Effect of Rates and Sources of N Fertilizer Application on Dynamics of Rice Brown Leaf Spot Disease (Bipolaris oryzae) Incidences in the Dry Zone of Sri Lanka
Bibliographic record
Abstract
Nitrogen inputs; sources or application amounts are key determinants of yield determination and determination of resistance or sensitivity to pathogen activities. This study aimed at assessing the impact of source and rate of application of N fertilizers on Rice Brown Leaf Spot (RBLS) disease incidences and dynamics in lowland irrigated rice crops. Leaf N using relative leaf chlorophyll content, leaf N concentration and crop yield were assessed during the wet 2018/19, 2019/20 and 2020/21 seasons and dry 2019 and 2020 seasons in the field research facility of Rajarata University of Sri Lanka. The conventional systems (The Department of Agriculture recommended inorganic fertilizer application at 100% N), integrated system (50% N with conventional through inorganic fertilizer and 25% N with organic manure mixture), and organic system (50% of N conventional through organic manure) were tested using a new improved rice variety Bg300, using a randomized complete block design with six replicates. Wet and dry seasons were contrastingly different in disease prevalence, where critical levels of incidences were visible earlier in the wet season compared to the dry season. Initial stages of the study, organic systems resulted in higher disease incidences, thus reaching infections of the full crop before conventional and integrated. Several seasons of continuous organic manure incorporation enhanced the resistance of organic systems to RBLS disease compared to the rest. The leaf N concentrations were higher in conventional, thus the RBLS incidences were relatively low, due to negative correlations between disease incidences. Rice yields also resulted in a significant negative correlation with disease incidences and were diminished in integrated and organic systems later. The yield suppression due to diseases such as RBLS in organic transition can be overcome by using an integrated approach and building a balanced substitutable nutrient management strategy.
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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 | 0.000 | 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) | 0.000 | 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".