Effect of Timing of Urea Application or Red Clover Incorporation on Forage and Animal Production
Bibliographic record
Abstract
Efficient use of land resources is a major objective for beef cattle producers. Incorporating high nutritive value forages into warm perennial grasses, increases animal production and reduces hay feeding, hence reducing production costs. The moment of chemical fertilizer application or the use of clovers to provide N in pastoral systems can be possible strategies to reduce inputs and decrease negative effects in the environment (N losses through leaching or evaporation) without affecting animal performance and gains per unit of land. In the conditions of the present experiment, the treatment with one application of N (as urea) in January (after grazing started) produced the same amount of forage, average daily gains (ADG), and gain per hectare than the treatment with two applications (November and January) at a lower cost of production. When red clover (legume) was used with annual ryegrass without any chemical fertilizer, forage and animal performance were reduced when compared to the other two treatments and cost of production was the greatest. Strategically applying N based on plant growth patterns and grazing management strategies, reduced cost of production without any impact on the animal-plant subsystem. Legumes as a source of N for annual pastures may not be enough to maintain appropriate production while seed cost should also be considered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".