Dry Mass Yield and Bromatological Composition of the Forage of Cynodon dactylon cv. Vaquero Submitted to Crescent Doses of Nitrogen, Applied as Swine Waste
Bibliographic record
Abstract
The present had as its objective to evaluate the response of dry mass total yield, and bromatological composition of the forage, submitted to different nitrogen doses, applied as swine waste in the pasture of Cynodon dactylon cv. ‘Vaquero’. The study was developed during the period of November 2017 until March 2018, in the experimental area of the ‘Pontifícia Universidade Católica do Paraná’, campus Toledo, located in the western region of Paraná, under latitude 24º43′13″ S and longitude 53º47′01″ W. The experimental design adopted was of randomized blocks with six treatments (0, 50, 100, 150, 200 and 250 kg ha-1) constituted by crescent doses of nitrogen as swine waste, with five replicates, totalizing 30 experimental plots. It was studied the dry mass yield (kg ha-1), crude protein (CP), dry mass (DM), neutral detergent fiber (NDF), acid detergent fiber (ADF), insoluble nitrogen in neutral detergent (NDIN), insoluble nitrogen in acid detergent (ADIN), lignin, ether extract (EE) and mineral matter (MM). It was verified that the N doses influenced in the dry mass yield of the forage, because it reached a productivity of 4520.57 kg ha-1 in the dry mass. The highest crude protein content of ‘Vaquero’ was obtained with the dose of 250 kg ha-1 of nitrogen, in which was observed a content of 13.72 g kg-1 of CP in the dry mass. The different nitrogen doses, applied as swine waste in the pasture of grass ‘Vaquero’, have an influence in the bromatological composition and in the dry mass yield, causing an increase in the forage contents of crude protein (CP), dry mass (DM), ether extract (EE), in forage produced under four cutting periods. The increase in the nitrogen doses improves the nutritional quality of grass ‘Vaquero’, once it causes decreases in the NDF and ADF content and increases in the CP and DM content.
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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 | 0.000 | 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) | 0.001 | 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".