4.6 Article

Ecology of Diaporthe eres, the causal agent of hazelnut defects

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PLOS ONE
卷 16, 期 3, 页码 -

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PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0247563

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  1. Fondazione Romeo ed Enrica Invernizzi

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The study investigated the effect of different abiotic factors on Diaporthe eres, with findings suggesting optimal temperatures for growth and development, as well as the importance of water activity for the fungal life cycle. The fitted data using nonlinear regression functions could be the basis for developing a predictive model for hazelnut infection by D. eres.
Diaporthe eres has been recently reported as the causal agent of hazelnut defects, with characteristic brown spots on the kernels surface and internal fruit discoloration. Knowledge regarding the ecology of this fungus is poor but, is critical to support a rationale and effective hazelnut crop protection strategy. Therefore, a study was performed to describe and model the effect of different abiotic factors such as temperature (T, 5-35 degrees C, step 5 degrees C) and water activity (a(w) 0.83-0.99, step 0.03) regimes on D. eres mycelial growth, pycnidial conidiomata development and asexual spore production during a 60-day incubation period. Alpha conidia germination was tested in the same T range and at different relative humidities (RH = 94, 97 and 100%) over 48 h incubation period. Fungal growth was observed from the first visual observation; regarding pycnidia and cirrhi, their development started after 8 and 19 days of incubation, respectively and increased over time. The optimum T for growth was 20-25 degrees C and for pycnidia and cirrhi development was 30 degrees C; a(w) >= 0.98 was optimal for the tested steps of the fungal cycle. The best condition for conidial germination of D. eres was at 25 degrees C with RH = 100%. Quantitative data obtained were fitted using non- linear regression functions (Bete, logistic and polynomial), which provided a very good fit of the biological process (R-2 = 0.793-0.987). These functions could be the basis for the development of a predictive model for the infection of D. eres of hazelnuts.

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