Prevalence and Eco-Friendly Management of Some Important Nursery Diseases of Mango in Bangladesh
Bibliographic record
Abstract
A survey was carried out to record the prevalence of the nursery diseases in four mango varieties (Gopalbhog, Langra, Amropali and Seedless) in some selected growing areas viz. Chapai Nawabganj, Rajshahi, Dinajpur and Mymensingh in Bangladesh. Altogether nine different diseases viz. anthracnose, die-back, malformation, scab, powdery mildew, sooty mould, red rust, gummosis and bacterial leaf spot were recorded during the period of survey. All the diseases were found in the nurseries of Chapai Nawabganj, but Gummosis was not observed in Rajshahi, Dinajpur and Mymensingh and die-back was also not found in Dinajpur. Only anthracnose was recorded in Kajla sinduri in Rajshahi and Kancha mithi in Dinajpur. Out of the 40 mango varieties surveyed occurrence of higher number of diseases were recorded in Amropali (9), Mollica (7), Langra (8), Aswina (8), Khirsapat (8), Fazli (8), Vustara (6), Bogra gooti (6), BARI Aam-2 (6), BARI Aam-4 (6), Mohananda (5), Polyembryony (7), Gopalbhog (7), Hybrid 10 (6), Nilambari (6), Mixed special (6) and Seedless (7). The efficacy of BAU-Biofungicide and four different fungicides viz. Amistar, Tilt 250 EC, Bavistin and Dithane M-45 in the nursery of four mango varieties (Gopalbhog, Langra, Amropali and Seedless) were evaluated in FTIP, Department of Horticulture, Bangladesh Agricultural University, Mymensingh. BAU-Biofungicide showed good effect in controlling anthracnose, die-back, powdery mildew, bacterial leaf spot and sooty mould of mango whereas Dithane M-45 was found best for controlling red rust of mango. Dithane M-45 followed by BAU-Biofungicide and Bavistin resulted reduction of powdery mildew incidence over control while the severity of powdery mildew was lowest in Bavistin followed by Dithane M-45 and BAU-Biofungicide.
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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 | 0.000 | 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".