Temperature-dependent sporulation of the fungus Coniella diplodiella, the causal agent of grape white rot

Author:

Ji Tao1,Languasco Luca2,Li Ming3,Rossi Vittorio4

Affiliation:

1. Università Cattolica del Sacro Cuore Dipartimento di Scienze delle Produzioni Vegetali Sostenibili, 550374, Department of Sustainable Crop Production (DI.PRO.VES.), Università Cattolica del Sacro Cuore, Via E. Parmense 84, Piacenza 29122, Italy, Piacenza, Emilia-Romagna, Italy;

2. Università Cattolica del Sacro Cuore Facoltà di Scienze Agrarie Alimentari e Ambientali, 198189, Department of Sustainable Crop Production , Via Emilia Parmense, 84, Piacenza, Emilia-Romagna, Italy, 29122, ;

3. National Engineering Research Center for Information Technology in Agriculture, 205339, Beijing, Beijing, China;

4. Università cattolica del Sacro Cuore, Entomology and Plant Pathology, Via E. Parmense 84, Piacenza, Piacenza, Italy, 29100, , ;

Abstract

White rot, caused by the fungus Coniella diplodiella, can severely reduce grapevine yields worldwide. Currently, white rot control mainly relies on fungicides applied on a calendar basis or following hailstorms that favor disease outbreak; however, the control achieved with this strategy is often inconsistent or otherwise unsatisfactory. Realizing more rational control requires an improved understanding of white rot epidemiology. Toward this end, we conducted experiments with grapevine berries of two Vitis vinifera cultivars (either injured or not before artificial inoculation with a conidia suspension of C. diplodiella) to determine the effect of temperature on the length of latency (i.e., the time between infection and onset of mature pycnidia on berries) and the production of pycnidia and conidia. Sporulation occurred between 10°C and 35°C, with the optimum detected at 20°C. The latency period was shorter at 25–35°C than at lower temperatures; the shortest latency period was 120 h at 30°C on injured berries. Affected berries produced abundant conidia at 15–30℃ (the optimum was 20℃) for more than two months following inoculation. Mathematical equations were developed that fit the data, with strong associations with temperature for latency period (R2 = 0.831) and for the production dynamics of secondary conidia (R2 = 0.918). These equations may contribute to the development of a risk algorithm to predict infection periods, which can inform risk-based rather than calendar-based disease control strategies.

Publisher

Scientific Societies

Subject

Plant Science,Agronomy and Crop Science

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