Peat Biogeochemistry and Greenhouse Gas Emissions during Peat Use in Horticulture
Bibliographic record
Abstract
Peatlands cover only 3% of the world's land cover, but in the northern hemisphere they store up to a third of organic carbon (C). The organic matter stored in peat bogs, known as peat, is highly sought-after as a growing medium for horticulture and agriculture in controlled environments. Peat extraction involves draining the natural peat bog and extracting the peat over several decades. The peat extracted is generally acidic and low in nutrients, so nutrients, limestone to raise the pH and other horticultural additives are added to enhance plant growth. Peat to which horticultural additives are added is called growing medium. Peat decomposition rates are relatively well known. However, data on the biogeochemical properties and decomposition rates of peat-based growing substrates are scarce but necessary to facilitate ongoing debates on the carbon footprint of peat use in horticulture. The aims of this thesis are i) to characterize growing substrates and compare how they differ from raw peat ii) to measure the decomposition rate of growing substrates iii) to explore the role of horticultural additives in increasing the decomposition rate of growing iv) analyze the decomposition rate of growing substrates on a Canadian scale and report the emission factor (EF) of peat use in horticulture and v) quantify the role of horticultural plants in increasing the decomposition rate of growing substrates. My work combines a variety of measurements ranging from Greenhouse Gas (GHG) flux measurements, δ13C, radiocarbon measurements, microbial analysis, soil biogeochemistry and modeling to arrive at the set objectives. I measured that the biogeochemistry and decomposition rates of growing substrates vary considerably from those of peat. To understand the causes of this increased decomposition rate in growing substrates, I show, using factorial experiments, that limestone added to increase pH increases the decomposition rate of growing substrates. Extrapolation of the values to a national scale shows that current IPCC Tier 1 emissions overestimate emissions from peat used in horticulture, and I suggest a revision to Intergovernmental Panel on Climate Change (IPCC) Tier 2 values. Growing lettuces and petunias in peat-based growing substrates in a controlled environment, I report on the different components of plant and soil respiration. Using radiocarbon measurements, I show that plant roots increase peat decomposition, but this could be species dependent. The results of this thesis help to revise the EFs related to the use of peat in horticulture in Canada and to improve the understanding of horticultural peat
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".