Modeling The Uptake Of Cationic Micronutrients And Rice Grain Yield At Different Graded Dose Of Nitrogen Fertilization
Bibliographic record
Abstract
The current study has been directed to find out a relationship whether such graded doses of nitrogen fertilizer can influence the pattern of micronutrient uptake and yield of the rice plant. The field experiment was carried out during the boro season of 2022 at Bidhan Chandra Krishi Viswavidyalaya, Nadia. The experiment was designed in a thrice replicated Randomized blocks with nitrogen fertilizer levels (0, 50, 100, 150 kg ha-1). A statistical analysis was carried out through Correlation- Regression studies which analyzed the relationship between nitrogen application and micronutrient uptake by rice. Correlation study of N uptake under graded doses of fertilizer application with the uptake of micronutrients followed the pattern Zn (r=0.985**)>Fe (r=0.962**)>Cu (0.953**)>Mn (r=0.947**) and Zn (r=0.980**)>Mn (r=0.948**)>Cu (0.944**)>Fe (r=0.939**) for rice grains and straw at harvest, respectively. The correlation of yield with grain N, straw N and N at maximum tillering stage also revealed a positive value. The Linear Regression (LR) models confirmed that incremental doses of N have a significant association with the uptakes of micronutrients. Thus, judicious management of N fertilizer can be a viable non-traditional approach for micronutrient nutrition in rice.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".