A process-based model to simulate sugarcane orange rust severity from weather data in Southern Brazil

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorResearch Centre for Agriculture and Environment-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorFerrero Trading Luxembourg-
Autor(es): dc.creatorValeriano, Taynara Tuany Borges-
Autor(es): dc.creatorde Souza Rolim, Glauco [UNESP]-
Autor(es): dc.creatorManici, Luisa Maria-
Autor(es): dc.creatorGiustarini, Laura-
Autor(es): dc.creatorBregaglio, Simone-
Data de aceite: dc.date.accessioned2022-08-04T22:09:55Z-
Data de disponibilização: dc.date.available2022-08-04T22:09:55Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2020-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s00484-021-02162-5-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/221820-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/221820-
Descrição: dc.descriptionForecasting the severity of plant diseases is an emerging need for farmers and companies to optimize management actions and to predict crop yields. Process-based models are viable tools for this purpose, thanks to their capability to reproduce pathogen epidemiological processes as a function of the variability of agro-environmental conditions. We formalized the key phases of the life cycle of Puccinia kuenhii (W. Krüger) EJ Butler, the causal agent of orange rust on sugarcane, into a new simulation model, called ARISE (Orange Rust Intensity Index). ARISE is composed of generic models of epidemiological processes modulated by partial components of host resistance and was parameterized according to P. kuenhii hydro-thermal requirements. After calibration and evaluation with field data, ARISE was executed on sugarcane areas in Brazil, India and Australia to assess the pathogen suitability in different environments. ARISE performed well in calibration and evaluation, where it accurately matched observations of orange rust severity. It also reproduced a large spatial and temporal variability in simulated areas, confirming that the pathogen suitability is strictly dependent on warm temperatures and high relative air humidity. Further improvements will entail coupling ARISE with a sugarcane growth model to assess yield losses, while further testing the model with field data, using input weather data at a finer resolution to develop a decision support system for sugarcane growers.-
Descrição: dc.descriptionCREA—Council for Agricultural Research and Economics Research Centre for Agriculture and Environment-
Descrição: dc.descriptionSchool of Agricultural and Veterinarian Sciences São Paulo State University (Unesp)-
Descrição: dc.descriptionHazelnut Company Division Ferrero Trading Luxembourg, Rue de Trèves, L-
Descrição: dc.descriptionSchool of Agricultural and Veterinarian Sciences São Paulo State University (Unesp)-
Idioma: dc.languageen-
Relação: dc.relationInternational Journal of Biometeorology-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectDisease forecasting-
Palavras-chave: dc.subjectDisease modeling-
Palavras-chave: dc.subjectProcess-based model-
Palavras-chave: dc.subjectPuccinia kuehnii-
Palavras-chave: dc.subjectSeverity index-
Título: dc.titleA process-based model to simulate sugarcane orange rust severity from weather data in Southern Brazil-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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