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Forecasting wildfire disease on tobacco: toward developing a high-accuracy prediction model for disease index using local climate factors and support vector regression

  • X. H. Cai
  • T. Chen
  • R. Y. Wang
  • Y. J. Fan
  • Y. Li
  • S. N. Hu
  • Z. M. Yuan
  • H. G. Li
  • X. Y. Li
  • S. Y. Zhao
  • Q. M. Zhou
  • W. Zhou
Original Paper
  • 19 Downloads

Abstract

Tobacco wildfire disease is common globally, and climate change may increase the risk of outbreaks. Therefore, there is an urgent need to establish an effective climate model to forecast the occurrence of wildfire disease. To design such a model, we collected data for 40 wildfire disease indices via tobacco field surveys and data for 15 climate factors of Guiyang County in China from 2012 to 2016. First, we built multiple linear regression (MLR), stepwise linear regression (SLR) and support vector regression (SVR) models using three climate features (precipitation, mean daily temperature and sunshine duration), and we could not find an effective model. Second, we built three corresponding models using expanded 15 climate features and an in-house WDEM method (the worst descriptor elimination multi-roundly), and the independent test results showed that the best SVR model had not only a higher predictive accuracy (\( {Q}_{ext}^2 \) = 0.94) but also a better stability. Finally, we further evaluated the biological significance of their retained climate features and the single-factor effects of the best model according to the interpretability analysis, and our results indicated that (1) the three climate factors (minimum value of wind velocity, daily range of temperature and daily pressure) strongly affected the occurrence of wildfire disease; (2) the ranges of relative humidity and sunshine hours were negatively correlated with the occurrence of wildfire disease, while daily mean vapour pressure was positively correlated with the occurrence of the disease. Our work enables a useful theoretical prediction for wildfire disease, especially in terms of climate-related predictions.

Notes

Acknowledgements

The authors thank other members of the laboratory at Department of Bioinformatics, College of Plant Protection, Hunan Agricultural University, Changsha, China for their help during the manuscript preparation.

Funding information

This research was supported by China Postdoctoral Science Foundation (No.2015T80870 and No.2014M562109), China Scholarship Council (No.201708430002) and Scientific Research Fund of Hunan Provincial Education Department (NO.17C0770).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Hunan Provincial Engineering and Technology Research Center for Agricultural Big Data Analysis and Decision-MakingHunan Agricultural UniversityChangshaChina
  2. 2.Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect PestsHunan Agricultural UniversityChangshaChina
  3. 3.Hunan Provincial Engineering and Technology Research Center for Biopesticide and Formulation ProcessingHunan Agricultural UniversityChangshaChina
  4. 4.College of AgricultureHunan Agricultural UniversityChangshaChina
  5. 5.Chenzhou Company of Hunan Tobacco CompanyChenzhouChina
  6. 6.Hunan Tobacco CompanyChangshaChina
  7. 7.Department of Soil and Crop SciencesTexas A&M UniversityCollege stationUSA

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