Les bois sans preneurs :
un approvisionnement potentiel pour la bioénergie
Mémoire
Claude Durocher
Maîtrise en sciences forestières Maître ès sciences (M.Sc.)
Résumé
La possibilité forestière représente le volume maximum des récoltes annuelles à perpétuité sans nuire à la capacité productive du milieu forestier. Les niveaux de récolte actuels dans la province de Québec, qui alimentent un réseau industriel dominé par la production de bois de sciage, de panneaux et de pâte, ne représentent en moyenne que 55% de la possibilité forestière, ce qui peut entraîner une dégradation graduelle de la ressource forestière si les peuplements de haute qualité sont récoltés en premier. Dans ce contexte, l’utilisation d’arbres de basse qualité et de peuplements moins désirés pour la production de bioénergie pourrait contribuer à améliorer à la fois les pratiques sylvicoles et la rentabilité de la chaîne de valeur. Le but de cette étude était d’identifier les facteurs biophysiques et socio-économiques qui affectent la proportion de la possibilité forestière récoltée dans les 74 unités de gestion du Québec, permettant ainsi d’identifier les peuplements qui pourraient être valorisés en bioénergie. Les résultats, issus d’analyse des données de possibilité et de récolte forestière pour la période 2008-2013, ont montré que la proportion de récolte était particulièrement faible pour les espèces feuillues, cette proportion pour les peupliers, le bouleaux et les érables variant seulement entre 19 et 38%. La distance entre la ressource et l’usine de pâte ou de panneaux la plus proche a été confirmée comme facteur principal du ratio récolte/possibilité forestière pour les espèces feuillues. Pour les espèces résineuses, plus la présence de peuplements feuillus est dominante dans une unité d’aménagement, plus le ratio de récolte/possibilité forestière est bas. Ainsi, les feuillus de qualité inférieure pourraient être utilisés comme une source importante de matière première dans le secteur de la bioénergie. Le développement d’une synergie entre les produits traditionnels et ceux de bioénergie pourrait faciliter l’application de saines pratiques sylvicoles et augmenter la rentabilité pour l’ensemble de la chaîne de valeur des produits forestiers.
Table des matières
Résumé iii
Table des matières v
Liste des tableaux vii
Liste des figures ix
Remerciements xi
Avant-propos xiii
Introduction 1
1 Untapped volume of surplus forest growth as feedstock for bioenergy 7
1.1 Résumé . . . 7
1.2 Abstract . . . 8
1.3 Introduction. . . 9
1.4 Material and methods . . . 12
1.5 Data analyses . . . 15
1.6 Results. . . 18
1.7 Discussion . . . 25
Conclusion 33
Liste des tableaux
1.1 Occurrence of zeros and ones in the observations of the dependent variable harvest/AAC ratio. ‘n’ is the total number of observations (Management Units)
and ‘Dist. family’ the distribution used in the models for each species group.. . 15
1.2 List of variables included in the full model. ‘MU’ is the management unit and
‘AAC’ is the annual allowable cut. . . 17
1.3 Mean and standard deviation of each covariate used in the study . . . 18
1.4 Annual allowable cut (AAC), total harvest and harvest/AAC ratio per species
group for all management units over the 2008-2013 period . . . 19
Liste des figures
1.1 Study forest area by MU and bioclimatic domain . . . 13
1.2 Distribution of harvest/AAC ratios per species group . . . 19
1.3 Observed harvest/AAC ratios per MU over the 2008-2013 period for the 4
spe-cies groups, 0 = No harvest, 100 = All the AAC was harvested . . . 20
1.4 Distances by road (km) between the centroid of the MUs and the nearest pulp
or fibreboard mill for the three groups of deciduous species . . . 21
1.5 Predictions of harvest/AAC ratio for birch as a function of the distance to a
pulpmill and the proportion of mixed forest in the management unit. . . 23
1.6 Predictions of harvest/AAC ratio for birch as a function of the distance to a
pulpmill and the total number of operating mills in the management unit. . . . 23
1.7 Predictions of harvest/AAC ratio for maple as a function of the distance to a
pulpmill and the road density index in the management unit. . . 24
1.8 Predictions of harvest/AAC ratio for poplar as a function of the distance to a pulp or fibreboard mill and the proportion of mixed forest in the management
unit. . . 25
1.9 Predictions of harvest/AAC ratio for the Spruce-pine-fir-larch group as a func-tion of the proporfunc-tions of mature and deciduous forests in the management
Remerciements
Avant tout, je tiens à remercier mes directeurs de recherche, Évelyne Thiffault et Alexis Achim. Merci à Évelyne pour son enthousiasme et son optimisme contagieux qui m’ont motivé tout au long de cette maîtrise. Grâce à son dynamisme, j’ai eu l’opportunité de présenter à quelques reprises mon travail et de faire un stage en Australie. Merci à Alexis pour sa disponibilité et son support continu dans mes études. Sa rigueur, son sens critique et dévouement pour la science et l’éducation sont admirables. Merci à vous deux de m’avoir impliqué dans différents projets. Je remercie également David Auty pour sa précieuse contribution à l’analyse statistique. Merci à mes collègues et amis : Anne, Manu, JR, Kay, Joëlle, André et mes autres acolytes du ABP-GHK. Je suis reconnaissante d’avoir côtoyé des gens aussi doués et agréables que vous. Merci à Jean-Romain de m’avoir ouvert les yeux sur ce qui est réellement efficace, j’utiliserai longtemps la sainte trinité : GNU/Linux, LATEX, R.
Merci à Marc Brown de University of Sunshine Coast et Ian Last de HQPlantations pour le stage à Gympie, Queensland. J’ai beaucoup de gratitude envers ceux qui ont fait de cette expérience une aventure inoubliable : Ian, Liz, Gabe, Lee, Kate, Michelle, Ken&Lee, Dom et tous mes autres mates.
Merci au Ministère des Forêts, de la Faune et des Parcs du Québec pour les données. Merci au réseau Biofuelnet pour le soutien financier qui a permis la réalisation du projet. Merci au Fonds de recherche du Québec – Nature et technologies pour l’appui financier qui m’a permis de réaliser un stage à l’international dans le cadre de ma maîtrise.
Finalement, merci à mes parents, mes sœurs et mes amis. Surtout, merci à meu companheiro de viagem, Raf, de m’avoir suivi à l’autre bout du monde !
Avant-propos
Ce mémoire est constitué d’un article rédigé en anglais, d’une introduction et d’une conclusion générale en français. L’article a été rédigé dans le but d’être publié dans une revue scientifique. Les questions et hypothèses de recherche ont été établies avec l’aide de ma directrice, Évelyne Thiffault et de mon codirecteur de recherche Alexis Achim. J’ai fait les analyses statistiques avec les données du Ministère des Forêts, de la Faune et des Parcs du Québec et du Bureau du Forestier en chef. J’ai interprété les résultats et rédigé le manuscrit ; je suis donc l’auteure principale de l’article. Ma directrice et mon codirecteur ont participé à l’élaboration du projet de recherche, et ont fourni d’indispensables avis tout au long du projet sur la méthodologie, l’analyse des résultats et la rédaction. Ils sont donc coauteurs de l’article. Également, David Auty a contribué aux analyses statistiques et à la rédaction par ses corrections et ses com-mentaires. Il est ainsi quatrième auteur de l’article inséré. Julie Barrette a également révisé et commenté l’article, et sera donc incluse comme cinquième auteure.
Introduction générale
Anticiper les changements climatiques, évaluer leurs impacts et concevoir des solutions, voici de grands défis du 21esiècle. L’augmentation rapide de la concentration des gaz à effet de serre
(GES) au cours du dernier siècle indique l’ampleur des changements (Dlugokencky and Tans,
2015). Le dioxyde de carbone (CO2) représente 76% des émissions de GES anthropiques (IPCC,
2015). Bien qu’il se retrouve naturellement dans l’environnement, sa forte concentration et son augmentation marquée le rendent préoccupant. Les données empiriques et leur modélisation démontrent de plus en plus l’influence des activités anthropiques sur le climat. En effet, une source majeure d’émission de CO2provient directement des carburants fossiles et des procédés
industriels. Selon le Groupe intergouvernemental d’experts sur l’évolution du climat (GIEC), le secteur de l’énergie contribuait à 35% des GES anthropiques en 2010 ; cette proportion ne cesse d’augmenter, malgré les accords internationaux tels que le Protocole de Kyoto et la Convention-cadre des Nations unies sur les changements climatiques (IPCC,2015). Il s’avère donc incontournable de trouver des solutions énergétiques à impact réduit sur le climat. Le système énergétique mondial actuel repose principalement sur des ressources qui s’épuisent, alors que les besoins énergétiques ne font qu’augmenter (IEA, 2017). En 2013, 78% de la consommation mondiale reposait sur les carburants fossiles (REN21,2015). Plusieurs auteurs considèrent cette dépendance comme un risque (Kruyt et al., 2009; Owen et al., 2010) ou une menace pour la sécurité énergétique de certains pays (Kruyt et al., 2009; Jansen and Seebregts,2010;Löschel et al.,2010). Ainsi, outre les raisons environnementales, les limites de cette ressource encouragent la recherche de sources d’énergie alternatives (Demirbas,2009). La bioénergie fait partie des solutions pour émettre moins de GES et progressivement rem-placer les combustibles fossiles. Par divers moyens technologiques, il est possible de convertir la biomasse en énergie sous plusieurs formes : gazeuses (biohydrogène, syngas), liquides (bio-diesel, bioéthanol, carburant synthétique) ou solides (copeaux, granules) (Demirbas, 2007;
Chhetri and Islam,2008;Lattimore et al.,2009;Rentizelas et al.,2009;Sims et al.,2010). La production mondiale de biodiesel et bioéthanol a pratiquement quadruplé de 2004 à 2014. Sur la même période de temps, la production de biocarburant au Canada est passée de 114 à 1143 milliers de tonnes équivalentes de pétrole (BP, 2015). Les biocarburants actuels sont princi-palement dits « de première génération », c’est-à-dire qu’ils sont produits à partir de matières
premières pouvant faire concurrence à la production de nourriture en accaparant des terres agricoles productives et augmentant les prix de biens alimentaires (FAO,2008;Mitchel,2008;
OECD,2009). Les biocarburants de seconde génération sont issus de résidus ligno-cellulosiques, comme les résidus agricoles et la biomasse forestière. Par conséquent, ils n’entrent pas en com-pétition avec la production alimentaire (Gallagher,2008;Koh and Wilcove,2008;Sims et al.,
2010;Gamborg et al., 2012) . Toutefois, même si la seconde génération de biocarburants est prometteuse pour contourner les désavantages de la première génération, plusieurs défis de taille restent à surmonter, dont la caractérisation des sources d’approvisionnement (Becker et al.,2009;Saddler et al.,2012;Dale et al.,2014).
Les sources de biomasse forestière sont variées : résidus d’usines de transformation (sciures, planures, copeaux), résidus de coupe (branches, houppiers, souches) et arbres entiers. En raison de l’importance des volumes potentiellement disponibles, la biomasse forestière compte parmi les sources d’approvisionnement ayant un grand potentiel pour les biocarburants de deuxième génération en Amérique du Nord (Parikka,2004;Mabee et al.,2006). Or, la récolte de biomasse en forêt peut avoir un impact sur l’environnement. Plusieurs études ont abordé les impacts écologiques de la récolte de résidus forestiers, notamment sur les sols et la biodiversité (Thiffault et al.,2010;Lamers and Junginger,2013). Les impacts de cette pratique varient d’un environnement à l’autre. Dans certains milieux sensibles, la récolte de biomasse devrait être limitée ou proscrite (Lattimore et al.,2009;Thiffault et al.,2010). La récolte d’un arbre entier de basse qualité pour la bioénergie peut entrainer d’autres enjeux quant au bilan d’émissions de GES (Laganière et al.,2017). Ces arbres détériorés ou mourants sont importants pour la biodiversité et l’habitat de plusieurs espèces (Larsson and Danell, 2001; Beese et al., 2003;
Weir et al., 2012). Une exploitation durable de résidus forestiers et d’arbres entiers pour la bioénergie est possible. Toutefois, il est nécessaire de respecter les conditions du site permettant l’exploitation.
Au Québec, la filière de la bioénergie provenant de la biomasse forestière prend lentement forme. La part de la biomasse dans la consommation totale d’énergie était de 7,3% en 2011 au Québec, soit trois millions de tonnes équivalentes de pétrole (Ministère de l’Énergie et des Ressources naturelles, 2015). Elle est consommée principalement dans les secteurs résidentiel et industriel (essentiellement par le secteur des pâtes et papiers). La production de granules de bois représente également une part grandissante de la filière bioénergétique. La province se place bien par rapport au reste du Canada puisque neuf usines sont installées au Québec sur les 41 usines du pays (Bernier et al.,2013; Ressources Naturelles Canada, 2013). Le plus grand marché pour les granules de bois se trouve en Europe, principalement pour la production de chaleur et d’électricité. C’est d’ailleurs la principale destination des granules de bois du Canada. Par contre, le Québec n’exportait que 19% de sa production en Europe en 2013, soit beaucoup moins que la Colombie-Britannique (82%) qui est pourtant beaucoup plus éloignée (Bradburn,2014).
Estimer le volume de biomasse forestière qui serait disponible pour la bioénergie reste à faire. Les quantités de résidus d’usine de transformation comme les sciures et les planures sont relativement bien connues (Mabee et al.,2004;Bradley, 2010;Ghafghazi et al.,2017). En ce qui concerne les résidus de coupe, des ratios pour estimer le volume de biomasse forestière ont été utilisés sur de grandes superficies (Smeets and Faaij,2007). Ces estimations de source potentielle de biomasse forestière sont faites à l’échelle nationale ou provinciale, souvent basées sur les volumes de récolte et les superficies forestières (Gronowska et al., 2009; Paré et al.,
2011;Parzei et al.,2014). Il est donc difficile de traduire l’information à une échelle plus fine, ce qui est pourtant nécessaire pour le développement de la filière. En plus des résidus,Dymond et al. (2010); Mansuy et al. (2017) ont évalué le potentiel de biomasse issu de perturbations naturelles (sous forme d’arbres entiers dégradés par les perturbations) à l’échelle nationale. Or, la littérature scientifique comporte des lacunes quant aux volumes d’essences et d’arbres ayant peu de potentiel commercial dans les filières de produits traditionnels du bois et qui pourraient être valorisés pour la bioénergie. Cette idée a été abordée à plusieurs reprises (Hassegawa et al.,
2015;Pothier et al.,2013;St-Pierre et al., 2013;Nguyen et al.,2015;Alam et al.,2012;Paré et al.,2011), sans pourtant avoir établi les volumes et le potentiel de valorisation.
Les industries forestières au Québec ne récoltent pas tous les bois inclus de la possibilité fo-restière, cette dernière représentant le volume maximum des récoltes annuelles à perpétuité, sans nuire à la capacité productive du milieu forestier. Selon le bureau du Forestier en Chef, environ 52-95% de la possibilité a été récolté en résineux et 14-48% en feuillus entre 1990 et 2008. Les calculs et les projections de la possibilité forestière reposent sur des hypothèses spécifiques, telles que les niveaux de récolte, leur distribution et d’autres activités sylvicoles comme les plantations et les éclaircies. Si ces hypothèses ne sont pas appliquées, les projections de la possibilité forestière deviendront inexactes (Paradis et al.,2013). Différents facteurs, liés à des facteurs écologiques et économiques, expliquent ces volumes non-récoltés. Une portion de ces volumes est constituée d’arbres ou de peuplements dont la valeur est trop basse dans les conditions actuelles pour justifier la récolte. Ces bois sans preneurs peuvent ainsi être qualifiés de « mal-aimés ». Le bois est considéré de basse qualité lorsqu’il ne possède pas les caracté-ristiques recherchées pour la transformation en produits (Briggs, 2010), ces caractéristiques recherchées variant selon la demande des marchés et la structure industrielle.
Un peuplement dégradé restera dégradé s’il n’y a pas d’intervention sylvicole pour l’amélio-rer (Hassegawa et al.,2015). Il a été démontré que certaines interventions forestières peuvent augmenter la valeur moyenne des arbres au sein du peuplement en retirant les tiges de mau-vaise qualité qui entrent en compétition pour les ressources avec les tiges de meilleure qualité (Skog et al., 2006; Pothier et al., 2013). De plus, des efforts de restauration peuvent être ac-complis dans les peuplements dont la structure et la composition ne correspondent pas aux caractéristiques préindustrielles du milieu (Delisle-Boulianne et al.,2014). Ces caractéristiques de forêts préindustrielles ne sont pas seulement recherchées, mais obligatoires avec la loi sur
l’aménagement durable du territoire forestier au Québec (Gouvernement du Québec, 2017). C’est pourquoi plusieurs mesures ont été prises pour assurer la récolte des arbres non vigou-reux en priorité (Delisle-Boulianne et al.,2014; Boulet et al.,2007). Pourtant, Boulet(2015) et le bureau du Forestier en Chef mentionnent l’ « incapacité à utiliser les bois de mauvaise qualité »de l’industrie. L’état actuel des forêts rend parfois difficile l’application des mesures en place pour des raisons financières (Pothier et al.,2013).
Le développement de nouvelles avenues de transformation fait partie des pistes de solutions pour répondre à cette problématique. Notamment, la récolte des arbres et des peuplements mal-aimés, présentement sans preneurs, pour produire de la bioénergie permettrait de remettre en production des sites ou d’améliorer la qualité des peuplements résiduels dans le contexte de coupes partielles. Cela serait bénéfique pour l’ensemble du tissu industriel forestier à long terme. Des recherches européennes ont démontré que l’ajout de la bioénergie favoriserait les in-dustries du sciage en raison de l’augmentation de la demande des coproduits (résidus de sciage) (Schwarzbauer and Stern,2010), mais défavoriserait celle des pâtes et papiers et des panneaux agglomérés (Schwarzbauer and Stern,2010;Trømborg and Solberg, 2010). Le contexte diffé-rent ne permet pas d’étendre ces résultats à nos conditions en raison, entre autres, de la grande disponibilité de la fibre non exploitée dans les forêts québécoises. À court terme, en ajoutant la bioénergie au panier de produits et en envoyant vers cette filière les arbres de faible qualité, nous posons l’hypothèse que cela encouragerait l’exploitation de certains peuplements qui ont actuellement un trop petit volume de bois désiré par les industries conventionnelles du sciage et de la pâte. À long terme, l’utilisation accrue d’arbres de faible qualité pourrait améliorer significativement l’état des forêts dégradées et donc la rentabilité de l’ensemble de la chaîne de valeur.
Objectif
Au Québec, la possibilité forestière n’est pas entièrement récoltée pour des raisons biophysiques (caractéristiques de la forêt) et industrielles/socio-économiques (caractéristiques du réseau in-dustriel). L’objectif général de ce projet de maîtrise est d’estimer les facteurs qui influencent le ratio de récolte de la possibilité forestière pour la période 2008-2013, et donc les volumes de bois sans preneurs. Les caractéristiques biophysiques des peuplements seront prises en compte pour tenter d’améliorer la compréhension des bois mal-aimés non récoltés par les industries conventionnelles du sciage et de la pâte. Également, la compréhension des paramètres de la structure industrielle régionale permettra d’établir les conditions qui déterminent la récolte, ou non, d’un volume de bois donné par groupe d’espèce. Cela pourra ouvrir la porte à la bioéner-gie, qui, par son ajout dans le panier de produits, pourrait permettre l’application de saines pratiques sylvicoles et l’intensification de l’aménagement dans des peuplements contenant une forte proportion de bois mal-aimés et présentement sans preneurs.
Le territoire d’étude comprend l’ensemble de la forêt aménagée du Québec, qui est divisée en 74 unités d’aménagement forestier (UA). L’analyse des données est effectuée à l’échelle de l’UA. L’ensemble des 74 UA permet de dresser le portrait provincial et de développer un modèle qui localise la matière ligneuse non récolté proportionnellement à la possibilité forestière. L’étude cible les facteurs qui limitent la récolte de la possibilité, permettant de quantifier les volumes potentiels pour la bioénergie.
L’étude poursuit deux objectifs spécifiques qui sont de :
— mesurer les relations entre les variables biophysiques et industrielles et la proportion des volumes récoltés de la possibilité forestière à l’échelle d’une UA ;
— cartographier les variables qui influencent la proportion des volumes récoltés de la pos-sibilité forestière.
Hypothèses
L’analyse s’appuie sur les deux hypothèses de recherche suivantes :
1. La proportion de la possibilité forestière réellement récoltée augmente avec la proximité des usines dont l’approvisionnement consiste en bois de qualité inférieure (c.-à-d. les usines de pâte et de panneaux)
2. La proximité de telles usines réduit le biais en faveur de la récolte de peuplements présentant les meilleures caractéristiques pour les produits de bois d’œuvre (sciage). Cette analyse a été conçue de manière à quantifier et cartographier le potentiel de la bioénergie à partir de la croissance forestière excédentaire à travers le Québec, ainsi que fournir une évaluation préliminaire du potentiel de biomasse forestière, sous forme d’arbres entiers, qui pourrait être allouée à la bioénergie.
Chapitre 1
Untapped volume of surplus forest
growth as feedstock for bioenergy
1.1
Résumé
La possibilité forestière représente le volume maximum des récoltes annuelles à perpétuité sans nuire à la capacité productive du milieu forestier. Les niveaux de récolte actuels dans la province de Québec, qui alimentent un réseau industriel dominé par la production de bois de sciage, de panneaux et de pâte, ne représentent en moyenne que 55% de la possibilité forestière, ce qui peut entraîner une dégradation graduelle de la ressource forestière si les peuplements de haute qualité sont récoltés en premier. Dans ce contexte, l’utilisation d’arbres de basse qualité et de peuplements moins désirés pour la production de bioénergie pourrait contribuer à améliorer les pratiques sylvicoles et la rentabilité de la chaîne de valeur. Le but de cette étude était d’identifier les facteurs biophysiques et socio-économiques qui affectent la propor-tion de la possibilité forestière récoltée dans les 74 unités de gespropor-tion du Québec, permettant ainsi d’identifier ce qui pourraient être valorisés en bioénergie. Les résultats, issus de l’analyse des données pour la période 2008-2013, ont montré que la proportion de récolte était particu-lièrement faible pour les espèces feuillues, cette proportion pour les peupliers, le bouleaux et les érables variant seulement entre 19 et 38%. La distance entre la ressource et l’usine de pâte la plus proche a été confirmée comme le facteur principal du ratio récolte/possibilité forestière pour les espèces feuillues. Pour les espèces résineuses, la présence de peuplements feuillus dans une région donnée influençait à la baisse le ratio de récolte/possibilité forestière. Ainsi, les feuillus de qualité inférieure pourraient être utilisés comme une source importante de matière première dans le secteur de la bioénergie.
1.2
Abstract
The annual allowable cut (AAC) is used in Canadian forestry to set the biophysical harvest limit of roundwood that will maintain the productive capacity of the forest. Current harvest levels in the province of Quebec, which feed an industrial network dominated by the production of lumber, panels and pulp, average only 55% of the AAC, which may cause a gradual deple-tion of the forest resource if stands that have the highest value are preferably selected. In this context, using surplus forest growth consisting of low quality trees and less desirable stands as bioenergy feedstock could help improve both silvicultural practices and wood value chain profitability. The aim of this study was to identify biophysical and socio-economic factors that affect the proportion of the AAC that is harvested in Quebec’s 74 management units. Results from the analysis of AAC and harvesting data for the period 2008-2013 showed the harvested proportion of the AAC was particularly low for hardwood species, with the proportions for poplar, birch and maple ranging between only 19 and 38%. The distance to the nearest pulp or fibreboard mill was confirmed as the prime factor determining the harvest/AAC ratio for de-ciduous species. For softwoods, the presence of dede-ciduous stands in a given region affected the harvest/AAC ratio. Low quality hardwoods could be used as an important source of feedstock for the bioenergy sector. Developing a synergy between traditional and bioenergy products could facilitate the application of sound silvicultural practices and increase profitability along the entire wood value chain.
1.3
Introduction
Growing concerns around rising greenhouse gas (GHG) emissions linked to the use of fossil fuels are driving the development of alternative energy sources, such as energy from biomass, or bioenergy (Gallagher, 2008; McKechnie et al., 2011; Paré et al., 2011). Deployment of sustainable biomass supply chains for bioenergy production can also contribute to local energy security, sustainable employment, and economic diversification, particularly in rural areas. The Intergovernmental Panel on Climate Change has notably highlighted the potential of forest biomass for bioenergy production, as part of a larger mobilisation of the forest sector for climate change mitigation (Nabuurs et al.,2007). Surplus forest growth, i.e. roundwood from whole trees that could be harvested in addition to current harvesting rates while still remaining within sustainable limits (Smeets and Faaij,2007), is a particularly abundant yet underutilized, poorly accounted for, and seemingly controversial source of biomass for the production of bioenergy (Lamers et al., 2013). Mobilisation of surplus forest growth can nevertheless be crucial for meeting the bioenergy production levels necessary for the transformation of global energy systems and climate change mitigation.
In Canadian forestry, the annual allowable cut (AAC) represents the biophysical annual harvest limit of industrial roundwood of forests. It is calculated for the managed forest areas under public tenure within each province, which represent approximately 90% of the total (Natural Resources Canada, 2017). Calculations are based on estimates of forest growth rates, and follow a policy of maintaining a non-declining future wood supply, considering a range of additional economical, social and ecological factors (such as maintenance of specific forest and lanscape features). The importance of individual factors to the AAC varies among provinces and even among forest management areas within provinces (Government of Canada,2016). The AAC is allocated among various industries for processing wood into conventional products (e.g. lumber, pulp, fibreboard, panels) based on specifications in terms of tree species and fibre quality. This allocation follows provincial forestry regulations and management regimes. Usually, stems of high quality are primarily allocated to sawtimber. Pulp and fibreboard mills then use by-products from sawmills (i.e. wood chips, shavings and sawdust) as feedstock and are also allocated lower-quality trees (often referred to as pulp logs in the literature).
In reality, however, the AAC is rarely completely harvested. In recent years, harvest levels in public forests in the province of Quebec have been much lower than the AAC (Parent,2010). This difference between long-term timber availability and actual harvest levels is due to several, often overlapping, ecological, industrial and economic factors. However, this difference may cause a gradual depletion of the quality of the forest resource if stands that have the highest value for conventional products are preferably selected (Deluca et al.,2009). The high-grading of forest stands, whereby the harvest is biased towards large, high-quality trees, has been reported in temperate hardwoods and mixed forests of northeastern North America (Deluca
et al.,2009;Nyland,1992). At a regional scale, depletion of the resource may occur by selecting stands that are likely to provide a higher profit, such as mature stands with desired species for lumber and little or no constraints in terms of road infrastructure and machine access. Logically, the consequences of such bias are that stands with high constraints remain intact and receive no silvicultural treatment. The problem is therefore deferred, since the non-harvested stands are usually included in the next round of AAC calculations (Bureau du Forestier en chef,2013). Calculation of the AAC rely on specific assumptions, such as harvest levels and the spatial distribution of harvesting and other silvicultural activities (e.g. planting, thinning, etc.). If these assumptions are not met in reality, the accuracy of AAC projections suffers (Paradis et al.,2013). In turn, inaccurate AAC calculations and a gradual depletion of the quality of forest resources can affect the potential for the forestry sector to contribute to climate change mitigation by reducing the strength of forest carbon sinks and slowing the production of long-lived wood products with high substitution potential (e.g. lumber used in construction). One possible solution is to add new processing pathways, such as bioenergy products, which can make use of the currently unharvested surplus forest growth i.e. trees and species with low economic potential for conventional products (Aguilar and Garrett,2009;
Barrette et al.,2015;Koning and Skog,1987;Munsell and Germain,2007).
In this context, using undervalued surplus forest growth as bioenergy feedstock could help improve both silvicultural practices and the profitability of the wood value chain by providing an outlet for underutilized fibre (Murphy et al., 2010). Case studies from the United States also suggest that it could prevent conversion of forests to other land-uses, by increasing the overall economic value of forest lands (Dale et al.,2017). Historically, only primary and sec-ondary forest residues have been considered for bioenergy products (i.e. by-products of forest management activities, such as tree tops and branches, and by-products of wood processing activities) (Daioglou et al.,2015). The use of roundwood as bioenergy feedstock has raised a number of concerns, notably related to the seemingly long time to carbon parity of bioenergy produced from whole trees i.e. the time span needed by a bioenergy system to recover the carbon levels of a baseline/reference fossil fuel-based scenario, before reduction in greenhouse gas (GHG) emissions to the atmosphere start to be recorded (Lamers and Junginger,2013). Therefore, most estimations of wood-based bioenergy feedstock from extensively managed forests generally exclude the use of roundwood (Paré et al.,2011).
To our knowledge, there are no systematic studies focusing on understanding the difference between harvest volume and the AAC that could help us assess the potential for bioenergy production from currently unutilized forest growth. It can be assumed that if a portion of the AAC in a given management unit (MU) is not harvested due to the presence of impor-tant access or operational constraints (e.g. steep slopes, poorly drained soils), this portion might not be considered as a potential bioenergy feedstock, since those constraints will also hinder the logistics and profitability for bioenergy producers. However, if some available forest
stands remain unharvested because their species composition and fibre quality do not meet the requirements of the existing industrial infrastructure, then these stands could likely be used as bioenergy feedstock. Therefore, both the industrial structure (e.g. number of logging agreements in the MU, distance between the forest resource and the wood processing facilities, quality of the existing road network, etc.) and forest characteristics (e.g. species composition, fibre quality, tree age distribution, etc.) could plausibly influence the harvest/AAC ratio, and ultimately the amount of forest growth that could be used as bioenergy feedstock.
The aim of this study was to identify and quantify the biophysical and economic factors that explain the distribution of surplus forest growth (defined as the difference between the AAC and actual harvest levels) in forests managed under public tenure in the province of Quebec. Using the province’s commercially managed estate (divided into 74 MUs) as a case study, factors related to stand, landscape and industrial network characteristics were analysed to understand the dynamics behind the allowable vs. actual wood consumption by existing forest industries. We hypothesized that the proximity to pulp and fibreboard mills, whose supply predominantly consists of sawmill by-products and lower-quality wood, is among the prime determinants of the proportion of the AAC that is actually harvested. In turn, we also hypothesized that the proximity to such mills will reduce the bias towards the harvesting of stands with the best characteristics for lumber. This analysis was designed to help quantify and map the potential for bioenergy from surplus forest growth across Quebec, while also providing a preliminary assessment of the potential of bioenergy to reduce the high-grading of the forest resource at the regional scale.
1.4
Material and methods
1.4.1 Study areaThe study area included managed commercial forests under public tenure (Crown land) in the province of Quebec (Canada), ranging from the northern temperate zone (45◦N) to the northern limit for harvest in the boreal forest (52◦N) (Figure 1.1). From South to North, the annual average temperature ranges from 6-7◦C to 0-1◦C, with forest types gradually changing from deciduous to coniferous stands and covering multiple bioclimatic domains: sugar maple-bitternut hickory, sugar maple-basswood, sugar maple-yellow birch, balsam fir-yellow birch, balsam fir-white birch and spruce-moss. There is also a rainfall gradient from West to East with an annual average rainfall of 600-650 mm to 850-900 mm (MDDELCC,2018). The southern part of the forest landbase has a higher population density, higher economic activity and a more developed road network. Forests under public tenure are divided into 74 management units, with an average size of 5,705 km2, which are distributed within nine administrative regions, covering 422,187 km2(Figure 1.1).
The study focused on four species groups that are important across the study area:
— Spruce-Pine-Fir-Larch (SPFL): white spruce (Picea glauca (Moench) Voss), black spruce (Picea mariana (Mill.) BSP), red spruce (Picea rubens Sarg.), tamarack (Larix laric-ina(Du Roi) Koch.), white pine (Pinus strobus L.), jack pine (Pinus banksiana Lamb.), red pine (Pinus resinosa Ait.), Scots pine (Pinus sylvestris L.) and balsam fir (Abies balsamea (L.) Mill.). The bulk of the volumes from these species are harvested in the boreal and mixed temperate bioclimatic domains of the province i.e. in the balsam fir-yellow birch, balsam fir-white birch and spruce-moss domains.
— Maple: sugar maple (Acer saccharum Marsh.), silver maple (Acer saccharinum L.) and red maple (Acer rubrum L.). These species are only found in the temperate zone span-ning the southern part of our study area i.e. in the sugar maple-bitternut hickory, sugar maple-basswood and sugar maple-yellow birch domains.
— Poplar: largetooth aspen (Populus grandidentata Michaux), balsam poplar (Populus balsamifera L.), eastern cottonwood (Populus deltoides Bartram) and trembling aspen (Populus tremuloides Michaux). While the first three species of this group are mostly found in temperate forests, most of the volume of this group consists of trembling aspen, which is mainly harvested in the boreal and mixed temperate bioclimatic domains. — Birch: white birch (Betula papyrifera Marsh.), grey birch (Betula populifolia Marsh.)
and yellow birch (Betula alleghaniensis Britt.). The harvest volume for this group is split mainly between yellow birch, which is more abundant in the temperate zone, and white birch, which is more prevalent in boreal forests. Yellow birch is found mainly in the balsam fir-yellow birch and sugar maple-yellow birch domains, while white birch predominates in the balsam fir-yellow birch, balsam fir-white birch and spruce-moss
domains.
For each of these four groups, data on AAC and on harvested volumes were compiled.
Figure 1.1 – Study forest area by MU and bioclimatic domain
1.4.2 AAC and harvest-level data
In Quebec, the AAC is calculated at the scale of each MU every five years by the Quebec Chief Forester’s Bureau, a governmental agency. For the purposes of this study, calculations for the most recent complete cycle were used i.e. from 2008 to 2013. This period also approximately represents an economic cycle within the North American forest sector, including the housing market crash in the United States and the subsequent (albeit partial) recovery of wood mar-kets. The AAC calculations are provided for each MU as annual available volume per species group and forest product category. For this study, we further divided product categories into two subcategories: high value (sawtimber products and veneer-based panels) and low value (pulp and fibreboard) products. Harvest data were gathered from reports provided by com-panies holding forest management and wood supply licenses on public land, which were made available by the Ministry of Forests, Wildlife and Parks upon request. These reports contained information for each MU regarding harvested volume per species group. A harvest/AAC ra-tio (harvested volume expressed as a percentage of the AAC) was then calculated for each MU and species group combination. Small, marginal AAC volumes (i.e. less than 5,000 m3)
were excluded from the calculations; those entries were mainly related to temperate hardwood species in MUs located at the northern limit of their geographical range.
Of the 254 calculated harvest/AAC ratios, 8 had a value greater than 100%, which was clearly abnormal. Most of these entries were for deciduous species, and likely arose from mismatches in species designations between data files. The entries were manually adjusted to a value of 100%.
1.4.3 Forest and industry attributes
Once the harvest/AAC ratios were calculated for each species group and MU combination, we compiled information regarding stand, landscape and industrial network characteristics for each MU, and relevant variables were identified using data obtained from the Quebec Ministry of Forests, Wildlife and Parks.
Our hypothesis was that the proportion of harvest would be influenced by variables related either to the biophysical characteristics of the forest or to aspects of the local wood industrial network. Biophysical variables describing forest composition, structure and ecological features were extracted from the provincial forest cartography database (Library Université Laval,
2018). In each MU, variables of interest were derived by calculating proportions of the MU area that was covered by forest stands of given species composition (defined as the proportion of the basal area occupied by coniferous and deciduous species; stands with >75% of basal area in coniferous or deciduous species are classified as coniferous or deciduous, respectively, and others are classified as mixed), level of maturity (stand age), stem quality grades, canopy height, soil drainage and slope.
We considered three variables to describe the structure of the wood industrial network in a given MU. The first was the number of individual logging agreements, which reflects the number of mills operating in the MU. We also anticipated that the presence of facilities that can process sawmill by-products (i.e. pulpmills, as well as oriented strand board and particle board mills for poplar) would be an important driver of harvesting levels. The distance between the wood resource (expressed as the centroid of a given MU) and the closest pulp or fibreboard mill was thus used as a proxy for this factor. Our second variable was hence defined as the distance (by road) to the nearest pulp or fibreboard mill that accepted the species of interest as part of its supply. Finally, since road construction and transport represent an important share of the costs of forest operations (Hassegawa et al.,2018), our third variable was an index of the road density network. It was calculated by dividing the total number of kilometres of existing, passable roads by the area of the MU. The reference year for this calculation was 2008.
1.5
Data analyses
To reduce bias arising from inter-annual economic variation, we analysed the effects of different variables on the harvest/AAC ratio over the five-year study period (2008-2013). Proportion data are usually modelled using beta regression, which uses the beta distribution to model the likelihood for the data (Ferrari and Cribari-Neto,2004). In such cases we assume the response is in the unit interval (0, 1). Since in this case the response could also take a value of zero (i.e. no harvest) or one (i.e. 100% of the AAC was harvested), we could not meet the classical regression assumptions with the standard modelling approach. Furthermore, the distributions and occurrences of zeros and ones varied between species groups. The data were therefore modelled with either a zero-inflated beta (BEINF0), one-inflated beta (BEINF1) or zero-one inflated beta (BEINF) distribution (see Table 1.1). The statistical analyses were conducted in the R statistical programming environment (R Core Team, 2017) using the gamlss package (Rigby et al.,2007).
Table 1.1 – Occurrence of zeros and ones in the observations of the dependent variable harvest/AAC ratio. ‘n’ is the total number of observations (Management Units) and ‘Dist. family’ the distribution used in the models for each species group.
Species group No. of zeros No. of ones n Dist. family
Birch 8 0 72 BEINF0
Maple 0 1 39 BEINF1
Poplar 10 5 69 BEINF
SPFL 2 2 74 BEINF
The function BEINF() defines the beta-inflated distribution with a four-parameter distribution (Equation 1.1) where µ describes the vector of location parameter values >0 and <1, σ the vector of scale parameter values >0 and <1, ν the vector of parameter values modelling the probability at zero and τ the vector of parameter values modelling the probability at one. The distribution family BEINF1() excludes zeros in the response variable, hence there is no ν parameter, while BEINF0() excludes values of one, so the τ parameter is not included. These distributions are appropriate for modelling continuous variables that have a known, restricted range, including the endpoints. Parameters of each component of the model are es-timated simultaneously and can be combined to give an overall probability estimate according to the selected covariates. The density function of the zero-inflated beta distribution, denoted by BEINF (µ, σ, ν, τ ) is defined as follows:
fY(y|µ, σ, ν, τ ) = p0 if y = 0 (1 − p0− p1)B(α,β)1 yα−1(1 − y)β−1 if 0 < y < 1 p1 if y = 1 (1.1)
for 0 < y < 1, where α = µ(1 − σ2)/σ2, β = (1 − µ) ∗ (1 − σ2)/σ2, p0= ν(1 + ν + τ )−1, p1= τ (1 + ν + τ )−1 so α > 0, β > 0, 0 < p0 < 1, 0 < p1< 1 − p0.
Hence BEINF (µ, σ, ν, τ ) has parameters µ = α/(α + β), σ = (α + β + 1)−1/2, ν = p0/p2, τ = p1/p2, where p2 = 1 − p0− p1. Finally, 0 < µ < 1, 0 < σ < 1, ν > 0 and τ > 0. E(y) = (τ + µ)/(1 + ν + τ ).
A full a priori , or saturated model was first developed for comparisons with models including only statistically significant variables. Selection of the variables to be included in the full model was based on the correlation between the candidate variables described above and the response variable (harvest/AAC ratio). The retention threshold was set at a Pearson’s r coefficient of 0.5. Multicollinearity between candidate variables was then checked using the variance inflation factor (VIF) (Zuur et al.,2010). When a VIF threshold of 3 was exceeded, we removed the variable least correlated with the response. The list of variables included in the full model is presented in Table 1.2. The following proxies (and associated classes) were used as descriptors of the forest biophysical context: proportion of forest cover comprised of mixed, (i.e. stands with 25 to 75% of deciduous trees as a proportion of basal area; mixed ratio) or deciduous,stands (i.e. stands with 75% of deciduous trees; deciduous ratio, tested only for the SPFL group), proportion of forest cover comprised of stands ≤ 50 years old, and > 50 years old (maturity ratio), and the proportion of the AAC used in production of high-value sawtimber and veneer logs (lumber ratio). For the industrial context, the list of variables included the number of logging agreements within the MU, the distance to the closest pulp (or fibreboard) mill and a road density index i.e. the total length of the road network per unit land area in the MU. The full model was fitted to each of the µ, σ, ν and τ components. A backward selection procedure was applied in which non-significant variables were succes-sively removed first from the µ component until all remaining variables were statistically
Table 1.2 – List of variables included in the full model. ‘MU’ is the management unit and ‘AAC’ is the annual allowable cut.
Type Variable name Definition
Biophysical context Maturity ratio Proportion of the area covered by stands > 50 years old (%) Mixed ratio Proportion of the area covered by
mixed stands (%)
Deciduous ratio Proportion of the area covered by deciduous stands (%)
Lumber ratio Proportion of the AAC consisting of sawtimber and veneer products (%) Industrial network Number of logging agreements Number of logging agreements
in the MU (count)
Distance to pulp or fibreboard mill Shortest road itinerary (km) Road density index Total length of the road network
per unit area in the MU (km/km2)
significant (p < 0.05). This process was then repeated for the σ, ν and τ , which gave a model (referred to as “model 1”) containing only statistically significant effects. Finally, the same selection procedure was applied, but this time starting with the τ component followed by ν, σ and µ (“model 2”). In cases where the models 1 and 2 differed, Vuong and Clarke tests were used to discriminate between them: the Vuong (1989) and Clarke(2007) tests are likelihood ratio-based tests that use the Kullback-Leibler information criterion. When these tests revealed no significant differences between models, the model with the lowest deviance and Akaike’s information criterion (AIC) was chosen (Akaike,1974). Finally, the difference in AIC was calculated to compare the chosen model and the full model.
1.6
Results
The compiled AAC for the different species varied from 6,686,025 m3to 107,468,500 m3for the
five-year period from 2008 to 2013. Table1.3presents mean values and standard deviations for all variables used to fit the full model for the four species groups. The calculated harvest/AAC ratio showed a wide variation among species. There were also large variations between MUs across the province, as illustrated in Figure1.3. The average harvest/AAC ratio was 21% for birch, 42% for maple, 35% for poplar and 64% for SPFL (Figure1.2and Table1.4).
Table 1.3 – Mean and standard deviation of each covariate used in the study
Species group Variables Mean SD
Birch Maturity ratio (%) 62.9 15.9
Distance to pulpmill (km) 201.0 108.3
Mixed ratio(%) 32.4 16.5
Number of logging agreements 8.5 5.5
Road density index 1.1 0.6
Lumber ratio (%) 22.1 7.2
Maple Maturity ratio (%) 57.7 10.9
Distance to pulpmill (km) 160.7 86.3
Mixed ratio(%) 44.9 9.5
Number of logging agreements 12.0 4.2
Road density index 1.3 0.7
Lumber ratio (%) 27.3 9.7
Poplar Maturity ratio (%) 63.7 15.6
Distance to pulp or fibreboard mill (km) 207.7 103.4
Mixed ratio(%) 32.2 16.3
Number of logging agreements 8.6 5.5
Road density index 1.1 0.6
Lumber ratio (%) 43.7 8.1
SPFL Maturity ratio (%) 63.4 16.2
Distance to pulpmill (km) 198.2 108.9
Mixed ratio(%) 32.4 16.8
Deciduous ratio(%) 32.4 16.8
Number of logging agreements 8.4 5.5
Road density index 1.1 0.7
Lumber ratio (%) 92.0 6.0
For all species groups, the full model (containing all covariates) was never preferred following the model selection procedure. In the reduced models, forest maturity ratio was retained in at least one component (i.e. µ, σ, ν or τ ) of the final model for each species group. Also, the distance to the nearest pulp or fibreboard mill was retained in at least one component of the final models for every deciduous species group. Distances by road between the centroid of each MU and the nearest pulp or fibreboard mill are plotted for hardwood groups in Figure1.4.
Figure 1.2 – Distribution of harvest/AAC ratios per species group
Table 1.4 – Annual allowable cut (AAC), total harvest and harvest/AAC ratio per species group for all management units over the 2008-2013 period
Harvest/AAC ratio
Species Total AAC (m3) Total harvest (m3) Mean (%) SD (%) Min (%) Max (%)
Birch 24 682 050 5 802 688 21 22 0 87
Maple 6 686 025 2 656 508 42 28 0 100
Poplar 14 504 000 5 897 468 35 36 0 100
SPFL 107 468 500 72 252 037 64 29 0 100
The following sections provide the details of the final models for each species group. Results are summarized graphically for the entire range of the response, from 0 to 100%, given by all components of each final model (Table 1.5).
1.6.1 Birch
For birch species, the variables that significantly influenced the harvest/AAC ratio were the number of logging agreements per MU, the distance to a pulpmill (fibreboard mill supplies were insignificant for this species group), and the proportions of the MU area covered by mixed and mature forests (Table1.5). For the µ component, the number of logging agreements and the proportion of mature forest were the most significant variables (P -value < 0.001) followed by the distance to the nearest pulpmill (P -value < 0.01). Both the proportions of mixed and mature forests were retained in the σ component (P -value < 0.01). The proportion of mixed forest was the only significant variable in the ν component of the model (P -value < 0.02), with low proportions of mixed forest being associated with a higher probability of zero harvest. The marginal effect of the distance to a pulpmill on the harvest/AAC ratio was rather weak, but trends became evident when coupled with other variables. The predicted harvest/AAC
Birch Poplar SPFL Maple 51 49 47 51 49 47 51 49 47 51 49 47 -80 -75 -70 -65 Harvest/AAC ratio 0 25 50 75 100
Figure 1.3 – Observed harvest/AAC ratios per MU over the 2008-2013 period for the 4 species groups, 0 = No harvest, 100 = All the AAC was harvested
51 49 47 51 49 47 51 49 47 -80 -75 -70 -65 Maple Poplar Birch
Distance to by-product mill (km) 100 200 300 400
Figure 1.4 – Distances by road (km) between the centroid of the MUs and the nearest pulp or fibreboard mill for the three groups of deciduous species
ratio increased with distance to a pulpmill in MUs with a low proportion of mixed forest (0-35%), while the opposite was observed in MUs with a higher proportion of mixed forest (>35%) (see Figure1.5). Similarly, in MUs with a relatively high proportion of mature forest (>60%) the model predicted an increase of the harvest/AAC ratio with increasing distance from a pulpmill, whereas we found the opposite for MUs with a lower cover of mature forest (result not shown). There was also a rapid decrease in the predicted harvest/AAC ratio with increased distance to a pulpmill when the number of logging agreements per MU was high (>10 mills). But when the overall number of mills was lower than this (≤10), the harvest/AAC ratio was predicted to increase gradually with the distance to a pulpmill (see Figure1.6).
1.6.2 Maple
The distance to the closest pulpmill (fibreboard mill supplies were insignificant for this species group), the total number of logging agreements (a proxy for the number of operating mills) in the MU and the road density index all significantly influenced the harvest/AAC ratio in the final model for maple (Table1.5). For the µ component, the road density index (P -value < 0.001) and the distance to a pulpmill were the most significant variables (P -value < 0.01), followed by the number of logging agreements (P -value < 0.05). The proportion of mature forest was also retained (P -value < 0.001) in the σ component, as well as the distance to a pulpmill (P -value < 0.05). Only the intercept was included in the τ component of the model, which indicates that none of the covariates could be related to the occurrence of 100% harvest. Based on the final model, the predicted harvest/AAC ratio for maple decreased abruptly when the distance to a pulpmill increased and when the road density index decreased (see
Table 1.5 –Final models for each species group. µ describes the vector of location parameter values >0 and <1, σ the vector of scale parameter values >0 and <1, ν the vector of parameter values modelling the probability at zero and τ the vector of parameter values modelling the probability at one. A value of 1 in the “model” column indicates that the selected model was subject to a backward selection procedure first applied to µ, then to σ, ν and τ . Values of 2 indicate that the selection was applied to components in the reverse order. Values of 1 in the explanatory variables indicate that only an intercept was fitted. See Table 1.2for definition of variables.
Species Model DF AIC Component Explanatory variables Full model ∆ AIC Birch 1 9 -150.23 µ Number of logging agreements+ Distance to pulpmill + Maturity ratio -5.89
σ Mixed ratio + Maturity ratio ν Mixed ratio
Maple 2 8 -17.94 µ Number of logging agreements+ Distance to pulpmill + Road density index -8.26 σ Distance to pulpmill + Maturity ratio
τ 1
Poplar 2 13 -30.79 µ Distance to pulpmill + Mixed ratio + Lumber ratio + Maturity ratio -0.35 σ Distance to pulp or fibreboard mill + Maturity ratio
ν Mixed
τ Distance to pulp or fibreboard mill + Mixed ratio
SPFL 2 7 8.84 µ Deciduous ratio + Maturity ratio 0.40 σ Maturity ratio
ν 1 τ 1
Figure 1.5 – Predictions of harvest/AAC ratio for birch as a function of the distance to a pulpmill and the proportion of mixed forest in the management unit.
Figure 1.6 – Predictions of harvest/AAC ratio for birch as a function of the distance to a pulpmill and the total number of operating mills in the management unit.
Figure 1.7). An increase in the harvest/AAC ratio was also predicted when the number of logging agreements (i.e. operating mills) or the proportion of mature forest (maturity ratio) within the MU increased (results not shown).
1.6.3 Poplar
For poplars, variables that significantly influenced the harvest/AAC ratio were the distance to a pulp or fibreboard mill, the lumber ratio and the proportions of mixed (mixed ratio) and
Figure 1.7 – Predictions of harvest/AAC ratio for maple as a function of the distance to a pulpmill and the road density index in the management unit.
mature forests (maturity ratio) within the MU (Table1.5). For the µ component, all of these variables were significant at P -values < 0.01, while only the distance to a pulp or fibreboard mill (P -values = 0.01) and the proportion of mature forest (maturity ratio) (P -values < 0.001) were retained in the σ component. The proportion of mixed forest cover (mixed ratio) within a MU is the only variable that was retained in the ν component (P -value = 0.01), with lower values associated with lower probabilities of zero harvest. In the τ component, the distance to a pulp or fibreboard mill (P -value < 0.05) and the proportion of mixed forest (mixed ratio) (P -value < 0.02) were both retained.
The predicted harvest/AAC ratio decreased when the distance to a pulp or fibreboard mill increased, and the decline was more pronounced in MUs with a low proportion of mixed forest (0-35%) (Figure1.8).
1.6.4 Spruce-Pine-Fir-Larch
In the case of the SPFL group, proportions of mature (maturity ratio) and deciduous forests (deciduous ratio) were the only two variables retained in the final model. For the µ component, both variables had a P -value < 0.001, while only the proportion of mature forest was retained in the σ component (P -value < 0.05) (Table1.5). However, none of the covariates could be related to the occurrences of zeros and ones, as only the intercepts were retained in the ν and τ components of the models.
The predicted harvest/AAC ratio decreased as the proportion of mature and deciduous forests increased (Figure1.9).
Figure 1.8 – Predictions of harvest/AAC ratio for poplar as a function of the distance to a pulp or fibreboard mill and the proportion of mixed forest in the management unit.
Figure 1.9 – Predictions of harvest/AAC ratio for the Spruce-pine-fir-larch group as a func-tion of the proporfunc-tions of mature and deciduous forests in the management unit.
1.7
Discussion
1.7.1 Pulp and fibreboard mills as catalysts for hardwood management
Overall, variables related to the structure of the industrial network (i.e. number of logging agreements, distance to pulp or fibreboard mill and road density index) had a larger influ-ence on the harvest/AAC ratio of deciduous species (birch, maple and poplar) than variables describing the biophysical characteristics of the forest (i.e. maturity ratio, mixed ratio and lumber ratio). The distance to the nearest pulp or fibreboard mill was confirmed as a prime
determinant of the surplus forest growth for birch, maple and poplar, thereby providing strong support for our first hypothesis. In the vicinity of such mills, a higher proportion of the AAC is actually harvested. In practice, this implies that hardwood pulpmills (with the addition of fibreboard mills for poplar) act as catalysts for forest management. This idea has often been mentioned in the literature (Hassegawa et al.,2015;Pothier et al.,2013;St-Pierre et al.,
2013; Nguyen et al., 2015; Alam et al., 2012; Paré et al., 2011), but until now it had rarely been formally analysed and quantified. Despite supporting our first hypothesis, we cannot be certain that the proximity to such mills reduces the bias towards the harvesting of stands with the best characteristics for lumber products. However, hardwoods can be separated into two distinct groups i.e. those growing in temperate deciduous forest regions and those that are more common in boreal forest regions. The different ecological dynamics at the stand and landscape levels in both forest zones are reflected in the silvicultural regimes that are applied, with consequences for the harvest/AAC ratio.
Management of temperate hardwoods
The temperate forests of Southern Quebec belong to the northern hardwoods forest type. They are dominated by deciduous tree species such as maples and yellow birch. Unfortunately, the AAC is calculated for all birch species, which prevented us from analysing yellow birch separately from white birch, the latter being more prevalent in the boreal forest. Despite this, the harvest/AAC ratio of both the maple and birch groups was higher in MUs that are located near to a pulpmill and that contained a high number of logging agreements (Table1.5). This reinforces the argument that a more developed industrial network with several processing pathways can be beneficial to forest management (Mendoza and Prabhu, 2000), especially in temperate hardwood forests with high species diversity.
Northern hardwood forests have been depleted by past harvesting practices, which often con-sisted of selecting the most valuable trees in selection cuts (Deluca et al., 2009; Nyland,
1992). To promote the restoration of these forests, stem marking rules have been introduced in Quebec’s public forests to promote the harvesting of low-vigour trees (Boulet et al.,2007;
Delisle-Boulianne et al., 2014). These rules, however, led to a decline in the proportion of high value sawtimber being harvested (Pothier et al.,2013;Havreljuk et al.,2014;Hassegawa et al.,2015), which in turn hindered the financial applicability of the newly implemented forest restoration measures. The consequence of this process is that more than half of the annual allowable cut of northern hardwoods remains unharvested.
Although the depleted state of the forest could be seen as a limiting factor for the applicability of silvicultural restoration practices (Pothier et al.,2013), our results suggest that one solution to this issue mainly lies in the presence of processing facilities for sawmill by-products and low-quality logs. When such facilities are located near the harvesting site, the harvested proportion of the annual allowable cut tends to increase, likely as a result of the contribution
of these end-products to the added value of the whole wood supply chain. Therefore, the proximity of processing facilities that can use sawmill by-products and low-quality roundwood is a key element that enables the application of silvicultural systems aimed at restoring the quality and vigour of northern hardwood forests.
Management of boreal hardwoods
The context is different for hardwoods associated with the boreal forest zone, such as poplar and white birch. These pioneer species specialize in rapid establishment and growth following a severe forest disturbance (e.g. fire, insect epidemic or clearcut) (McCarthy, 2001). The poplar species group had a low harvest/AAC ratio, averaging only 35%, while for the birch group it was even lower at 21% (Table 1.4). These harvest/AAC ratios were partially tied to biophysical characteristics of the forest, such as the proportion of mixed and mature stands (Table 1.5). As the boreal forest landscape matures, there is a transition from mixed to coniferous stands. Therefore, these pioneer species gradually become scarce in the long-term absence of severe disturbances.
Accordingly, the harvest/AAC ratio of poplar tended to be higher in MUs that contained a high proportion of mixed stands (Figure1.8). A similar trend was observed for birch, although for proportions of mixed stands between 0 and 35% the harvest/AAC ratio tended to increase slightly with the distance to the pulpmill Figure 1.5. The reason for this unexpected trend may be linked to the harvesting of remote SPFL stands containing a small proportion of white birch. Indeed, this species tends to be maintained for a longer period of time than poplar in the absence of severe disturbance (Kneeshaw and Bergeron,1998). Results for SPFL showed that conifer-dominated forests, which are likely to contain significant volumes of white birch, had higher harvest/AAC ratios, regardless of the distance to the nearest pulpmill.
Boreal hardwoods rapidly colonize sites following a severe disturbance, thus creating even-aged forest stands. Kneeshaw and Bergeron(1998) observed that gaps in 78-year-old stands of the southeastern boreal forest region were caused primarily by mortality of poplar species, which suggests that stands of this age and older are subject to synchronous mortality. For trembling aspen specifically, such mortality events occur as early as 60 years following a stand-replacing disturbance, and they increase in frequency and magnitude over time (Hill et al.,2005;Pothier et al.,2004). This process associated with the presence of pioneer species leaves large volumes of standing decaying trees, which are included in the AAC and are likely to become part of the surplus forest growth. Results of Pothier et al. (2004) showed that volume losses, defined as the sum of the standing dead aspen volume and the decayed stem volume and percentage of aspen basal area bearing conks of Phellinus tremulae, represent from 5% in a 60-year-old stand to 22% at 120 years of age. Therefore, if this resource remains unharvested, high mortality levels will be observed over a relatively short period of time (Kneeshaw and Bergeron,1998;
The impact of the lumber ratio on the level of harvest is another factor suggesting that surplus poplar growth can lead to significant economic losses. While poplar is generally not a desired species, poplar veneer logs are highly sought-after as feedstock for high value products(Achim et al.,2011). However, such logs need to be large, straight and virtually defect-free (Balatinecz and Kretschmann,2002). Obviously, logs of this type are more likely obtained from young, vigorous stands, before the advent of mortality and stem decay. However, even in the best conditions, veneer logs represent a relatively small proportion of the harvested volume, because they are only found in the lower bole of defect-free trees. Therefore, to gain access to this high-value resource, processing pathways for lower quality tree sections must also be available. For the 2008-2013 period 8,606,532 m3of poplar remained unharvested across the public forests of the province. A large proportion of this volume is likely to remain unutilized, unless there is a significant change in the industrial network (e.g. addition of industrial stakeholders that can process low-quality fibre). Again, our results suggest that, for the hardwood value chain, the structure of the local industrial network is more crucial to effective forest management interventions than the characteristics of the forest resource.
Softwood availability through hardwood management
The SPFL group represents the bulk of coniferous species harvested from Quebec’s boreal forest zone. Unlike for deciduous species, the distance to a pulpmill was not included in the final model. This is due to differences in processing pathways. In Quebec, softwood pulpmills rely almost exclusively on chips, shavings and sawdust from sawmills, whereas hardwood pulpmills also utilize logs. Therefore, the proximity of sawmills is probably the most important factor explaining the level of harvest for coniferous species (a variable not taken into account in our analysis due to the very large number of sawmills and the complexity of their network across the province).
The harvest/AAC ratio of SPFL was only related to biophysical characteristics of the forest, i.e. proportions of mature and deciduous forest stands in the MU. The negative influence of the proportion of deciduous forests on the harvest/AAC ratio confirms that finding profitable processing pathways for hardwoods is the key to the overall reduction of surplus forest growth in Quebec, not only for hardwoods, but also for coniferous species.
In this context, the negative relationship between the proportion of mature forest and the harvest/AAC ratio was surprising at first. However, it is presumably because boreal areas with high proportions of mature forests are often located in remote, inaccessible areas. In these remote forests, harvesting activities are negatively affected by poorly developed road networks (Prestemon and Wear,2000) (although our analysis was likely not sensitive enough to detect the influence of the road density index on this species group).
1.7.2 Time frame as a limitation of the study
The time frame of this study (2008-2013) limits the extrapolation of the results because of the particular economic conditions associated with the housing crisis in the United States, which severely affected the Canadian forestry industry. Analysis of data from the 2013-2018 period could therefore yield different results. On the other hand, the observed trends for 2008-2013 in the harvest patterns relative to the AAC are well aligned with information related to the downturn of markets for low-quality/pulp-grade wood during the last decade. Indeed, for the 1998-2008 period, the harvest/AAC ratio has always been 52–96% for SPFL and 14–48% for other species (mainly deciduous) (Bureau du Forestier en Chef,2018).
1.7.3 Bioenergy as an additional pathway
Our results show that in Quebec, a large part of the annual allowable cut remains unhar-vested, and that an important factor leading to increased levels of harvest is access to nearby facilities that can process sawmill by-products and low-quality logs, especially from deciduous species. This suggests that finding an outlet for such feedstock will have a mobilising effect for sawtimber, notably by unlocking access to coniferous volumes dispersed within currently unwanted hardwood stands. Although providing new processing pathways may appear as a simple solution that could lead to optimised forest management and greater economic benefits, the current reality of the forest value chain is that maintaining existing pathways is already proving to be a challenge. The Canadian pulp and paper production capacity has undergone a significant downturn in recent years. Contributing factors include the substitution of paper by electronics (Hujala,2011), the increasing use of recovered fibre (Holik,2013) and growing in-ternational competition (FAO,2008). The production of paper and paperboard has decreased in Western countries over the past decade, while it has increased in the emerging economies of Asian and Latin American countries (Hujala et al., 2012;FAO, 2008). The consequences of this trend, such as mill closures leading to increased unemployment in various sectors (e.g. transportation, forest operations), could be severe, particularly for remote rural economies. This has been portrayed as contributing to the long-term decline of forestry-related industries (Brandeis and Guo,2016). The addition of new commodity products to the value chain would therefore help promote a welcome revitalisation of the Canadian forest sector. To this end, bioenergy (including biomass for heat, power or biofuels for transportation) is increasingly being considered by both federal and provincial governments.
As another example, wood pellet mills (which could either contribute to the flourishing export trade with the European Union (Thrän et al.,2017) or to domestic markets) could provide an alternative pathway in locations where the pulp production capacity has declined. Brandeis and Guo (2016) compared the economic contribution of a wood pellet mill with that of a pulpmill, and found that one job in the wood pellet mill would have a higher overall impact on forestry and logging operations than one job in the pulpmill; even if a pellet mill operates
