Effect of Different Nutrient Management Systems on Yield and Yield Components of Rice Crop (<em>Oryza sativa</em> L.) in the Dry Zone of Sri Lanka
Bibliographic record
Abstract
Purpose: Integrated and organic nutrient management has recently become the focus in Sri Lanka for seeking better perspectives on food quality and environmentally friendly production. This study was conducted to understand the magnitude of yield and yield components under selective nutrient management systems in major cropping seasons within the transitional period of a conventional rice-based cropping system. Research Method: Rice yield components and yield were measured with different nutrient management systems; conventional, integrated, and organic from Yala 2019 to Maha 2020/21. An ANOVA was carried out using the Repeated Measures MIXED model to determine the effect of nutrient management systems on yield and yield components for four continuous cropping seasons. Findings: Total tillers per hill and productive tillers per hill significantly varied with conventional, integrated, and organic systems in descending order. The number of filled spikelets per panicle (43) was significantly increased, and the number of hollow spikelets per panicle (8) and thousand-grain weight (21.5g) were significantly decreased with an organic system in the Yala 2019 season only. Although the biological and expected grain yields of the Yala 2019 season were significantly higher with the conventional and integrated systems, these did not change significantly with the organic system in the last two seasons. Research Limitations: Yield parameters fluctuated due to weather changes in different seasons; thus specific impacts of different nutrient managements have been masked to a certain degree. Originality/ Value: The attempt to convert conventional crop production systems in Sri Lanka to organic can be effectively achieved using the integrated use of nutrients and crop rotations.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".