Effect of Sulphur and Zinc on Rice Performance and Nutrient Dynamics in Plants and Soil of Indo Gangetic Plains
Bibliographic record
Abstract
Rice is the staple food of more than three billion people in the world, most of who live in Asia.Rice is important crop of Indo Gangetic Plains of Bihar, productivity of system is stagnate and somewhere going down, to ascertain the role of sulphur and zinc an experiment was conducted at main campus of ICAR Research Complex of Eastern Region Patna with four levels of both nutrients i.e. sulphur and zinc, total 16 treatments were tested in Randomized Block Design. Both the nutrients were applied to rice and their direct and residual response was ascertained to rice and lentil in sequence. Based on three years of experimentation, results revealed that rice plant height is significantly affected by sulphur and zinc. Tallest plant (101.7cm) was recorded at maturity with application 6kg Zn application Zn. With the advance of stage dry matter accumulation was increased, it was not like the LAI which was decreased after Panicle initiation stage. Highest LAI (4.29) at anthesis was produced in the plots treated with Zn at 6 kg/ha. Dry matter share of root was in general less than 15% across the levels of sulphur and zinc during all the phenological stages. Maximum rice yield (7.63 t/ha) was recorded with combined application of 30kg sulphur and 6kg zinc, whereas corresponding minimum rice yield (7.09 t/ha) was recorded with absolute control plots where no application of zinc and sulphur was done during entire experimentation period.Maximum (281.2 kg/ha) nitrogen uptake was recorded with 6kg zinc treatment. However highest uptake of P (91.1 kg/ha) and K (150.4 kg/ha) was recorded in the plot supplemented with no Zn and sulphur at 40 kg/ha, respectively. Soil parameters viz., pH, EC and organic carbon content did not influenced with the S and Zn. N, P, K, S and Znwere affected significantly due to sulphur and zinc nutrition.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".