Removing Solids Improves Response of Grass to Surface‐Banded Dairy Manure Slurry: A Multiyear Study
Bibliographic record
Abstract
Removing solids from slurry manure helps balance nutrients to plant needs and may increase soil infiltration rate toreduce loss of ammonia. The long-term effects of applying the separated liquid fraction (SLF) of dairy slurry with surface banding applicators are not well known. This 6-yr study compared the yield, N recovery, and stand persistence of tall fescue (Festuca arundinacea Schreb.) receiving SLF at 300 (SLF300) and 400 (SLF400) kg ha(-1) yr(-1) of total ammoniacal N (TAN); whole dairy slurry (WS) at 200 (WS200), 300 (WS300), and 400 (WS400) kg TAN ha(-1) yr(-1); and mineral fertilizerat 300 kg N ha(-1) yr(-1). The slurries were applied four times per year by surface banding, a technique that reduces ammonia emission and canopy contamination. Grass yield and N uptake were significantly higher for SLF300 than WS300 atequivalent rates of TAN. At similar total N, yield and N uptake were much greater for SLF than WS (2 Mg DM ha(-1) and 75 kg N ha(-1), respectively). Apparent total N recoverywas 63% greater for SLF300 than WS300 due to less ammonia loss and less immobile N. The apparent recovery of total N was 31% higher for Fert300 than for SLF300. Yield and N uptake for SLF300 and WS300 were similar in Harvests 1 and4, but SLF had higher values under hot and dry conditions in Harvests 2 and 3. Using SLF rather than WS will increase crop yield and allow higher application volumes near barns, whichwill reduce hauling costs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".