Forage Yield and Quality of Sweet Sorghum as Influenced by Sowing Methods and Harvesting Times
Bibliographic record
Abstract
Sowing methods and harvesting times are the important management considerations for getting the optimum yield and quality of fodder crops. This study, investigated the influence of sowing methods and harvesting times on the growth, yield and quality of sweet sorghum. Chinese sweet sorghum was grown by broadcast method, 30 cm apart lines and 45 cm apart lines and harvested after 60, 75 and 90 days after sowing, respectively. All the tested sowing patterns and harvesting times considerably affected the growth, yield and quality of sweet sorghum. However, sowing in 30 cm apart rows produced maximum leaves per plant (13.09), fresh forage yield (38.1 t ha-1), dry matter yield (4.85 t ha-1), crude proteins (8.9%), ash contents (11%) and sugar contents (12.8%), respectively. Similarly, harvesting after 90 days of sowing gave highest leaves per plant (14.72), fresh forage yield (45.1 t ha-1), dry matter yield (5.60 t ha-1), ash contents (12.2%) and sugar contents (14.1%), respectively. These results suggested that sowing in 30 cm apart lines and harvesting after 90 days of sowing improved the growth, yield and quality of sweet sorghum under the semiarid region of Faisalabad.
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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".