Nitrogenous Fertilizer Coated With Zinc Improves the Productivity and Grain Quality of Rice Grown Under Anaerobic Conditions
Bibliographic record
Abstract
An ample quantity of water and sufficient nutrients are required for economical rice production to meet the challenges of ever-increasing food demand. Currently, slow-release nitrogenous fertilizers for efficient inputs utilization and maximum economic yield of field crops are in the limelight for researchers and farmers. In this study, we evaluated the comparative efficacy of conventional urea and coated urea (zinc and neem) on rice grown under aerobic and anaerobic regimes in greenhouse conditions. For the aerobic regime, field capacity was maintained at 80-100% to keep the soil aerated. On the other hand, for the anaerobic regime, pots were covered with a polythene sheet throughout the experimentation to create flooded conditions. All forms of urea, conventional and coated (zinc and neem), improved plant growth, gas exchange, yield, yield contributing parameters, and quality characteristics of rice crop. However, better performance in all attributes was found in the case of zinc-coated urea. Gas exchange attributes (photosynthetic rate, 30%, and stomatal conductance 24%), yield parameters like plant height (29%), tillers per plant (38%), spikelets per spike (31%), grains per panicle (42%), total biomass (53%), and grain yield (45%) were recorded to be maximum in rice plants treated with zinc-coated urea. The highest grain and straw nitrogen contents, grain protein contents, and grain water absorption ratio were also found in plants with zinc-coated urea applications. In irrigation practices, the anaerobic regime was found to be more responsive compared to the aerobic regime regarding rice growth, productivity, and quality traits. Thus, to enhance the productivity and quality of rice grown in anaerobic conditions, zinc-coated urea is best suited as it is more responsive when compared to other forms of urea.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".