EFFECT OF MULTI-YEAR SURFACE-BANDING OF DAIRY SLURRY ON GRASS
Bibliographic record
Abstract
Applying liquid manure by surface banding increases short-term yield compared to broadcasting pro-bably by reducing NH3 loss. However, the response to surface-banding slurry manure on grass N after several years of application has not been reported. This study compared the effects of commercial fertili-zer with drag-shoe applied dairy slurry on yield, N uptake and soil parameters of a tall fescue (Festuca arundinacea) sward in years 7-8 of a trial in south-coastal British Columbia, Canada. At equivalent rates of mineral-N, annual grass yield (average of 200 and 2001) was 2-3 Mg ha-1 greater with manure than fer-tilizer whereas at equivalent rate of total-N (400 kg ha-1) annual yield was 1.3 mg ha-1greater with fertili-zer. N-uptake was 6 and 11 kg ha-1 greater from manure than from fertilizer at 200 and 400 kg mineral-N ha-1, respectively, suggesting a relatively small benefit from historical applications of N. Apparent N reco-very for both fertilizer and manure was about 80 and 70 % at 200 and 400 kg mineral-N ha-1, respectively. Alternating manure/fertilizer (400 kg TAN ha-1) produced high yield and N-uptake with less applied total-N than manure alone. There was 230, 309 and 519 kg ha-1 of unrecovered applied N annually (average of 2000 and 2001) for the low manure, alternating and high manure applications, respectively. High manure plots had significant higher total soil N (approximately 1000 kg ha-1) and available soil P and K, but there was less fall soil NO3 with manure than with fertilizer. The study indicates that high yields of tall fescue can be maintained by banding slurry manure with or without mineral fertilizer at annual total-N rates of 400- 600 kg ha-1 with little risk of ground water contamination but significant amounts of applied N are lost from the system.
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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".