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DEFHAZ: A Mechanistic Weather-Driven Predictive Model for Diaporthe eres Infection and Defective Hazelnut Outbreaks

The browning of the internal tissues of hazelnut kernels, which are visible when the nuts are cut in half, as well as the discolouration and brown spots on the kernel surface, are important defects that are mainly attributed to Diaporthe eres. The knowledge regarding the Diaporthe eres infection cyc...

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Autores principales: Camardo Leggieri, Marco, Arciuolo, Roberta, Chiusa, Giorgio, Castello, Giuseppe, Spigolon, Nicola, Battilani, Paola
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9784339/
https://www.ncbi.nlm.nih.gov/pubmed/36559665
http://dx.doi.org/10.3390/plants11243553
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author Camardo Leggieri, Marco
Arciuolo, Roberta
Chiusa, Giorgio
Castello, Giuseppe
Spigolon, Nicola
Battilani, Paola
author_facet Camardo Leggieri, Marco
Arciuolo, Roberta
Chiusa, Giorgio
Castello, Giuseppe
Spigolon, Nicola
Battilani, Paola
author_sort Camardo Leggieri, Marco
collection PubMed
description The browning of the internal tissues of hazelnut kernels, which are visible when the nuts are cut in half, as well as the discolouration and brown spots on the kernel surface, are important defects that are mainly attributed to Diaporthe eres. The knowledge regarding the Diaporthe eres infection cycle and its interaction with hazelnut crops is incomplete. Nevertheless, we developed a mechanistic model called DEFHAZ. We considered georeferenced data on the occurrence of hazelnut defects from 2013 to 2020 from orchards in the Caucasus region and Turkey, supported by meteorological data, to run and validate the model. The predictive model inputs are the hourly meteorological data (air temperature, relative humidity, and rainfall), and the model output is the cumulative index (Dh-I), which we computed daily during the growing season till ripening/harvest time. We established the probability function, with a threshold of 1% of defective hazelnuts, to define the defect occurrence risk. We compared the predictions at early and full ripening with the observed data at the corresponding crop growth stages. In addition, we compared the predictions at early ripening with the defects observed at full ripening. Overall, the correct predictions were >80%, with <16% false negatives, which confirmed the model accuracy in predicting hazelnut defects, even in advance of the harvest. The DEFHAZ model could become a valuable support for hazelnut stakeholders.
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spelling pubmed-97843392022-12-24 DEFHAZ: A Mechanistic Weather-Driven Predictive Model for Diaporthe eres Infection and Defective Hazelnut Outbreaks Camardo Leggieri, Marco Arciuolo, Roberta Chiusa, Giorgio Castello, Giuseppe Spigolon, Nicola Battilani, Paola Plants (Basel) Article The browning of the internal tissues of hazelnut kernels, which are visible when the nuts are cut in half, as well as the discolouration and brown spots on the kernel surface, are important defects that are mainly attributed to Diaporthe eres. The knowledge regarding the Diaporthe eres infection cycle and its interaction with hazelnut crops is incomplete. Nevertheless, we developed a mechanistic model called DEFHAZ. We considered georeferenced data on the occurrence of hazelnut defects from 2013 to 2020 from orchards in the Caucasus region and Turkey, supported by meteorological data, to run and validate the model. The predictive model inputs are the hourly meteorological data (air temperature, relative humidity, and rainfall), and the model output is the cumulative index (Dh-I), which we computed daily during the growing season till ripening/harvest time. We established the probability function, with a threshold of 1% of defective hazelnuts, to define the defect occurrence risk. We compared the predictions at early and full ripening with the observed data at the corresponding crop growth stages. In addition, we compared the predictions at early ripening with the defects observed at full ripening. Overall, the correct predictions were >80%, with <16% false negatives, which confirmed the model accuracy in predicting hazelnut defects, even in advance of the harvest. The DEFHAZ model could become a valuable support for hazelnut stakeholders. MDPI 2022-12-16 /pmc/articles/PMC9784339/ /pubmed/36559665 http://dx.doi.org/10.3390/plants11243553 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Camardo Leggieri, Marco
Arciuolo, Roberta
Chiusa, Giorgio
Castello, Giuseppe
Spigolon, Nicola
Battilani, Paola
DEFHAZ: A Mechanistic Weather-Driven Predictive Model for Diaporthe eres Infection and Defective Hazelnut Outbreaks
title DEFHAZ: A Mechanistic Weather-Driven Predictive Model for Diaporthe eres Infection and Defective Hazelnut Outbreaks
title_full DEFHAZ: A Mechanistic Weather-Driven Predictive Model for Diaporthe eres Infection and Defective Hazelnut Outbreaks
title_fullStr DEFHAZ: A Mechanistic Weather-Driven Predictive Model for Diaporthe eres Infection and Defective Hazelnut Outbreaks
title_full_unstemmed DEFHAZ: A Mechanistic Weather-Driven Predictive Model for Diaporthe eres Infection and Defective Hazelnut Outbreaks
title_short DEFHAZ: A Mechanistic Weather-Driven Predictive Model for Diaporthe eres Infection and Defective Hazelnut Outbreaks
title_sort defhaz: a mechanistic weather-driven predictive model for diaporthe eres infection and defective hazelnut outbreaks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9784339/
https://www.ncbi.nlm.nih.gov/pubmed/36559665
http://dx.doi.org/10.3390/plants11243553
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