Impact of Row Spacing on Yield and its Components on Lentil Varieties Under the Agroecological Conditions of Quetta
Bibliographic record
Abstract
A field experiment conducted at the Balochistan Agriculture Research Institute (ARI) in Quetta on November 15, 2022, assessed the impact of varying row spacings on the growth and yield of two lentil varieties: Local Panjgur Black and Dasht-21. The study utilized a Randomized Complete Block Design (RCBD) with three replications, examining five row spacings: 20 cm, 25 cm, 30 cm, 35 cm, and 40 cm. Results showed that at 60 days, Local Panjgur Black exhibited a higher average leaf count (10.86 leaves per plant) compared to Dasht-21 (8.53 leaves). The widest row spacing of 40 cm resulted in the maximum number of leaves (18.49 leaves), while the narrowest spacing of 20 cm had the fewest (11.33 leaves). Local Panjgur Black had more branches (8.0 branches per plant) than Dasht-21 (6.73 branches). The 40 cm row spacing yielded the highest branch count (8.00 branches), whereas the 20 cm spacing had the lowest (6.16 branches). The tallest plants were observed in Local Panjgur Black (34.00 cm). Increasing row spacing led to taller plants, with the 40 cm spacing producing the tallest plants (38.00 cm) and the 20 cm spacing the shortest (27.33 cm). Local Panjgur Black produced more pods per plant (57.60) than Dasht-21 (38.00). The 40 cm row spacing resulted in the highest number of pods (44.33), while the 20 cm spacing had the fewest (22.8). Local Panjgur Black achieved a higher seed yield (1,411.7 kg/ha) compared to Dasht-21 (1,170.1 kg/ha). The 40 cm row spacing yielded the most seeds (1,616.5 kg/ha), whereas the 20 cm spacing had the lowest yield (1,002.5 kg/ha). In conclusion, adopting wider row spacings, such as 40 cm, can significantly improve the growth and yield of lentil varieties like Local Panjgur Black. This practice offers a viable strategy for enhancing lentil productivity in similar agro-climatic regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".