Long‐Term Effects of Semisolid Beef Manure Application to Forage Grass on Soil Mineralizable Nitrogen
Bibliographic record
Abstract
Livestock manure is an important source of N for forage grass production. The long‐term effects of semisolid beef manure application to forage grass on potentially mineralizable N ( N 0 ), mineralizable N pools, and field estimates of soil N supply were evaluated in dike‐land (heavy textured, poorly drained) and upland (medium‐textured) soils in Nova Scotia, Canada. Treatments included an unfertilized control, annual spring application of 100 kg N ha −1 mineral fertilizer or annual applications of 75, 150, or 300 kg total Kjeldhal N ha −1 as manure (M75, M150, and M300, respectively) from 1995 to 2004. Soil samples collected in fall 2004 were used to estimate N 0 using a 44‐wk aerobic incubation at 25°C. The N 0 values were 62 and 49% higher in the M300 treatment (324 and 480 kg N ha −l ) than the other manure treatments (199 and 323 kg N ha −1 ) for the upland and dike‐land soils, respectively. The mineralization rate coefficient ranged from 0.045 to 0.082 wk −1 Manure application increased the readily mineralizable N pool (Pool I); higher rates of application resulted in higher values in both soils. The intermediate and stable mineralizable N pools were increased only by the M300 treatment in the upland soil but not in the dike‐land soil. Long‐term manure application of the M300 treatment resulted in high N 0 with no yield benefits, which can increase the risk of N losses to the environment in both soils, whereas Pool I was responsive to all manure application rates.
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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) | 0.001 | 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".