Dynamics in concentrations of Blumeria graminis f. sp tritici conidia and its relationship to local weather conditions and disease index in wheat
Dynamics in concentrations of Blumeria graminis f. sp tritici conidia and its relationship to local weather conditions and disease index in wheat
复制标题
Blumeria graminis f 浓度的动态变化。
DOI：
10.1007/s10658-011-9898-8
复制
发表时间：
2012-04-01
影响因子：
1.8
通讯作者：
Luo, Yong
中科院分区：
文献类型：
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作者：
Cao, Xueren;Duan, Xiayu;Luo, Yong
2007 - 2008、2008 - 2009和2009 - 2010三个季节，在中国河北省廊坊市一块种植对白粉病敏感品种的小麦田中进行了试验。用小麦白粉菌（Blumeria graminis f. sp. tritici，Bgt）对植株进行接种，并使用容量式孢子采样器收集空气中的Bgt分生孢子。每周记录病情严重程度。分析了空气中分生孢子浓度与气象因素以及病情指数之间的关系。在所有三个季节中，接种后约20天首次检测到分生孢子，然后随时间逐渐增加。2008年和2009年5月中旬以及2010年5月下旬，在生长阶段（GS）10.5.4观察到空气中分生孢子浓度最高。接种后（GS 5）至乳熟期（GS 11.1）空气中Bgt分生孢子浓度与温度、太阳辐射呈正相关，与相对湿度和水汽压亏缺（VPD）呈负相关。利用多元回归分析构建了基于气象因素的空气中Bgt分生孢子浓度预测模型。使用自回归移动平均（ARIMA）（p，d，q）模型进行的时间序列分析表明，三个季节的数据各自都可以用简单的ARIMA（1，0，0）模型拟合。冠层内的分生孢子浓度显著高于冠层上方（P
（最后一个单词“P”似乎不完整，如果还有遗漏信息，请补充完整后再次提问。）
Experiments were conducted for 3 seasons, 2007-2008, 2008-2009 and 2009-2010 in a wheat field planted with a cultivar susceptible to powdery mildew in Langfang City, Hebei Province, China. Plants were inoculated with Blumeria graminis f. sp. tritici (Bgt) and conidia of Bgt in the air were trapped using volumetric spore samplers. Disease severity was recorded weekly. The relationships between airborne conidial concentrations and meteorological factors, as well as disease index were analyzed. Conidia were first detected about 20 days after inoculation in all three seasons, and then increased gradually with time. The highest conidial concentrations in the air were observed in mid-May 2008 and 2009 and late May 2010 at growth stage (GS) 10.5.4. The concentrations of Bgt conidia after inoculation (GS 5) to milky ripe (GS 11.1) in the air were positively correlated with temperature, solar radiation, and negatively with relative humidity and vapor pressure deficit (VPD). Prediction models of Bgt conidial concentrations in the air based on meteorological factors were constructed using multiple regression analysis. Time series analysis, using autoregressive integrated moving average (ARIMA) (p, d, q) models, showed that each of the three season's data can be fitted with simple ARIMA (1, 0, 0) models. Conidial concentrations within the canopy were significantly higher than those above the canopy (P