WPSII-8 Forage yield, pasture quality and economic performance after pasture rejuvenation in an organic beef cattle farm.
Bibliographic record
Abstract
Beef cattle production on productive pastures can be very profitable, but over time, pastures decline in productivity. The following methods of rejuvenation were investigated from 2015–2017 on an organic beef cattle farm: sub-soiling, break & re-seeding, a combination of manure application plus subsoiling, high stock density grazing, bale grazing, pasture rest, as well as direct seeding in spring and fall. Measurements of pasture dry matter (DM) yield and quality were made to determine the most effective and profitable rejuvenation methods in comparison to a complete break and reseeding scenario. The top 3 forage DM yielders were bale grazing, followed by manure application plus subsoiling and then break & re-seeding methods in that order. Overall, all rejuvenation methods investigated produced 5 - 100% higher forage DM yield than control. Of the 15 forage nutritive value parameters measured, only 3 (forage CP, Ca and P) were significantly affected by rejuvenation methods later in the study. Other nutritive value parameters were similar for all methods of rejuvenation investigated including control. Compared to control, the economic analysis showed that the highest input cost was from break and re-seeding. However, when the cost of the hay used for feeding cows for bale grazing was factored into the input costs, then bale grazing would by far have the highest total variable cost/ha. The returns were higher for bale grazing and high stock density grazing than other methods including control. Without having to break and re-seeding old pastures, the 3 top suggested methods of pasture rejuvenation that are expected to reduce time for rejuvenation and loss of pasture productivity, are bale grazing, manure application plus sub-soiling and high stock density grazing.
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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 | 0.001 | 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) | 0.001 | 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".