马尾松毛虫灾情指数的方差分析周期外推预报
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摘要
为了提高马尾松毛虫Dendrolimus punctatus Walker灾情预报结果的准确性,为提高防治效果提供科学依据,本文采用方差分析周期外推预报法,以安徽省潜山县的马尾松毛虫各代幼虫累计发生量和当代发生面积计算求得的灾情指数为依据,进行方差分析周期外推预报,并对预报结果进行验证。预报结果的历史符合率,1989-2016年全年灾情指数预报结果历史符合率为89.29%;预报2017年的结果与实况一致。1989-2016年的越冬代历史符合率为89.29%,2017年预报值与实际完全一致。1997-2016年的一代预报结果历史符合率为100%。1989-2016年的二代预报结果的历史符合率为85.19%。2017年由于越冬代飞防使用灭幼脲,使当年一、二代虫口大幅度降低,预报值高于实况。方差分析周期外推预报法对马尾松毛虫的灾情指数预报是一种较理想的预报方法。
In order to improve the accuracy of disaster forecast of Dendrolimus punctatus, and provide scientific basis for improving the control efficacy, the periodic extrapolation prediction method of variance analysis was performed on the basis of the damage index calculated by the cumulative amount of larvae and the area of occurrence in each generation of D.punctatus in Qianshan county, Anhui province, and the prediction results were verified. The historical conformity rate of the annual disaster index forecast results was 89.29% for 1989-2016, and the forecasts for 2017 were in line with the actual data. The historical conformity rates of the overwintering and 2 nd generations were 89.29% and 85.19% for 1989-2016, respectively, and the forecasts were in line with the actual data for 2017, while the historical conformity rate of the 1 st generation was 100% in 1997-2016. In 2017, due to the application of chlorbenzuron by aerial spraying, the 1 st and 2 nd generations were greatly reduced and the forecast value was higher than the actual value. The method of variance analysis and periodic extrapolation is an ideal method for predicting the occurrence of D.punctatus.
In order to improve the accuracy of disaster forecast of Dendrolimus punctatus, and provide scientific basis for improving the control efficacy, the periodic extrapolation prediction method of variance analysis was performed on the basis of the damage index calculated by the cumulative amount of larvae and the area of occurrence in each generation of D.punctatus in Qianshan county, Anhui province, and the prediction results were verified. The historical conformity rate of the annual disaster index forecast results was 89.29% for 1989-2016, and the forecasts for 2017 were in line with the actual data. The historical conformity rates of the overwintering and 2 nd generations were 89.29% and 85.19% for 1989-2016, respectively, and the forecasts were in line with the actual data for 2017, while the historical conformity rate of the 1 st generation was 100% in 1997-2016. In 2017, due to the application of chlorbenzuron by aerial spraying, the 1 st and 2 nd generations were greatly reduced and the forecast value was higher than the actual value. The method of variance analysis and periodic extrapolation is an ideal method for predicting the occurrence of D.punctatus.
引文
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[2] 邹运鼎,程扶玖,查光济.松针内含物与马尾松毛虫生存发育关系的研究[J].林业科学,1990,26(2):142-148.
[3] 萧刚柔.中国森林害虫(第二版)[M].北京:林业出版社,1992:948-953.
[4] 张真,李典谟.马尾松毛虫暴发机制分析[J].林业科学,2008,44(1):140-150.
[5] 张爱兵,陈建,王正军,等.BP网络模型和LOGIT模型在森林害虫测报上的应用初报——以安徽省潜山县马尾松毛虫为例[J].生态学报,2001,21(12):2159-2165.
[6] PARK Y S,CEREGHINO R,COMPIN A.Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters [J].Ecological Modelling,2003,160(3):265-280.
[7] 贾春生.利用马尔可夫链方法测报马尾松毛虫发生级别[J].东北林业大学学报,2006,34(5):21-22.
[8] ZHANG W J,ZHONG X Q,LIU G H.Recognizing spatial distribution patterns of grassland insects:neural network approaches [J].Stochastic Environmental Research and Risk Assessment,2008,22(2):207-216.
[9] 陈绘画,王坚娅,徐志宏.基于响应面方法的马尾松毛虫发生量混沌特性检测及其预测[J].东北林业大学学报,2011,39(9):94-96.
[10] 田万银,徐华潮.浙江沿海防护林马尾松毛虫的预测预报模型[J].环境昆虫学报,2012,34(4):401-406.
[11] 费海泽,王鸿斌,孔祥波等.马尾松毛虫发生相关气象因子筛选及预测[J].东北林业大学学报,2014,41(1):136-140.
[12] 许章华,李聪慧,刘健.马尾松毛虫害等级的Fisher判别分析[J].农业机械学报,2014,45(6):275-283.
[13] 王庆,毕猛,杜婷.基于气象因子的马尾松毛虫发生率空间格局研究[J].林业科学研究,2016,29(2):256-260.
[14] 余燕,李尚,王振兴,等.马尾松毛虫幼虫发生严重程度的预测研究[J].安徽农业大学学报,2017,44(5):882-893.
[15] 周夏芝,王振兴,余燕,等.马尾松毛虫幼虫高峰期发生量的预测模型研究[J].应用昆虫学报,2017,54(6):1031-1043.
[16] 国家林业局森林病虫害防治总站.林业有害生物监测预报技术[J].北京:中国林业出版社,2013:117-118.
[17] 张孝羲,翟保平,牟极元,等.昆虫生态及预测预报[M].第3版.北京:中国农业出版社,2002:262-265.
[18] 吴劲松.用方差分析周期及随机时间序列法作赫章6-8月总降雨量预报[J].贵州气象,1999,23(1):13-14.
[19] 张梅,陈玉光,杨冰.方差分析周期叠加法预测农作物生长季积温[J].现代农业科技,2017(21):237-240.
[20] 杨长登.用方差分析周期叠加外推法预报年降水量[J].贵州气象,1998,22(1):23-25.
[21] 康晓慧,陈浩,张梅.3种时间序列分析模型在水稻稻瘟病预测中的应用[J].西北农林科技大学学报(自然科学版),2011,39(6):173-184.
[22] KWON J H.Crystal graphs and the combinatorics of young tableaux [M].Handbook of Algebra.Vol 6,North-Holland,New York,2009:473-504.
[23] 武汉中心气象台,武汉大学数学系.方差分析周期外推法在长期预报中的应用[J].数学学报,1974,17(3):156-163.