Surface‐Banding Liquid Manure over Aeration Slots: A New Low‐Disturbance Method for Reducing Ammonia Emissions and Improving Yield of Perennial Grasses
Bibliographic record
Abstract
Low‐disturbance methods for applying slurry manure on forages are needed that can maximize crop response and minimize loss of nutrients to the environment. A new implement [Aerway SSD (subsurface deposition slurry applicator)] that bands manure over aeration‐type slots was assessed relative to conventional broadcasting and surface banding. The comparison was based on immediate and residual crop responses to single and multiple applications of dairy slurry by tall fescue ( Festuca arundinacea Schreb.) and orchardgrass ( Dactylis glomerata L.). Also, ammonia emissions were compared using both semiopen chamber and micrometeorological (integrated horizontal flux) methods. The aeration slots without manure generally did not have a significant effect on yield or N uptake. Averaged over all harvests, surface banding increased yield and N uptake over broadcasting by 6.9 and 6.8%, respectively. The SSD increased yield and N uptake over surface banding by 4.4 and 7.5%, respectively. The relative effectiveness of the techniques on yield varied among experiments. In the ammonia volatilization trials (micrometeorological method), loss of applied total ammoniacal N in the 2 wk after application ranged from 36 to 61% for broadcast manure compared with 17 to 32% for SSD‐applied manure. With both micrometeorological and semiopen chamber, ammonia emissions from applied manure were 46 to 48% lower with the SSD than with broadcasting. Emissions from surface‐banded manure (chamber method) averaged 33% greater with surface banding than with the SSD. The results indicate that the SSD manure applicator reduced ammonia loss and increased yield and N uptake relative to broadcasting and surface‐banding techniques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".