Reducing peat in growing media: impact on nitrogen content, microbial activity, and CO<sub>2</sub> and N<sub>2</sub>O emissions
Bibliographic record
Abstract
Renewable materials including coir, biochar, and composts are investigated worldwide in the horticultural industry to partially substitute peat in growing media. In this study, we assessed the effects of biochar and vermicompost as partial substitution of peat and compared these peat-based growing media with coir in terms of NH 4 + -N and NO 3 − -N content, CO 2 -C and N 2 O-N emissions and their microbial biomass carbon and nitrogen. Six growing media mixtures (peat; peat + biochar 9:1 v/v; peat + vermicompost 9:1 v/v; coir; coir + biochar 9:1 v/v; coir + vermicompost 9:1 v/v) replicated three times were incubated in growth chambers during a 60 d period. At day 0 of incubation (DAI), peat amended with biochar retained around 12.81% of NH 4 + -N compared with peat alone. The concentrations of NO 3 − -N peaked at 275 mg·kg −1 at 33 DAI for peat and 552 mg·kg −1 at 46 DAI for coir amended with vermicompost. The substitution of peat with biochar resulted in large CO 2 -C [2070 μg CO 2 -C·g −1 dry weight (DW)] and N 2 O-N (62.78 μg N 2 O-N·g −1 DW) emissions, but not coir. The substitution of coir with vermicompost increased N 2 O-N emissions at a much lower level (47.53 μg N 2 O-N·g −1 DW) than peat (111.82 μg N 2 O-N·g −1 DW). Our results showed that supplements of vermicompost in peat and coir improved N supply which could benefit plant growth, while substituting part of peat with biochar increased CO 2 -C and N 2 O-N emissions. In contrast, no effect of biochar was observed with coir, which is beneficial for the environmental footprint of short-cycle growing crops.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".