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Record W7052733078

SITE-SPECIFIC CARBON STOCK ASSESSMENTS OF SHELTERBELT TREES IN SASKATCHEWAN

2021· dissertation· en· W7052733078 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2021
Typedissertation
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsWindbreakClimate changeCarbon stockCarbon sequestrationStock (firearms)Tree planting
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.184
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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