Growth and Yield Parameters of Mesta Varieties as Influenced by Spacing and Nutrient Sources
Bibliographic record
Abstract
Field experiment was conducted at GKVK, University of Agricultural Sciences, Bangalore, Karnataka to study the growth and yield parameters of mesta as influenced by varieties, spacing and nutrient sources.The plant height at harvest stage varied significantly due to different plant spacing, varieties and nutrient sources. Among the varieties, HS-108 recorded significantly higher plant height (275.5 cm) and total dry matter (25.01 g) than AMV-4. The seed yield differed significantly due to different plant spacing, varieties and nutrient sources. Among the varieties, AMV-4 recorded significantly higher seed yield (754.5 kg ha-1) than HS-108 (581.5 kg ha-1). Significantly higher seed yield was recorded under 45 cm x 10 cm spacing (687 kg ha-1) than 30 cm x 10 cm (649.5 kg ha-1). Further, application of 5 t of FYM per ha along with 40:20:20 kg NPK per ha fertilizer registered higher seed yield (698.0 kg ha-1) compared to 100 per cent N equivalent through FYM (625.5 kg ha-1). The fibre yield differed significantly due to different plant spacing, varieties and nutrient sources. Among the varieties, HS-108 recorded significantly higherfibre yield (948 kg ha-1) than AMV-4 (850 kg ha-1). Significantly higher fibre yield was recorded under 45 cm x 10 cm spacing (923 kg ha-1) than 30 cm x 10 cm (875 kg ha-1). Further, application of 5 t of FYM per ha along with 40:20:20 kg NPK per ha fertilizer registered higher fibre yield (962 kg ha-1) compared to 100 per cent N equivalent through FYM (803 kg ha-1). The interaction effects between varieties, plant spacing and nutrient sources were found to be significant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".