Establishment of a model for the occurrence and prediction of bayberry decline disease
In order to develop the key prevention and control technologies of bayberry decline disease, and grasp the regularity of the disease, one model of disease prediction and forecasting is needed to be established. In this study, orchards with bayberry decline disease were selected in nine locations of...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Zhejiang University Press
2022-04-01
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| Series: | 浙江大学学报. 农业与生命科学版 |
| Subjects: | |
| Online Access: | https://www.academax.com/doi/10.3785/j.issn.1008-9209.2021.09.021 |
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| Summary: | In order to develop the key prevention and control technologies of bayberry decline disease, and grasp the regularity of the disease, one model of disease prediction and forecasting is needed to be established. In this study, orchards with bayberry decline disease were selected in nine locations of Zhejiang Province from 2018 to 2020, and the diseased trees were randomly selected. This study investigated the disease index, measured the nutrient elements of soil and leaves, vegetative growth parameters and fruit quality parameters. The results showed that the disease index of bayberry decline disease was significantly correlated with the available boron, available phosphorus, and available potassium contents of soil, and zinc, potassium, manganese, and boron contents of leaves, and twig length, twig thickness, leaf length, leaf width of vegetative growth parameters, and the mass of single fruit, titratable acid, soluble solid and vitamin C contents of fruits. The twig length and leaf width, which are easy to measure, were selected to fit with the disease index. SPSS software was used for data analysis, and the prediction model of bayberry decline disease was successfully established. Finally, we get the prediction equation of bayberry decline disease of Y=－0.058X<sub>1</sub>－5.255X<sub>2</sub>＋165.35 (R<sup>2</sup>=0.64, P=0.02), then we randomly chose three orchard samples for accuracy detection, and found the prediction precision rates were all more than 90%. The establishment of this model lays a foundation for monitoring forecasting and prevention of bayberry decline disease. |
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| ISSN: | 1008-9209 2097-5155 |