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Potentials and Limitations of Existing Forecasting Models for Alternaria on Potatoes: Challenges for Model Improvement
Authors:Sofie Landschoot  Jolien De Reu  Kris Audenaert  Pieter Vanhaverbeke  Geert Haesaert  Bernard De Baets  Willem Waegeman
Affiliation:1.Department of Applied Bioscience Engineering, Faculty of Bioscience Engineering,Ghent University,Ghent,Belgium;2.KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Faculty of Bioscience Engineering,Ghent University,Ghent,Belgium;3.Agricultural Centre for Potato Research,Kruishoutem,Belgium
Abstract:Alternaria species, including A. solani and A. alternata, are a serious threat to potato cultivation and cause necrotic leaf spots, leading to premature defoliation and yield losses. To reduce the impact of the disease, a timely prediction of a disease outbreak is important. Worldwide, modelling attempts have been made to predict the occurrence of Alternaria in order to take adequate measures. In the present paper, we made an effort to classify the existing prediction models and subdivided them into three categories: plant-based, pathogen-based and plant-pathogen-based models. Plant-based models predict the susceptibility of the host crop and presume that Alternaria inoculum is abundantly present and not the restrictive factor, whereas pathogen-based models consider one or more stages of the Alternaria life cycle and suppose that the host crop is always susceptible. The plant-pathogen-based models try to take into account the complete plant-pathogen-environment relationship. In this paper, a critical review of the described models for Alternaria leaf spot is presented. To illustrate the discrepancy between the predicted and the observed dates of the first Alternaria symptoms or the discrepancy between the suggested first treatment and necessity to treat Alternaria, the existing models were subjected to the Belgian weather conditions. It turns out that these models are not applicable in Belgium or similar regions. This can be partially attributed to the fact that most of the currently available models are too simplistic (only plant- or pathogen-based) for regions where the disease pressure highly fluctuates between growing seasons and between locations within one season. Finally, perspectives for model improvement are given taking into account both plant, pathogen and environment.
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