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Record W4407381983 · doi:10.1111/ppa.14065

Clubroot Disease in South Asia: Distribution and Management Practices

2025· article· en· W4407381983 on OpenAlexaff
Ashish Ghimire, Shilpa Devkota, Ananya Sarkar, Priyanka Mittapelly

Bibliographic record

VenuePlant Pathology · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Resistance and Genetics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBiologyClubrootDisease managementDistribution (mathematics)MEDLINEAgronomy

Abstract

fetched live from OpenAlex

ABSTRACT Clubroot, caused by the biotrophic protist Plasmodiophora brassicae , is a devastating root disease that affects all members of the Brassicaceae family. Significant progress has been made in understanding its occurrence, life cycle and management strategies. This disease has posed a considerable threat to producers and breeders globally, including South Asia. The disease has been reported in India, Nepal and parts of Bangladesh, Bhutan, Pakistan and Sri Lanka. The cool and moist climatic conditions in the northern Himalayan belt, combined with acidic soils and inadequate disease management systems, are key factors, particularly in areas of India and Nepal prone to clubroot outbreaks. Disease management strategies currently focus on soil amendments, the use of synthetic fungicides and fumigants and the application of resistant genotypes. However, limited research has been conducted on modes of disease transmission. Recent studies revealed that contaminated soil and infected seedlings are major contributors to the spread of the pathogen. Brassica crops such as B. juncea (mustard), B. napus (canola) and B. oleracea (cabbage) are particularly vulnerable. However, resistant cultivars like B. napus ‘Midas’ and B. oleracea ‘Big Sun 111’, ‘Nepa Star’ and ‘Kathmandu Local’, have been identified in India and Nepal, offering some potential for mitigating the disease. To combat clubroot effectively, there is an urgent need for integrated disease management strategies and the development of resistant genetic materials. These efforts should involve diverse stakeholders, including producers, industries, government sectors and academicians, aiming to advance the understanding of clubroot challenges and bridging critical research in South Asia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.124

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.234
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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