Vegetable grafting: A sustainable and eco-friendly strategy for soil-borne pest and disease management
Bibliographic record
Abstract
Vegetable production around the world is more and more hampered by the unfavourable soil and environmental conditions as well as biotic ones as soil-borne pests and diseases. Among all management tactics, vegetable grafting is considered as eco-friendly for sustainable vegetable production as a result of the resistant rootstock reduces the dependency upon agrochemicals needed treating the soil-borne diseases and has opened a new vista in organic farming of vegetables. The production and cultivation of grafted solanaceous and cucurbitaceous plants are ever-increasing across Asia, Europe, and North America because of its ability to provide tolerance to biotic stress and abiotic stresses. These grafted seedlings provide resistance against biotic/abiotic stresses and also increase the yield of the cultivars. At present grafting is regarded as a rapid alternative tool to the relatively slow breeding methodology and helpful in sustainable farming that takes low input for future agriculture system. This tactic has rapidly expanded due to intensification of production practices, reliance on susceptible cultivars to satisfy specific market demands, a global movement and local invasion of novel pathogens, accrued use of organic practices, the fast adoption of high tunnel production systems, use of appropriate technologies for resource-limited farmers and the ban on methyl bromide via Montreal Protocol (Sakata et al. 2007). Further, inventions in mechanised and robotic grafting have given a positive stimulus to this novel eco-friendly approach. Mechanisation can significantly reduce the cost of grafted seedling production in the future. Because of the high post graft mortality of seedlings, this technology is still in infancy in India. For its commercial application in India, sharpening of grafting skills and healing environment need to be standardised.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".