Influence of Sowing Depth in the Emergence of Urochloa and Panicum
Bibliographic record
Abstract
Sowing depth of forage seed is an important factor in seed germination and emergence and varies according to crop. Ideal sowing should be performed at a depth sufficient to promote rapid and uniform germination, with minimal reserve expenditure and facilitating nutrient uptake by the plant. The objective of this study was to evaluate the influence of sowing depth on seed germination of forage species in the field. The experiment was implemented in September 2018. The design was completely randomized in a 4 × 4 factorial scheme with four replicates, being the first factor four forage cultivars: Urochloa ‘Xaraés’; Urochloa ‘Piatã’; Urochloa ruziziensis and Panicum maximum ‘Mombaça’; and the second factor four sowing depths: 0; 4; 7 and 10 cm. The variables evaluated were initial emergence; emergence at 10 days after sowing; emergence at 21 days after sowing and emergence speed index. Seeding at 0 cm provides greater emergence of seedlings for Urochloa brizantha ‘Piatã’, Urochloa brizantha ‘Xaraés’, Urochloa ruziziensis and Panicum maximum ‘Mombaça’. Seedling emergence reduced when sowing was performed at greater depths (4, 7, and 10 cm). If necessary, Urochloa brizantha ‘Piatã’ should be sown up to 7 cm. The sowing at 10 cm depth is not recommended for any of the studied cultivars.
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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 | 0.001 |
| 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".