Logotipo do repositório
 

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

dc.contributor.authorValeriano, Taynara Tuany Borges
dc.contributor.authorde Souza Rolim, Glauco [UNESP]
dc.contributor.authorManici, Luisa Maria
dc.contributor.authorGiustarini, Laura
dc.contributor.authorBregaglio, Simone
dc.contributor.institutionResearch Centre for Agriculture and Environment
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionFerrero Trading Luxembourg
dc.date.accessioned2022-04-28T19:40:49Z
dc.date.available2022-04-28T19:40:49Z
dc.date.issued2021-01-01
dc.description.abstractForecasting 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.en
dc.description.affiliationCREA—Council for Agricultural Research and Economics Research Centre for Agriculture and Environment
dc.description.affiliationSchool of Agricultural and Veterinarian Sciences São Paulo State University (Unesp)
dc.description.affiliationHazelnut Company Division Ferrero Trading Luxembourg, Rue de Trèves, L
dc.description.affiliationUnespSchool of Agricultural and Veterinarian Sciences São Paulo State University (Unesp)
dc.identifierhttp://dx.doi.org/10.1007/s00484-021-02162-5
dc.identifier.citationInternational Journal of Biometeorology.
dc.identifier.doi10.1007/s00484-021-02162-5
dc.identifier.issn1432-1254
dc.identifier.issn0020-7128
dc.identifier.scopus2-s2.0-85108384475
dc.identifier.urihttp://hdl.handle.net/11449/221820
dc.language.isoeng
dc.relation.ispartofInternational Journal of Biometeorology
dc.sourceScopus
dc.subjectDisease forecasting
dc.subjectDisease modeling
dc.subjectProcess-based model
dc.subjectPuccinia kuehnii
dc.subjectSeverity index
dc.titleA process-based model to simulate sugarcane orange rust severity from weather data in Southern Brazilen
dc.typeArtigopt
dspace.entity.typePublication
unesp.author.orcid0000-0002-3807-4633[1]
unesp.campusUniversidade Estadual Paulista (UNESP), Faculdade de Ciências Agrárias e Veterinárias, Jaboticabalpt

Arquivos