Nitrogen Availability from Composts for Humid Region Perennial Grass and Legume–Grass Forage Production
Bibliographic record
Abstract
Perennial forages may be ideally suited for fertilization with slow N release amendments such as composts, but difficulties in predicting N supply from composts have limited their routine use in forage production. A field study was conducted to compare the yield and protein content of a binary legume-grass forage mixture and a grass monocrop cut twice annually, when fertilized with diverse composts. In all three years from 1998-2000, timothy (Phleum pratense L.)-red clover (Trifolium pratense L.) and timothy swards were fertilized with ammonium nitrate (AN) at up to 150 and 300 kg N ha(-1) yr(-1), respectively. Organic amendments, applied at up to 600 kg N ha(-1) yr(-1) in the first two years only, included composts derived from crop residue (CSC), dairy manure (DMC), or sewage sludge (SSLC), plus liquid dairy manure (DM). Treatments DM at 150 kg N ha(-1) yr(-1) and CSC at 600 kg N ha(-1) yr(-1) produced cumulative timothy yields matching those obtained for inorganic fertilizer. Apparent nitrogen recovery (ANR) ranged from 0.65% (SSLC) to 15.1% (DMC) for composts, compared with 29.4% (DM) and 36.5% (AN). The legume component (approximately 30%) of the binary mixture acted as an effective "N buffer" maintaining forage yield and protein content consistently higher, and within a narrower range, across all treatments. Integrating compost utilization into livestock systems that use legume-grass mixtures may reduce the risk of large excesses or deficits of N, moderate against potential losses in crop yield and quality, and by accommodating lower application rates of composts, reduce soil P and K accumulation.
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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".