Influence of Weather and Soil Parameters on Development of Wet Root Rot in Pulse Crops and Virulence Analysis of Rhizoctonia solani Isolates
Bibliographic record
Abstract
Wet root rot caused by Rhizoctonia solani Kühn in pulse crops was favored by wide range of soil parameters like temperature, humidity, pH, electric conductivity and soil texture. The areas surveyed for the collection of the isolates showing variable atmospheric temperature and relative humidity and low to medium levels of soil organic carbon and high level of available phosphorus influenced the development of the disease incidence from 2-48%. Seventy three cultivars of mungbean, twenty eight cultivars of urdbean and eight cultivars of cowpea were evaluated against virulent isolate of R. solani (RASC 30) to design a set of differential cultivars for virulence analysis. Two cultivars of urdbean, namely, NDU3-4 and IPU2-14, one cultivar of mungbean, namely, HUM 1 and three cultivars of cowpea, namely, V240, V585 and DCP7 showed resistant reactions. Four cultivars of urdbean, namely, TU94-2, KU323, KUG216 and B3-8-8, one cultivar of mungbean, namely, PDM54 and two cultivars of cowpea, namely, V578 and DCP13 were moderately resistant against the pathogen. The virulence analysis of 90 isolates of the pathogen representing 7 anastomosis groups (AGs) isolated from pulse crops of 16 agro-ecological regions of India on a set of differential cultivars, namely, HUM 1, PDM 54 and Pusa Vishal of mungbean, NDU 3-4, KU 323, Uttara of urdbean and V 240, V 578 and Pusa Sukomal of cowpea grouped the isolates into five pathotypes. The differential cultivar for each pathotype was identified. The pathotypes were not corresponding to the AG type of the isolates. Except one pathotype (isolate RMPG28 belonging to AG2-3), each pathotype had the isolates from different AGs.
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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) | 0.000 | 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".