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陇南山区小麦蚜虫发生气象条件及程度预测模型 被引量:14

Epidemic Meteorologic Conditions and Degree Prediction Model of Wheat Aphids in Longnan Mountainous Area
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摘要 对陇南山区1992~2007年小麦蚜虫发生情况调查监测资料和相应气象资料进行对比分析,结果表明:陇南山区地形复杂,气候类型多样化,小麦蚜虫的发生具有特殊性,在不同的气候带以不同的虫态越冬,尤以海拔1100~1400m高度的向阳台地数量最大,在盛暑期间,陇南各月平均气温都在22℃以上,平均最高气温已超过30℃,麦蚜的数量急剧降低。其主要危害期由南向北、由低到高主要发生在4~5月,小麦蚜虫发生程度与上年9月到当年3月逐月平均气温呈正相关,与该时段降水量总体呈反相关,而与4~8月逐月平均气温呈反相关,与该时段降水量呈正相关。从农业气象角度出发,探讨出陇南山区小麦蚜虫发生流行时空变化规律、危害特点、预测模型,2008年和2009年业务试报趋势正确。 Based on analyzing the monitoring data of epidemic wheat aphids and corresponding meteorologic conditions from 1992 to 2007 in Longnan mountainous area, the results showed that the epidemic of wheat aphids was of particularity because of complex topographic form and various climate conditions in Longnan mountainous area. Wheat aphids' overwintering state was different in different climate zone at the ahitucle of 1 100 - 1 400 m, and its quantity was sharply reduced during midsummer because'monthly average temperature was above 22 ℃ and the maximum temperature was above 30 ℃ during this time in Longnan. Hazard of wheat aphids mainly happened from April to May from south to north and from piedmont to mountaintop. There was a positive relationship between the epidemic degree of wheat aphids and average temperature from last September to this March, but there was a negative relationship between the epidemic degree of wheat aphids and precipitation during this time. However, there was a negative relationship between the epidemic degree of wheat aphids and average temperature from April to August, but there was a positive relationship between the epidemic degree of wheat aphids and precipitation during this time. From the agrometeorological angle, the regularity of temporal and spatial variation, hazard characteristics and prediction models were studied, and service prediction accorded with observation in 2008 and 2009.
出处 《安徽农业科学》 CAS 北大核心 2009年第33期16419-16422,共4页 Journal of Anhui Agricultural Sciences
基金 甘肃省气象局项目(2005-18)
关键词 小麦蚜虫 发生规律 预测模型 Wheat aphids Regularity of epidemic Prediction model
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