Combining perennial grass–legume forages and liquid dairy manure contributes to nitrogen accumulation in a clayey soil
Bibliographic record
Abstract
Repeated applications of liquid dairy manure (LDM) and perennial crops generally favor nitrogen (N) stocks in soils, but in ways that may differ with soil type and other management practices. The objective of this study was to assess the long-term (21 yr) changes in soil N stocks (0–50 cm) of a silty clay soil, in a cool humid climate, in response to mineral fertilization (MIN) or LDM, combined with two tillage practices [chisel plow (CP), or moldboard plow (MP)], and two crop rotations [cereal monoculture (monoculture) or cereal–perennial forage rotation (forage-based rotation)]. The forage-based rotation favoured a greater accumulation of N in the first 20 cm of soil (+50 kg N·ha−1·yr−1) when compared with the monoculture. Tillage practices did not impact N stocks in the whole soil profile, but influenced its vertical distribution, with greater accumulation at the surface with CP, and at depth with MP. Annual input of LDM increased N stocks at the surface (0–20 cm) compared with MIN, especially when combined with the forage-based rotation. After 21 yr, soil N stocks (0–50 cm) with LDM were 32% (+2 t N·ha−1) higher in the forage-based rotation than in the monoculture, suggesting better retention and more efficient use of manure-N with perennial forages than cereals. Comparisons between the N mass balance computed for each cropping system, and the changes in soil N stocks indicated that accumulation of N under the forage-based rotation was largely due to symbiotic fixation by legumes in the forage mixture.
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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 | 0.001 | 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".