Sward Structure and Herbage Accumulation of Massai Guineagrass Pastures Managed According to Pre-Grazing Heights, in the Northeast of Brazil
Bibliographic record
Abstract
The forage sward height measurement is a practical and potential tool for grazing management. Thus, the objective of this study was to evaluate the structure of pasture and forage accumulation related to sward pre-grazing height of Panicum maximum cv. Massai, before being grazed by sheep. The study was conducted in the Federal University of Rio Grande do Norte, Macaíba, Brazil. The treatments were the pre-grazing sward heights at: 35, 40, 45 and 50 cm. The post-grazing height was 15 cm for all treatments. The interaction between the pre-grazing sward heights and grazing cycles was only statistically significant for light interception (LI) and leaf area index (LAI). The LI had linear and positive effect to the pre-grazing heights in only one of three grazing cycles, with approximately 1% increase in LI for each centimeter grown in the sward. The total forage mass had linear regression, every centimeter increased in height, there was a correspondent dry matter (DM) increase of 187 kg ha-1 in forage mass. There was a linear response between leaf blade mass and dead material with sward height. The post-grazing lowest LI was 29.42% at 42.05 cm high. The lowest amount of LI was 29.42% at 42.05 cm high. The minimum LAI was 0.69. The top DM and mineral matter (MM) accumulation rate were linear and had 58.32 and 20.46 kg ha-1 day-1 MS, respectively. Massai guineagrass grazed by sheep must be handled between 35 and 40 cm high at pre-grazing when associated with post-grazing height of 15 cm.
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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) | 0.000 | 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 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".