Seasonal nitrate levels in the soil solution of an organic pasture managed for nature conservation
Bibliographic record
Abstract
Extensive management of pastures is considered as an option to enhance nature conservation in agricultural landscapes. Partial reduction of the grazing frequency from four to two rotations and discontinuing the tending of sod and sward have been tested for extensive management of a mixed pasture at the Nova Scotia College of Agriculture in Truro since 2004. It is not well known how extensification affects the N cycle of organic pastures without mineral fertilizer or manure applications. Moisture and temperature of the topsoil and nitrate content of the leachate extracted with ceramic suction cups at 0.5 m depth were monitored prior to grazing and during sward regrowth in the first and second rotation in spring of 2005 and 2006. In each year, more than 75 % of all samples contained less than 2.5 mg NO3-N L-1. A significant seasonal nitrate dynamic suggests that plant N uptake reduces nitrate leaching in the first rotation, while soil biological activity increases N supply due to soil warming in excess of plant uptake, thus raising the nitrate level in the second rotation. Extensification significantly reduced soil temperature with a concomitant albeit small decrease of nitrate levels in the soil leachate. The pasture cycled nitrogen very tightly with little room for leakages as a result of imposed treatments during the spring season. Key words: Pasture, organic, temperature, nitrogen, leaching
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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".