花生褐斑病表观侵染速率、空中分生孢子密度与气象因素的相关性分析

为明确花生褐斑病表观侵染速率、空中分生孢子密度与气象因素之间的相关性，采用田间小区试验系统调查了花生褐斑病流行动态情况，并定期捕捉了空中分生孢子飞散情况，结合田间环境监测分析不同变量之间的相关性。结果表明，花生褐斑病的季节流行曲线为典型S形曲线，病害指数增长期为花生出苗至7月上旬，逻辑斯蒂增长期为7月上旬至9月中旬，衰退期为9月中旬至花生生育期结束。整个生长季花生褐斑病的表观侵染速率呈波浪式正态分布，其变化趋势可反映不同流行时期该病害病情变化快慢。花生褐斑病菌空中分生孢子密度与各供试花生品种的病害表观侵染速率之间呈显著正相关，说明通过空中分生孢子密度可准确分析花生褐斑病病情变化。花生褐斑病表观侵染速率和空中分生孢子密度与当日气象因素之间的相关性较差，仅空中分生孢子密度与当日降雨量呈显著负相关，相关系为-0.454*，说明较强降雨对空中孢子飞散有明显的冲刷作用。花生褐斑病表观侵染速率、空中分生孢子密度分别与前7 d平均气温、平均相对湿度、平均叶面湿润时数、累计降雨量呈现显著或极显著正相关。可将这些相关性较高的气象因素作为预测模型关键的输入变量，为花生褐斑病的科学管理和防治决策提供理论依据。

Correlation between visible infection rate,airborne conidia density of peanut early leaf spot and meteoro? ogical factors

 To explore the correlation between visible infection rate of peanut early leaf spot, airborne conidia density of Cercospora arachidicola Hori and environmental factors by using 5 varieties as material, epidemic dynam? ics of peanut early leaf spot was investigated, airborne conidia of C. arachidicola and meteorological factors were monitored in field. Combined with meteorological data, the relationship among variables was analyzed. The seasonal epidemic curve of peanut early leaf spot showed a typical S-shape. Exponential phase was from peanut seedling to early July, logistic phase was from early July to mid-September, and decline phase was from mid-September to the end of peanut growth period. The apparent infection rate of the disease showed a normal distribution in the whole growing season, and its trend could reflect the change of disease epidemic rate in different epidemic phases. There was a significant positive correlation between the airborne conidia density and the detectable infection rate of the tested peanut varieties, which indicated that the disease index could be accurately analyzed by the airborne conidia density. Both infection rate and airborne conidia had no significant correlation with meteorological factors of the day. Only airborne conidia density had significant negative correlation with rainfall of the day, and the correlation coeffi? cient was -0.454, which indicated that strong rainfall had a significant settling effect on airborne conidia dispersion. The infection rate and airborne conidia density of peanut early leaf spot were both significantly positively correlated with mean temperature, mean relative humidity, mean leaf wetness and accumulated rainfall of previous 7 days re? spectively. The above results showed that the meteorological factors with high correlation could be used as the key input variables in the prediction model to providescientific management and control of peanut early leaf spot.