Impact of Plant-Based Amendments on Water-Soluble Nitrogen Release Dynamics in Cultivated Peatlands
Bibliographic record
Abstract
Drained cultivated peatlands have been an essential agricultural resource for many years. To slow and reduce the degradation of these soils, which increases with drainage, the use of plant-based amendments (straw, wood chips, and biochar) has been proposed. Literature on the effects of such amendments in cultivated peatlands is scarce, and questions have been raised regarding the impact of this practice on nutrient cycling, particularly nitrogen (N) dynamics. By means of a six-month incubation experiment, this study assessed the effects of four plant-based amendments (biochar, a forest mix, willow, and miscanthus) on the release kinetics of water-soluble N pools (mineral and organic) in two histosols of differing degrees of decomposition (Haplosaprist and Haplohemist). The amendment rate was set at 15 Mg ha−1 on a dry weight basis. The N release kinetics were significantly impacted by soil type and amendment. Miscanthus and willow were the amendments that most reduced the release of soluble organic N (SON) and mineral N (minN). The addition of plant-based amendments reduced the total amount of released N pools during the incubation (cumulative N pools) by 50.3 to 355.2 mg kg−1, depending on the soil type, the N pool, and the type of amendment. A significant relationship was found between microbial biomass N, urease activity, and the cumulative N at the end of the incubation. The results showed that the input of plant-based amendments in cultivated peatland decreases N release, which could have a beneficial impact by decreasing N leaching; however, it could also restrict crop growth. Further research is needed to fully assess the impact of such amendments used in cultivated peatlands on N and on C fluxes at the soil–plant and soil–atmosphere interfaces to determine if they constitute a long-term solution for more sustainable agriculture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.004 | 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 teacher head, 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".