Predicting airborne ascospores of Sclerotinia sclerotiorum through machine learning and statistical methods

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dc.contributor.authorReich, Jonathan
dc.contributor.authorMcLaren, Debra
dc.contributor.authorKim, Yong Min
dc.contributor.authorWally, Owen
dc.contributor.authorYevtushenko, Dmytro P.
dc.contributor.authorHamelin, Richard
dc.contributor.authorChatterton, Syama
dc.date.accessioned2024-08-20T20:54:52Z
dc.date.available2024-08-20T20:54:52Z
dc.date.issued2024
dc.descriptionOpen access article. Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license (CC BY-NC-ND 4.0) applies
dc.description.abstractA main biological constraint of dry bean (Phaseolus vulgaris) production in Canada is white mould, caused by the fungal pathogen Sclerotinia sclerotiorum. The primary infectious propagules of S. sclerotiorum are airborne ascospores and monitoring the air for inoculum levels could help predict the severity of white mould in bean fields. Daily air samples were collected in commercial dry bean fields in Alberta, Manitoba and Ontario and ascospores were quantified using quantitative PCR. Daily weather data was obtained from in-field weather stations. The number of ascospores on a given day was modelled using 63 different environmental variables and several modelling methods, both regression and classification approaches, were implemented with machine learning (ML) (random forests, logistic regression and support vector machines) and statistical (generalized linear models) approaches. Across all years and provinces, ascospores were most highly correlated with ascospore release from the previous day (r ranged from 0.15 to 0.6). This variable was also the only variable included in all models and had the greatest weight in all models. Models without this variable had much poorer performance than those with it. Correlations of ascospores with other environmental variables varied by province and sometimes by year. A comparison of ML and statistical models revealed that they both performed similarly, but that the statistical models were easier to interpret. However, the precise relationship between airborne ascospore levels and in-field disease severity remains unclear, and spore sampling methods will require further development before they can be deployed as a disease management tool.
dc.description.peer-reviewYes
dc.identifier.citationReich, J., McLaren, D., Kim, Y. M., Wally, O., Yevtushenko, D., Hamelin, R., & Chatterton, S. (2024). Predicting airborne ascospores of Sclerotinia sclerotiorum through machine learning and statistical methods. Plant Pathology, 73(6), 1586-1601. https://doi.org/10.1111/ppa.13902
dc.identifier.urihttps://hdl.handle.net/10133/6874
dc.language.isoen
dc.publisherWiley
dc.publisher.departmentDepartment of Biological Sciences
dc.publisher.facultyArts and Science
dc.publisher.institutionLethbridge Research and Development Centre
dc.publisher.institutionUniversity of British Columbia
dc.publisher.institutionBrandon Research and Development Centre
dc.publisher.institutionHarrow Research and Development Centre
dc.publisher.institutionUniversity of Lethbridge
dc.publisher.urlhttps://doi.org/10.1111/ppa.13902
dc.subjectAerobiology
dc.subjectDisease forecasting
dc.subjectDry bean
dc.subjectMachine learning
dc.subjectModelling
dc.subjectWhite mould
dc.subjectAirborne ascospores
dc.subject.lcshDried beans--Diseases and pests--Canada
dc.subject.lcshSclerotinia sclerotiorum
dc.titlePredicting airborne ascospores of Sclerotinia sclerotiorum through machine learning and statistical methods
dc.typeArticle
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