).

This software computes annual climatic variables for a given site using the North American

Normals database (Canada–USA 1981–2010), which contains monthly statistics over the

most recent 30-year period before the trees were sampled. Geographic coordinates of latitude,

longitude, and elevation from all study sites were set as BioSIM inputs. The software was

parameterized to generate climate information using an interpolation between the eight

18

closest available weather stations. BioSIM is a simulator that generates a series of daily

values with a random component. Averages are reported along with their mathematical

expectation of average, variance, autocorrelation, and cross-correlation of minima and

maxima. All simulations were conducted in a single operation with 500 iterations to ensure

regular results. More than 20 climatic variables were generated using this method.

Statistical analysis

The dependent variable used in this study was the red heartwood proportion (RHP) at 1.3 m

in height, representing the ratio of the discoloured wood radius to the total radius of the core.

This choice was made to reflect the typical grade sawing of hardwoods (Steele 1984), which

consists of extracting noncoloured wood boards from the bark towards the pith until the

discoloured core is reached. The radial proportion of discoloured wood was preferred to the

cross-sectional area proportion as it was found to be normally distributed.

All statistical tests were performed using the R statistical programing software (R

Development Core Team 2012). Mixed linear models were developed for each species

separately using the nlme package. Region and site nested in region were considered as

random effects, in accordance with the sampling strategy (Gelman and Hill 2007). The initial

steps of the analysis aimed to describe the local and regional patterns of variation in red

heartwood. To assess the importance of different variables associated with RHP, models were

compared using the corrected Akaike information criterion (AICc) (Burnham and Anderson

2002). This statistic was used to guide model development by balancing the fit (i.e., log-

likelihood) and parsimony (i.e., number of parameters) of each model. To avoid over-fitting

and reporting spurious effects, a limited set of candidate models were used to test biologically

plausible hypotheses (Burnham and Anderson 2002; Anderson 2008).

Model development

Twenty a priori multilevel linear models were developed and compared to identify the main

factors related to RHP in sugar maple and yellow birch. The first group of candidate models

included individual tree descriptors such as tree height and age (i.e., cambial age at 1.3 m),

19

which are known to be associated with red heartwood size in other hardwood species

(Wernsdorfer et al. 2006; Giroud et al. 2008).

A second group of candidate models was created to describe tree development. The

explanatory variables “growth rate”, or periodic annual increment, and “growth trend”,

representing the rate of change in mean annual growth within a given time period (Bigler and

Bugmann 2003), were derived from annual ring increments. Such variables have been used

successfully in mortality models because of their link with tree vigor (Bigler and Bugmann

2003; Hartmann et al. 2008). Both variables were represented using ring area increments

(RAI, cm2/year), which are recognized as a more meaningful indicator of tree growth and

vigor than ring width (LeBlanc 1996). Five year growth rate was measured on each sample

in a period that ended at the discoloured wood boundary (RAIBC5). This variable was deemed

to be representative of the size of the live crown (Valentine and Mäkelä 2005) and hence the

number and size of live branches at the time when the ring at the edge of the discoloured

zone was formed. Likewise, the slope of a local linear regression applied to ring area over a

period of 10 years spanning the current boundary between discoloured and noncoloured

wood (i.e., five rings on each side) (SLR10) was used as a potential explanatory variable. This

was deemed to represent the growth trend at the time when the ring at the limit of the

discoloured zone was formed. If RAIBC5 can be considered as an indicator of the number and

size of the live branches present at that time, SLR10 could be indicative of further growth

prior to branch death. A 10 year time interval was chosen in this case to increase the number

of points in the regression and limit the influence of unusually large or narrow rings on the

obtained value. Average annual tree increment (AAI) over the whole pith-to-bark profile was

used as a long-term measure of tree vigor. Because this variable was not normally distributed,

a logarithmic transformation was applied prior to model fitting.

The third set of candidate models included the basal area of the stand (StandBA, m2/ha). It

was hypothesized that RHP would be positively correlated with basal area because increased

competition for light may lead to faster crown recession and, therefore, to increased branch

mortality (Shmulsky and Jones 2011).

20

Climatic variables were then added to the fourth group of candidate models. Only a limited

number of climatic variables generated by BioSIM and those relevant to our hypotheses were

chosen based on preliminary analyses and data exploration. Explanatory variables related to

temperature (annual means and extremes) and precipitation (annual sums) were included in

the final set of candidate models.

Finally, an intercept-only model was added to ensure it did not perform better than more

complex models. A summary of all candidate variables is presented in Table 1.3.

Table 1.3 List of explanatory variables screened in the modeling process.

Variable Description

Age (years) Cambial age at 1.3 m

Height (m) Height of the tree

AAI (cm2/year) Log-transformed mean ring area increment

RAIBC5 (cm2/year) 5-year mean ring area increment at the limit of the red heartwood zone

SLR10 Linear regression slope of ring area values over 10 years spanning the limit of the red

heartwood zone (i.e., 5 rings on each side)

StandBA (m2/ha) Stand basal area

LTMin (°C) Annual lowest daily minimum temperature

MTMean (°C) Annual mean daily average temperature

Precip (mm) Annual total precipitation

Autocorrelation between variables was checked during the model construction process using

a variance inflation factor (VIF) (Zuur et al. 2010). The usual assumptions for regression

analysis were checked and no problems were detected. All selected models presented

normally distributed errors, homogeneous variances and no extreme values. For the

simplification of interpretation and to limit the number of candidate models, only additive

effects were considered (i.e., no interactions). Before model comparison, four sugar maple

and five yellow birch trees were rejected from the analyses because of missing discoloured

heartwood and thus RAIBC5 and SLR10 data. Model selection was performed using the

AICcmodavg package in R (Mazerolle 2012). This allowed uncertainties regarding the

selection of the best model to be assessed using a model averaging technique (also referred

to as “multimodel inference”). The package computes the weighted estimates of the

predictions for a given predictor variable across all models. The weighting of parameter

estimates is given by the model probabilities, which are derived from Akaike's weights. For

21

a detailed description of this approach see Burnham and Anderson (2002) or Mazerolle

(2006).

Results

Of the 192 samples per species, only four sugar maple and five yellow birch cores did not

contain discoloured heartwood. The mean radial proportions (±SD) of red heartwood were

36.4 ± 14.9% and 36.8 ± 15.5% for sugar maple and yellow birch, respectively. Important

variations of RHP were observed between the sampling regions (Figure 1.3).

Figure 1.3 Mean red heartwood proportion (RHP) for sugar maple and yellow birch in the 12

sampling regions. Two study sites are included in each region and eight trees were sampled

from each site. Error bars represent standard errors.

There were no differences in RHP between bioclimatic domains, but significant differences

(p < 0.05) were detected between subdomains. In sugar maple, mean RHP was approximately

44% in the western bioclimatic subdomains, compared with 29% in the eastern subdomains.

However, no such trend was observed for yellow birch (Figure 1.4).

22

Figure 1.4 Mean red heartwood proportion (RHP) for sugar maple and yellow birch across

bioclimatic subdomains. Eastern Balsam fir – Yellow birch subdomain (n = 92), Western

Balsam fir – Yellow birch subdomain (n = 64), Eastern Sugar maple – Yellow birch

subdomain (n = 92), and Western Sugar maple – Yellow birch subdomain (n = 128). Error

bars represent standard errors.

The mean age of trees sampled in the western subdomains was greater (97 and 86 years for

sugar maple and yellow birch, respectively) than in their eastern counterparts (88 and 78

years for sugar maple and yellow birch, respectively). Annual ring measurements on the

sample cores revealed that (i) age varied substantially between the samples despite the limited

range of DBH and (ii) RHP was strongly related to age for both species (Figure 1.5).

23

Figure 1.5 Number of discoloured wood rings as a function of the total number of rings at

1.3 m.

The best model for predicting RHP in sugar maple was model 13 (AICc = 1335.06, wi =

0.75), which included tree, growth, and climate variables, followed by model 17 with a

difference in AICc of 3.72 units (Table 1.4). The uncertainty regarding the best model (model

13) was assessed using model averaging. According to the 95% confidence intervals (CI) for

the model-averaged regression estimates, RAIBC5 (CI = 2.25–3.08), SLR10 (CI = −7.89 to

−0.66), Age (CI = 0.11–0.23), and LTMin (CI = −2.12 to −0.57) all showed strong evidence

of having a significant influence (effect ≠ 0 with p < 0.05) on RHP (Figure 1.6). However,

there was insufficient evidence for Height (CI = −0.87 to 0.34) and StandBA (CI = −0.38 to

0.35) to be included in model 17. The adjusted R2 of model 13 was 0.67, but this decreased

to 0.60 when calculated from the fixed effects only. Parameter estimates are presented in

Table 1.5.

24

Table 1.4 Comparison of the multiple linear regression models for sugar maple red heartwood

proportion (RHP).

Model group Explanatory variables ID Log-

likelihood K AICc ∆i wi

Intercept only Null 1 -725.98 4 1460.17 125.11 0.00

Tree Age 2 -720.12 5 1450.56 115.50 0.00

Age + Height 3 -719.99 6 1452.44 117.38 0.00

Tree + Growth AAI 4 -724.82 5 1459.98 124.92 0.00

RAIBC5 5 -684.32 5 1378.96 43.90 0.00

RAIBC5 + SLR10 6 -680.47 6 1373.41 38.35 0.00

Age + Height + RAIBC5 7 -665.12 7 1344.87 9.81 0.01

Age + Height + RAIBC5 + SLR10 8 -662.43 8 1341.66 6.60 0.03

Age + RAIBC5 + SLR10 9 -662.80 7 1340.21 5.15 0.06

AAI + Height 10 -723.85 6 1460.17 125.11 0.00

Tree + Growth +

Stand Age + Height + RAIBC5 + SLR10 + StandBA 11 -662.42 9 1343.86 8.80 0.01

AAI + Height + StandBA 12 -723.58 7 1461.78 126.72 0.00

Tree + Growth +

Climate Age + RAIBC5 + SLR10 + LTMin 13 -659.13 8 1335.06 0.00 0.75

AAI + LTMin 14 -720.17 6 1452.79 117.73 0.00

Age + RAIBC5 + SLR10 + Precip 15 -662.39 8 1341.58 6.52 0.03

AAI + Precip 16 -723.91 6 1460.28 125.22 0.00

Full model Age + Height + RAIBC5 + SLR10 + StandBA + LTMin 17 -658.77 10 1338.78 3.72 0.12

AAI + Height + StandBA + LTMin 18 -719.26 8 1455.33 120.27 0.00

Age + Height + RAIBC5 + SLR10 + StandBA + Precip 19 -662.01 10 1345.26 10.20 0.00

AAI + Height + StandBA + Precip 20 -722.71 8 1462.22 127.16 0.00

Note: K is the total number of parameters (including an intercept and random terms), ∆i is the difference in

AICc with the best model, wi is the ratio of the ∆i for a given model to that of the whole set of candidate models.

A description of the abbreviations used is given in Table 1.3.


best-fit model parameters for sugar maple.

25

Table 1.5 Parameter estimates and their standard errors (±SE) for the final models.

Parameter Sugar maple Yellow birch

Intercept -40.14 ± 16.22 -18.43 ± 7.14

RAIBC5 2.66 ± 0.21 2.19 ± 0.17

SLR10 -4.29 ± 1.85 –

Age 0.17 ± 0.03 0.23 ± 0.03

Height – 1.30 ± 0.36

LTMin -1.32 ± 0.43 –

Note: A description of the abbreviations used is given in Table 1.3.

For yellow birch, the best model for predicting RHP was model 7 (AICc=1361.03; wi =0.52),

which included tree and growth variables, but several more complex models had very similar

AICc values (Table 1.6). There was sufficient empirical support for models 8 (∆i = 1.72; wi

= 0.22), 19 (∆i = 3.34; wi = 0.10), 17 (∆i = 3.66; wi = 0.08) and 11 (∆i = 3.92; wi = 0.07) as

the choice of the best model. Through model averaging, RAIBC5 (CI = 1.86−2.54), Age (CI

= 0.18−0.30) and Height (CI = 0.58−1.98) were confirmed to have a significant effect on

RHP (Figure 1.7). However, there was insufficient evidence for SLR10 (CI= −2.04 to 4.29),

StandBA (CI = −0.49 to 0.69), MTMean (CI = −0.47 to 4.92) and Precipitation (CI = 0−0.02)

to be included. The competing models all had the same basic structure as model 7, but

contained additional explanatory variables. Model averaging showed that the inclusion of

these additional covariates did not substantially improve the model fit. The adjusted R2 of

model 7 was 0.65, decreasing to 0.60 when calculated from the fixed effects only. Parameter

estimates are presented in Table 1.5.

26

Table 1.6 Comparison of the multiple linear regression models for yellow birch red

heartwood proportion (RHP).

Model group Explanatory variables ID Log-

likelihood K AICc ∆i wi

Intercept only Null 1 -752.04 4 1512.30 151.27 0.00 Tree Age 2 -743.01 5 1496.35 135.32 0.00

Age + Height 3 -731.76 6 1475.98 114.96 0.00 Tree + Growth AAI 4 -751.90 5 1514.13 153.10 0.00

RAIBC5 5 -702.67 5 1415.67 54.64 0.00

RAIBC5 + SLR10 6 -702.53 6 1417.52 56.49 0.00

Age + Height + RAIBC5 7 -673.20 7 1361.03 0.00 0.52

Age + Height + RAIBC5 + SLR10 8 -672.97 8 1362.74 1.72 0.22

Age + RAIBC5 + SLR10 9 -678.71 7 1372.05 11.03 0.00

AAI + Height 10 -740.87 6 1494.20 133.18 0.00 Tree + Growth +

Stand Age + Height + RAIBC5 + SLR10 + StandBA 11 -672.96 9 1364.94 3.92 0.07

AAI + Height + StandBA 12 -739.47 7 1493.57 132.54 0.00

Tree + Growth + Climate

Age + RAIBC5 + SLR10 + MTMean 13 -676.35 8 1369.51 8.49 0.01

AAI + MTMean 14 -751.25 6 1514.97 153.94 0.00

Age + RAIBC5 + SLR10 + Precip 15 -677.73 8 1372.26 11.24 0.00

AAI + Precip 16 -751.68 6 1515.83 154.80 0.00

Full model Age + Height + RAIBC5 + SLR10 + StandBA + MTMean 17 -671.72 10 1364.69 3.66 0.08

AAI + Height + StandBA + MTMean 18 -739.41 8 1495.63 134.60 0.00

Age + Height + RAIBC5 + SLR10 + StandBA + Precip 19 -671.56 10 1364.37 3.34 0.10

AAI + Height + StandBA + Precip 20 -738.81 8 1494.43 133.40 0.00

Note: K is the total number of parameters (including an intercept and random terms), ∆i is the difference in

AICc with the best model, wi is the ratio of the ∆i for a given model to that of the whole set of candidate models.

A description of the abbreviations used is given in Table 1.3.


best-fit model parameters for yellow birch.

Discussion

The results of this study confirm that there is regional variation in the proportion of

discoloured wood in both sugar maple and yellow birch. Our initial hypothesis was generally

supported for both species, since tree age and growth variables explained a large percentage

27

of the regional variation in RHP. Tree age was strongly related to the observed variation from

west to east, as older trees of both species were generally found in the western part of the

study area. The fact that discolouration is an age-dependent process (Figure 1.5) is consistent

with several observations made in other hardwoods (Campbell and Davidson 1941;

Hallaksela and Niemisto 1998; Knoke 2003; Giroud et al. 2008; Drouin et al. 2009; Belleville

et al. 2011). As proposed by Giroud et al. (2008), the effect of tree age on the discoloured

wood proportion may be explained by the accumulation of injuries over time and the normal

decrease in tree vigor. Indeed, branch mortality and the subsequent processes of branch

degradation, self-pruning, and wound healing continue over the lifespan of the tree. This

suggests that red heartwood formation is a traumatic but unavoidable process that occurs in

some hardwood species.

Several studies have identified dead branches as the main entry point for discolouration in

many hardwood species (Hallaksela and Niemisto 1998; Wernsdorfer et al. 2005; Belleville

et al. 2011). One possible explanation is that dead branch stubs create pathways for oxygen

and microorganisms (Shigo 1967; Sorz and Hietz 2008). Therefore, for a given tree age,

heartwood size could vary as a function of either the cumulative area of dead branches or of

the elapsed time until a wound heals. Our results for both species showed that larger red

hearts were associated with larger ring area at the limit of the discoloured wood column

(RAIBC5). This variable could be considered as an indicator of the number and (or) size of

the external defects, mainly in the form of knots, linked to the discolouration column. Indeed,

large ring widths at the edge of the discoloured wood boundary are indicative of previous

vigorous growth, which in turn is driven by a large live crown containing large and (or)

numerous branches. Based on the findings from tree dissection studies (Wernsdorfer et al.

2005; Giroud et al. 2008; Belleville et al. 2011), we can assume that most of the live branches

present when those rings were formed had died by the time our sampling was conducted.

Therefore, an early period of vigorous radial growth, followed by a period of crown

recession, would likely lead to a high proportion of red heartwood for a given tree age.

This would also explain the negative effect of the trend in ring area increments at each side

of the red heartwood boundary (SLR10) observed in the final model for sugar maple. In this

case, a negative slope of the local regression might be indicative of a slower healing process

28

(i.e., a longer time before branch occlusion), leading to an increase in RHP. As demonstrated

by Hein (2008) in European beech, a long period between branch death and occlusion can

lead to a greater proportion of heartwood. Moreover, the extent of the red heartwood column

was shown to be closely related to wound size (Shigo 1966; Hein 2008), so that the effect of

smaller branches might be limited (Eisner et al. 2002). The effect of SLR10 was not included

in the final model for yellow birch, even though this variable was associated with some of

the best candidate models.

Unlike in sugar maple, tree height had a significant positive effect on RHP in yellow birch.

This may reflect the fact that taller stems tend to have more branch scars. This result contrasts

with the findings of Giroud et al. (2008) and Wernsdorfer et al. (2006), but these studies

described the indirect effect of tree height through stem taper, rather than of tree height as a

stand-alone variable. In addition, they were both conducted on a restricted number of sites.

The significance of tree height in our study might also reflect the influence of other factors

such as soil properties or site fertility on the development of red heartwood, which we did

not directly consider.

At the stand scale, it is well-established that branch mortality is driven mainly by stand

density (Makinen 2002; Shmulsky and Jones 2011). Because high levels of tree-to-tree

competition normally lead to faster crown recession, it was expected that increased stand

basal area would stimulate red heartwood formation. However we found no evidence linking

stand basal area with RHP. This could reflect a trade-off between branch mortality and branch

size (i.e., the higher branch mortality in dense stands may be compensated for by their smaller

size).

The observed regional variation in RHP is in contrast with the study of Yanai et al. (2009)

who found no significant differences among six northern US states in sugar maple RHP. This

might be explained by the larger variations in growth conditions in the current study or to the

fact that variation in RHP is larger at the northern edge of the species distribution range. In

our study, the lowest daily minimum temperature over the year (LTMin) had a significant

effect on RHP in sugar maple, over and above the additive effects of age and radial growth.

LTMin was more closely associated with longitude than latitude, probably because of the

29

lower sampled range in the latter. Pither (2003) tested a climate-based hypothesis related to

the distribution range of a large sample of North American species and showed that low

extremes in temperatures (the lowest value of average daily minima during January) had a

significant limiting effect. Sakai and Weiser (1973) and Burke et al. (1976) obtained similar

results for other species. It is likely that sugar maple is more sensitive to extreme cold

temperatures than yellow birch because it has a more southerly distribution range (Godman

et al. 1990).

Low temperatures have often been associated with an irregular traumatic heartwood

formation called “blackheart” or “frost heart” (Burke et al. 1976; Taylor et al. 2002). Frost

heart formation episodes were reported in F. sylvatica, Acer species, and Fraxinus excelsior

L. after the severe European winters of 1928–1929 and 1941–1942, when temperatures fell

below –30 °C (Liese (1930) in Hillis (1987)). In sugar maple, the freezing resistance of the

xylem is –40 °C (Sakai and Weiser 1973). As some of the sites we sampled fall below this

threshold, it is possible that the negative relationship described between RHP and LTMin

could be related to increased proportions of frost cracks at these temperature extremes.

However, an attempt was made to limit the impact of these potentially confounding

observations by sampling only vigorous trees with no sign of such injuries.

Although further work is required to reach more conclusive explanations for regional

variations in RHP, the findings of this study provide useful insights for managers of northern

sugar maple and yellow birch hardwood forests. The best models for each species both had

a high predictive potential. There was limited improvement in the proportion of the variance

explained for each model between the fixed and random effects (i.e., from 60% to 67% in

sugar maple and from 60% to 65% in yellow birch). This suggests that there is limited

residual variance associated with random region and site effects. According to our results,

regional variation in discoloured wood proportion is most closely related to tree age and

growth-related variables. The strong positive effect of tree age on RHP implies that shorter

rotation lengths would be beneficial for appearance products. However, this may come at the

expense of the production of a defect-free bole (Shmulsky and Jones 2011). Our results

suggest that a promising strategy for forest managers would be to promote the regeneration

of dense cohorts in gaps created by partial cutting. A period without intervention would

30

promote crown recession and limit branch diameter. Once the desired length of clear bole is

reached, the best stems could be selected as crop trees and released (Perkey and Wilkins

1993). This would speed up the occlusion of dead branch stubs while maintaining the vigor

of live branches. In some situations, manual pruning could be considered at a young age, but

further studies are needed to assess the long-term effect and cost efficiency of this treatment

on heartwood proportion. In other instances, predictive models could be used to guide site

selection for timber or veneer production. Attention should be focused on extreme climatic

conditions and species range limits (Pither 2003; Burton et al. 2008). The unexplained

variation in the best model for each species might be associated with other factors, such as

soil type and genotypic variability, which should be considered in further studies. In addition,

supplementary investigations about healing rate and time to wound closure in hardwoods that

form traumatic heartwood are necessary for better understanding of the occurrence and

magnitude of red heartwood.

Conclusion

In this study, we tested the hypothesis that regional variations in RHP within stems of sugar

maple and yellow birch can be mainly explained by differences in tree age and growth-related

variables. A strong positive relationship between tree age and the proportion of discoloured

wood was observed for both species. A 5 year average of ring area increment at the limit of

the discoloured wood column was positively related to the RHP. Conversely, the growth

trend, expressed as the slope of the regression of ring area increments over a 5 year period

on either side of the discoloured wood boundary, was negatively related to the RHP in sugar

maple. These factors were interpreted as being related to the size and time before occlusion

of dead branches, which are known to act as initiation points for discolouration. In the case

of sugar maple, the lowest daily minimum temperature over the year also had a strong

negative effect on the RHP, over and above that of tree age and growth-related variables.

Overall, the fixed effects of the developed models were able to explain around 60% of the

variance in RHP for both species, and could be used to predict its regional variation and

inform forest management and wood procurement decisions. Further work should aim to

investigate the effects of soil characteristics and genetic factors on the proportion of red

heartwood within tree stems.

31

Acknowledgments

The authors are grateful to the Fonds de recherche du Québec – Nature et technologies

(FRQNT) for the financial support for this project. We wish to express our thanks to staff

from the Ministère des Ressources naturelles du Québec, Station touristique de Duchesnay

(SÉPAQ), and Groupement forestier de Portneuf for their help with locating sampling sites.

We are also grateful to Marine Bouvier, Stéphanie Cloutier, Simon Delisle-Boulianne,

Amélie Denoncourt, Élisabeth Dubé, Louis-Vincent Gagné, Louis Gauthier, Julia Maman,

and Marie-Pier Arsenault for their assistance in collecting the samples. Thanks are extended

to Rémi St-Amant and Jacques Régnière (Canadian Forest Service) for BioSIM

parameterization advice, to Marc J. Mazerolle for statistical advice, to David Auty for

grammatical revision of the manuscript, and to the anonymous reviewers for their valuable

comments on an earlier version of the manuscript. Special thanks to S.Y. Zhang who had the

original idea for this project.

33

Chapitre 2

Integrating standing value estimations into tree marking

guidelines to meet wood supply objectives2

2 Version intégrale d’un article publié / Intergral version of a published paper:

Havreljuk, F., Achim, A., Auty, D., Bédard, S. and Pothier, D. 2014. Integrating standing value estimations

into tree marking guidelines to meet wood supply objectives. Can. J. For. Res. 44(7), 750–759. doi:

10.1139/cjfr-2013-0407

34

Abstract

The identification of low-vigor trees with potential for sawlog production is a key objective

of tree marking guidelines used for partial cuts in northern hardwoods. The aim of this study

was to measure the impact of various vigor-related defects on the monetary value of

hardwoods. To achieve this, we sampled 64 sugar maple (Acer saccharum Marshall) and 32

yellow birch (Betula alleghaniensis Britton) trees from two locations in southern Quebec,

Canada. We identified over 420 defects, which were grouped into 8 categories. The trees

were then harvested and processed into lumber, and the value per unit volume of each stem

was calculated from the value of the product assortment (lumber, chips, and sawdust). We

found that visible evidence of fungal infections (sporocarps and (or) stroma) and cracks had

the largest negative influence on value in both species. A model that included these defects

was almost as good at predicting value as one that included a specifically designed quality

classification. A more accurate assessment of value could be achieved using wood decay

assessment tools and by considering site-specific variables. Results from this study showed

that visual identification of fungal infections and cracks could be used to enhance tree

marking guidelines for hardwoods. This would meet both the silvicultural objective of

selection cuts, by removing low-vigor trees, and the wood supply objective, by improving

stem quality assessment prior to harvest.

Key words: hardwoods, tree marking, defects, tree vigor, tree quality.

35

Résumé

L’identification des arbres moribonds qui ont un potentiel pour la production de billes de

sciage est un objectif clé des directives de martelage lors de coupes partielles dans les

peuplements de feuillus nordiques. Cette étude avait pour but de mesurer l’impact de divers

défauts reliés à la vigueur sur la valeur monétaire des feuillus. À cette fin, nous avons

échantillonné 64 érables à sucre (Acer saccharum Marshall) et 32 bouleaux jaunes (Betula

alleghaniensis Britton) à deux endroits dans le sud du Québec, au Canada. Nous avons

identifié plus de 420 défauts que nous avons regroupés en huit catégories. Ces arbres ont

ensuite été récoltés et transformés en sciage et la valeur par unité de volume de chaque tige

a été calculée à partir de la valeur de l’assortiment de produits (sciages, copeaux et sciures).

Nous avons constaté que les signes visibles d’infection fongique (sporocarpes ou stroma) et

les fentes avaient la plus grande influence négative sur la valeur des deux espèces. Un modèle

qui incluait ces défauts était presque aussi bon pour prédire la valeur qu’un modèle basé sur

un système de classes de qualité spécifiquement conçu. On pouvait obtenir une évaluation

plus précise de la qualité en utilisant des outils pour évaluer la carie du bois ou en tenant

compte de variables propres à la station. Les résultats de cette étude montrent que

l’identification visuelle des infections fongiques et des fentes peut être utilisée pour améliorer

les directives de martelage chez les feuillus. Cela devrait satisfaire tant les objectifs sylvicoles

des coupes de jardinage, en éliminant les arbres moribonds, que les objectifs

d’approvisionnement en bois, en améliorant l’évaluation de la qualité des tiges avant la

récolte.

Mots-clés : feuillus, martelage, défauts, vigueur des arbres, qualité des arbres.

36

Introduction

The natural disturbance regime of North American temperate deciduous forests is

characterized by mortality of individual trees, or group of trees, with very rare occurrence of

severe, large-scale disturbances (Seymour et al. 2002). To maintain the resulting uneven-

aged structure of the stands, selection cutting is often the preferred silvicultural system. Such

an approach aims to maintain or improve forest productivity by removing low-vigor trees,

which are expected to die before the next harvesting cycle (Arbogast 1957; Majcen et al.

1990; Nyland 1998). This strategy replaces several decades of management that focused on

selecting the best quality stems, which resulted in depleted stands (Nyland 1992). Under such

a system, the search for short-term profit is likely to conflict with the goals of sustainable

forest management.

The long-term success of selection cutting depends on making the appropriate choice of

stems that should be harvested. Under most tree marking systems, tree assessment is based

on the identification of defects that may affect both vigor and stem quality (Leak et al. 1987;

Majcen et al. 1990; OMNR 2004; Meadows and Skojac 2008; SWDNR 2013). In this study,

vigor is defined as the risk of tree mortality occurring before the next scheduled harvesting

cycle, and quality is defined with reference to the log and stem attributes that determine actual

and potential monetary value. When the criteria for assessing tree vigor are unclear or

misinterpreted, the objective of stand quality improvement associated with selection cutting

may be compromised (Bédard and Brassard 2002; Meadows and Skojac 2008).

Tree vigor assessment is a complex task that requires detailed knowledge of defects that

affect tree survival (OMNR 2004). Although tree vigor and quality are related concepts,

Fortin et al. (2009) showed that tree vigor, as assessed by the system of Boulet (2007), was

a poor predictor of log quality. This suggests that defects influencing the likelihood of tree

survival may not have an equivalent effect on stem value. To achieve the full production

potential of hardwood forests, an effective tree marking system should consider both

silvicultural and wood supply objectives for sustainable management. Thus, the harvesting

of non-vigorous trees that contain high quality sawlogs should be prioritized (Pothier et al.

2013).

37

One approach towards improving stem quality estimation when applying a tree vigor

evaluation system consists of using an additional standing tree classification (Hanks 1976;

Monger 1991). Like tree vigor assessment systems, existing hardwood stem quality

classification systems visually assess the presence, size, and distribution of defects along the

stem but focusing mainly on those that might affect conversion into wood products. In

addition to stem diameter, the quality class of each individual tree therefore depends mainly

on clear bole length (i.e., free of defects), curvatures, and cull deductions. Such rules are

consistent with log grade assessments (Rast et al. 1973; Petro and Calvert 1976), a fact that

was confirmed empirically by Fortin et al. (2009b). However, there is still little information

available about the specific factors that influence the product basket composition and

monetary value of hardwoods.

Drouin et al. (2010) showed that wood quality and value of white birch (Betula papyrifera

Marsh.) decreased when internal characteristics of the stem, such as discoloration and decay,

were considered. Detection and characterization of internal defects can therefore be improved

when non-destructive tools are used in combination with external visual inspection (Wang

and Allison 2008). Some tools, such as wood-drilling instruments (Costello and Quarles

1999; Barrette et al. 2013), are designed to detect the changes in wood density (i.e., internal

decay). Others use acoustic velocity (i.e., stress-wave propagation) to estimate wood

physico-mechanical properties (Wang et al. 2007; Paradis et al. 2013). However, data

acquisition using such tools may be expensive (Greifenhagen and Marilyn 2005; Leong et al.

2012), and their utilisation in classification systems for hardwoods remains to be evaluated.

For integration in tree marking, estimations of vigor and quality must be rigorous, yet

practicably applicable. For example, the integration of Monger’s (1991) tree grade

classification with Boulet’s (2007) vigor assessment would result in a 16-class system (4 × 4

matrix of vigor and quality grades), which would be difficult to apply operationally. The

objective of this study was thus to facilitate the integration of standing tree value estimations

into tree marking guidelines by identifying the main vigor-related variables affecting the

value of the product assortment from hardwood stems. This was applied to sugar maple (Acer

saccharum Marshall) and yellow birch (Betula alleghaniensis Britton) trees, which are the

dominant species in the hardwood forests of southern Québec, Canada.

38

Material and methods

Study sites

The study was conducted in two northern hardwoods forests located on public land in

southern Québec, Canada (Figure 2.1). The first site was located in Duchesnay (DU) near

Québec City, within the balsam fir (Abies balsamea [L.] Mill.) – yellow birch bioclimatic

domain. The second was located near Mont-Laurier (ML), in the Laurentian region, within

the sugar maple – yellow birch bioclimatic domain (Robitaille and Saucier 1998). According

to the climate estimations generated by BioSIM for the 1981–2010 period (Régnière et al.

2012), the mean and minimum annual temperatures are 3.2 °C and −36.5°C and 2.3 °C and

−41.2 °C, for the DU and ML sites, respectively.

Figure 2.1 Location of the study areas.

At DU, total annual precipitation is 1368 mm with about 33% falling as snow, while at ML

it is 1013 mm with 34% as snow. Both stands had an approximate area of 50 ha. The total

basal area (BA) of merchantable standing trees was 23.5 m² ha −1 for the DU site and 25.0

m² ha−1 for the ML site. At the DU site, sugar maple and yellow birch represented 38% and

39

36% of total BA, respectively, while the remaining BA was composed of American beech

(Fagus grandifolia Ehrh.). The ML site was dominated by sugar maple (87% of total BA),

followed by yellow birch (6%), American beech (5%), and red maple (Acer rubrum L.) (2%).

Soils at both sites were moderately to well-drained glacial tills.

Sampling

A sample of 64 sugar maple and 32 yellow birch trees, equally distributed between the two

sites, was selected. To ensure that a wide range of tree vigor and quality combinations were

included, the sampling was stratified according to a matrix of vigor and quality classes

provided by Boulet (2007) and Monger (1991), respectively.

The tree vigor classification system aims to identify stems with the highest risk of dying

before the next cutting cycle. Harvest priority is based on the signs and symptoms of more

than 250 observable defects, which are grouped into eight categories: (1) fungal infection

(through observations of visible sporocarps and stroma on the stem), (2) cambial necrosis,

(3) bole deformations and injuries, (4) butt and root defects, (5) stem and bark cracks, (6)

woodworms and sap wells, (7) crown defects, and (8) branching and pruning defects.

Depending on the presence and severity of the observed defects, trees are ranked into four

harvest priorities: (1) moribund trees (M), (2) surviving trees (S), (3) growing trees to be

conserved (C), and (4) reserve stock trees (R). Moribund trees are expected to die before the

next cutting cycle, while S trees are defective and declining but are assumed to survive until

the next harvest. Class C corresponds to growing trees with minor defects, while reserve trees

are those without defects and with the highest survival probability that should be retained in

the stand.

Standing tree quality assessment was achieved using Monger’s (1991) classification. Four

grades (A, B, C, and D) are used to describe stem potential for producing sawlogs, where the

highest tree quality corresponds to grade A and the lowest to grade D (i.e., trees with no

sawlog potential). This classification is based mainly on stem size and on the observable

defects on the lower 5 m of the tree. Trees must meet minimum diameter at breast height

(DBH, 1.3 m above ground level) thresholds for each grade (A > 39 cm, B > 32 cm, C and

D > 23 cm). The quality assessment consists of first identifying the “best” 3.7 m stem section

40

within the lower 5 m of the stem (i.e., the section with the fewest defects). Then, the chosen

section is separated into four equal faces circumferentially, to estimate the clear wood yield

of each face. For each tree, a quality grade is assigned according to the clear wood length of

the third-best face and the estimated percentage reduction in volume due to observable cull,

rot, cracks, and sweep measured in the whole stem section. These rules were derived from

the tree grading system developed by Hanks (1976).

Tree selection

To complete the sampling matrix, trees were initially classified according to the two systems

described above. The assessments were limited to stems with a DBH greater than 23 cm,

which is considered the lower merchantable limit. To incorporate stem size heterogeneity,

trees were randomly selected from two DBH categories for each quality–vigor combination

(i.e., under and over 46, 40, and 34 cm for grades A, B, and C-D, respectively). At each site,

one yellow birch and two sugar maple sample trees were randomly selected from each

quality-vigor combination, giving a total of 32 and 64 sample trees, respectively, for each

species across the two sites. At the DU site, some specific combinations were not found inside

the study area. In these cases, two missing grade A trees for each of the M and S vigor classes

were replaced by two B quality trees. To ensure the accuracy of sample tree assessment, all

vigor and quality evaluations were verified and approved by certified and trained tree

markers. A summary of the mean sample tree characteristics is presented in Table 2.1.

Sample tree measurements

On each sample tree, the position and size of all observable defects were determined using a

measuring tape on the lower 5 m section of the stem and a hypsometer (Vertex IV Ultrasonic

Hypsometer, Haglöf AB, Sweden) in the upper part. Total tree height (m), the height of the

first living branch (m), and the height of the main fork or primary branches (m) were also

measured using a hypsometer.

41

Table 2.1 Mean sample tree characteristics for the sugar maple and yellow birch data.

No. of stems Mean DBH ±SD [range]

(cm)

Mean height ±SD

(m)

Mean gross merchantable

volume ±SD (dm³)

Sugar maple 64 38.4 ±7.6 [23.5-58.5] 21.7 ±2.5 1093 ±530

M 16 38.7 ±6.9 [29.3-50.1] 21.4 ±2.2 1073 ±433

S 16 38.6 ±9.1 [23.5-58.5] 21.7 ±2.6 1136 ±692

C 16 38.3 ±7.2 [26.2-55.0] 21.8 ±2.6 1098 ±533

R 16 37.8 ±7.7 [24.6-50.5] 21.9 ±2.7 1063 ±477

Yellow birch 32 39.1 ±7.9 [26.6-62.4] 22.4 ±2.2 1216 ±572

M 8 43.4 ±10.4 [29.9-62.4] 21.5 ±1.8 1471 ±823

S 8 39.3 ±6.5 [28.6-48.3] 23.7 ±1.7 1272 ±439

C 8 35.8 ±7.3 [26.6-46.9] 21.0 ±2.5 958 ±480

R 8 38.0 ±6.0 [31.0-47.5] 23.3 ±2.0 1163 ±434

Total 96 38.6 ±7.7 [23.5-62.4] 21.9 ±2.4 1134 ±544

Note: Trees are classified by harvest priorities: moribund trees (M), surviving trees (S), growing trees to be

conserved (C), and reserve stock trees (R) (Boulet 2007).

An evaluation of the internal decay of all study trees was performed using the wood-drilling

instrument Resistograph F-500SE (Instrumenta Mechanik Labor System GmbH (IML),

Germany). This device measures the resistance to drilling in a tree. In the presence of decay

and cavities into the stem, drilling resistance decreases (Mattheck et al. 1997), and the extent

of these defects can be estimated. In this study, two perpendicular drillings were made at 1.3

m above ground level (Greifenhagen and Marilyn 2005). Resistograph readings were

analyzed following the general principles described by Mattheck et al. (1997). To simplify

their visual interpretation, readings were categorized into two classes: sound wood and

decayed wood, the latter characterized by a decrease in wood density caused by cavities and

cracks. Two perpendicular drilling profiles were fully obtained on each tree. The depth of

sound wood (Costello and Quarles 1999) was used as a potential predictor variable in the

subsequent analyses, as it is closely related to the number and size of high grade lumber

pieces. This was calculated as the amount of non-decayed wood from the inner bark to the

first decayed point, averaged from four radii around the tree (i.e., two complete linear

drillings). Fourteen trees (13 sugar maple and one yellow birch) had incomplete radial profile

readings due to either large stem size or the presence of major defects. For those trees, the

analyses were made using only the complete radii (i.e., cambium to pith).

42

Standing tree quality of sugar maple and yellow birch was also evaluated using acoustic

measurements. Acoustic velocity in wood can be used to estimate its physico-mechanical

properties (Kumar 2004; Wang and Allison 2008). Generally, sound waves propagate more

slowly in a damaged or decayed wood than in sound wood (Leong et al. 2012). We used the

IML Hammer (Instrumenta Mechanik Labor (IML) GmbH, Germany) to obtain radial

velocity measurements. The general approach consisted of generating a sound wave by

hammer impact on a probe located on one side of the stem at 1.3 m above ground level. The

resulting dilatational wave traveled to the other side of the stem where a receiving probe was

located. Acoustic velocity was taken as the ratio of the distance between the probes to the

transit time. The average of two perpendicular measurements was used for each tree. In both

sites, trees were measured by the same person to minimize any potential operator bias.

Tree felling and bucking

Felling operations were conducted in the fall of 2009 and 2010 at the DU and ML sites,

respectively. Full-length trees were harvested and efforts were made to minimize logging

damage on the sample trees and to preserve any large branches with sawing potential. Felled

trees from each site were transported to the Duchesnay sawmill (Sainte-Catherine-de-la-

Jacques-Cartier, Québec), where they were inspected and cut into logs according to the Petro

and Calvert (1976) grading rules. At this stage, we included an additional sawlog category to

accommodate small diameter logs (bolts) (Bumgardner et al. 2001), thus ensuring the full

potential value of each tree was obtained (Giguère 1998). Efforts were made to identify

veneer logs, but these were combined with sawlogs in subsequent analyses because they

occurred very rarely.

Sawmill conversion

A total of 128 logs were sawn at the Duchesnay sawmill, while 61 bolts were transformed

using a portable sawmill (Wood-Mizer Products Inc., USA) at Laval University’s wood

research center. All logs were sawn using a grade sawing approach (Richards et al. 1979),

which consists of maximizing the production of high-grade lumber (i.e., knot-free sapwood)

by rotating the log around its pith (Steele 1984). For consistency, logs from both study sites

were sawn by the same qualified workers. A total of 2236 boards were produced, and their

43

provenance (i.e., tree and log) was traced during the conversion process. Lumber pieces were

all standard 2.54 cm thick (1 inch), with variable widths and lengths ranging from 7.6 to 35.6

cm (3 to 14 inches) and 1.22 to 3.62 m (4 to 12 feet), respectively. Each board was graded

according to the standards of the National Hardwood Lumber Association (2011). The initial

step in the lumber grading was to determine the number of board feet in each piece. One

board foot (2360 cm³ ) is the equivalent of a board measuring 30.48 cm long × 30.48 cm wide

× 2.54 cm thick (1 foot × 1 foot × 1 inch). For both species, standard grades were divided

into several quality classes (FAS, F1F, Selects, No. 1 Common, No. 2A Common, No. 3A

Common, and No. 3B Common), which are mainly related to the size of clear face cuttings.

The best grade is FAS, and the lowest is No. 3B Common, which in practice corresponds to

pallet lumber. Boards of a lower quality than this grade were rejected. In addition, board

color was also assessed, as it can affect lumber prices. According to NHLA (2011) rules,

sugar maple boards were classified as No. 1 White Maple, No. 2 White Maple, or Sap,

depending on the extent of sapwood on the faces and edges of cuttings. For yellow birch, the

color premium could either be associated with non-colored wood (Sap birch) or discolored

heartwood (Red birch), as long as it was uniform. For consistency, boards from both sites

were graded by the same qualified inspector. The distribution of boards is presented in Table

2.2 and 2.3.

44

Table 2.2 Lumber grade distribution among the sawn boards.

NHLA grades Total no. of

boards

No. of board

feet % of board feet

Sugar maple 1460 5469 100.0

FAS 61 372 6.8

F1F 61 370 6.8

SEL 98 348 6.4

1C 265 1029 18.8

2A 324 1160 21.2

3A 252 850 15.5

3B 399 1340 24.5

Yellow birch 776 2989 100.0

FAS 51 307 10.3

F1F 39 214 7.2

SEL 61 239 8.0

1C 182 731 24.5

2A 178 609 20.4

3A 120 397 13.3

3B 145 492 16.5

Note: FAS represents the best grade and No. 3B Common (i.e., pallet lumber) the lowest.

Table 2.3 Distribution of NHLA grades among tree quality classes.

Tree quality

classes

NHLA Grades (% of board feet)

FAS F1F SEL 1C 2A 3A 3B

Sugar maple

A 7.5 8.5 6.8 23.1 20.8 13.5 19.7

B 9.1 7.9 6.3 17.9 21.7 14.7 22.3

C 1.8 3.8 7.4 14.5 19.6 19.5 33.3

D 2.0 0.0 2.9 16.6 23.3 19.5 35.8

Yellow birch

A 16.3 11.3 4.4 28.3 17.8 10.0 11.9

B 7.2 3.8 11.5 24.5 21.7 12.4 18.9

C 2.3 1.4 11.8 14.2 23.3 22.1 24.9

D 0.0 8.5 7.8 27.0 26.2 16.3 14.2

Note: Grade A is the highest quality, while grade D is the lowest (i.e., trees with no sawlog potential).

Value estimations

Lumber value estimations

Lumber value for sugar maple and yellow birch was determined using Hardwood Market

Report (2008–2012) price lists for northern hardwoods. A five-year average price (2008 to

2012) was used for rough green wood (US dollars per 1000 board feet – MBF) (Table 2.4).

45

The value of each piece was obtained according to its NHLA grade and board size. For

consistency with the lumber value of each grade, the F1F and FAS grades were combined,

as were the No. 1 and No. 2 White Maple color grades.

Table 2.4 Mean sugar maple and yellow birch lumber values from 2008 to 2012.

NHLA grades Hard maple Birch

Unselected Sap No. 1&2 White Unselected Red Sap

FAS 1195 1295 1420 1105 1410 1380

Selects 1175 1275 1400 1085 1390 1360

No. 1C 760 860 860 685 990 960

No. 2A 540 640 640 430 735 705

No. 3A 355 455 – 280 585 555

No. 3B (pallet) 225 – – 225 – –

Note: Values are presented in US$ per MBF for 2.54 cm thick (1 inch) boards of random widths and lengths

that are rough and green. 1000 board feet (MBF) = 2.36 m³.

Pulpwood value estimations

Pulpwood prices vary widely among buyers, tree species, and location. For simplicity, it was

estimated at 40 USD per cubic meter (solid), delivered to the mill (i.e., includes harvesting

and hauling). This represents the estimated 2008 to 2012 average pulpwood value reported

for the northeastern area of North America (NC State University 2013; SPFRQ 2013). The

same value was used for both sugar maple and yellow birch trees, as they are generally sold

together as mixed hardwoods. The estimated pulpwood value was applied to all the pulpwood

logs (i.e., pieces over 2.5 m long and with a top diameter over 9 cm inside bark) in our study

that did not produce any sawn lumber, while waste logs were omitted from the study since

they constituted a negligible proportion of the total. To incorporate wood chips and sawdust

into estimations of tree value per cubic meter, a pulpwood price of 40 USD per cubic meter

was assigned to the difference between the net round wood volume of each log and the total

volume of lumber produced.

Tree value estimations

The estimated value per unit volume (VAL) of each stem, which was the dependent variable

in this study, was expressed in US dollars per cubic meter of round wood ($·m−3 ). This was

estimated by dividing the sum of values from each product by the gross volume of the stem.

46

The latter was derived from a volume equation using DBH and total tree height as predictors

(Perron 2003). VAL was preferred to total stem value, because it is less influenced by tree

size and it corresponds to the units used in the industry for log procurement.

Statistical analysis

In line with the general objective, statistical analysis was used to study the links between the

visual criteria included in the classifications of standing trees and their value per cubic meter

(VAL). The influence of tree classification systems on value was tested using analysis of

variance (ANOVA). When significant differences were found, multiple comparisons were

carried out to discriminate between each class. This was performed using Tukey’s honestly

significant difference test (Tukey HSD), with a robust procedure used to account for the

unbalanced nature of the data (Herberich et al. 2010). Differences between means were

considered significant at a value of p < 0.05. To build a predictive model of VAL, the first

step consisted of determining the relationship between VAL and DBH. Several equations

were tested and an exponential function (eq. (1)), previously described by Barrette et al.

(2012), was chosen as it provided the best fit to the data:

(1) 𝑉𝐴𝐿 = 𝑏1𝐷𝐵𝐻𝑏2𝑏3𝐷𝐵𝐻

where b1, b2 and b3 are the model parameters to be estimated. To facilitate the integration of

other covariates in this model, this equation was linearized through a log-transformation

process, as follows:

(2) ln(𝑉𝐴𝐿) = 𝑏1′ + 𝑏2 ln(𝐷𝐵𝐻) + 𝑏3

′ 𝐷𝐵𝐻

where ln(VAL) represents the natural logarithm of VAL and the indices refer to linearized

parameters from Eq. (1).

Analyses of variance were conducted to assess the effect of each of the eight defect categories

described by the vigor classification on VAL. Both discrete (presence/absence) and

continuous (number and size) variables for each defect category were tested. In this step, we

also considered potential interactions between defect classes and DBH. Defects that had a

significant effect on VAL were included in the model as follows:

47

(3) ln(𝑉𝐴𝐿) = 𝑏1′ + 𝑏2 ln(𝐷𝐵𝐻) + 𝑏3

′ 𝐷𝐵𝐻 + 𝑏′𝑍

where Z represents the additional covariates in the final model described below.

Model selection

To test the effectiveness of stand- and tree-level characteristics for predicting tree value per

unit volume, models of increasing complexity were successively fitted to the data. Several

combinations of variables were screened in the models. First, a null (i.e., intercept-only)

model was fitted, to be used as the reference level for the more complex models. Next,

variables relating to site- and tree-level characteristics were added, followed by variables

relating to tree classification (i.e., vigor, quality, and defects). Finally, variables obtained

using non-destructive methods (i.e., Resistograph and IML-Hammer) were assessed. In total,

fifteen candidate models were developed and compared using the corrected Akaike

information criterion (AICc) and Akaike weights (wi), which assess the relative likelihood of

each model across all candidate models (Burnham and Anderson 2002; Anderson 2008). In

addition, fit indices (R²) were calculated for each model, but these were not used as a criterion

for model selection.

All candidate models were checked for error normality, homogeneity of variance, and the

influence of extreme values. Multicollinearity between variables was checked using the

variance inflation factor (VIF) (Zuur et al. 2010), with an upper limit value of 5 if variables

were to be included in candidate models. Predicted values of log-transformed equations were

corrected for bias using Sprugel’s (1983) correction factor. All statistical analyses were

performed using libraries contained in the R statistical programing environment (R Core

Team 2013).

Results

Effect of vigor and quality on tree value

Multiple comparisons between vigor classes (M-S-C-R) revealed that differences in VAL

were only significant between the M and R vigor classes (p = 0.004). A similar result was

48

observed when the model (eq. (3)) included both the vigor classification and DBH. M trees

were less valuable than R trees (Figure 2.2). In contrast, significant differences in VAL were

observed among all quality classes (A–C: p = 0.012; A–D: p = <0.001; B–C: p = 0.018; B–

D: p = < 0.001; C–D: p = 0.012), except between the A and B classes (p = 0.977). When the

quality classes were combined with DBH in eq. (3), predicted tree value per unit volume

decreased, as expected, from class A to class D (Figure 2.3). Overall, VAL was inversely

correlated with tree diameter in the A and B quality classes. There was a lower correlation

between VAL and DBH for the C and D classes, although the highest values tended to occur

at a DBH of about 35 cm. Since no significant differences were found between the study

species, data for sugar maple and yellow birch were combined in the remaining analyses.

Figure 2.2 Predicted VAL (US$·m−3) in relation to vigor classification. Trees are classified

by harvest priorities: moribund trees (M), surviving trees (S), growing trees to be conserved

(C), and reserve stock trees (R) (Boulet 2007).

49

Figure 2.3 Predicted VAL (US$·m−3) in relation to quality classification. The four grades (A,

B, C, and D) are used to describe the potential for sawlog production, with grade A being the

highest and grade D the lowest (i.e., trees with no sawlog potential) (Monger 1991).

Influence of defects on tree value

More than 400 defects associated with the tree vigor classification were identified on our

sample trees (Table 2.5). Of the eight categories of defects that are considered in tree marking

classification systems, two had a significant effect on VAL. The presence of both fungal

infection and cracks were negatively correlated with tree value per unit volume. In the former

case, the effect was greater in larger stems (result not shown). When both these variables

were included in the same model along with DBH, and with the inclusion of an interaction

term between DBH and fungal infection, the AICc was reduced compared to models that did

not include defects (Table 2.6 and Figure 2.4). In addition to the presence or absence of

defects, the inclusion of the raw counts of defects in the models was also tested, but in each

case there was no significant reduction in AICc. In addition, cracks longer than 1.5 m that

were associated with decay appeared to have a greater effect on tree value than shorter cracks

with no decay, but differences were not significant (results not shown).

50

Table 2.5 Proportion of study trees (%) affected by a given defect category.

Site Defect group

1 2 3 4 5 6 7 8

Duchesnay 5.2 6.3 10.4 10.4 37.5 6.3 25.0 33.3

Sugar maple 3.1 2.1 8.3 7.3 26.0 5.2 17.7 22.9

Yellow birch 2.1 4.2 2.1 3.1 11.5 1.0 7.3 10.4

Mont-Laurier 10.4 8.3 18.8 7.3 39.6 11.5 12.5 14.6

Sugar maple 7.3 5.2 12.5 3.1 29.2 11.5 10.4 7.3

Yellow birch 3.1 3.1 6.3 4.2 10.4 0.0 2.1 7.3

Note: Proportions do not sum to 100% because individual trees can exhibit several defects simultaneously.

Categories of defects were as follows: fungal infection (1), cambial necrosis (2), bole deformations and injuries

(3), butt and root defects (4), stem and bark cracks (5), woodworms and sap wells (6), crown defects (7), and

branching and pruning defects (8).

Table 2.6 Parameter estimates (± SE) and p-values for the model including DBH, fungal

infections, and cracks given by eq. (3).

Parameter Variable Estimate SE P-value

b’1 Intercept -12.532 5.116 0.016

b2 ln(DBH) 6.237 1.933 0.002

b’3 DBH -0.147 0.050 0.004

b’4 Fungi 1.086 0.806 0.181

b’5 Crack -0.335 0.118 0.006

b’6 DBH*Fungi -0.044 0.020 0.032

Fit statistics

n 96

R² 0.303

RSE 0.483

Bias 1.124

Note: n is the number of observations, R² is the coefficient of determination, RSE is the residual standard error,

and Bias is equal to e(RSE^2/2).

51

Figure 2.4 Predicted VAL (US$·m−3) of sugar maple and yellow birch in relation to the

presence of the main tree defects.

Final model for predicting tree value

The ranking of the fitted models, based on AICc, is presented in Table 2.7. These results

revealed that the utilization of wood decay assessment tools could improve tree value

predictions. The best model for predicting VAL in sugar maple and yellow birch included

the variables site, DBH, quality classification, and Resistograph readings (model 14; AICc =

77.9, wi = 0.97). The next best was model 15 (AICc = 85.0, wi = 0.03), which included site,

DBH, fungal infections, cracks, and Resistograph readings. Under the model ranking criteria

(AICc and Akaike weights), these models were considerably better than all the other

candidate models (Table 2.7). The adjusted R² of model 14 and model 15 were 0.65 and 0.63,

respectively. The effect of sound wood depth, as measured by the Resistograph, on stem

value per unit volume is presented in Figure 2.5 for quality classification and Figure 2.6 for

fungal infections and cracks.

Among the simpler alternatives, the model that included fungal infection, cracks and DBH

(model 7; AICc = 142.0) was also very similar, in terms of AICc, to the model that included

both quality classification models and DBH (model 6; AICc = 141.8). In comparison, the

52

model that included vigor classification and DBH performed poorly (model 5; AICc = 158.0).

The analysis also highlighted important differences in tree value between the two

experimental sites, since its inclusion as a predictor variable improved the fit of all models.

Table 2.7 Comparison of the linearized models for predicting the value per unit volume

(VAL) of each stem.

Model

group ID Explanatory variables Rank K AICc ∆i wi LL Adj. R²

Intercept 1 Null 15 2 170.7 92.8 0.0 -83.3 0

Site 2 Site 12 3 153.7 75.9 0.0 -73.7 0.17

Tree 3 DBH 14 4 166.9 89.0 0.0 -79.2 0.06

4 DBH + Site 11 5 145.6 67.7 0.0 -67.5 0.26

Tree +

Classifications

5 DBH + Vigor 13 7 158.0 80.2 0.0 -71.4 0.18

6 DBH + Quality 9 7 141.8 63.9 0.0 -63.3 0.30

7 DBH * Fungi + Cracks 10 7 142.0 64.1 0.0 -63.3 0.30

8 DBH + Vigor + Site 8 8 131.8 53.9 0.0 -57.1 0.38

9 DBH + Quality + Site 3 8 110.1 32.3 0.0 -46.2 0.51

10 DBH * Fungi + Cracks + Site 6 8 122.2 44.3 0.0 -52.3 0.44

Tree +

Classifications

+ Non-

destructive

evaluation

11 DBH + Quality + Resistograph 5 8 118.8 41.0 0.0 -50.6 0.46

12 DBH + Quality + Acoustic 7 8 126.7 48.8 0.0 -54.5 0.41

13 DBH * Fungi + Cracks + Resistograph 4 8 116.4 38.5 0.0 -49.4 0.47

14 DBH + Quality + Resistograph + Site 1 9 77.9 0.0 0.97 -28.9 0.65

15 DBH * Fungi + Cracks + Resistograph + Site 2 9 85.0 7.1 0.03 -32.4 0.63

Note: Rank is the model ranking according to the AICc, K is the total number of parameters (including the

model intercept), ∆i is the difference between the AICc and that of the best model, wi is the ratio of the ∆i for a

given model to that of the whole set of candidate models, LL is the log-likelihood. NB: DBH corresponds to

DBH + ln(DBH), except for the interaction with fungi, where it represents DBH * Fungi + ln(DBH).

53

Figure 2.5 Predicted VAL (US$·m−3) in relation with sound wood depth for quality

classification (Monger 1991).

Figure 2.6 Predicted VAL (US$·m−3) in relation with sound wood depth for main defects.

54

Discussion

Tree marking systems for hardwoods must meet both silvicultural and wood supply

objectives while remaining practically applicable. This study confirmed that a tree marking

system based on tree vigor, such as the one currently used in Quebec, is poorly related to the

standing value of sugar maple and yellow birch trees. These results are in agreement with

those of Fortin et al. (2009b), who have demonstrated the low ability of the same

classification system to determine log grades in standing trees. We argue that it would be

possible to improve tree selection by discriminating based on vigor-related variables that also

affect tree value. Of these, our results show that visible signs of fungal infections (i.e.,

sporocarps and (or) stroma) and cracks are the main variables that affect the current market

value of sugar maple and yellow birch stems. Therefore, trees that are affected by other types

of defects, such as cambial necrosis, stem deformations, mechanical injuries, woodworms,

sap wells, or crown and branching defects, might have a low probability of survival with no

appreciable loss of value. Therefore, our results suggest that these stems should be prioritized

for harvest to achieve both the main silvicultural objective, by removing low-vigor trees, and

the wood supply objective of selection cuts (Leak et al. 1987; Majcen et al. 1990; Nyland

1998; Pothier et al. 2013). Statistically, the use of Monger’s (1991) quality classification

brought only a marginal improvement over the alternative model that simply considered the

presence of fungal infections and cracks. In forest surveys intended to establish a more

precise estimate of standing tree value, quality classification, with the potential addition of

data from wood decay assessment tools, should be preferred (Model 11, Table 2.7). However,

because of the need for simplicity and cost-effectiveness, we recommend the use of the model

based on the presence of fungal infections and cracks in hardwood tree marking operations

(Model 7, Table 2.7).

In this study, visible evidence of fungal infections was one of the main vigor-related variables

affecting standing tree value per unit volume. These are known to be related to a higher

probability of stem mortality (Guillemette et al. 2008) and are usually associated with

decayed wood (Lavallée and Lortie 1968; Boulet 2007). The effect of fungal infections on

tree value increased with stem diameter, probably because larger trees are generally older

and contain a greater proportion of decayed wood (Basham 1991). The reduction in tree value

55

was greatest when both fungal infections and cracks occurred simultaneously on the tree

(Figure 2.4). While the incidence of frost cracks has been associated with larger trees (Burton

et al. 2008), we did not observe an interaction between cracks and stem diameter in our study.

Cracks are often associated with undesirable internal characteristics, such as discoloration or

decay (Shigo 1966; Lavallée and Lortie 1968), which in turn may decrease the value of raw

material designated for appearance wood products (Wiedenbeck et al. 2004).

In our study, tree value per unit volume decreased with dbh even in stems in the A and B

quality classes, i.e., those that are virtually free of external defects. Since the value was

expressed in terms of volume, this suggests that lumber yield was lower in larger stems. In

southern Appalachian species, Prestemon (1998) also observed that the probability of

obtaining higher quality grades tended to decrease in large trees. In our study, the maximum

value for both species was reached at DBH values between 40 and 45 cm. This is less than

the optimal DBH observed by Pothier et al. (2013), where the joint probability of obtaining

both low-vigor and high quality trees was highest at around 58 and 64 cm for sugar maple

and yellow birch, respectively. However, the latter study grouped all trees of Monger’s

(1991) A, B, and C classes into a “high quality” category (i.e., trees likely to contain at least

one sawlog). In the current study, the decreasing value of large standing trees of the highest

quality grades indicates that a decline in quality can occur even in the absence of external

signs of degradation (Shigo 1984). The larger trees in a stand are likely to have progressively

accumulated internal defects over time, such as heartwood discoloration, resulting in loss of

quality (Erickson et al. 1992; Baral et al. 2013). This suggests that diameter thresholds could

be included in tree marking criteria to avoid financial losses due to this potential degradation.

While the exact diameter limits still need to be determined, results reported by Hansen and

Nyland (1987) indicate that sawn timber volume and value from sugar maple decline in stems

over 50 cm in diameter. For sugar maple, Majcen et al. (1990) suggested a maximum

diameter of between 45 and 60 cm, depending on site quality, while Leak et al. (1987)

suggested a maximum of 40 to 50 cm.

Unsurprisingly, the inclusion of variables associated with the extent of the decay in the stem

tended to improve value estimations in our models. Of these, Resistograph readings gave

more accurate estimations of tree value than acoustic velocity measurements. In fact, the

56

coefficient of determination (R²) increased by around 15% when the depth of sound wood

was included in the models, compared with models with no Resistograph measurements.

Wood-drilling instruments have already proven their utility in tree-risk assessment (Costello

and Quarles 1999; van Wassenaer and Richardson 2009). However, the high cost of data

acquisition for these methods (Greifenhagen and Marilyn 2005; Leong et al. 2012) might

only be justified either in circumstances where knowledge of the internal wood properties of

specific trees is required or where regional-scale assessments of the variation in wood quality

are desirable (Moore et al. 2009; Barrette et al. 2013).

The analysis revealed that there were important differences in standing tree value between

the sites used in this study. The significance of the site effect remained even when it was

combined with the wood decay assessment variables in the models, suggesting that some

among-site variability might be attributable to internal defects that were not characterized by

the tested devices. For instance, the proportion of discolored wood, which is known to vary

with tree growth rate and tree age at the regional scale (Havreljuk et al. 2013), may also vary

significantly between sites. However, more study sites are required to quantify the true site-

to-site variability in standing tree value.

It must be pointed out that our study assessed standing tree value in terms of traditional end-

products. While stems without fungal infection and cracks may represent the best financial

opportunity for lumber industries, those affected by such defects might have some potential

for other end-uses, such as wood extractives (St-Pierre et al. 2013) or bioenergy (Lestander

et al. 2012). In addition, the retention of low-vigor, low quality trees in the residual stand

must be envisaged, as these can bring further benefits to forest ecosystems, including the

provision of habitat for important plant and animal species (Leak et al. 1987; Kenefic and

Nyland 2007). However, clear guidelines would have to be developed to avoid the ever-

present risk of high-grading hardwood stands. Additionally, the contamination risk to the

residual stand from low-vigor trees with various defects is still to be addressed (Horsley et

al. 2002; Fortin et al. 2013).

After a selection cut, the residual stand should be composed of vigorous trees that are likely

to improve in grade over time (Leak et al. 1987; Bastien and Wilhelm 2000). Although the

57

results of our study could be used to integrate stem quality considerations into Boulet’s

(2007) classification, our data does not allow for an evaluation of the vigor classes used in

this system. Yet, Hartmann et al. (2008) have suggested that it could be simplified further.

The authors found that trees from the lowest vigor class (M class) have a greater probability

of dying than high-vigor trees (R class), but there were no differences between the

intermediate vigor classes, suggesting that a vigor assessment needs just two categories (i.e.,

mortality and survival). Thus, a potential simplification of both the quality and vigor

classifications might result in a four-class hybrid system representing vigorous and non-

vigorous trees, with or without sawing potential. Comparable systems containing two to six

classes are already in use in parts of North America (Arbogast 1957; Leak et al. 1987; OMNR

2004; Meadows and Skojac 2008; SWDNR 2013). This approach would be similar to that of

Majcen et al. (1990), which has been previously applied in Quebec, although it would be

based on a more detailed description of stem defects. However, as Boulet’s (2007) tree

classification system was implemented in 2005, further studies will be required to evaluate

its applicability for predicting tree death, or to include potential simplifications to the

assessment of vigor.

Conclusion

The objective of this study was to improve a tree marking system focusing only on tree vigor

by identifying the main variables that affect the monetary value of hardwood stems. The

effectiveness of the current marking system for predicting standing tree value was found to

be low. The identification and quantification of various types of defects on sugar maple and

yellow birch stems showed that fungal infections (i.e., visible sporocarps and (or) stroma)

and cracks are the main factors that affect stem value. Predictive models of standing tree

value using these variables performed almost as well as more complex models that included

a full standing tree quality classification in the predictors. We conclude that enhanced tree

marking guidelines, based on the visual identification of fungal infections and cracks, would

be both practical to apply and less costly than the inclusion of a comprehensive stem quality

assessment. The proposed amendments may help achieve both the silvicultural objective of

selection cuts, by removing low-vigor trees, and the wood supply objective, by improving

quality assessment in standing trees.

58

Acknowledgements

This study was financially supported by the Fonds de recherche du Québec – Nature et

technologies (FRQNT). The authors are grateful to the staff from the Ministère des

Ressources naturelles du Québec, Station touristique de Duchesnay (SÉPAQ) and

Coopérative Forestière des Hautes-Laurentides (CFHL) for providing and harvesting the

sampling sites. We wish to express our thanks to staff from École de foresterie et de

technologie du bois de Duchesnay for their valued collaboration in this project. We also thank

Frauke Lenz, Élisabeth Dubé, and Jean-Philippe Gagnon for their assistance in field work,

Jocelyn Hamel, Étienne Boulay, and Roch Boulerice for tree grading validation and to Julie

Barrette, Emmanuel Duchateau, and Normand Paradis for their help during the sawmill trial.

Thanks are extended to the staff of Centre de recherche sur le bois (CRB – Laval University)

and FPInnovations for their assistance in this project.

59

Chapitre 3

Predicting lumber grade occurrence and volume

recovery in sugar maple and yellow birch logs

60

Abstract

Forest production from the North American temperate deciduous forests is mainly associated

with the processing of hardwoods by the appearance wood products industries. To improve

the supply decisions for these industries, it is important to understand the factors that affect

the manufactured products assortment from trees and logs. The objective of this study was

to investigate and model the relationship between log characteristics and sawn board

attributes in sugar maple (Acer saccharum Marshall) and yellow birch (Betula alleghaniensis

Britton). We harvested 64 sugar maple and 32 yellow birch trees from two locations in

southern Quebec, Canada, which were processed into sawlogs, prior to being converted into

lumber. A total of 2236 boards were assessed for grades and colors specifications according

to the rules of the National Hardwood Lumber Association (NHLA). We developed statistical

models taking into account the high proportion of zeros in the data, for predicting the volume

recovery of the various lumber grades and color specification. In both species, board grades

were strongly related to the log length, the position of the log in the stem, the small-end

diameter of the log and the diameter of decay at the small-end of the log. Lumber color was

related to the net volume of the log and red heartwood volume for sugar maple, and to the

log length, the small-end diameter of the log and the red heartwood diameter at the large-end

of the log for yellow birch. These models outperformed a specifically designed log

classification system in predicting the lumber volume recovery. From a management point

of view, the production of valuable lumber should focus on the butt log and could be achieved

by promoting a fast radial growth of the stem, thereby limiting the development of red

heartwood.

61

Résumé

La production forestière dans les forêts feuillues tempérées d’Amérique du Nord est

principalement associée aux industries des produits d'apparence en bois. Afin d’améliorer les

décisions d'approvisionnement de ces industries, il est important de comprendre les facteurs

qui influencent le panier de produits provenant d'arbres et de billes. L'objectif de cette étude

était de modéliser la relation entre les caractéristiques des billes et les attributs des sciages de

l'érable à sucre (Acer saccharum Marshall) et du bouleau jaune (Betula alleghaniensis

Britton). Nous avons récolté 64 érables à sucre et 32 bouleaux jaunes provenant de deux

endroits au sud du Québec, Canada, qui ont été transformés en billes de sciage, puis en

planches. Au total, 2236 planches ont été classifiées selon les grades et les catégories de

couleurs de la National Hardwood Lumber Association (NHLA). En tenant compte de la

forte proportion de zéros dans les données, nous avons mis au point des modèles statistiques

pour prédire le rendement en volume des différents grades et catégories de couleurs des

sciages. Pour les deux espèces, les grades des planches étaient fortement liés à la longueur

de la bille, à la position de la bille dans l’arbre, au diamètre au fin bout de la bille et au

diamètre de la carie au fin bout de la bille. Pour l'érable à sucre, la couleur des sciages était

liée au volume net de la bille et au volume de la coloration de cœur, tandis que pour le bouleau

jaune, elle était liée à la longueur de la bille, au diamètre au fin bout de la bille et au diamètre

de la coloration de cœur au gros bout de la bille. La capacité prédictive du rendement en

volume de sciage de ces modèles a été supérieure à celle du système de classification des

billes spécifiquement conçu à cette fin. Du point de vue de l’aménagement, la production de

bois de grande valeur devrait se concentrer sur la bille de pied et pourrait être atteinte en

favorisant une croissance radiale rapide de l’arbre afin de limiter le développement de la

coloration de cœur.

62

Introduction

The production of added-value wood products from the North American temperate deciduous

forests is mainly associated with the processing of hardwoods by the appearance wood

products industries. Accordingly, the demand and market value of sugar maple (Acer

saccharum Marshall) and yellow birch (Betula alleghaniensis Britton) trees depend on the

visual characteristics of their manufactured products assortment. Variations in quality

between and within stems imply that logs must be separated into different potential uses

before being supplied to the relevant mill. This can be achieved using tree grading rules

(Hanks 1976; Monger 1991) that were developed to assess stem quality prior to harvest. Like

log grading rules also in use (Rast et al. 1973; Petro and Calvert 1976), these classification

systems aim to predict the potential for lumber production, except that standing tree quality

is based only on an assessment of the butt log.

Fortin et al. (2009b) developed a two-part conditional model to address the issue of predicting

the log grading assortment from an assessment of standing tree quality. In this model, log

grades are based on estimates of the merchantable value of the lumber pieces that can be

extracted from a given sawlog (Petro and Calvert 1976). Lumber value predictions can be

obtained by adjusting the prices of the of the National Hardwood Lumber Association

(NHLA 2011) lumber grades to their current market value. However, such lumber value

estimates are based on a single study, conducted almost four decades ago, that provided little

information about the factors that induce variations in the lumber products assortment, and

thus monetary value among or within log grades.

In practice, the lumber value of sugar maple and yellow birch trees is determined by the

grades and the color of sawn pieces (Hardwood Market Report 2011). Drouin et al. (2010)

found that tree diameter is the most important variable affecting the distribution of NHLA

grades in white birch, while wood color variability was mostly affected by the diameter, age

and vigor of standing trees. Other studies showed the link between the occurrence and size

of red heartwood and the presence of external defects and injuries in standing trees (Shigo

1967; Knoke 2003; Wernsdorfer et al. 2005; Belleville et al. 2011; Baral et al. 2013),

suggesting that lumber color distribution can vary according to the occurrence of defects.

63

Because of this emphasis on quality, a major challenge in northern hardwood forests has been

to supply the appearance wood products industries while avoiding the gradual depletion of

the resources (Erickson et al. 1990; Nyland 1992). In a context where managed forests have

to provide multiple, and sometimes conflicting, services (Leak et al. 2014), it is important to

strive to extract maximum value from each harvested tree. Understanding the factors that

affect lumber assortment in trees and logs can help attain this objective by optimizing the

forest value chain, from designing adequate silvicultural systems to improving wood

procurement and allocation choices (Muñoz et al. 2013; Bennett 2014).

Whereas some studies have investigated in details the composition of the products basket in

white birch (Betula papyrifera Marshall) (Drouin et al. 2010) and in softwoods (Barrette et

al. 2012; Auty et al. 2014), no such work is available, to our knowledge, for sugar maple and

yellow birch. Yet, these two species have considerable economic importance in the North

American wood market, and their monetary value is known to vary considerably with

changes in stem or wood properties (Wiedenbeck et al. 2004; Havreljuk et al. 2014). Even if

tree value was related to log or stem characteristics in these studies, it is not possible to

determine to which extent a change in tree value was caused either by the variation in lumber

volume or lumber grades, or both.

The objective of this study was to investigate the relationship between log characteristics and

sawn board attributes in sugar maple and yellow birch. More specifically, we aimed to

develop models for predicting the occurrence and volume of each NHLA lumber grade in a

given log. From a statistical point of view, the modeling of product assortment from trees or

logs is challenging because of the multiple lumber grades and the excess of “zeros” in the

responses for certain classes of logs or board types. We therefore used generalized additive

models with a beta inflated distribution (Rigby and Stasinopoulos 2009) as a framework to

predict the occurrence and the volume of the various lumber grades that can be produced

from logs of both species.

64

Material and methods

Study area

Two study sites were selected for this study, both located in the temperate forest zone of the

southern Québec, Canada. The first site (S1) was located near Québec City (46°56’ N; 71°40’

W) within the balsam fir (Abies balsamea [L.] Mill.) – yellow birch bioclimatic domain

(Robitaille and Saucier 1998). The sampled stand has a total basal area (BA) of merchantable

standing trees of 23.5 m² ha-1, composed of sugar maple (38%), yellow birch (36%) and

American beech (Fagus grandifolia Ehrh.) (26%). The second site (S2) was located near

Mont-Laurier (46°39’ N; 75°38’ W), within the sugar maple – yellow birch bioclimatic

domain (Robitaille and Saucier 1998). The measured total BA of merchantable standing trees

was 25.0 m² ha-1, and was mainly composed of sugar maple (87%) with minor components

of yellow birch (6%), American beech (5%) and red maple (Acer rubrum L.) (2%). Both sites

were located on moderately to well-drained glacial tills and extend on an area of about 50 ha.

The mean annual temperature and precipitation was estimated at 3.2 °C / 1368 mm and 2.3

°C / 1013 mm for S1 and S2, respectively (Régnière et al. 2012).

Tree selection

At each study site, 32 sugar maple and 16 yellow birch trees were selected for a total of 96

stems. In order to incorporate stem heterogeneity into the sampling, sample trees were

selected according to the wide range of tree vigor and quality classes provided by Boulet

(2007) and Monger (1991), respectively. The tree vigor classification is a four-class system

that aims to identify stems with the highest risk of dying before the next cutting cycle

according to the presence and severity of observable defects on the tree (Boulet 2007). The

tree quality classification characterizes stem potential for producing sawlogs, according to

the stem size and the observable defects on the lower 5 m of the tree (Monger 1991). To be

consistent with the merchantable limit of the hardwoods intended for sawing, the assessments

were limited to stems with a diameter at breast height (DBH, 1.3 m above ground level)

greater than 23 cm.

65

Log classification

Felling operations were conducted in 2009 and 2010 at the Duchesnay and Mont-Laurier

sites, respectively. Full-length trees, including large branches with sawing potential, were

transported to the Duchesnay sawmill (Sainte-Catherine-de-la-Jacques-Cartier, Québec) for

log grading. Log grading was made according to the Petro and Calvert (1976) log grading

rules, which are similar to those used by the US Forest Service (Rast et al. 1973). The factors

considered in log grading include log size (length and small-end diameter), log position along

the bole, number of clear face cuttings (defect-free sections), straightness and soundness of

the log (Hanks et al. 1980). Three grades (F1, F2 and F3) were used to describe sawlog

potential for factory lumber grade products, where the highest quality corresponds to grade

F1 (i.e., Factory-1) and the lowest to grade F3 (i.e., Factory-3). An additional sawlog category

(F4) was included to represent small diameter bolts (Bumgardner et al. 2001). Other low-

grade logs were classified as pulpwood or waste logs. Veneer logs were distinguished

throughout the bucking stage, but these were combined with sawlogs in the analyses because

of their scarce occurrence. Log volume measurements were calculated using Smalian’s

equation (Avery and Burkhart 2001). A summary of the mean log characteristics is presented

in Table 3.1.

Table 3.1 Mean log characteristics for the sugar maple and yellow birch data.

No.

Mean length ±SD

[range] (cm)

Small-end

diameter

±SD (cm)

Large-end

diameter

±SD (cm)

Mean gross

volume ±SD

[range] (dm3)

Mean net volume

±SD [range] (dm3)

Red heartwood

volume ±SD

(dm3)

Sugar maple 123 247 ±72 [128-382] 30 ±5 33 ±7 202 ±109 [46-535] 196 ±106 [46-535] 47 ±36

F1 5 338 ±28 [316-379] 34 ±1 43 ±4 375 ±27 [335-401] 373 ±25 [335-401] 52 ±31

F2 27 319 ±45 [252-382] 34 ±5 39 ±5 332 ±81 [208-535] 325 ±79 [208-534] 70 ±40

F3 49 267 ±28 [245-381] 28 ±5 31 ±5 187 ±62 [82-363] 181 ±56 [82-326] 49 ±36

F4 42 165 ±37 [128-254] 28 ±5 30 ±6 115 ±61 [46-424] 111 ±55 [46-382] 29 ±22

Yellow birch 66 247 ±74 [126-381] 29 ±6 33 ±7 200 ±113 [38-497] 196 ±111 [38-485] 60 ±54

F1 5 326 ±33 [293-381] 38 ±3 43 ±4 412 ±78 [332-497] 403 ±68 [332-485] 143 ±39

F2 17 296 ±44 [253-379] 33 ±3 38 ±5 292 ±66 [191-460] 286 ±64 [188-438] 99 ±62

F3 25 272 ±41 [250-381] 27 ±5 30 ±5 176 ±65 [89-373] 173 ±64 [89-373] 42 ±30

F4 19 148 ±25 [126-194] 27 ±6 28 ±6 94 ±48 [38-198] 90 ±45 [38-196] 28 ±27

Total 189 247 ±73 [126-382] 30 ±6 33 ±7 201 ±110 [38-535] 196 ±197 [38-535] 51 ±44

66

Sawmill conversion

A total of 189 sawlogs were produced throughout the bucking stage. Twelve sugar maple and

four yellow birch trees generated only pulp logs and were discarded from the study. The

majority of sawlogs (128) were processed at the Duchesnay sawmill. Because of their smaller

size, the 61 bolts were sawn using a portable sawmill (Wood-Mizer Products Inc., USA) at

Laval University’s Renewable Materials Research Centre (Quebec City, Canada). The

production of high grade lumber (i.e., knot-free sapwood) was maximized using the grade

sawing approach (Richards et al. 1979), which consists of progressively rotating the log by

90 degrees once the heartwood is reached on one sawn face (Steele 1984). For consistency,

logs from both study sites were sawn by the same qualified workers and the provenance (i.e.,

tree and log) of each board was traced during the conversion process.

Lumber classification

The sawing trial resulted in a total of 2236 lumber pieces with standard 2.54 cm thickness (1

inch) and variable widths and lengths ranging from 7.6 to 35.6 cm (3 to 14 inches ) and 1.22

to 3.62 m (4 to 12 feet), respectively. Sugar maple and yellow birch boards were graded

according to the standards of the National Hardwood Lumber Association (2011). The initial

step in the lumber grading consisted in measuring the number of board feet in each piece.

One board foot (2360 cm³) is the equivalent of a 30.48 cm long x 30.48 cm wide x 2.54 cm

thick (1 foot x 1 foot x 1 inch) board. Standard lumber grades applied to both species were

mainly related to the size of clear face cuttings (i.e., free of defects) and were divided classes

of similar quality (FAS, FAS one face (F1F), Selects, No. 1 Common, No. 2A Common, No.

3A Common and No. 3B Common, with the best grade being FAS and the lowest No. 3B

Common). Boards of a lower grade than No. 3B Common (i.e., pallet lumber) were discarded

from the study. All sawn boards were also assessed for color specifications according to the

NHLA (2011) rules. Color selection for sugar maple and yellow birch is optional, but its

assessment can help specify the end uses and, therefore, the market value of boards.

Depending on the extent of non-discolored wood (wrongly referred to as “sapwood” in the

wood processing industry (Baral et al. 2013)) on the faces and edges of each piece, sugar

maple boards were classified as No. 1 White Maple, No. 2 White Maple or Sap Hard Maple

67

in descending order of quality. In the No. 1 White Maple category, both faces and both edges

of the required cuttings need to be non-discolored, while in the No. 2 White Maple category

the same rules apply except that only one face, both edges and over 50% of the reverse side

of the cuttings should not contain discolored heartwood. The requirements for Sap Hard

Maple were the same as those for the Sap Birch, where a cutting needs to have at least one

non-discolored face. Yellow birch lumber color assessment was also considered for “Red”

birch, where each required cuttings need to have one clear heartwood (i.e., discolored) face.

For both species, lumber pieces not satisfying the NHLA color specifications were classified

as regular wood. For consistency, boards from both sites were graded by the same qualified

inspector. The distribution of sawn lumber is presented in Table 3.2.

Table 3.2 Lumber grade distribution among the sawn boards.

NHLA grades Total no. of boards No. of board feet % of board feet

Sugar maple 1460 5469 100.0

FAS 61 372 6.8

F1F 61 370 6.8

Selects 98 348 6.4

No. 1C 265 1029 18.8

No. 2A 324 1160 21.2

No. 3A 252 850 15.5

No. 3B 399 1340 24.5

Yellow birch 776 2989 100.0

FAS 51 307 10.3

F1F 39 214 7.2

Selects 61 239 8.0

No. 1C 182 731 24.5

No. 2A 178 609 20.4

No. 3A 120 397 13.3

No. 3B 145 492 16.5

Statistical approach

The response variable of the statistical model was the lumber volume recovery calculated as

the ratio of the volume of each lumber grade over the total net volume of the log. For the

sake of clarity, we will use “VRG” and “VRC” when referring to the lumber volume recovery

of the various board grades and board colors, respectively. Prior to the calculation of lumber

volume recovery, the lumber grade volume represented in board feet was replaced by its

metric equivalent (1 board feet = 0.0023597 cubic meter [m³]) to obtain a ratio of cubic

68

meters of sawn wood divided by a number of cubic meters of roundwood. Therefore, both

VRG and VRC are continuous variables with values theoretically bound between 0 and 1. In

practice, there were no occurrences of 1, but there were many zero values for certain classes

of boards (Figure 3.1 and Figure 3.2).


recovery of lumber grades (VRG).

69


recovery of the lumber colors (VRC).

This abundance of zeros is caused by the more restrictive criteria for better grades. In our

study, we used generalized additive models (Hastie and Tibshirani 1986) to predict the

product assortment from a given log. The modeling of the mean part and the excess of zeros

of the dependent variable was assessed simultaneously using the beta inflated distribution

with abundance of zeros (BEINF0) implemented in the GAMLSS package (Rigby and

Stasinopoulos 2009) of the R statistical programing environment (R Development Core

Team 2014). The beta distribution is very flexible for modeling data that are measured in a

continuous scale on the open interval (0, 1) since its density can take different shapes

depending on the values of the two parameters that index the distribution (Ospina and Ferrari

2010). The probability function of the zero-inflated beta distribution, denoted by BEINF0

(𝜇, 𝜎, 𝑣) is defined by Eq. 1:

70

(1) 𝑓𝑌(𝑦|𝜇, 𝜎, 𝑣) = {𝑝0 if 𝑦 = 0

(1 − 𝑝0)1

𝐵(𝛼,𝛽)𝑦𝛼−1(1 − 𝑦)𝛽−1 if 0 < y < 1

for 0 ≤ 𝑦 < 1, where

(1.1) 𝛼 = 𝜇(1 − 𝜎2)/𝜎²

(1.2) 𝛽 = (1 − 𝜇)(1 − 𝜎2)/𝜎²

(1.3) 𝑝0 = 𝑣(1 + 𝑣)−1

So, 𝛼 > 0, 𝛽 > 0, 0 < 𝑝0 < 1

Hence BEINF0 (𝜇, 𝜎, 𝑣) has parameters

(1.4) 𝜇 = 𝛼/(𝛼 + 𝛽)

(1.5) 𝜎 = (𝛼 + 𝛽 + 1)−1/2

(1.6) 𝑣 = 𝑝0/1 − 𝑝0

So, 0 < 𝜇 < 1, 0 < 𝜎 < 1, 𝑣 > 0

(1.7) 𝐸(𝑦) =𝜇

(1+𝑣)

The three parameters 𝜇 (mean), 𝜎 (dispersion) and 𝑣 (probability of zero) were each modelled

using the independent explanatory variables. To respect the parameter hierarchy when fitting

the model (Rigby and Stasinopoulos 2009), the 𝜇 parameter was fitted before the 𝜎

parameter. The explanatory variables were allowed to differ for each part of the model (i.e.,

beta and probability of zero).

Model selection

Prior to fitting, we made several hypotheses about the variables that could have an effect on

the fit of each part of the model. Log grade and log length were expected to have a strong

effect on the abundance of zeros in the model because the valuable lumber grades are

characterized by longer and larger pieces, which are not present in bolts and lower log grades.

For example, the minimum board size for lumber grades FAS and F1F is 15 cm (6 inch) wide

71

by 2.5 m (8 feet) long so that the probability for low log grades to produce such a lumber

piece is low. Lumber grades and colors were considered separately in order to limit the

number of potential combinations of product assortments. For the lumber grade models, we

tested the interaction between NHLA lumber grades and the two species studied, while

separate models were calibrated for color specifications to reflect the fact that, in this case,

the grading specifications varied between species.

Prior to model selection, all variables were tested individually according to their relationship

with lumber grades and colors. This step allowed to discriminate the most important variables

to be included in the model selection process. Then, the main independent variables included

in the modeling of the different parameters of the BEINF0 distribution were subsequently

eliminated through a backward elimination procedure. The corrected Akaike information

criterion (AICc) (Burnham and Anderson 2002) was used to determine if the contribution of

a variable to the model fit was significant or not. The AICc was preferred to the AIC statistic

(Akaike 1974) because it is more appropriate for small samples and includes a greater penalty

for extra parameters (Anderson 2008). Models with the lowest AICc were preferred. Models

were compared to the intercept-only model, specified for each part. In total, 1323

observations were used for the model fitting of the VRG (i.e., 189 logs x 7 lumber grades),

while 492 (i.e., 123 logs x 4 lumber colors) and 198 observations (i.e., 66 logs x 3 lumber

colors) were used for the sugar maple and yellow birch VRC, respectively. The goodness-of-

fit of each model was assessed by checking the independence of residuals on the response

variable and the residual normality, as described by Stasinopoulos and Rigby (2007). Fit

indices (pseudo-R²) were also calculated for each model, but these were not used as a criterion

for model selection. In addition, to present the general performance of the best model in a

straightforward manner, the predictions for each board grade were converted to their

monetary value (Hardwood Market Report 2008–2012) (Table 3.3), before being summed

per log grade.

72

Table 3.3 Mean sugar maple and yellow birch lumber values from 2008 to 2012.

NHLA grades Hard maple Birch

Regular Sap No. 1&2 White Regular Red Sap

FAS 1195 1295 1420 1105 1410 1380

Selects 1175 1275 1400 1085 1390 1360

No. 1C 760 860 860 685 990 960

No. 2A 540 640 640 430 735 705

No. 3A 355 455 – 280 585 555

No. 3B (pallet) 225 – – 225 – –

Note: Values are presented in US$ per MBF for 2.54 cm thick (1 inch) boards of random widths and lengths

that are rough and green. 1000 board feet (MBF) = 2.36 m³.

73

Results

Lumber grade models

The volume proportion of the best lumber grades (No. 1C and Better) was 59.5%, 49.8% and

30.6% for sugar maple log grades F1, F2 and F3, respectively. For yellow birch, the

corresponding values were 67.9%, 60.6% and 34.9% (Table 3.4).

Table 3.4 Proportion of NHLA lumber grades and colors among log grades.

Lumber grades proportion (%) Color specifications proportion (%)

FAS F1F Selects No.

1C

No.

2A

No.

3A

No.

3B

No. 1

White

No. 2

White Sap Red Regular

Sugar maple 6.8 6.8 6.4 18.8 21.2 15.5 24.5 18.5 10.1 14.8 – 51.9

F1 17.3 15.6 7.8 18.8 17.6 5.8 17.1 26.1 16.1 11.3 – 46.5

F2 12.8 11.1 6.5 19.4 21 10.9 18.4 22.4 14.1 12.1 – 51.4

F3 1.9 3.8 6.7 18.2 21.1 20.2 28.1 13.5 7.2 16.5 – 62.8

F4 0 0 4.8 18.8 23.6 19.8 33.1 17.7 4.8 18.5 – 59.1

Yellow birch 10.3 7.2 8.0 24.5 20.4 13.3 16.5 – – 37.9 10.3 51.9

F1 22 13 7.2 25.7 13.2 7 11.9 – – 31.7 16.3 52.1

F2 14.1 9.4 7.6 29.5 17.9 10.1 11.6 – – 37.6 13.5 48.9

F3 3.5 4.1 9.6 17.7 26.5 19 19.6 – – 41.7 4.7 53.6

F4 0 0 5.6 25.3 21.2 16.6 31.2 – – 35.9 7.2 56.9

Even if the overall proportion of the best lumber grades was higher for each log grade in

yellow birch logs than in sugar maple, both species were considered together in the VRG

model because the goodness of fit was not significantly improved by the inclusion of

interactions between species and other covariates. The list of models tested is presented in

Table 3.5.

The best model predicting VRG was model 8 (AICc = -1523.1), followed by model 9 (AICc

= -1503.1, ∆i =20.0), which contained more variables, suggesting that the inclusion of

additional covariates, such as the number of clear wood faces, did not substantially improve

the model fit (Table 3.5). The probability of zero occurrence (𝑣) and the mean (𝜇) were

explained by a slightly different list of covariates. The occurrence of zeros was mostly

explained by the small-end diameter of the log, log length and log position, while the main

74

variables related to the mean were the small-end diameter of the log, log position and the

decay diameter at the small-end of the log.

Table 3.5 List of models predicting the VRG of sugar maple and yellow birch.

Model group ID Part Explanatory variables

Individual

components Complete model

AICc R² d.f. LL AICc ∆i R²

Intercept 0 . Null . . 3 277.9 -549.9 973.2 0.00

Mean 1

µ Grade -614.3 0.06

21 593.9 -1145.0 378.1 0.38 σ Grade -539.6 0.00

ν Grade -1073.2 0.33

Log grading

classification 2

µ Grade x Petro -650.6 0.11

82 790.1 -1405.3 117.8 0.54 σ Grade x Petro -536.9 0.03

ν Grade x Petro -1296.5 0.45

Volumetric

covariates

3

µ Grade x Net volume -698.6 0.12

42 779.5 -1472.1 51.0 0.53 σ Grade x Net volume -565.3 0.03

ν Grade x Net volume -1305.6 0.45

4

µ Grade x Net volume + Grade x Decay volume -704.7 0.14

77 830.0 -1496.4 26.7 0.57 σ Grade x Net volume -565.3 0.03

ν Grade x Net volume + Grade x Clear faces -1326.4 0.48

Other log

measurements

5

µ Grade x Small-end diameter -701.7 0.13

42 738.7 -1390.6 132.5 0.50 σ Grade x Small-end diameter -564.5 0.03

ν Garde x Small-end diameter -1226.7 0.41

6

µ Grade x Length -628.0 0.08

42 678.6 -1270.4 252.7 0.45 σ Grade x Length -546.1 0.02

ν Grade x Length -1185.6 0.39

7

µ Grade x Small-end diameter + Grade x Length -700.1 0.13


ν Grade x Small-end diameter + Grade x Length -1299.6 0.45

8

µ Grade x Small-end diameter + Grade x Position

+ Grade x Small-end decay -722.8 0.16


ν Grade x Small-end diameter + Grade x Position + Grade x Length

-1331.7 0.47

9

µ Grade x Small-end diameter + Grade x Position + Grade x Small-end decay + Clear faces

-708.4 0.19


ν Grade x Small-end diameter + Grade x Position

+ Grade x Length + Clear faces -1333.9 0.49

Full model 10

µ

Grade x (Region + Species + Small-end

diameter + Length + Position + Small-end

decay + Clear faces)

-702.6 0.21

167 940.4 -1498.2 24.9 0.63 σ Grade x Species -531.3 0.00

ν

Grade x (Region + Species + Small-end

diameter + Length + Position + Small-end

decay + Clear faces)

-1334.0 0.51

Note: d.f. is the degrees of freedom, LL is the Log-Likelihood, ∆i is the difference in the AICc and that of the

best model and R² is pseudo-R². The fit indices for individual components of the model are presented for

illustrative purposes only and were not used for the model fitting.

75

The most important variations in VRG for all covariates were observed with the lowest lumber

grade (No. 3B) and the best lumber grades (FAS and F1F). An important decrease in the VRG

of No. 3B with increasing log length was compensated by an increase in the VRG of the best

lumber grades, around 2.50 and 3.50 m (Figure 3.3). Similarly, the VRG of the lowest lumber

grades decreased with an increase of the small-end diameter of the log and was exceeded by

the VRG of the best lumber grades at a diameter of about 38 cm (Figure 3.3). Higher

proportions of VRG for the FAS, F1F and Selects were observed with logs that originated

from the bottom part of the tree. Conversely, the VRG of No. 3A and No. 3B grades increased

in the upper logs. No clear trend was observed between VRG and the decay diameter at the

small-end of the log even if the overall lumber volume recovery was higher for the lowest

lumber grades (Figure 3.3). Throughout the model calibration process, the zero-part (𝑣) was

more sensitive to the addition of variables as these brought larger increases to the overall

model fit (Table 3.5). Parameter estimates for the best model predicting VRG are presented

in Table 3.6.

Figure 3.3 Predicted lumber volume recovery for each lumber grade (VRG) plotted against

the small-end diameter of the log (cm) of the best model (model 8). Lines represent loess

smoothing functions with standard error through the predictions.

76

Table 3.6 Parameter estimates (and standard errors) for the best model to predicting VRG (model 8).

Model part : Mean (µ)

Board grade Intercept Grade Position Small-end diameter Decay diameter Grade x Position Grade x Small-end

diameter

Grade x Decay

diameter

FAS -3.3999 (0.8134) 0 -0.0009 (0.0006) 0.0439 (0.0248) -0.0332 (0.0251) 0 0 0

F1F -3.3999 (0.8134) -0.0670 (0.9911) -0.0009 (0.0006) 0.0439 (0.0248) -0.0332 (0.0251) 0.0014 (0.0007) -0.0164 (0.0301) 0.0658 (0.0321)

Selects -3.3999 (0.8134) 1.5013 (0.9168) -0.0009 (0.0006) 0.0439 (0.0248) -0.0332 (0.0251) 0.0004 (0.0007) -0.0607 (0.0283) 0.0369 (0.0301)

No. 1C -3.3999 (0.8134) 0.8111 (0.8889) -0.0009 (0.0006) 0.0439 (0.0248) -0.0332 (0.0251) 0.0008 (0.0007) -0.0249 (0.0273) 0.0467 (0.0285)

No. 2A -3.3999 (0.8134) 1.5286 (0.8685) -0.0009 (0.0006) 0.0439 (0.0248) -0.0332 (0.0251) 0.0010 (0.0007) -0.0488 (0.0266) 0.0308 (0.0281)

No. 3A -3.3999 (0.8134) 2.2931 (0.8702) -0.0009 (0.0006) 0.0439 (0.0248) -0.0332 (0.0251) 0.0015 (0.0007) -0.0855 (0.0267) 0.0262 (0.0279)

No. 3B -3.3999 (0.8134) 3.4951 (0.8435) -0.0009 (0.0006) 0.0439 (0.0248) -0.0332 (0.0251) 0.0013 (0.0006) -0.1164 (0.0258) 0.0851 (0.0265)

Model part : Dispersion (σ)

Board grade Intercept Grade Small-end diameter Grade x Small-end diameter

FAS -3.9118 (1.3326) 0 0.0750 (0.0393) 0

F1F -3.9118 (1.3326) 0.8891 (1.7790) 0.0750 (0.0393) -0.0376 (0.0526)

Selects -3.9118 (1.3326) 1.59122 (1.5232) 0.0750 (0.0393) -0.0542 (0.0457)

No. 1C -3.9118 (1.3326) 2.1067 (1.4139) 0.0750 (0.0393) -0.0576 (0.0422)

No. 2A -3.9118 (1.3326) 2.3989 (1.3813) 0.0750 (0.0393) -0.0670 (0.0411)

No. 3A -3.9118 (1.3326) 3.0668 (1.4112) 0.0750 (0.0393) -0.0969 (0.0422)

No. 3B -3.9118 (1.3326) 3.1503 (1.3660) 0.0750 (0.0393) -0.0104 (0.0405)

Model part : Probability of zero (ν)

Board grade Intercept Grade Length Position Small-end diameter Grade x Length Grade x Position Grade x Small-end diameter

FAS 10.1162 (1.9744) 0 -0.0115 (0.0037) 0.0045 (0.0013) -0.2145 (0.0512) 0 0 0

F1F 10.1162 (1.9744) 2.0545 (2.8324) -0.0115 (0.0037) 0.0045 (0.0013) -0.2145 (0.0512) -0.0071 (0.0054) -0.0026 (0.0016) -0.0037 (0.0707)

Selects 10.1162 (1.9744) -6.1606 (2.3210) -0.0115 (0.0037) 0.0045 (0.0013) -0.2145 (0.0512) 0.0041 (0.0045) -0.0016 (0.0015) 0.1299 (0.0613)

No. 1C 10.1162 (1.9744) -5.8310 (2.5571) -0.0115 (0.0037) 0.0045 (0.0013) -0.2145 (0.0512) 0.0134 (0.0051) -0.0028 (0.0015) -0.0427 (0.0745)

No. 2A 10.1162 (1.9744) -6.7677 (2.9493) -0.0115 (0.0037) 0.0045 (0.0013) -0.2145 (0.0512) 0.0056 (0.0062) -0.0028 (0.0016) 0.0134 (0.0865)

No. 3A 10.1162 (1.9744) -7.9821 (2.4636) -0.0115 (0.0037) 0.0045 (0.0013) -0.2145 (0.0512) 0.0044 (0.0049) -0.0061 (0.0015) 0.1493 (0.0671)

No. 3B 10.1162 (1.9744) -12.9144 (2.8608) -0.0115 (0.0037) 0.0045 (0.0013) -0.2145 (0.0512) 0.0188 (0.0059) -0.0049 (0.0017) 0.1593 (0.0765)

77

Lumber color models

For sugar maple, the proportion of non-discolored wood (No. 1 White Maple, No. 2 White

Maple and Sap Hard Maple) was greater for logs of highest quality (Table 3.4). For yellow

birch, the proportion of Sap Birch was constant through the log grades, while a decreasing

trend was observed from F1 to F4 for the Red Birch category (Table 3.4). Details of the

model fitting process for sugar maple and yellow birch are presented in Table 3.7 and Table

3.8, respectively.

The main variables related to VRC in the best sugar maple model (model 4, AICc = -736.7)

were the net volume of the log and the volume of red heartwood. The inclusion of clear wood

faces and decay volume did not improve the fit of the second best model (model 5, ∆i = 7.4).

Other models including other log measurements gave lower fit than log volume. The VRC of

No. 1 and No. 2 White Maple color specifications increased with the log net volume, while

it decreased for Sap Hard Maple. A non-significant change was observed in the VRC for the

regular color category. Conversely, the VRC of the regular lumber category increased with

the red heartwood volume of the log, while a decreasing trend was observed for the No.1

White Maple and Sap Hard Maple (Figure 3.4).

Contrary to the VRC model for sugar maple, which was related to volumetric covariates, the

most important variables for yellow birch were related to other log measurements. The best

model for predicting the VRC in this species was model 9 (AICc = -311.7), followed by model

10 (AICc = -306.6, ∆i = 5.1) that contained some additional variables. The main covariates

associated with the mean volume recovery (𝜇) of a given color grade were the small-end

diameter of the log, log length and the large-end diameter of heartwood, while the occurrence

of zeros (𝑣) was related to the large-end diameter of heartwood. No clear trends in VRC of

Sap Birch and regular lumber were found against log length and the small-end diameter of

the log, but the VRC of the Red Birch category tended to increase with an increase of the log

dimensions (Figure 3.5). In addition, for logs with a large-end diameter of red heartwood

over 16 cm, the proportion of the Regular wood and Red Birch categories increased, while

Sap Birch decreased (Figure 3.5). Parameter estimates for the best model to predicting VRC

of sugar maple and yellow birch are presented in Table 3.9.

78

Table 3.7 List of models predicting the VRC of sugar maple.


Individual



Intercept 0 . Null . . 3 109.2 -212.3 524.4 0.00

Mean 1

µ Color -502.3 0.45

12 317.1 -609.5 127.2 0.45 σ Color -225.6 0.04

ν Color -303.6 0.18

Log grading

classification 2

µ Color -502.3 0.45

24 339.5 -628.5 108.2 0.61 σ Color -225.6 0.04

ν Color x Petro -323.2 0.25

Volumetric covariates

3

µ Color x Net volume -502.7 0.46

20 341.5 -641.1 95.6 0.61 σ Color -225.6 0.04

ν Color x Net volume -331.2 0.24

4

µ Color x Net volume + Color x Red heartwood volume -592.3 0.56

28 398.1 -736.7 0.0 0.69 σ Color -225.6 0.04

ν Color x Net volume + Color x Red heartwood volume -355.4 0.30

5

µ Color x Net volume + Color x Red heartwood volume

+ Color x Decay volume + Color x Clear faces -585.7 0.57

36 403.6 -729.3 7.4 0.70 σ Color -225.6 0.04

ν Color x Net volume + Color x Red heartwood volume -355.4 0.30

Other log measurements

6

µ Color x Large-end red heartwood diameter -550.2 0.51

20 349.0 -656.2 80.5 0.62 σ Color -225.6 0.04

ν Color x Small-end red heartwood diameter -313.4 0.21

7

µ Color x Length -500.8 0.46

20 328.9 -616.1 120.6 0.59 σ Color -225.6 0.04

ν Color x Length -311.5 0.21

8

µ Color -502.3 0.45

16 335.0 -636.9 99.8 0.60 σ Color -225.6 0.04

ν Color x Small-end diameter -331.2 0.24

9


28 384.7 -709.3 27.4 0.67 σ Color -225.6 0.04

ν Color x Small-end red heartwood diameter + Color x Small-end diameter + Color x Length

-367.8 0.32

10

µ Color x Large-end red heartwood diameter + Color x

Clear faces + Color x Position -556.2 0.53

36 396.4 -715.0 21.7 0.69 σ Color -225.6 0.04

ν Color x Small-end red heartwood diameter + Color x

Small-end diameter + Color x Length -367.8 0.32

Full model 11

µ

Color x (Small-end diameter + Large-end red

heartwood diameter + Large-end decay + Length +

Position + Clear faces)

-578.0 0.58

60 421.5 -706.0 30.7 0.72 σ Color -225.6 0.04

ν

Color x (Small-end diameter + Small-end red

heartwood diameter + Large-end decay + Length + Position + Clear faces)

-346.0 0.32




79

Table 3.8 List of models predicting the VRC of yellow birch.


Individual



Intercept 0 . Null . . 3 18.2 -30.2 281.5 0.00

Mean 1

µ Color -75.9 0.22

9 108.4 -197.8 113.9 0.60 σ Color -43.5 0.08

ν Color -144.1 0.45

Log grading classification

2

µ Color -75.9 0.22

18 118.1 -196.3 115.4 0.64 σ Color -43.5 0.08

ν Color x Petro -143.5 0.50

Volumetric

covariates

3

µ Color -75.9 0.22

12 124.4 -223.0 88.7 0.66 σ Color -43.5 0.08

ν Color x Net volume -169.6 0.53

4

µ Color x Net volume + Color x Red heartwood volume -138.8 0.47

18 166.8 -293.8 17.9 0.78 σ Color -43.5 0.08

ν Color x Red heartwood volume -186.7 0.57

5

µ Color x Net volume + Color x Red heartwood volume

+ Color x Clear faces -135.5 0.48

24 169.0 -283.0 28.7 0.78 σ Color -43.5 0.08

ν Color x Red heartwood volume + Color x Clear faces -180.1 0.57

Other log

measurements

6


15 149.7 -266.8 44.9 0.74 σ Color -43.5 0.08

ν Color x Large-end red heartwood diameter -189.4 0.58

7

µ Color x Length -74.3 0.24

15 114.7 -196.7 115.0 0.62 σ Color -43.5 0.08

ν Color x Length -143.9 0.47

8

µ Color x Small-end diameter -75.9 0.22

15 130.3 -227.9 83.8 0.68 σ Color -43.5 0.08

ν Color x Small-end diameter -179.5 0.55

9

µ Color x Length + Color x Small-end diameter + Color

x Large-end red heartwood diameter -155.8 0.53

21 179.5 -311.7 0.0 0.80 σ Color -43.5 0.08

ν Color x Large-end red heartwood diameter -189.4 0.58

10

µ

Color x Length + Color x Small-end diameter + Color

x Large-end red heartwood diameter + Color x Small-

end red heartwood diameter

-158.9 0.55

27 184.7 -306.6 5.1 0.81 σ Color -43.5 0.08

ν Color x Large-end red heartwood diameter + Color x

Small-end red heartwood diameter -183.6 0.58

Full model 11

µ




-149.2 0.57

45 189.3 -261.3 50.4 0.82 σ Color -43.5 0.08

ν




-157.5 0.58




80

Figure 3.4 Predicted lumber volume recovery for each color category (VRC) plotted against the covariates of the best model for sugar

maple (model 4). Lines represent loess smoothing functions with standard error through the predictions.

Figure 3.5 Predicted lumber volume recovery for each color category (VRC) plotted against the covariates of the best model for yellow

birch (model 9). Lines represent loess smoothing functions with standard error through the predictions.

81

Table 3.9 Parameter estimates (and standard errors) for the best model to predicting VRC of sugar maple (model 4) and yellow birch

(model 9).

Model part : Mean (µ) for sugar maple

Board color Intercept Color Log net volume Red heartwood volume

Color x Log net volume Color x Red heartwood volume

No. 1 White Maple -2.0745 (0.1421) 0 0.0028 (0.0008) -0.0137 (0.0029) 0 0

No. 2 White Maple -2.0745 (0.1421) -0.6179 (0.2255) 0.0028 (0.0008) -0.0137 (0.0029) -0.0013 (0.0011) 0.0106 (0.0038)

Sap Hard Maple -2.0745 (0.1421) 0.2168 (0.1836) 0.0028 (0.0008) -0.0137 (0.0029) -0.0044 (0.0010) 0.0130 (0.0038)

Regular -2.0745 (0.1421) 1.1010 (0.1641) 0.0028 (0.0008) -0.0137 (0.0029) -0.0052 (0.0009) 0.0259 (0.0032)

Model part : Dispersion (σ) for sugar maple

Board color Intercept Color

No. 1 White Maple -1.2749 (0.0814) 0

No. 2 White Maple -1.2749 (0.0814) -0.3968 (0.1245)

Sap Hard Maple -1.2749 (0.0814) -0.2549 (0.1117)

Regular -1.2749 (0.0814) -0.1386 (0.1102)

Model part : Probability of zero (ν) for sugar maple

Board color Intercept Color Log net volume Red heartwood

volume Color x Log net volume

Color x Red heartwood

volume

No. 1 White Maple 0.56874 (0.6147) 0 -0.0271 (0.0070) 0.0561 (0.0155) 0 0

No. 2 White Maple 0.56874 (0.6147) 1.1488 (0.7240) -0.0271 (0.0070) 0.0561 (0.0155) 0.0158 (0.0070) -0.0523 (0.0174)

Sap Hard Maple 0.56874 (0.6147) -3.2628 (0.8821) -0.0271 (0.0070) 0.0561 (0.0155) 0.0234 (0.0080) -0.0278 (0.0188)

Regular 0.56874 (0.6147) -20.1978 (2124) -0.0271 (0.0070) 0.0561 (0.0155) 0.0271 (12.400) -0.0561 (36.50)

Model part : Mean (µ) for yellow birch

Board color Intercept Color Length Small-end diameter Red heartwood

diameter Color x Length

Color x Small-

end diameter

Color x Red

heartwood diameter

Sap Birch -0.5259 (0.3047) -2.4303 (0.4911) 0.0013 (0.0007) 0.0685 (0.0149) 0.0738 (0.0140) 0.0019 (0.0011) 0.1836 (0.0243) -0.2321 (0.0240)

Red Birch -0.5259 (0.3047) -2.5941 (0.9847) 0.0013 (0.0007) 0.0685 (0.0149) 0.0738 (0.0140) 0.0030 (0.0021) 0.0636 (0.0328) -0.0049 (0.0279)

Regular -0.5259 (0.3047) 0 0.0013 (0.0007) 0.0685 (0.0149) 0.0738 (0.0140) 0 0 0

Model part : Dispersion (σ) for yellow birch

Board color Intercept Color

Sap Birch -1.5412 (0.1002) 0.1589 (0.1418)

Red Birch -1.5412 (0.1002) -0.1763 (0.1887)

Regular -1.5412 (0.1002) 0

Model part : Probability of zero (ν) for yellow birch

Board color Intercept Color Red heartwood diameter Color x Red heartwood diameter

Sap Birch -18.63 (2289) -0.0000 (3237) 29.330 (2289) 0.0000 (129.4)

Red Birch -18.63 (2289) -0.0000 (3237) 29.330 (2289) 0.0000 (183.1) Regular -18.63 (2289) 0 0 0

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Log grading classification

Model comparison confirmed that log grades could be used to predict the lumber grades

assortment (Model 2, AICc = -1405.3), but better results were achieved when log net volume

or other log measurements were used as covariates (Table 3.5). The model considering only

the small-end diameter of the log and log length (Model 7, AICc = -1466.0) was comparable

to the model using only the net volume of the log (Model 3, AICc = -1472.1).

Better predictions of VRC for sugar maple could be achieved by including log grades (Model

2, AICc = -628.5) in the mean model (Model 1, AICc = -609.5), even if the overall fit

remained low (Table 3.7). Conversely, no improvement in AICc was observed when the log

grades (model 2, AICc = -196.3) were added to the yellow birch model (model 1, AICc = -

197.8), suggesting that log grading was not related to the VRC of this species (Table 3.8).

Model evaluation

Observed and predicted frequencies of the volume recovery for each lumber category are

presented in Figure 3.1 and Figure 3.2 for grades and colors, respectively. The overabundance

of zeros justified the use of the zero-inflated beta distribution (BEINF0). Even if the

distribution of the predicted values was satisfactory when compared to the observations for

the grades or color classes that have an overabundance of zeros (FAS, F1F, Red Birch), the

predicted distribution was poor for the grades (No. 1C, No. 2C, No. 3A) and colors (No. 1

White Maple, Sap Hard Maple). However, the overall sum of predictions for each grade or

color class was close to the observed values. Value predictions fitted well the observed data,

although log value was slightly overestimated for the F4 log grade (Figure 3.6). This bias

was probably due to the limited range of net volumes for these small logs and the distribution

problems described earlier.

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Figure 3.6 Predicted value recovery against log net volume established from the predicted

VRG (model 8). Lines represent linear smoothing functions with standard error plotted

against the observed value recovery.

Discussion

Modeling the log (Buongiorno et al. 1993; Fortin et al. 2009b; Rankovic et al. 2013) or

lumber (Lyhykäinen et al. 2009; Drouin et al. 2010; Auty et al. 2014) products assortments

is important to estimate the monetary value of a given tree or forest stand, which are known

to change over time as lumber prices fluctuate (Erickson et al. 1990). In the current study,

we developed a set of models for predicting the lumber yields for grades and colors of sugar

maple and yellow birch from external log characteristics. The volume proportion of the

various lumber grades was similar to those obtained in other similar studies on northern

hardwoods (Petro and Calvert 1976; Hanks et al. 1980; Wengert and Meyer 1994). In the

study of Hanks et al. (1980), nearly 20 000 logs were sawn throughout the Eastern United

States and F1 logs were predicted to yield 60% or more of No. 1C and Better lumber, whereas

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it was 40 to 60% for F2 logs, and less than 40 % for F3 logs. In our study, all volume

proportions were in the same ranges, except the F2 log grade for yellow birch, which was

slightly higher.

The main covariates related to the best lumber grade model (Model 8, Table 3.5) were the

log length, the position of the log in the stem, the small-end diameter of the log and the

diameter of decay at the small-end of the log. As expected, the small-end diameter of the log

and the log length were closely related to the board size minima for a given lumber grade

(NHLA 2011). Similarly, Muñoz et al. (2013) found that the diameter over bark at the small

end of the log was the best predictor of structural sawn lumber in oak (Quercus robur L.).

Log position along the bole could be an indirect indicator of internal wood characteristics

(Petro 1971; Hanks 1976). The butt log is generally considered as the most valuable part of

the tree (Fortin et al. 2009b), as it is larger and more likely to contain a section free of defects

such as knots. This was the case in our study, as more high-grade lumber was found in the

butt logs. Conversely, decay is often associated with volume loss and quality depletion in

standing trees (Basham 1991). Havreljuk et al. (2014) found that visible evidence of fungal

infections and cracks had a negative influence on the value of sugar maple and yellow birch

trees. In the current study, the decay diameter at the small-end of the log had only a limited

effect on the lumber grades assortment. However, the range of decay values was limited, as

many logs with large extent of decay were considered as pulp logs and were discarded from

sawing. Among the other variables that were estimated in the log grading process, the number

of clear face cuttings did not improve the predictions of the VRG. This result is surprising

because this variable is deemed to be an indicator of defects inside the log and, consequently,

of clear face cuttings in the sawn boards. High quality logs are hence characterized by longer

defect-free areas (i.e., clear face cuttings). It is probable that the grouping of all external

defects (i.e., knots, deformations, etc.) in a four-class variable to represent the number of

clear face cuttings on the log resulted in a loss of precision that would explain the lack of

relationship with lumber grades.

The investigation of the relationship between VRG and the log characteristics showed that

the most valuable lumber grades were found at the bottom of the tree, in logs longer than 2.5

m and with a minimum diameter at the small-end above 30 cm. This result is consistent with

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the specifications of the best log grades in the log classification systems (Hanks 1976; Petro

and Calvert 1976). With the exception of the log position in the stem, the variables included

in the best VRG model can be easily measured and are derived from a log grading system,

such as Petro and Calvert’s (1976). Simplicity is an important advantage of a model based

on a limited number of classes. Although Petro and Calvert’s (1976) log grading system

proved useful to discriminate the VRG between log categories (Figure 3.6) and provided good

predictions with a pseudo-R² of 0.54, the log grading model (Model 2, Table 3.5) was not as

good as model 8, which was based on precise log measurements. The model presented in this

study permitted to quantify to which extent each log characteristic is related to the VRG. As

a result, the curves corresponding to the effect of single log measurements on the VRG for a

given lumber grade may be used to establish some thresholds related to log production

objectives.

In this study, we also presented several models for predicting the assortment of lumber colors.

To our knowledge, this is the first study that describes the relationship between log

characteristics and volume recovery of the various color specifications contained in the

NHLA lumber grading system. In log grading systems (Hanks 1976; Petro and Calvert 1976;

MRNFQ 2011), the red heartwood is only considered when the discolored wood begins to

show some signs of decay, or when it is associated with decayed wood. As a result, some

logs could be downgraded or rejected. Considerations of the diameter of the red heartwood

column at the log cross sections could improve the lumber product assortment, because its

size was found to be positively related to the proportion of the regular lumber for sugar maple

and to the proportion of the regular and “Red Birch” lumber for yellow birch.

Although this study was conducted on the logs and product assortment, the findings may

provide useful guidance for the management of sugar maple and yellow birch in northern

hardwood stands. For a given volume of round wood, our results suggest that forest managers

should aim to produce larger sawlogs, because a higher proportion of the valuable lumber,

both in grades and in colors, was positively related to the log dimensions. However, sawn

timber volume or value in these species is known to decrease beyond DBH thresholds varying

between 40 and 70 cm (Hansen and Nyland 1987; Majcen et al. 1990; Prestemon 1998;

Pothier et al. 2013; Saucier et al. 2014). The accurate assessment of large trees with tree

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grading systems (Rast et al. 1973; Monger 1991) could be difficult, because of the presence

of some internal defects (Havreljuk et al. 2014). Even if no direct relationship was established

in this study between defect-free sections and lumber volume recovery, the production of

defect-free boles remains desirable as it could limit the proportion of red heartwood in a tree

(Giroud et al. 2008; Belleville et al. 2011; Baral et al. 2013; Havreljuk et al. 2013).

From a modeling point of view, the models for predicting the lumber yields presented in this

study could replace the old yield tables of Petro and Calvert (1976). The estimates of volume

recovery for the F4 logs are also a new contribution that could be implemented in growth

simulators (Fortin et al. 2009a). Theoretically, it would be possible to predict the lumber

assortment from yellow birch or sugar maple stems by using a model that predicts the log

grading assortment from a standing tree quality (Fortin et al. 2009b). The predictions (i.e.,

log net volume) from the Fortin et al. (2009b) model could be integrated in our model that

uses log net volume as input. However, prior to such use, a model evaluation should be

performed on the final predictions resulting from both models, to assess potential issues

related to bias and error propagation.

Although the number and size of lumber pieces is mostly related to log dimensions, the final

product assortment is also modulated by complex trade-offs between optimizing for lumber

volume or lumber value (Auty et al. 2014). Thus, smaller pieces with better characteristics

(i.e., grades and colors) may be preferred to larger lumber pieces of lower quality, because

of their relatively higher market value. However, the processing cost of such smaller pieces

could also be higher. The sawing operator has to consider potential lumber grades, colors and

volumes throughout the sawing process in order to produce lumber with the overall highest

value. As a result, there could be a bias related to the sawmill operator. In our study, this bias

was minimized because the same skilled workers processed all conventional and bolt logs

from both study sites.

The use of the beta inflated distribution with abundance of zeros proved useful for predicting

lumber volume recovery. As it could be observed in figures 3.1 and 3.2, the assessment of

the dispersion parameter needs to be improved to better represent each lumber grade and

color category. We tested other distributions, where the values of the dependent variable were

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not bound between 0 and 1, but these were associated with higher bias. Even with some

potential sources of bias and distribution problems, the predicted VRG and VRC per log were

close to the observed values. Therefore, we recommend using our models to obtain estimates

the volume recovery for a group or a population of logs. If they are used to generate

predictions for an individual log, the estimations could be inaccurate.

Conclusion

The objective of this study was to develop a set of models for predicting the lumber volume

recovery for grades and colors from external log characteristics in sugar maple and yellow

birch. No differences were found between these two species and they were considered

together in the assessment of the volume recovery related to lumber grades. The volume

recovery of the most valuable grades increased with log length and with the small-end

diameter of the log. In addition, the best lumber grades were found in the butt log. Log

dimensions were also positively related to the proportion of the most desired color

specifications, which did not contain red heartwood. Conversely, the red heartwood size was

related to the higher proportion of the regular (i.e., discolored) wood, that is less desirable

for both species, but also to a higher proportion of “Red Birch”, that generally has higher

value in yellow birch than the regular color. These variables were better predictors of volume

recovery than the specifically designed log grading rules. However, for a better accuracy,

they should be used to generate estimations of the volume recovery for a group of logs, rather

than at an individual level.

Acknowledgements

We are grateful to the Fonds de recherche du Québec – Nature et technologies (FRQNT) for

the financial support of this study. We wish to express our thanks to the staff from the

Ministère des Forêts, de la Faune et des Parcs du Québec (MFFP – Direction de la recherche

forestière), Station touristique de Duchesnay and Coopérative Forestière des Hautes-

Laurentides for providing and harvesting the sampling sites and to the staff of Centre de

recherche sur les matériaux renouvelables (CRMR) and FPInnovations for their assistance in

this project. The authors wish to acknowledge the valued collaboration of the staff from École

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de foresterie et de technologie du bois de Duchesnay for lumber processing and grading. We

are also grateful to Frauke Lenz, Élisabeth Dubé, Jean-Philippe Gagnon, Julie Barrette,

Emmanuel Duchateau and Normand Paradis for their assistance in the field work.

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Conclusion générale

Le succès d’un aménagement forestier étant lié de près à la qualité des arbres et des billes

produites (Erickson et al. 1992), il importe de favoriser la croissance des arbres sains et de

qualité, mais aussi de pouvoir évaluer cette qualité avant la récolte. L’objectif principal de

cette thèse était d’améliorer les prévisions d’approvisionnement des usines de transformation

de bois feuillu en reliant l’évaluation de la qualité des arbres sur pied à leur valeur monétaire

et au panier de produits transformés. L’érable à sucre et le bouleau jaune ont été choisis à

cause de leur abondance dans les forêts feuillues du Québec et leur importance commerciale.

En effet, les deux espèces sont principalement utilisées par l’industrie du sciage pour la

fabrication de divers produits dits « d’apparence », tels que les meubles et les parquets.

Le premier apport de cette thèse a permis de quantifier la proportion radiale de la coloration

de cœur des deux espèces dans 12 localisations couvrant l'ensemble de la zone tempérée du

sud du Québec. Un mesurage non destructif de la coloration de cœur en utilisant des carottes

de sondage a permis d’inventorier plusieurs arbres dans une vaste étendue, et ce, dans un

temps et aux coûts raisonnables. À notre connaissance, c’est la première étude scientifique

qui a mis en évidence le lien entre l’âge des arbres, leur vitesse de croissance et la proportion

de la zone colorée chez l’érable à sucre et le bouleau jaune. Des différences de la proportion

radiale de la coloration de cœur ont été observées entre les régions d’étude pour les deux

espèces et elles semblent être majoritairement attribuables à des facteurs liés au

développement des arbres. D’une part, nos résultats ont montré que l'âge cambial avait un

effet positif sur la proportion radiale de la coloration des deux espèces. La coloration de cœur

étant d’origine traumatique chez l’érable à sucre et le bouleau jaune, son développement et

sa propagation nécessitent des portes d’entrée dans l’arbre. L’effet d’âge observé serait ainsi

attribuable à une accumulation de blessures à travers le temps, principalement causées par la

mortalité des branches, et par une perte de vigueur de l’arbre (Giroud et al. 2008). Ces

résultats indiquent que l’occurrence de la coloration de cœur serait ultimement inévitable

chez les deux espèces. D’autre part, l’analyse de l’accroissement radial autour de la zone

colorée a révélé un effet positif de la superficie des anneaux de croissance à la limite de la

zone colorée et, dans le cas de l'érable à sucre, un effet négatif de la pente d’une régression

appliquée aux valeurs de superficie des anneaux couvrant une période de cinq ans de part et

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d’autre de cette même limite. Conséquemment, pour un âge donné, une période de croissance

rapide suivie d’un ralentissement associé une récession de la cime favorisera le

développement d’une plus grande colonne de coloration. Au-delà des effets de l’âge et de

l’accroissement, une partie de la variabilité était aussi associée à la température minimale

annuelle d’une localisation dans le cas de l’érable à sucre. Cette variable climatique peut

favoriser l’apparition de gélivures et d’autres blessures dans l’arbre et semble liée de près à

la limite nordique de l’aire de distribution de l’érable à sucre. Par ailleurs, aucune autre

variable climatique n’a permis d’expliquer les variations régionales de la coloration de cœur,

et ce, même si un effet régional entre les sous-domaines bioclimatiques, caractérisés par

différents régimes de précipitations, a été observé pour l’érable à sucre.

Les résultats du deuxième chapitre ont permis de déterminer les principales variables qui

affectent la valeur monétaire de l’érable à sucre et du bouleau jaune sur pied. Un mesurage

détaillé de 64 érables à sucre et 32 bouleaux jaunes qui ont été dûment inspectés et classés,

avant d’être abattus, tronçonnés et transformés en planches, nous a permis d’établir que le

seuil maximal de valeur par mètre cube de bois rond pour l’érable à sucre et le bouleau jaune

se situait entre 40 et 45 cm de dhp. Ces résultats sont très proches de ceux du récent rapport

du Comité sur l'impact des modalités opérationnelles des traitements en forêt feuillue

(CIMOTFF) (Saucier et al. 2014), où il a été établi que pour les forêts du Québec, les

diamètres de maturité financière (i.e., diamètres au-delà duquel les arbres laissés sur pied

perdent de la valeur) pour la production de bois d’œuvre variaient de 46 à 50 cm pour l’érable

à sucre et le bouleau jaune. Dans notre étude, nous avons également observé une diminution

de la valeur par volume unitaire de bois rond avec une augmentation du diamètre des arbres

qui n’avaient aucun défaut apparent (ex. qualités A et B). Cela a permis de mettre en évidence

les limites des systèmes de classement de la qualité des arbres sur pied, qui ne sont pas en

mesure d’évaluer les défauts internes des arbres. Ils permettent aussi de questionner la

recommandation du CIMOTFF sur l’application d’un seuil de dhp au-delà duquel tous les

arbres devraient être récoltés. En effet, selon nos résultats, il serait pertinent d’ajouter un seuil

de dhp au-delà duquel un arbre devrait être laissé sur pied puisque sa qualité interne est

susceptible d’être faible, et ce, même s’il rencontre les critères externes l’associant aux

meilleures classes de qualité. Cette diminution de la valeur des tiges de bonne qualité a été

causée par une perte de volume liée à la carie. L’utilisation d’une sonde perceuse s’est avéré

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un outil utile et complémentaire au classement visuel pour identifier les arbres cariés.

Toutefois, aucun lien n’a pu être établi entre le profil de la sonde perceuse et la présence de

coloration de cœur, qui diminue aussi la valeur des arbres, si cette coloration n’est pas

accompagnée de pourriture.

Les résultats du deuxième chapitre ont aussi mis en évidence le fait que parmi tous les types

de défauts qui doivent être pris en considération lors du marquage des arbres, les signes

visibles d’infection fongique et les fentes avaient la plus grande influence négative sur la

valeur des deux espèces. En n’utilisant que ces deux types de défaut dans nos systèmes

guidant la sélection des arbres, les critères de marquage des tiges peuvent être simplifiés, tout

en permettant d’assurer une évaluation de la qualité de l’arbre comparable aux systèmes plus

complexes. L’approche proposée est plus simple et moins coûteuse que celle des systèmes

complexes actuellement en place (p. ex., le système à quatre classes ABCD de Monger

(1991)). De plus, elle s’avère une solution de rechange aux approches dont l’efficacité n’a

pas été démontrée scientifiquement (p. ex., le système à deux classes O-P (MRNFQ 2006)).

Le troisième chapitre a mis en évidence le lien existant entre les caractéristiques des billes

destinées au sciage et les variables déterminantes qui font varier le rendement en produits

transformés. L’analyse des sciages a montré que la proportion des meilleurs grades augmente

avec la longueur et le diamètre des billes. De plus, le rendement en volume de sciages de

haute qualité était plus élevé dans le bas de l’arbre, parce que les grosses billes ont

généralement moins de défauts internes comme les nœuds. Chez l’érable à sucre, les

dimensions des billes de sciages étaient également positivement liées à un rendement plus

élevé en bois sans coloration, tandis que chez le bouleau jaune, elles étaient associées à une

plus forte proportion de la coloration de cœur. Dans tous les cas, les billes présentant une

grande zone colorée ont produit une forte proportion de bois « régulier » de valeur moindre.

Les modèles mis au point au troisième chapitre ont mieux prédit les rendements

volumétriques liés aux grades que les systèmes de classement de billes, ce qui indique qu’ils

pourraient remplacer les anciennes tables de rendement (Petro et Calvert 1976). D’autre part,

les modèles liés aux prévisions des rendements selon les couleurs des sciages constituent une

nouveauté permettant de mieux décrire le panier des produits transformés. Du point de vue

de la modélisation, le modèle de prévision du panier de produits de sciage pourrait être

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intégré à un simulateur de croissance comme SaMARE (Fortin et al. 2009a). À partir des

prévisions du volume par catégorie de billes provenant des modèles de Fortin et al. (2009b),

il serait possible de prédire le rendement en grades et en couleurs des planches. Par contre,

avant une telle application, il faudra évaluer et minimiser les biais tout en analysant la

propagation des erreurs d’un modèle à l’autre.

En reprenant les principaux résultats de ce projet de recherche, l’étude de sciages nous a

montré que la valeur au m³ de bois rond des arbres sur pied diminuait après un certain seuil,

alors que la qualité moyenne des planches avait tendance à être plus élevée dans les grosses

billes. Ces résultats peuvent paraitre contradictoires au premier abord. En effet, le fait que les

grosses billes soient généralement de plus grande valeur suggère qu’il faudrait tendre vers

une sylviculture axée sur la production de gros arbres. Techniquement, cela pourrait être vrai

tant et aussi longtemps que ces gros arbres produisent des billes de sciage de qualité. Par

contre, la valeur au m³ de bois rond des arbres sur pied décroit après un certain seuil, puisque

ces arbres produisent moins de billes qui ont les caractéristiques nécessaires au sciage. Cela

met en évidence l’importance de voir la section transversale d’une bille, qui peut ou non être

rejetée du sciage, contrairement aux arbres sur pied pour lesquels les systèmes de classement

de la qualité ne sont pas en mesure d’évaluer les défauts internes. Il n’est pas simple de

déterminer le moment idéal pour couper un arbre en maximisant le volume et la qualité du

sciage, et donc sa valeur. Comme nous l’avons décrit précédemment, l’utilisation des seuils

diamétraux peut être une solution. De plus, l’âge de l’arbre devrait aussi idéalement être

considéré dans l’établissement des seuils, puisque à croissance égale, un arbre plus vieux

devrait avoir plus de défauts internes qu’un arbre plus jeune, entre autres à cause d’une plus

grande zone colorée. Toutefois, en pratique, l’âge des arbres n’est pas une variable connue

des sylviculteurs œuvrant dans les forêts feuillues de structure typiquement inéquienne. La

vaste gamme d’âge obtenue parmi les échantillons du premier article de cette thèse démontre

bien qu’il serait difficile d’estimer cette variable de manière juste.

Pour minimiser le développement de la coloration de cœur des arbres sur pied, tout en

maximisant la valeur des sciages, il est préférable d’avoir une croissance radiale lente en bas

âge, puis plus rapide par la suite. Ainsi, nous proposons de faire croitre les arbres sous forte

compétition lorsqu’ils sont jeunes afin de favoriser l’élagage naturel et limiter le nombre de

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grosses branches. Par la suite, lorsque les arbres ont atteint une bonne hauteur de fût élagué,

un dégagement pourrait être fait pour augmenter la croissance radiale et ainsi favoriser la

production de billes de sciage (Baral et al. 2013). Les arbres ayant le meilleur potentiel pour

produire des sciages de qualité devraient être choisis comme des arbres d’avenir sur lesquels

appliquer ce scénario (Perkey and Wilkins 1993). En effet, un aménagement orienté vers la

production de petits arbres libres de croître, tout en maintenant une faible proportion de

grosses tiges résiduelles, peut être bénéfique pour la production de tiges destinées au sciage

(Buongiorno et al. 1993). D’autre part, les gros arbres ayant un faible accroissement ont une

probabilité de mortalité plus élevée (Woodall et al. 2005). Ainsi, l’aménagement des arbres

feuillus doit être orienté vers les individus qui ont un bon accroissement et qui possèdent un

potentiel d’amélioration du point de vue de qualité (Leak et al. 1987; Bastien and Wilhelm

2000). La très grande variabilité entre les âges des arbres échantillonnés au premier chapitre,

qui étaient pourtant de diamètres semblables et provenaient des mêmes stations, tend à

démontrer que l’environnement compétitif peut avoir un impact important sur la vitesse de

croissance radiale deux espèces.

Pour conclure, il a été démontré que la coupe de jardinage était très rarement appliquée selon

les règles de l’art, autant au Québec (Bédard and Brassard 2002) qu’au nord des États-Unis

(Pond et al. 2014). La volonté de restaurer le massif forestier, tout en assurant la rentabilité

des opérations forestières, s’est traduite par l’apparition de plusieurs traitements alternatifs

au jardinage ces dernières années (Saucier et al. 2014). Ces nouveaux traitements sylvicoles

et le retour vers un contexte plus favorable dans le secteur de la transformation du bois

(Fédération des producteurs forestiers du Québec 2015) font ressortir l’importance d’évaluer

avec justesse la qualité des arbres récoltés afin d’assurer un approvisionnement adéquat des

usines de transformation et des autres preneurs de bois feuillu. Les résultats de cette thèse

apportent plusieurs réponses face à cette problématique et peuvent avoir des retombées à

plusieurs niveaux. Tout d’abord, les modèles prévisionnels de la coloration de cœur et ceux

liés aux principaux défauts affectant la valeur des arbres sur pied peuvent servir d’outil au

sylviculteur lors de l’élaboration des prescriptions sylvicoles pour un peuplement. D’autre

part, l’identification visuelle des infections fongiques et des fentes en forêt peut être utilisée

pour améliorer les directives de martelage chez les feuillus. De plus, les modèles permettant

de prédire le rendement en grades ou en couleurs des sciages peuvent fournir aux producteurs

94

du bois une meilleure estimation du panier de produits, tout en offrant la possibilité d’être

intégrés dans les simulateurs de croissance. Finalement, dans cette étude, nous avons

uniquement considéré la valeur du bois découlant des produits transformés (sciages et pâte).

Afin de dresser un portrait économique et financier plus complet de la valeur des feuillus sur

pied, les recherches futures devraient, d’une part, inclure les coûts (p. ex., matière ligneuse,

transformation, etc.) et, d’autre part, considérer les autres utilisations possibles des bois

feuillus (p. ex., composés extractibles, granules, etc.), qui peuvent être complémentaires ou

non au sciage. De plus, les recherches futures pourraient porter sur les paniers de produits

alternatifs et de seconde transformation dans lesquelles certaines usines sont spécialisées,

plutôt que de se rattacher au bois de grade d’apparence, ce qui aurait l’avantage de se

rapprocher des marchés et d’une diversité de produits secondaires (p. ex., planchers,

ameublements, cabinets, etc).

95

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109

Annexe 1 : Données utilisées pour le chapitre 1

1A : Variables observées par site d’étude pour l’érable à sucre

ID Region name Stand

ID Latitude Longitude Elevation

Ecol.

type

Total stand

basal area

(m2/ha)

LT

Min

(°C)

MT

Mean

(°C)

Precip

(mm)

1 Témiscamingue 1 47°14'4" -78°47'47" 384 FE32 32.0 -43.8 2.1 1010.3


2

Rapides-des-

Joachims 1 46°20'16" -77°44'39" 314 FE32 27.0 -37.7 3.6 946.4

2

Rapides-des-

Joachims 2 46°19'34" -77°41'20" 248 FE32 26.7 -36.8 4.1 925.8

3 Réservoir Cabonga 1 47°26'42" -77°2'22" 402 FE32 28.0 -45.1 1.4 961.5


4 Outaouais 1 46°30'40" -76°19'53" 411 FE32 26.0 -40.5 2.7 971.3

4 Outaouais 2 46°29'1" -76°18'54" 328 FE32 25.3 -39.8 3.1 967.5

5 Ste-Véronique 1 46°35'33" -74°58'38" 371 FE32 24.0 -40.0 3.1 1071.4

5 Ste-Véronique 2 46°33'45" -74°56'44" 347 FE32 31.3 -39.8 3.3 1069.2

6 Mauricie 1 46°51'43" -72°43'6" 319 FE32 29.0 -41.3 2.7 1103.4

6 Mauricie 2 46°48'17" -72°40'44" 276 FE32 27.2 -40.3 3.0 1118.1

7 Portneuf 1 47°8'30" -72°5'26" 416 FE32 30.0 -39.4 2.1 1247.0

7 Portneuf 2 47°3'59" -72°6'47" 376 FE32 28.0 -38.9 2.4 1256.8

8 Duchesnay 1 46°52'20" -71°40'20" 293 FE32 30.0 -36.5 3.1 1345.1

8 Duchesnay 2 46°55'22" -71°37'32" 225 FE32 21.0 -36.4 3.4 1349.0

9 Lac Mégantic 1 45°22'13" -70°56'55" 591 FE32 36.0 -33.4 3.6 1349.9

9 Lac Mégantic 2 45°26'23" -70°40'37" 544 FE32 38.0 -34.0 3.6 1170.9

10 Montmagny 1 46°51'39" -70°31'7" 367 FE32 19.3 -33.9 3.2 1208.4

10 Montmagny 2 46°50'55" -70°32'10" 361 FE32 29.0 -33.9 3.2 1210.5

11 Charlevoix 1 47°58'7" -69°57'10" 342 MS12 28.5 -34.6 2.0 1026.5

11 Charlevoix 2 47°57'16" -70°0'2" 296 FE32 28.0 -34.9 2.1 1010.1

12 Squatec 1 47°55'52" -68°30'39" 367 FE32 33.0 -36.0 2.2 1135.4

12 Squatec 2 47°54'39" -68°29'8" 408 FE32 23.0 -36.5 2.0 1142.2

110

1B : Variable observées par site d’étude pour le bouleau jaune


ID Latitude Longitude Elevation

Ecol.

type

Total stand

basal area

(m2/ha)

LT

Min

(°C)

MT

Mean

(°C)

Precip

(mm)


1 Témiscamingue 2 47°13'18" -79°8'45" 327 MJ12 28.0 -41.8 2.7 955.1

2 Rapides-des-Joachims 1 46°19'34" -77°41'20" 248 FE32 26.7 -36.8 4.1 925.8

2 Rapides-des-Joachims 2 46°20'35" -77°43'6" 288 MJ12 29.0 -37.4 3.8 937.3



4 Outaouais 1 46°28'59" -76°18'45" 357 FE32 24.7 -40.0 3.0 968.3

4 Outaouais 2 46°29'59" -76°20'33" 352 FE32 28.0 -40.0 3.0 969.3

5 Ste-Véronique 1 46°36'39" -74°54'55" 344 MJ12 35.0 -40.0 3.3 1067.4

5 Ste-Véronique 2 46°33'45" -74°56'44" 347 FE32 31.3 -39.8 3.3 1069.2

6 Mauricie 1 46°49'16" -72°41'35" 343 FE32 20.0 -40.7 2.6 1116.7

6 Mauricie 2 46°48'17" -72°40'44" 276 FE32 27.2 -40.3 3.0 1118.1

7 Portneuf 1 47°11'43" -72°3'20" 493 MJ22 21.3 -40.3 1.6 1240.7

7 Portneuf 2 47°5'47" -72°6'27" 393 MJ12 29.0 -39.0 2.2 1260.7

8 Duchesnay 1 46°56'26" -71°44'9" 237 FE32 24.7 -36.0 3.4 1386.9

8 Duchesnay 2 46°55'22" -71°37'32" 225 FE32 23.0 -36.4 3.4 1349.0

9 Lac Mégantic 1 45°24'33" -70°40'13" 566 MJ12 27.0 -34.1 3.5 1181.6

9 Lac Mégantic 2 45°29'42" -71°9'39" 569 MJ12 24.0 -33.8 3.6 1424.1

10 Montmagny 1 46°51'36" -70°31'21" 362 MJ12 23.0 -33.9 3.2 1207.3

10 Montmagny 2 46°50'43" -70°31'34" 380 MJ12 25.0 -34.3 3.1 1216.1

11 Charlevoix 1 47°58'7" -69°57'10" 342 MS12 28.5 -34.6 2.0 1026.5

11 Charlevoix 2 47°57'16" -70°0'2" 296 FE32 28.0 -34.9 2.1 1010.1

12 Squatec 1 47°55'51" -68°30'40" 370 FE32 30.7 -36.1 2.2 1136.8

12 Squatec 2 47°55'28" -68°30'40" 413 FE32 32.0 -36.5 2.0 1149.7

111

1C : Variables dendrométriques pour l’érable à sucre


ID N

Mean tree

DBH ± SD

(cm)

Mean tree

height ± SD

(m)

Mean tree

age ± SD

(years)

Mean

discoloration age

± SD (years)

Mean radius proportion

of discoloration ± SD

(%)

Mean basal area

proportion of

discoloration ± SD (%)

1 Témiscamingue 1 8 29.1 ± 3.1 20.1 ± 1.2 119 ± 19 65.5 ± 16.9 52.5 ± 11.8 28.7 ± 11.2

1 Témiscamingue 2 8 27.9 ± 3.0 19.7 ± 1.0 101 ± 24 54.6 ± 16.9 57.0 ± 6.9 32.9 ± 8.0

2 Rapides-des-Joachims 1 8 27.2 ± 2.9 20.7 ± 2.6 82 ± 7 37.6 ± 3.7 38.3 ± 6.1 15.0 ± 5.0


3 Réservoir Cabonga 1 8 30.0 ± 3.3 20.3 ± 1.7 114 ± 23 54.9 ± 13.8 41.9 ± 7.0 17.9 ± 5.3


4 Outaouais 1 8 28.3 ± 2.8 22.2 ± 1.5 130 ± 9 77.9 ± 10.9 44.3 ± 6.9 20.1 ± 5.8

4 Outaouais 2 8 28.5 ± 3.4 20.8 ± 1.1 98 ± 22 50.8 ± 18.2 43.6 ± 9.7 19.8 ± 8.6

5 Ste-Véronique 1 8 27.5 ± 2.9 22.3 ± 1.5 108 ± 25 53.4 ± 23.7 40.5 ± 12.0 17.6 ± 9.9

5 Ste-Véronique 2 8 27.2 ± 2.8 19.4 ± 1.8 74 ± 10 31.9 ± 5.8 40.0 ± 10.6 17.0 ± 8.5

6 Mauricie 1 8 29.7 ± 2.8 22.6 ± 1.6 112 ± 23 51.8 ± 21.9 42.3 ± 8.1 18.4 ± 6.6

6 Mauricie 2 8 28.4 ± 3.0 19.2 ± 1.7 94 ± 8 41.6 ± 7.4 35.1 ± 12.4 13.7 ± 9.5

7 Portneuf 1 8 26.6 ± 2.2 20.2 ± 1.9 75 ± 15 33.6 ± 13.7 48.5 ± 9.2 24.2 ± 8.7

7 Portneuf 2 8 27.6 ± 2.4 20.1 ± 1.9 83 ± 12 38.5 ± 13.5 44.1 ± 10.4 20.4 ± 8.6

8 Duchesnay 1 8 28.8 ± 3.1 19.1 ± 1.0 94 ± 8 33.8 ± 7.3 42.7 ± 11.2 19.3 ± 9.7

8 Duchesnay 2 8 28.1 ± 2.4 22.8 ± 1.5 93 ± 23 44.1 ± 13.4 39.4 ± 9.0 16.2 ± 7.7

9 Lac Mégantic 1 8 30.8 ± 2.0 21.5 ± 1.9 78 ± 11 19.1 ± 9.7 21.6 ± 7.2 5.1 ± 3.1

9 Lac Mégantic 2 8 26.8 ± 2.0 22.7 ± 2.0 77 ± 5 28.8 ± 10.2 26.2 ± 14.0 8.6 ± 7.1

10 Montmagny 1 8 27.4 ± 3.0 21.4 ± 0.8 92 ± 17 28.4 ± 18.1 22.1 ± 16.6 7.3 ± 7.0

10 Montmagny 2 8 29.1 ± 2.8 21.1 ± 1.6 68 ± 9 19.0 ± 5.1 28.2 ± 12.2 9.2 ± 8.3

11 Charlevoix 1 8 28.9 ± 3.3 17.2 ± 1.5 94 ± 13 30.5 ± 7.4 24.5 ± 8.8 6.7 ± 4.8

11 Charlevoix 2 8 29.0 ± 2.9 17.3 ± 1.4 100 ± 28 33.4 ± 26.4 25.3 ± 18.8 9.5 ± 10.5

12 Squatec 1 8 27.8 ± 2.8 23.2 ± 1.2 82 ± 8 26.3 ± 15.2 23.3 ± 18.1 8.3 ± 9.7

12 Squatec 2 8 27.8 ± 2.1 20.5 ± 2.2 80 ± 9 21.5 ± 11.9 15.5 ± 11.0 3.5 ± 4.0

112

1D : Variables dendrométriques pour le bouleau jaune

ID Region name Site N

Mean tree

DBH ± SD

(cm)

Mean tree

height ± SD

(m)

Mean tree age

± SD (years)

Mean

discoloration age

± SD (years)

Mean radius

proportion of

discoloration ± SD

(%)

Mean basal area

proportion of

discoloration ± SD (%)

1 Témiscamingue 1 8 28.0 ± 2.9 19.3 ± 2.5 93 ± 8 41.3 ± 15.3 39.9 ± 19.2 19.1 ± 18.8

1 Témiscamingue 2 8 28.5 ± 2.1 18.7 ± 1.7 88 ± 16 33.8 ± 10.4 38.2 ± 7.4 15.0 ± 5.8





4 Outaouais 1 8 27.1 ± 3.0 19.7 ± 1.9 80 ± 19 30.9 ± 12.5 30.4 ± 12.5 10.6 ± 7.0

4 Outaouais 2 8 27.3 ± 2.7 19.8 ± 2.0 87 ± 27 39.5 ± 18.1 34.2 ± 12.2 13.0 ± 8.2

5 Ste-Véronique 1 8 27.9 ± 3.3 21.2 ± 1.9 98 ± 5 37.1 ± 8.8 49.2 ± 6.1 24.5 ± 5.9

5 Ste-Véronique 2 8 28.8 ± 3.0 21.4 ± 1.6 87 ± 7 29.0 ± 6.8 42.0 ± 9.0 18.3 ± 7.5

6 Mauricie 1 8 28.1 ± 3.4 19.3 ± 1.3 73 ± 33 31.0 ± 25.7 25.6 ± 15.3 8.6 ± 9.2

6 Mauricie 2 8 28.5 ± 2.5 20.5 ± 1.7 86 ± 12 41.1 ± 14.1 37.2 ± 12.9 15.3 ± 11.8

7 Portneuf 1 8 28.8 ± 3.0 16.2 ± 1.4 57 ± 23 13.4 ± 19.1 14.3 ± 13.9 3.7 ± 4.1

7 Portneuf 2 8 28.7 ± 3.5 19.4 ± 2.1 66 ± 18 26.6 ± 16.4 30.0 ± 13.5 10.6 ± 7.7

8 Duchesnay 1 8 28.1 ± 2.9 19.7 ± 1.3 68 ± 13 28.3 ± 10.1 39.5 ± 14.6 17.5 ± 10.6

8 Duchesnay 2 8 27.4 ± 2.6 23.5 ± 1.5 87 ± 12 44.3 ± 11.1 51.0 ± 11.5 27.2 ± 11.3

9 Lac Mégantic 1 8 27.7 ± 2.2 19.6 ± 1.2 74 ± 17 27.9 ± 7.9 29.5 ± 6.0 9.0 ± 3.3

9 Lac Mégantic 2 8 28.4 ± 2.4 19.0 ± 1.2 97 ± 9 44.6 ± 20.8 48.9 ± 14.4 25.7 ± 15.6

10 Montmagny 1 8 27.2 ± 3.5 19.9 ± 1.3 92 ± 9 44.0 ± 14.1 43.0 ± 11.4 19.6 ± 9.7

10 Montmagny 2 8 27.7 ± 2.3 19.3 ± 1.8 51 ± 5 11.8 ± 5.8 20.4 ± 8.7 4.8 ± 3.6

11 Charlevoix 1 8 27.3 ± 2.8 17.6 ± 0.9 70 ± 21 25.6 ± 14.0 26.2 ± 11.0 7.9 ± 5.5

11 Charlevoix 2 8 27.8 ± 3.1 17.2 ± 1.3 76 ± 18 24.3 ± 12.5 24.7 ± 9.6 6.9 ± 4.4

12 Squatec 1 8 27.9 ± 2.3 20.3 ± 2.3 79 ± 27 40.4 ± 27.2 45.9 ± 23.6 25.9 ± 17.0

12 Squatec 2 8 27.4 ± 2.8 20.5 ± 1.1 82 ± 38 44.3 ± 29.8 45.0 ± 20.4 23.9 ± 17.4

113

Annexe 2 : Données utilisées pour les chapitres 2 et 3

ID Site ID

Species DBH (cm)

Height (m)

Quality (Monger

1991)

Harvest priority (Boulet 2007)

Main defect (Boulet 2007)

Sound wood depth

(cm) (Resisto-graph)

Mean acoustic velocity

(m/s) (IML Hammer)

VAL ($/m³)

1 DU Sugar maple 39.7 22.5 B M FE06A 18.3 656.7 96.6

2 DU Sugar maple 23.5 20.6 C S EN03X 10.8 1238.8 47.0

3 DU Yellow birch 33.4 18.4 B C FE02A 11.8 1149.0 103.8

4 DU Yellow birch 26.6 17.0 D C FE02A 12.4 957.0 37.2

5 DU Yellow birch 34.7 19.9 C C NC07X 12.3 971.8 51.0

6 DU Sugar maple 47.5 21.2 C C FE06X 10.9 424.0 39.0

7 DU Sugar maple 30.1 18.8 C C VP01X 13.0 1157.5 44.7

8 DU Sugar maple 44.3 21.7 D S NC01X 3.3 481.0 42.0

9 DU Sugar maple 49.3 21.5 D M SP14A 12.3 411.2 25.6

10 DU Yellow birch 40.9 20.8 B M PR03A 5.3 596.0 41.4

11 DU Sugar maple 30.8 21.6 C M SP14A 7.8 826.7 39.4

12 DU Sugar maple 29.3 23.3 D M FE06A 7.3 488.2 35.4

13 DU Sugar maple 28.7 20.0 C R DB05X 10.8 1225.8 53.2

14 DU Yellow birch 38.1 17.9 D M SP15X 3.9 257.0 36.7

15 DU Yellow birch 62.4 23.2 A M PR03A 14.2 430.0 62.8

16 DU Sugar maple 35.2 19.0 B M FE06A 10.5 373.7 82.5

17 DU Sugar maple 28.0 21.6 D S DB05A 11.7 1291.0 54.7

18 DU Yellow birch 29.9 21.2 C M HP04X 14.1 918.0 63.0

19 DU Sugar maple 36.4 19.9 C M HP04X 15.0 1064.0 50.7

20 DU Yellow birch 39.3 25.6 A S EN03X 18.3 1128.2 103.8

21 DU Sugar maple 33.9 20.2 B R FE02X 15.7 1340.5 125.4

22 DU Sugar maple 42.7 25.8 C R FE02X 19.0 1163.0 110.5

23 DU Sugar maple 42.4 26.0 B C FE08X 19.5 690.0 81.6

24 DU Yellow birch 39.4 24.5 B R FE16X 16.1 1014.7 108.5

25 DU Sugar maple 46.1 24.7 A R HP01X 15.5 982.3 124.5

26 DU Sugar maple 40.5 24.7 A C FE06X 15.8 883.7 96.8

27 DU Sugar maple 39.3 17.5 B S DB05A 19.3 1333.3 83.4

28 DU Yellow birch 47.5 25.9 A R PR01X 21.6 1063.5 124.6

29 DU Yellow birch 31.0 23.7 C R EN01X 14.5 1054.0 92.3

30 DU Sugar maple 38.3 22.7 C S DB20A 14.9 1135.3 62.4

31 DU Yellow birch 42.5 22.5 A C NC07X 19.8 1120.7 128.5

32 DU Sugar maple 31.5 18.8 D C VP01X 13.3 1275.3 37.7

33 DU Sugar maple 37.7 22.0 B C EN04X 14.5 1215.8 94.6

34 DU Sugar maple 33.2 20.5 B S FE04A 14.3 1150.5 83.4

35 DU Yellow birch 38.8 26.2 B S EN03X 18.2 1159.5 121.8

36 DU Yellow birch 37.5 21.0 D R FE02X 17.6 1007.7 56.7

37 DU Sugar maple 24.6 22.9 D R FE02X 9.9 1207.0 46.2

38 DU Yellow birch 28.6 23.0 D S SP10X 4.3 477.8 35.1

39 DU Sugar maple 42.3 19.9 D C PR07X 12.0 753.0 46.2

40 DU Yellow birch 46.9 23.9 C S EN03X 18.2 823.8 64.2

41 DU Sugar maple 42.2 27.2 B R PR01X 16.4 1124.3 118.1

42 DU Sugar maple 37.2 22.0 B M SP14A 16.0 1269.7 113.7

43 DU Sugar maple 39.4 25.8 A R FE15X 18.7 1290.3 110.8

44 DU Sugar maple 46.7 25.0 B M PR03A 17.6 1024.8 81.5

45 DU Sugar maple 38.7 21.6 B S EN03X 17.8 1399.2 151.4

46 DU Sugar maple 58.5 26.4 B S EN03X 20.9 754.2 100.6

47 DU Sugar maple 40.7 20.0 A C EN04X 18.6 1304.0 137.2

48 DU Sugar maple 35.8 21.5 D R FE15X 14.5 1298.8 82.0

114

49 ML Yellow birch 46.9 23.5 B C FE06X 20.0 1089.8 62.7

50 ML Yellow birch 45.1 21.0 A R AR01X 20.8 998.3 99.9

51 ML Yellow birch 48.3 23.3 C S SP10X 16.6 1023.5 23.7

52 ML Yellow birch 48.8 20.8 B M FE06A 8.6 399.8 12.8

53 ML Yellow birch 42.4 21.8 A C FE06X 13.3 716.5 110.8

54 ML Yellow birch 28.1 20.7 C C DB10X 13.6 813.5 34.0

55 ML Yellow birch 31.4 22.9 C R DB05X 15.1 913.0 80.4

56 ML Yellow birch 31.9 24.1 D C DB10X 13.4 586.3 19.6

57 ML Yellow birch 49.5 23.2 A M HP07X 15.3 955.8 68.0

58 ML Yellow birch 38.2 25.7 B R PR01X 18.1 1008.8 66.9

59 ML Yellow birch 32.6 22.2 C M NC02X 9.3 705.5 51.1

60 ML Yellow birch 44.7 22.7 D M NC07A 15.1 934.2 17.4

61 ML Yellow birch 40.7 22.0 A S SP10X 18.4 1047.0 60.2

62 ML Yellow birch 38.9 24.6 B S EN03X 15.5 852.3 38.6

63 ML Yellow birch 32.9 21.1 D S EN03X 15.1 815.0 35.5

64 ML Yellow birch 34.2 21.3 D R FE02X 15.7 1031.5 50.1

65 ML Sugar maple 43.0 21.7 A S EN03X 19.5 1191.3 65.7

66 ML Sugar maple 37.3 18.6 C R DB05X 15.4 629.0 64.1

67 ML Sugar maple 37.1 20.0 D R FE01X 16.3 854.8 40.7

68 ML Sugar maple 37.2 22.2 C M SP14A 13.5 1111.5 55.3

69 ML Sugar maple 25.4 18.8 D R DB05X 7.7 806.3 14.2

70 ML Sugar maple 44.1 20.4 A R AR01X 20.5 1194.7 88.1

71 ML Sugar maple 36.5 25.0 C S SP09X 13.3 1096.8 45.4

72 ML Sugar maple 37.5 22.2 C C FE08X 13.0 895.5 37.1

73 ML Sugar maple 46.8 22.3 A M VP02X 19.1 1029.5 31.8

74 ML Sugar maple 30.0 18.9 C M SP14A 6.1 655.2 19.4

75 ML Sugar maple 27.2 18.8 C S VP01A 12.3 806.8 59.5

76 ML Sugar maple 29.7 20.9 C C VP01X 13.3 1204.8 57.3

77 ML Sugar maple 44.7 25.1 A M FE06A 16.2 895.8 74.6

78 ML Sugar maple 39.7 22.5 B C FE06X 11.6 947.5 58.1

79 ML Sugar maple 44.0 20.1 B S HP11A 17.8 1007.0 49.2

80 ML Sugar maple 32.1 21.8 D M NC07A 10.1 664.7 24.3

81 ML Sugar maple 32.9 18.6 D S EN03X 14.6 1193.0 61.0

82 ML Sugar maple 36.9 19.4 B S VP01A 13.7 1164.7 64.6

83 ML Sugar maple 50.5 21.0 A R FE02X 13.1 426.3 33.1

84 ML Sugar maple 38.8 21.0 D M SP08X 5.1 627.8 30.3

85 ML Sugar maple 36.4 19.0 B R DB14X 15.4 564.3 55.9

86 ML Sugar maple 55.0 26.2 A C VP01X 11.5 480.8 31.4

87 ML Sugar maple 36.2 22.8 B C DB20X 16.7 1342.2 80.2

88 ML Sugar maple 32.5 22.6 C R AR01X 14.8 1284.5 53.2

89 ML Sugar maple 26.2 17.2 D C DB10X 7.5 566.0 32.5

90 ML Sugar maple 48.7 21.3 B R DB05X 19.6 728.3 79.7

91 ML Sugar maple 35.1 17.6 B M SP16X 7.6 980.0 26.8

92 ML Sugar maple 41.1 24.1 A C FE06X 17.9 748.7 63.4

93 ML Sugar maple 50.1 18.2 B M SP12X 11.1 956.8 13.2

94 ML Sugar maple 40.2 24.8 D S SP09X 10.8 1071.5 51.1

95 ML Sugar maple 34.8 21.9 D C FE06X 12.6 652.8 16.6

96 ML Sugar maple 53.3 25.5 A S NC01X 20.8 944.3 54.1

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Évaluation visuelle des arbres feuillus sur pied et valeur ... · Alexis Achim, qui a toujours eu confiance en moi et qui a été le premier à me donner la chance de poursuivre