Biological Control of Groundnut Root Rot in Farmer’s Field
Bibliographic record
Abstract
Groundnut is an important oilseed crop predominantly grown in Rajasthan, India and has suffered a 55 to 85 percent root rot disease caused by multiple pathogen complex mainly Aspergillus niger, Apergillus flavus, Sclerotium rolfsii, Thievaliopsis basicola, Rhizoctonia solani and Pythium aphanidermatum perennating in soil and seed. Trichoderma harzianum (Th3) was used against Groundnut varieties (GG-10, GG-20, M-13 and Local varieties) to reduce the yield loss by root rot disease during the year 2009 and 2010 in farmers’ fields in twelve villages in the Jaipur district of Rajasthan. The field trials were conducted by the application of Trichoderma harzianum in the form of powder and liquid bio-formulation. Trials were conducted by treating the soil, seed and foliage with powdered bio-formulation (Th3 SD, SA) at 5 g per kg seed/soil followed by spray treatment with liquid bio-formulation (Th3 FS) at 5 ml/l along with recommended IPM practices. The crops under farmer practice were significantly lower in yields with the diagnostic blackening symptoms travelling from roots to stem affecting the vascular system followed by shredding at root-stem internodes resulting in complete wilting and plant death while in Th3 treated crop blackening reduced and the root vascular system was free of disease. Maximum values of yield (39.17 Q/ha), R.C. Index (0.15), C.F.U. (38.5 x 106), and lowest root rot incidence (14.03%) was recorded in the Th3 treated groundnut crops. Increase in annual income also encouraged farmers to use the Trichoderma technology. Participatory approach and interaction between researcher and farmers helped in quick adoption and dissemination of use of biocontrol agents for groundnut growers in Rajasthan state, India.
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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".