Aerating Grassland before Manure Application Reduces Runoff Nutrient Loads in a High Rainfall Environment
Bibliographic record
Abstract
The effect of mechanically aerating grassland before liquid manure application in the fall on surface runoff and transport of nutrients and solids was studied in a high rainfall area. The two treatments were control and aeration, the latter receiving one pass with an aerator perpendicular to the slope before fall application of liquid manure (dairy in Years 1-3 and swine in Year 4). Treatments were randomly assigned on 3 to 5% sloping land with a silt loam surface soil (Aquic Dystroxerept) planted in orchardgrass (Dactylis glomerata L.). Runoff from natural rainfall events was sampled for nutrient and solids analysis. Aeration significantly reduced runoff and loads of suspended solids, total Kjeldahl N (TKN), and dissolved reactive P in all years. Annual runoff amounts were reduced by 47 to 81%, suspended and volatile solid loads by 48 to 69% and 42 to 83%, respectively, TKN loads by 56 to 81%, and total P (TP) loads by 25 to 75%. Loads of the soluble nutrient NH4-N, dissolved reactive P, and K were reduced by 41 to 83%. The first three runoff events after manure application accounted for approximately one-third of the annual total runoff and solid and nutrient loads when averaged across treatments, with loads of TKN, K, and NH4-N totaling 4.4, 3.3, and 1.9 kg ha-1, respectively. Aeration slightly increased downward movement of NO3-N, but not other nutrients in the soil. Thus mechanical aeration can be an effective tool for reducing runoff and loads of solids and nutrients after surface application of liquid manure on sloping grassland.
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 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 | 0.001 | 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".