A network meta-analysis provides new insight into fungicide scheduling for the control of Botrytis cinerea in vineyards

  • Elisa Gonzalez Dominguez
  • , Giorgia Fedele
  • , Tito Caffi
  • , Laurent Delière
  • , Pierre Sauris
  • , David Gramaje
  • , José Luis Ramos-Saez de Ojer
  • , Emilia Díaz-Losada
  • , Ana M Díez-Navajas
  • , Paul Bengoa
  • , Vittorio Rossi*
  • *Autore corrispondente per questo lavoro

Risultato della ricerca: Contributo in rivistaArticolo

10 Citazioni (Scopus)

Abstract

BACKGROUND: Control of Botrytis bunch rot (BBR) is currently based on the application of fungicides at four timings corresponding to specific growth stages of vines: end of flowering (A), pre-bunch closure (B), veraison (C) and before harvest (D). The current research provides a network meta-analysis of 116 studies conducted between 1963 and 2016 in nine countries, in which 14 strategies (based on combinations of 1, 2, 3, or 4 sprays applied in A, B, C, and/or D) were compared. RESULTS: When a one-spray strategy was applied, BBR control was more effective with sprays applied in A, C, or D than B. With a two-spray strategy, strategy AC provided similar control as strategy BC; strategy CD also provided good control. For a 3-spray strategy, the best disease control was consistently obtained with strategy ACD. Four-spray strategy ABCD provided the best control but often involved needless sprays so that the routine application of four sprays is not justified. CONCLUSIONS: Spraying at timing A seems to be very important for achieving efficient and flexible disease control. Flexibility is reduced by spraying at timing B rather than A. © 2018 Society of Chemical Industry.
Lingua originaleInglese
pagine (da-a)324-332
Numero di pagine9
RivistaPest Management Science
Volume75
Numero di pubblicazione2
DOI
Stato di pubblicazionePubblicato - 2019

All Science Journal Classification (ASJC) codes

  • Agronomia e Scienze della Produzione Vegetale
  • Entomologia

Keywords

  • Botrytis bunch rot
  • disease management
  • grey mould
  • network meta-analysis

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