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 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.

Automated summary

Researchers developed a mechanistic model called DEFHAZ to predict hazelnut defects. The model used meteorological and georeferenced data, and was validated by comparing predictions with observed data. The results showed that the model had an accuracy of over 80% and a false negative rate of less than 16%.