Effectiveness of Dormancy-Breaking Treatments on the Germination of Indigofera zollingeriana
Bibliographic record
Abstract
The availability of high-quality forage remains a major challenge in ruminant livestock systems, particularly during the dry season. Indigofera zollingeriana is a promising leguminous species with high protein content, yet its cultivation is constrained by high seed dormancy. This study aimed to evaluate the effectiveness of various dormancy-breaking treatments on the germination of I. zollingeriana seeds, both in general (across treatment groups) and specifically (to determine the most effective treatment). The experiment was arranged in a completely randomized design (CRD) with 17 non-factorial treatments and three replications, using two germination test methods: Top of Paper (TOP) and Between Paper (BP). Data were analyzed using ANOVA and orthogonal contrast tests at 5% and 1% significance levels. Results showed that all treatments (K1–K16) significantly increased germination percentage compared to the control, with averages of 49.88% vs. 36.00% (TOP) and 53.69% vs. 41.00% (BP). The most effective treatment was immersion in 95% sulfuric acid (H₂SO₄) for 10 minutes, which achieved the highest germination rate (88%) and the lowest dormancy intensity (0%), significantly outperforming physical, biological, and hormonal treatments. These findings indicate that short-duration chemical scarification can serve as a standard treatment for large-scale I. zollingeriana seed production. Further studies are needed to evaluate the physiological safety of the treatment and its impact on early seedling growth in field conditions.
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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 | 0.001 |
| 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".