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Enregistrement W7029982298

Lumber recovery, a leaf area model, and water use in response to commercial thinning, for plantation grown black spruce (Picea mariana [Mills] B.S.P.) / by Steven M. Young.

2017· dissertation· en· W7029982298 sur OpenAlexaboutno aff

Notice bibliographique

RevueKnowledge Commons (Lakehead University) · 2017
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueForest ecology and management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésThinningBlack spruceBoomCrown (dentistry)Yield (engineering)Water useSoftwoodSilvicultureSite index
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Three black spruce (Picea mariana [Mills] B.S.P.) plantations (Airstrip, Boom Lake and Tyrol Lake), in northwestern Ontario, with varied stand densities (2806 stems ha[superscript -1], 2285 stems ha[superscript -1], and 3578 stems ha[superscript -1], respectively) and site indices (11.8 m, 13.3 m, 10.9 m, respectively) were studied to determine lumber properties and yield and to develop a model to predict leaf area. The Boom Lake site was also studied to assess the impacts of site disturbance and residual tree damage from commercial thinning on tree water use.
\nWith respect to lumber recovery, all three sites produced over 87% No. 2 and Better grades lumber. Wane and skip were the two most prominent defects causing lumber downgrades. The lowest quality site with the highest stand density produced significantly stronger (modulus of rupture, MOR) and stiffer (modulus of elasticity, MOB) lumber than either the best quality site with a lower stand density or the medium quality site with a similar stand density. The best site with the lowest stand density also produced significantly stronger and stiffer lumber than the medium site with higher stand density. Lumber sawn from log sections taken from below the live crown were significantly stronger but not significantly stiffer than lumber sawn from logs from within the live crown. There were no significant differences in bending MOB and MOR between visual grades. Economy grade lumber was denser than the other grades and had a smaller average ring width than the Grades No. 1 and No. 2. There were weak negative relationships between average ring width and basic wood density. There were weak positive relationships between basic wood density and MOB or MOR. Three scenarios were examined for relative economic return. Regardless of economic scenario, the Boom site (best site quality) produced the highest value per hectare followed by Tyrol (poorest site
\nquality) and then Airstrip. Generally, MSR grading produced the highest value per hectare (maximum $28,000/ha at the Boom site).
\nThe model to predict whole tree leaf area was developed from 37 plantation grown black spruce trees collected from all 3 sites. Allometric equations to describe needle weight (W[subscript n]), leaf area (A,) and A[subscript 1]: breast height sapwood area (A[subscript s]) relationships are described.
\nSpecific leaf area (SLA, m[superscript 2]/g ) was measured on needles from 4 needle age classes on 4 subsampled branches taken from 3 crown sections of equal length on each of the 37 trees. SLA varied by site, needle age and crown section. SLA of needles from the Airstrip was
\nsignificantly larger than those from the Tyrol site, while needle SLA from the Boom site was not significantly different from either. Linear and nonlinear allometric models were built to appropriately scale up to whole tree leaf area based on live branch diameters measured in the field, and compared against trees (9 total, 3 from each site) where the entire crown was collected. Nonlinear models for crown sections 1 (top section) and 2 (middle section) revealed an unbiased predictive ability whereas both linear and nonlinear models showed a general tendency to over predict the needle weight of crown section 3 (bottom section). Nevertheless, a linear model to predict A[subscript 1] from A[subscript s] was developed that showed to no evidence of bias.
\nSapflow was measured from May 2007 to August 2008 to assess the impacts of site disturbance and residual tree damage from commercial thinning on tree water use. Twelve black spruce trees were instrumented with thermal dissipation sap flow sensors in the spring following a fall season harvest. Soil resistance to penetration was measured with an Eijkelkamp 06.15.SA model Penetrologger Set. The twelve trees were classified based on damage and adjacency to trails resulting in 4 treatment groups; TD (adjacent to the trail with apparent damage), TN (adjacent to the trail with no damage), ID (away from the trail with apparent damage) 
\nand IN (away from the trail with no damage). There was no significant difference between the soil resistance to penetration on or between harvest trails. Daily water use (Q[subscript d]) was as high as 20.711 day[superscript -1] and as little as 0.06 1 day[superscript-1]. There were no significant differences detected between treatment groups for diameter at breast height (DBH), height (H), length of live crown (EC), sapwood area (A[subscript s]) or leaf area (A[subscript1]). The TD trees appeared to have the lowest Qd, while the TN trees appeared to have lowest mean sap velocity ( v[subscript s]) and low Qd. Transpiration (Qi) and stomatal conductance (G c) levels appeared to generally be lower for the TN group when compared to other treatment groups. The data suggests that Q, and Gc may not be affected by adjacency to trails and/or mechanical damage in the first year following thinning treatment.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,788
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,021
Tête enseignante GPT0,238
Écart entre enseignants0,217 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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