Carbon balance and greenhouse gas emissions from horticultural plants grown in peat-based growing media
Bibliographic record
Abstract
Peat-based growing substrates are commonly used in specialty crop production. The decomposition rates of peat and the respiration dynamics of plants grown in peat mixtures are poorly understood. We grew lettuce ( Lactuca sativa ) and petunia ( Petunia sp.), representing food and ornamental plant growth, in peat-based media and measured the exchange of carbon dioxide (CO 2 ), nitrous oxide (N 2 O), and methane (CH 4 ) over 3 to 4 months. We used radiocarbon isotopes to partition ecosystem respiration (ER) into autotrophic respiration (AR) and heterotrophic respiration (HR) and estimated the priming effect of roots to enhance peat HR. Average (± standard deviation) N 2 O emissions were 2.69 ± 3.47 mg m −2 day −1 , while CH 4 emissions were variable and small. HR measured from peat alone was on average 0.28 ± 0.15 g CO 2 -C m −2 day −1 . Average net ecosystem exchange (NEE) and ER measurements for pots containing lettuce were −1.17 and 2.09 g CO 2 -C m −2 day −1 , respectively, and NEE and ER for pots containing petunia were −0.62 and 2.96 g CO 2 -C m −2 day −1 , respectively. Without considering the priming effect, HR contributed 9% and 13% to the total ER in lettuce and petunia, respectively. Radiocarbon partitioning of ER revealed that HR contributes 10% and 18% for lettuce and petunia, showing a statistically significant positive priming ( p = 0.007) effect in petunia but not in lettuce. Our measurements provide a basis for the reporting of GHG emissions from horticultural plants grown in peat-based growing media.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".