March
2015
, Volume
99
, Number
3
Pages
395
-
400
Authors
Xueren Cao, State Key Laboratory for Biology of Plant Disease and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, and Key Laboratory of Integrated Pest Management on Tropical Crops, Ministry of Agriculture, Environment and Plant Protection Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou 571001, China;
Dongming Yao, State Key Laboratory for Biology of Plant Disease and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, and College of Plant Protection, Anhui Agricultural University, Hefei 230036, China;
Xiangming Xu, East Malling Research, New Road, East Malling, Kent ME19 6BJ, UK;
Yilin Zhou, State Key Laboratory for Biology of Plant Disease and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193;
Kejian Ding, College of Plant Protection, Anhui Agricultural University, Hefei 230036, China;
Xiayu Duan and
Jieru Fan, State Key Laboratory for Biology of Plant Disease and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China; and
Yong Luo, Department of Plant Pathology, China Agricultural University, Beijing 100193, China
Affiliations
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Accepted for publication 23 September 2014.
Abstract
Abstract
Disease severity of wheat powdery mildew, caused by Blumeria graminis f. sp. tritici, was recorded weekly in fungicide-free field plots for three successive seasons from 2009 to 2012 in Langfang City, Hebei Province, China. Airborne conidia of B. graminis f. sp. tritici were trapped using a volumetric spore sampler, and meteorological data were collected using an automatic weather station. Cumulative logit models were used to relate the development of wheat powdery mildew to weather variables and airborne conidia density. Density of airborne conidia was the most important variate; further addition of weather variables, although statistically significant, increased model performance only slightly. A model based on variables derived from temperature and humidity had a generalized R2 of 72.4%. Although there were significant differences in model parameters among seasons, fine adjustment did not increase model performance significantly.
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