Organic Lawn Clipping Silage as a Potential Livestock Feed
Bibliographic record
Abstract
Lawns are recognized as being an amenity for quality of life, but they are generally viewed as having little agricultural value. The ability of ruminants to digest grass clippings harvested from lawns has the potential to convert these otherwise nonproductive landscapes into feed. This study of organic lawn care compared organic fertilizer-amended plots to unamended plots, to demonstrate the feasibility of fermenting fresh lawn clippings into a grass silage to be used as winter livestock feed, and to evaluate the silage palatability and quality. Silage fermentation was performed in plastic bags by air removal through a vacuum and analyzed 200 days after harvest. Except for increasing yield with fertilizer, there were no differences in silage quality between treatments. Silage average dry matter (DM) content was 41%, Protein Crude Soluble 19%, Acid Detergent Fiber 66%, Ash 9.6%, Total Volatile Fatty Acid 7.7%, Lactic Acid 7.8, Acetic Acid 1%, Propionic and Butyric Acids <1%, pH 4.2, and Net Energy Lactation 3152 calories lb-1. The fermented clipping pH was sufficiently acidic, and the other measured indicators were comparable to common types of silage. Because organic lawns often supply more phosphorous (P) than is needed for lawn maintenance, and many New Jersey turf soils already have soil test P levels above the optimum range, adding more P from organic fertilizers is not desirable. This study found that a single harvest of clippings for silage may remove about 10 kg ha-1 of P from the lawn soil.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".