Neocosmospora rubicola, a stem rot disease in potato: Characterization, distribution and management
Bibliographic record
Abstract
Potato (Solanum tuberosumL.) is one of the most important crops in maintaining global food security. Plant stand and yield are affected by production technology, climate, soil type, and biotic factors such as insects and diseases. Numerous fungal diseases includingNeocosmospora rubicola, causing stem rot, are known to have negative effects on potato growth and yield quality. The pathogen is known to stunt growth and cause leaf yellowing with grayish-black stems. The infectivity ofN. rubicolaacross a number of crops indicates the need to search for appropriate management approaches. Synthetic pesticides application is a major method to mitigate almost all potato diseases at this time. However, these pesticides significantly contribute to environmental damage and continuous use leads to pesticide resistance by pathogens. Consumers interest in organic products have influenced agronomists to shift toward the use of biologicals in controlling most pathogens, includingN. rubicola. This review is an initial effort to carefully examine current and alternative approaches to controlN. rubicolathat are both environmentally safe and ecologically sound. Therefore, this review aims to draw attention to theN. rubicoladistribution and symptomatology, and sustainable management strategies for potato stem rot disease. Applications of plant growth promoting bacteria (PGPB) as bioformulations with synthetic fertilizers have the potential to increase the tuber yield in both healthy andN. rubicolainfested soils. Phosphorus and nitrogen applications along with the PGPB can improve plants uptake efficiency and reduce infestation of pathogen leading to increased yield. Therefore, to controlN. rubicolainfestation, with maximum tuber yield benefits, a pre-application of the biofertilizer is shown as a better option, based on the most recent studies. With the current limited information on the disease, precise screening of the available resistant potato cultivars, developing molecular markers for resistance genes againstN. rubicolawill assist to reduce spread and virulence of the pathogen.
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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 | 0.000 | 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".