Effectiveness of Nano-Chitosan, Biological Agents, and Plant Oils on Enhancing Yield and Reducing Pink Ear Rot Disease in Maize
Bibliographic record
Abstract
This study aimed to manage pink ear rot disease caused by Fusarium verticillioides and to evaluate the impact of tested treatments on maize yield parameters which applied as sprays either once, twice, or three times. Single applications of treatments like Bacillus subtilis, garlic oil, camphor oil, and chitosan were generally ineffective, while Topsin-M70 fungicide reduced disease severity by 25.02%. When applied twice, chitosan was most effective, reducing disease severity by 46.44%, followed by nano-chitosan, camphor oil, and fungicide Topsin-M70. When applied three times, fungicide Topsin-M70 showed the most effective, reducing disease severity by 84.62%, with Bacillus subtilis, nano-chitosan, and camphor oil also performed well. Repeated applications of Trichoderma harzianum, carnation oil, and garlic oil increased disease severity. Also, the study evaluated the impact of tested treatments on maize yield parameters. Results indicated that nano-chitosan and camphor oil were the most effective in enhancing yield parameters such as the number of rows per ear, kernels per row, 100-kernel weight, and grain yield per plant, with nano-chitosan showing the highest improvements across most parameters. Fungicide Topsin-M70 also demonstrated significant efficacy, particularly in grain yield per plant. Overall, multiple applications of the treatments led to greater improvements in maize yield parameters.
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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".