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Influence of Climatic Variables on Incidence of Whitefly-Transmitted Begomovirus in Soybean and Bean Crops in North-Western Argentina.
Reyna, Pablo; Suarez, Franco; Balzarini, Mónica; Rodriguez Pardina, Patricia.
Afiliação
  • Reyna P; Unidad Ejecutora UFYMA-INTA-CONICET, Córdoba X5020ICA, Argentina.
  • Suarez F; Instituto Nacional de Tecnología Agropecuaria (INTA), Instituto de Patología Vegetal (IPAVE), Av. 11 de Septiembre, Córdoba 4755 X5014MGO, Argentina.
  • Balzarini M; Unidad Ejecutora UFYMA-INTA-CONICET, Córdoba X5020ICA, Argentina.
  • Rodriguez Pardina P; Facultad Ciencias Agropecuarias, Universidad Nacional de Córdoba, Córdoba 5000, Argentina.
Viruses ; 15(2)2023 02 07.
Article em En | MEDLINE | ID: mdl-36851675
Over the last 20 years, begomoviruses have emerged as devastating pathogens, limiting the production of different crops worldwide. Weather conditions increase vector populations, with negative effects on crop production. In this work we evaluate the relationship between the incidence of begomovirus and weather before and during the crop cycle. Soybean and bean fields from north-western (NW) Argentina were monitored between 2001 and 2018 and classified as moderate (≤50%) or severe (>50%) according to the begomovirus incidence. Bean golden mosaic virus (BGMV) and soybean blistering mosaic virus (SbBMV) were the predominant begomovirus in bean and soybean crops, respectively. Nearly 200 bio-meteorological variables were constructed by summarizing climatic variables in 10-day periods from July to November of each crop year. The studied variables included temperature, precipitation, relative humidity, wind (speed and direction), pressure, cloudiness, and visibility. For bean, high maximum winter temperatures, low spring humidity, and precipitation 10 days before planting correlated with severe incidence. In soybeans, high temperatures in late winter and in the pre-sowing period, and low spring precipitations were found to be good predictors of high incidence of begomovirus. The results suggest that temperature and pre-sowing precipitations can be used to predict the incidence status [predictive accuracy: 80% (bean) and 75% (soybean)]. Thus, these variables can be incorporated in early warning systems for crop management decision-making to reduce the virus impact on bean and soybean crops.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Glycine max / Begomovirus Tipo de estudo: Incidence_studies / Prognostic_studies / Risk_factors_studies País/Região como assunto: America do sul / Argentina Idioma: En Revista: Viruses Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Argentina País de publicação: Suíça

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Glycine max / Begomovirus Tipo de estudo: Incidence_studies / Prognostic_studies / Risk_factors_studies País/Região como assunto: America do sul / Argentina Idioma: En Revista: Viruses Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Argentina País de publicação: Suíça