Influence Of Varietal Duration And Nitrogen Fertilization In Augmenting Micronutrient Uptake And Yield Of Rice
Bibliographic record
Abstract
Lack of micronutrients in diet lead to health anomalies in human, to solve which diet-based availability can be looked upon. The goal of the current research is to ascertain how micronutrient uptake and yield of boro rice are influenced by the duration of the variety and graded doses of nitrogen fertilization. The field trial was conducted in the boro season of 2022 at Bidhan Chandra Krishi Viswavidyalaya, Gayeshpur, Nadia in a thrice replicated split plots with nitrogen fertilizer levels (0, 50, 100, 150 kg ha-1) in main plot and varieties (short duration-Satabdi, medium duration-Pratikshya and long duration-Swarna masuri) in sub-plots. At both maximum tillering and harvest stage, increase in nitrogen fertilization levels significantly increased the uptake of Zn, Cu, Fe and Mn and biomass yield significantly. However, while Fe and Mn concentrations were highest for N application at 150 kg ha-1, N application at 100 and 150 kg ha-1 statistically registered at par Zn and Cu values. Varietal influence on grain and straw yield as well as micronutrient uptake in harvest may be linked to their life cycle pattern where Partikshya considerably outperformed short duration Satabdi variety. Worst performance of Swarna Masuri variety may possibly be attributed to the inherent varietal characteristics and exposure to terminal moisture and heat stress at the months of May and June. Judicious management of nitrogen fertilizer may be a viable non- traditional approach for micronutrient nutrition in 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 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 | 0.000 | 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".