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Record W6959358316 · doi:10.7939/r3-kq8d-ba45

Fusarium root rot of canola (Brassica napus): Occurrence, pathogenicity and yield losses in Alberta, Canada

2024· dissertation· en· W6959358316 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2024
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Resistance and Genetics
Canadian institutionsnot available
Fundersnot available
KeywordsFusarium proliferatumCanolaRoot rotFusariumInternal transcribed spacerInoculationFusarium oxysporumGibberella

Abstract

fetched live from OpenAlex

Root rot is a soilborne disease of canola (Brassica napus) caused by a pathogen complex. Canola crops were surveyed in Alberta, Canada, in 2021 and 2022 to assess the composition and diversity of fungi associated with root rot in this region. Isolates were identified to genus and/or species based on colony and morphological characteristics, with Fusarium spp. found to be predominant. The identity of Fusarium spp. was confirmed by analysis of the translation elongation factor 1-α (TEF-1α) and internal transcribed spacer (ITS) sequences. Fusarium avenaceum was recovered most frequently, followed by F. redolens and F. solani. Most isolates caused moderate to severe disease on canola under greenhouse conditions, with F. avenaceum and F. sporotrichioides among the most aggressive species. Fusarium sporotrichioides and F. commune were identified for the first time as canola root rot pathogens. Principal component analysis demonstrated the utility of combining disease severity and growth measurements in assessments of pathogenicity. Analysis of the ITS sequence successfully distinguished various Fusarium species, with the exception of closely related phylogenetic species like F. avenaceum, F. acuminatum, and Fusarium tricinctum. The TEF-1α sequence analysis facilitated species differentiation. Concatenating the ITS and TEF-1α sequences provided more diversified grouping information. No geographic or year effects were observed on fungal diversity or aggressiveness. In another study, the impact of F. avenaceum and Fusarium proliferatum on canola yields was evaluated in field and greenhouse experiments, and the interaction between F. proliferatum and F. oxysporum was also explored. Inoculation with any of the three species resulted in significant disease severity and reduced seedling emergence compared with non-inoculated controls, leading to yield reductions of up to 35%. Notably, there was a strong correlation (r = 0.93) between root rot severity at the seedling stage and at maturity. Regression analysis indicated a linear decline in emergence with increasing disease severity. Furthermore, disease severity at maturity adversely affected pod number per plant and seed weight per plant, with both parameters ultimately reaching zero at a severity of four on a 0-4 scale. Co-inoculation with F. oxysporum and F. proliferatum induced more severe root rot than inoculation with each species on its own, suggesting synergistic interactions between these fungi. In a final study, the host range of F. proliferatum was investigated through greenhouse inoculation experiments involving wheat, barley, faba bean, pea, lentil, canola, lupine and soybean. All crops were at least partly susceptible, developing mild to severe root rot at the seedling and adult plant stages, and showing significant reductions in growth. In general, barley and wheat demonstrated higher tolerance to infection, followed by faba bean and pea. Soybean, canola, lupine, and lentil were most susceptible. Canola and soybean were particularly vulnerable to F. proliferatum at the pre-emergence stage, while infection greatly reduced lentil biomass. Reductions in barley emergence and other growth parameters, however, occurred only under a high inoculum concentration. Understanding the identity, pathogenicity, and relative prevalence of Fusarium spp., along with improved knowledge of their impact on yields, will contribute to better management of canola root rot.

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 score0.193
Threshold uncertainty score0.461

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 head0.006
GPT teacher head0.160
Teacher spread0.154 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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