SITE-SPECIFIC CARBON STOCK ASSESSMENTS OF SHELTERBELT TREES IN SASKATCHEWAN
Notice bibliographique
Résumé
Climate change is threatening the entire planet, so collective efforts to mitigate it are crucial to our future. Planting trees is one of the easiest alternatives known to help sequester the carbon causing problem, yet this procedure is underutilized, especially in agricultural areas. This dissertation is an attempt to link the theoretical and practical issues regarding carbon stock assessments on a farm-level basis that could underpin policies aimed at mitigating climate change in rural areas. The goal of this study was to establish a method to retrieve past tree growth using increment cores, and to test if this information is reliable as input data to model carbon stocks on a farm-level basis. To do so, data was collected from shelterbelt grown trees of varying ages, species, and management conditions in farms across Saskatchewan, Canada, during the summers of 2018 and 2019. This dissertation presents many of the theoretical and historical aspects of shelterbelts in the early chapters, and then works towards finding practical solutions to some of the carbon modeling issues from shelterbelts in the later chapters. The first manuscript I developed discusses early shelterbelt history in Canada, and the environmental benefits that they have provided over the last century. I focus on the carbon sequestration potential from both above- and below-ground accrual, by examining the historical changes in the publication records on the subject through time. Contrasting shelterbelt effects on crops is also illustrated by comparing many studies from around the world, and how these effects change for different crops grown adjacent to shelterbelts. The many facets of carbon sequestration potential of shelterbelts were assessed by examining agroforestry studies from around the world. Conclusions drawn from this process are that shelterbelts represent a great potential for carbon sequestration and global warming mitigation. I argue that shelterbelts should therefore be more heavily applied to the existing agricultural land base, and discuss some potential policy changes that would assist in motivating more planting of trees to be implemented. Without a major change in policy, I argue that the full potential of carbon sequestration from shelterbelt systems will not occur. The goal of my second manuscript was to derive a precise and practical method to retrieve past tree growth using increment cores, and to better understand the associated error that came with such derivations. If accomplished, then shelterbelt carbon stock assessments could be improved, by allowing for a quicker and easier method of assessment. Factors such as the number of increment cores used, if a core reached the pith or the center of the tree, as well as species, age, and tree shape were assessed. Fifty-six combinations of these factors as well as their associated errors were processed, and the conclusion was that the more increment cores used reaching the pith, the better. The study also concludes that dendrochronologically derived increment core data, although not currently used for allometric purposes, is reliable as a mechanism to retrieve growth data (e.g., diameter at breast height, or basal area), which is commonly only measured in forestry operations from repeated visits and repeated measurements. In my last manuscript, the goal was to use data retrieved using the method described in my second manuscript, as input variables in the 3-PG model. This common forestry model was used to assess accuracy and how geographically specific fitting needs to be for the most precise estimates of past growth. It was found that using increment-core derived growth data yields a strong fitting, and that tree-level and site-specific fittings are more precise than the regional methods currently described in the literature. Both these results support that better carbon stock models could be made with this knowledge for two reasons. First, a greater database, i.e., ex-situ radial-growth data from tree-ring databanks, or in-situ obtained tree data, can be now considered for modeling purposes. Second, precise site-specific modeling can be used to calculate a farm-specific carbon footprint, which can ultimately be used as a tool to implement carbon incentivizing policies. The implications of being able to make a farm-based carbon stock model, is that it can support farm-specific carbon footprint calculations. This was the only theoretical-based factor remaining, a factor that precluded incentivization policies from being implemented by federal or provincial governments. Such policies could support a carbon market among farmers or/and a policy rewarding the carbon that many farmers are already sequestering. These policy/market changes would motivate shelterbelt tree planting, which would help the landowners, the agricultural sector, and most importantly, everyone, by assisting in the mitigation of global warming concerns.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».