Abstract:

【Background】 Since 1995, Fusarium head blight (FHB) has gradually spread and risen from a secondary disease to a major disease in Haihe Plain, from sporadic occurrence to continuous occurrence, showing the characteristics of rapid outbreak, large area and heavy loss in epidemic years. To realize effective prevention and control of FHB, accurate forecasting technology is an important prerequisite for controlling the occurrence and development of FHB. 【Objective】According to the occurrence of FHB in Haihe Plain, the prediction model of FHB suitable for Haihe Plain was established to provide technical supports for scientific prevention and control of FHB.【Method】Based on the data about spike rate of FHB and meteorological factors of key growth stage of wheat in 21 counties of Haihe Plain from 2001 to 2016, the key meteorological factors which have significant influences on the FHB occurrence in Haihe Plain were screened by stepwise regression analysis, and the prediction models of FHB occurrence based on multiple linear regression model and boosted regression tree model were constructed, respectively.【Result】When the learning efficiency (lr) of the boosted regression tree model was 0.005 and the complexity (tc) of the tree was 6, the prediction deviation of the model was the lowest, and the residual standard error was 0.006311. Eight key meteorological factors, including MRH₁₅, Rain_-35, MRH_-55, SD₁₅, LT_-65, MWS_-55, MT_-25 and DRain₁₅, which had a significant impact on the occurrence of FHB in Haihe Plain, were screened out, and a multiple linear regression model with eight predictive variables was established (R²=0.8158, corrected R ²=0.8018, P<2.2×10 ^-16). Meanwhile, the importance of each key meteorological factor was evaluated by using the boosted regression tree model, with the values of 69.62%, 14.08%, 4.89%, 4.34%, 3.35%, 2.02%, 1.20% and 0.50%, respectively. According to the key predictive variables, the prediction model was further simplified, and a multiple linear regression model with four predictive variables was constructed (y=-19.45376+0.11689MRH₁₅+0.17346Rain_-35+0.04185SD₁₅+0.26592MRH_-55, R²=0.7575, corrected R ²=0.7468, P<2.2×10 ^-16). When the prediction variables was reduced from 8 to 4, the prediction accuracy of the multiple linear regression model decreased from 88.43% to 85.90%, but the prediction accuracy on the disease spike rate of the boosted regression tree model increased from 87.72% to 91.23%, which was verified by using the historical data of Anxin, Dingzhou and Guantao, etc in 2008, 2010 and 2012. The prediction accuracy on the disease spike rate of the multiple linear regression model and the boosted regression tree model changed from 87.53% to 87.42% and from 89.20% to 89.21%, respectively, but there was no significant difference between the multiple linear regression model and the boosted regression tree model, when they were verified with the historical data of Zhengding and Luancheng from 2001 to 2016. In a word, the prediction accuracy of multiple linear regression model showed a downward trend, while the prediction accuracy of boosted regression tree model showed an upward trend.【Conclusion】In this study, the boosted regression tree model with four predictive variables was constructed, with the prediction accuracy of 89.21%. At the same time, the disease spike rate predicted by the boosted regression tree model was basically consistent with the observed fluctuation trend, indicating that the boosted regression tree model had a good application prospect in the prediction of FHB in Haihe Plain.

Key words: Fusarium head blight (FHB), Fusarium graminearum, prediction model, boosted regression tree (BRT)