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RISS 인기검색어
- 검색키워드
- 저자 : Qian Guangjing (검색결과 3 건)
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The status of neutral insects, mosquitoes in the food of natural enemies in tea gardens
Guangjing Qian,Xueyu Song,Shang Li,Zhenxing Wang,Shoudong Bi,Xiazhi Zhou,Yunding Zou 한국응용곤충학회 2019 Journal of Asia-Pacific Entomology Vol.22 No.4
In order to provide scientific basis for the comprehensive pest management in tea garden, the status of the main natural enemies, Tetragnatha squamata and T. maxillosa in their prey on neutral insects, mosquitoes and mayflies in tea garden from spring to summer was verified. The quantity, temporal and spatial relationships of mosquitoes, mayflies, five main pests and T. squamata, T. maxillosa were analyzed by angle cosine coefficient, temporal niche similarity coefficient and the ranges of spatial dependence of theoretical model of semivariogram in geostatistics. The assay was conducted in Huangshan large-leafed tea gardens in 2013 and 2014, Anji white tea gardens in 2014 and 2015, and, Wuniuzao and Baihaozao tea gardens. The results showed that the top three preys, Monolepta hieroglyhpica, thrips and mosquitoes, had a close relationship with two types of natural enemies. The top three preys had a close relationship with T. squamata, mayflies were the sixth. The top three prey M. hieroglyhpica, thrips and mosquitoes had a close relationship with T. maxillosa. Neutral insects, mosquitoes were the main prey of the potential natural enemies, T. squamata and T. maxillosa from March to May in tea gardens, mayflies were the fifth to sixth. There was no significant difference in the individuals of mosquitoes in different tea gardens.
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Prediction of peak occurrence of Dendrolimus punctatus larvae based on Bayes discriminant method
Qian Guangjing,Song Xueyu,Sun Jiazhao,Zhang Shuping,Zhou Xiazhi,Zhang Guoqing,Zou Yunding,Fang Guofei,Zhang Zhen,Yan Ping,Bi Shoudong 한국곤충학회 2020 Entomological Research Vol.50 No.8
To improve the accuracy of forecasting the peak occurrence of Dendrolimus punctatus Walker, we here used the Bayes discriminant analysis to predict this peak occurrence for the first and second generation of Dendrolimus punctatus larvae based on these data from 1983 to 2016 in Qianshan County, Anhui Province. Our present results showed that this discriminant equation for the first generation was as follows: f (1) = 3.2588-6.2700x1 + 1.2870x2 + 0.7920x3 + 0.4152x4; f (2) = 14.5215- 8.5710x1 + 2.9790x2 + 2.0280x3 + 0.5031x4; f (3) = 3.5264; f (4) = 66.8312- 12.5216x1 + 5.1740x2 + 4.7162x3 + 0.6033x4. And that the prediction accuracy for the first generation was 97.22%. Whilst this discriminant equation for the second generation was as follows: f (1) = 3.536-1.192x5 + 1.338x6 + 0.638x70.025x8; f (2) = 7.317-1.337x5 + 4.240x6 + 1.010x70.295x8; f (3) = 16.488- 3.192x5 + 4.955x6 + 1.900x7–0.411x8; f (4) = 34.502- 4.184x5 + 7.484x6 + 2.583x7–0.443x8. The prediction accuracy for the second generation was 85.71%. Overall, our findings revealed that the Bayes discriminant analysis could screen out key factors to significantly improve the prediction accuracy of peak occurrence of Dendrolimus punctatus larvae.
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Zhang Nan,Qian Guangjing,Zhang Lin,Song Xueyu,Zou Yunding,Bi Shoudong 한국곤충학회 2021 Entomological Research Vol.51 No.7
To improve the accuracy of forecasting the peak period of Dendrolimus punctatus, stationary time series, periodic distance method, stepwise regression model and the Bayes discriminant analysis were used. RSME value, kappa coefficient and accuracy were used as evaluation criteria to predict the peak period for the second generation egg of D. punctatus with over 33 years from 1983 to 2016 in Qianshan County, Anhui Province. The predictions of these models were verified in 2017 and 2018. The prediction of the stationary time model and Bayes discriminant analysis for 2017 was one level lower than the actual result and for 2018 was one level higher than the actual result, while the prediction of the periodic distance method was identical to the actual result for 2017 and greatly different from the actual result for 2018. The accuracy for stationary time series (RMSE = 0.92 kappa = 0.76) and periodic distance method (RMSE = 2.96, kappa = 0.81) from 1983 to 2018 were 87.88% and 85.71%, respectively. When taking into consideration the standard error was based on differential, the accuracy for the prediction of stepwise regression model (RMSE = 0.25, kappa = 1.00) from 1983 to 2018 was 100%. The accuracy of Bayes discriminant method (RMSE = 0.71, kappa = 0.96) was 97.14%. Comparatively speaking, the stepwise regression model and Bayes discriminant analysis method were better than the stationary time series and periodic distance method in RMSE value, kappa coefficient and accuracy. So they were relatively ideal forecast methods.
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