Effect of chemical weed management on growth, yield and economics of drum seeded rice (Oryza sativa)
Bibliographic record
Abstract
Field experiment was conducted at Periyanarkunam, Bhuvanagiri Taluk, Cuddalore District during Kuruvai 2021 and 2022 to study the effect of different herbicidal weed management on growth, yield and economics of drum seeded rice (Oryza sativa L.) variety ‘ASD 16’. The experiment was conducted in randomized block design with four replications. The treatments comprised of six weed management practices and are made up of a combination of herbicides (pre-emergence, early-post and post-emergence herbicides) with hand weeding, which was com- pared with unweeded control and twice hand weeding on 25 and 45 DAS. Application of bensulfuron methyl 0.6% + pretilachlor 6% GR @ 660 g a.i./ha PE fb metsulfuron methyl 10% + chlorimuron ethyl 10% WP @ 4 g a.i./ha PoE on 25 DAS fb hand weeding on 45 DAS recorded the highest weed control efficiency of 77.55 and 74.23% during Kuruvai 2021 and 2022 respectively, growth attributes at 60 DAS viz., plant height (96.82 and 91.79 cm), tillers/m2 (367 and 343) and dry matter production (8.01 and 7.78 t/ha), yield attributes like productive tillers/m2 (319 and 312), filled grains/panicle (100.64 and 98.42), grain yield (6.19 and 6.11 t/ha) and benefit cost ratio (2.78 and 2.49) during Kuruvai 2021 and 2022, respectively over bispyribac sodium 10% SC @ 25 g a.i./ha PoE on 20 DAS fb hand weeding on 45 DAS (farmers practice). Hence, it is an efficient weed management practice and an economically feasible method for achieving the maximum growth, yield and economics of drum seeded rice.
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 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 | 0.000 | 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 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".