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山西晋南麦区蛴螬种群动态预测模型研究 被引量:4

Prediction models of grub population dynamics in wheat lands of South Shanxi Province
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摘要 为对蛴螬种群进行准确预测和适时防治,本试验于1982~2002年在山西晋南麦区8个市(县)选择代表性地块,采用棋盘式取样法调查分析了正茬和回茬麦田蛴螬的发生规律。筛选出4个蛴螬发生的预测因子,即发生程度(Y)与虫口密度基数(X1)、8月份降雨量(X2)、9月下旬降雨量(X3)和9月下旬5cm地温(X4)相关。运用通径分析,确证了对蛴螬发生程度而言虫口基数是首要影响因素,气象因子居次要地位。同时研究了不同茬口下气象因子对麦田蛴螬发生动态的影响差异,并采用多元统计预测法,建立了预测回归方程,正茬小麦田为Y=0.0379+0.5969X1+0.0019X2-0.0098X3+0.0460X4,回茬田为Y=1.7655+0.6348X1-0.0052X2-0.0037X3-0.0035X4。经回归检验,两个预测模型在1982~2006年间历史拟合率分别高达88%和92%,可用于预测未来蛴螬发展动态。 In order to correctly predict the grub population dynamics for a timely control, we investigated and analyzed grub genesis regulation in first-cropping and continuous-cropping wheat fields by chessboarol-sampling method using 1982~2002 data collected in the wheat lands of South Shanxi Province. Furthermore, the influence of weather factors on grub population dynamics for different cropping systems was investigated and regression equations established by the adoption of multi-analysis method. Four factors including grub original density (X1 ) , August rainfall (X2 ) , rainfall in the last 10 days of September (X3 ) and the 5 cm depth soil temperature in last 10 days of September ( X4 ) were selected and their correlations with infection degree of grub genesis (Y) determined. Based on path-analysis, the most important factor is grub original density, followed by weather factors. Regression equation for the first-cropping wheat field is Y=0.037 9 +0.596 9X1 +0.001 9X2 -0.009 8X3 +0.0460X4 and that for the continuous-cropping wheat field is Y = 1. 765 5 + 0. 634 8X1 - 0. 005 2X2 - 0. 003 7X3 - 0. 003 5X4. For 1982~2006, the prediction rate of the models are 88% and 92% respectively. The results show that the prediction model is highly suitable for forecasting grub population dynamics.
出处 《中国生态农业学报》 CAS CSCD 2008年第4期929-932,共4页 Chinese Journal of Eco-Agriculture
基金 山西省科技厅攻关项目(002018) 山西省农业科学院攻关项目(YGG0723)资助
关键词 小麦 蛴螬 种群动态 预测模型 虫口基数 气象因子 Wheat, Grub, Population dynamics, Forecasting model, Grub original density, Weather factor
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