Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1119506
Title: Monitoring Pyricularia sp. airborne inoculum in Passo Fundo, Rio Grande do Sul, Brazil.
Authors: DANELLI, A. L. D.
FERNANDES, J. M. C.
MACIEL, J. L. N.
BOARETTO, C.
FORCELLINI, C. A.
Affiliation: Anderson Luiz Durante Danelli, Pós-Graduação em Agronomia-PPG Agro-Universidade de Passo Fundo (UPF), BR-285, CEP: 99052-900, Passo Fundo, RS, Brasil, Bolsista Capes/Prosup/UPF
JOSE MAURICIO CUNHA FERNANDES, CNPT
JOAO LEODATO NUNES MACIEL, CNPT
Cristina Boaretto, Pós-Graduação em Agronomia-PPGAgro-Universidade de Passo Fundo (UPF), BR-285, CEP: 99052-900, Passo Fundo, RS, Brasil
Carlos Alberto Forcelini, Professor, Faculdade de Agronomia e Medicina Veterinária, UPF, BR-285, CEP: 99052-900, Passo Fundo, RS, Brasil.
Date Issued: 2019
Citation: Summa Phytopathologica, v. 45, n. 4, p. 361-367, 2019.
Description: The fungus Pyricularia sp., the causal agent of wheat blast, produces light, dry and hyaline conidia that can be removed from sporulating lesions by the wind and transported over long distances. Experiments were performed with the aim of (a) determining the relationship between the climate variables and the quantity of conidia of Pyricularia sp.,and (b) obtaining technical data that can be used in the elaboration of blast forecasting models.From February 2nd, 2013 to June 7th, 2014, the number of Pyricularia sp. conidia in the air was monitored by using a spore trap and glass slides smeared with vaseline. Several climate variables were hourly recorded during the spore capturing period. The data were explored based on classification trees and relationships between the weather-based predictors and the number of trapped conidia day-1. The strongest predictors were mean relative humidity, daily mean temperature, precipitation lower than 5 mm day-1,and number of hours when temperature was between 15 and 35 °C and relative humidity > 93%.
Thesagro: Triticum Aestivum
Oryza Sativa
Keywords: Weather factors
Forecasting models
DOI: 10.1590/0100-5405/178086
Type of Material: Artigo de periódico
Access: openAccess
Appears in Collections:Artigo em periódico indexado (CNPT)

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