Physiological Studies of Fusarium oxysporum Causing Root Rot of Mulberry in Maharashtra
Bibliographic record
Abstract
A Scientific Investigation of mulberry root rot was conducted in 18 districts of Maharashtra State to assess disease incidence in farmers' fields. The disease incidence was recorded based on the number of infected or dead plants showing typical root rot symptoms. The pathogen was isolated from infected mulberry roots and identified as Fusarium oxysporum through morphological characteristics. Physiological studies were carried out to determine the nutritional requirements of the pathogen using Czapek’s Dox agar medium supplemented with different carbon, nitrogen, phosphate, amino acids, vitamins, salts, oxides, and trace elements. Among carbon sources, D-glucose, maltose, mannitol, D-xylose, and lactose supported maximum growth, while D-galactose was least favourable. Calcium nitrate, potassium nitrate, magnesium nitrate, and urea were most effective nitrogen sources, whereas ammonium salts were less supportive. Di-potassium hydrogen orthophosphate and calcium phosphate enhanced growth among phosphate sources. Several amino acids, including L-cysteine, L-glutamic acid, glycine, and L-lysine, promoted abundant growth. Inositol and thiamine were found to be the most favourable vitamins, while others such as folic acid and niacin were less effective. Magnesium chloride showed growth comparable to control among salts. Molybdenum oxide and ferric oxide were stimulatory among oxides, while magnesium sulphate, manganese sulphate, and sodium sulphate enhanced growth among trace elements. These findings highlight the wide physiological adaptability of F. oxysporum and provide insights into its nutritional ecology, which may be useful in devising management strategies for mulberry root rot disease.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".