Effect of Nitrogen on Growth and Yield of Sorghum Forage (Sorghum Bicolor ( L.) Moench Cv.) under Three Cuttings System
Bibliographic record
Abstract
Abstract A field experiment was conducted on sorghum (Sorghum bicolor (L.) Moench cv.) under three cutting system to determine the effect of nitrogen on growth and yield at University of Agriculture Faisalabad, Pakistan, during the season 2010-2011. The experiment was laid out in Randomized Complete Block Design (RCBD), using three replications. There were four levels of nitrogen 0, 50, 75 and 100 kg N/acre in the form of urea. The growth parameters like plant height, number of leaves, leaf area is determined periodically. First reading taken after 20 days of sowing while second and third was taken after 15 days of first cutting. Yield parameter like plant population, fresh and dry weight was determined in three cuttings. Results showed that increasing nitrogen dose increased all growth attributes. Results revealed for first, second and third cuttings showed significant differences at all growth attributes. Thus, the maximum plant height was observed in N4 (100 kg N/acre), having plant height 193.92, 195.24 and 192.79 cm in first, second and third cutting, respectively, which was followed by the treatment N3 (75 kg N/acre), having 179.70 cm in first cutting, while second and third cutting have same plant height 168.62 cm. The exception was the plant population showed non significant behavior in second and third cutting while number of leaves per plant in second cutting only and protein % in third cutting showed non significant difference with nitrogen application.
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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".