CHALLENGE OF BIOASSAY PLANTS IN A MONITORED OUTDOOR ENVIRONMENT

被引:9
作者
FRANCL, LJ
机构
[1] Department of Plant Pathology, North Dakota State University, Fargo, ND
来源
CANADIAN JOURNAL OF PLANT PATHOLOGY-REVUE CANADIENNE DE PHYTOPATHOLOGIE | 1995年 / 17卷 / 02期
关键词
BIPOLARIS SOROKINIANA; DRECHSLERA TRITICI-REPENTIS; SEPTORIA NODORUM; STAGONOSPORA NODORUM; YELLOW LEAF SPOT;
D O I
10.1080/07060669509500704
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Mobile potted plants placed in a field can discretize episodes conducive or nonconducive to disease processes in a fluctuating environment. Models of infection and other disease components derived in controlled environments can be confirmed with this bioassay method. In 1992 healthy wheat plants (Triticum aestivum) were placed in a wheat field 32 times for 24 h periods and then subjected to a wet period of 24 h or returned directly to a growth chamber. Pyrenophora tritici-repentis, Phaeosphaeria nodorum, and Cochliobolus sativus were the most common pathogens. Pyrenophora tritici-repentis caused the greatest number of lesions and exhibited the highest infection efficiency of the three pathogens. Amount of infection was positively correlated among the pathogens but correlations of tan spot and spot blotch with septoria nodorum leaf blotch were lower when there was an added wet period. Weather was monitored to find correlates with infection period. Leaf wetness duration of 6-7 h was minimal for infection by P. tritici-repentis, confirming results obtained at constant temperatures. Infection periods could also be related to unconventional variables such as wind direction, indicative of environmental associations that can be revealed by this bioassay technique. Current analytical techniques promise a better understanding and predictability of plant disease epidemics.
引用
收藏
页码:138 / 143
页数:6
相关论文
共 25 条
[1]  
Arizmendi C.M., Sanchez J.R., Ramos N.E., Ramos G.I., Time series predictions with neural nets: Application to airborne pollen forecasting, Int. J. Biometeorol, 37, pp. 139-144, (1993)
[2]  
Campbell C.L., Madden L.V., Introduction to Plant Disease Epidemiology, (1990)
[3]  
Coakley S.M., McDaniel L.R., Line R.F., Quantifying how climatic factors affect variation in plant disease severity: A general method using a new way to analyze meteorological data, Climatic Change, 12, pp. 57-75, (1988)
[4]  
Cook D.F., Wolfe M.L., A back-propagation neural network to predict average air temperatures, AI Applications, 5, pp. 40-46, (1991)
[5]  
Everts K.L., Lacy M.L., Influence of environment on conidial concentration of alternaria porri in air and on purple blotch incidence on onion, Phytopathology, 80, pp. 1387-1391, (1990)
[6]  
Francl L.J., Madden L.V., Rowe R.C., Riedel R.M., Correlation of growing season environmental variables and the effect of early dying on potato yield, Phytopathology, 80, pp. 425-432, (1990)
[7]  
Hau B., Kranz J., Mathematics and statistics for analysis in epidemiology, Epidemics of Plant Diseases, pp. 12-52, (1990)
[8]  
Hirst J.M., Changes in atmospheric spore content: Diurnal periodicity and the effects of weather, Trans. Brit. Mycol. Soc, 36, pp. 375-393, (1953)
[9]  
Hosford R.M., Larez C.R., Hammond J.J., Interaction of wet period and temperature on pyrenophora tritici-repentis infection and development in wheats of differing resistance, Phytopathology, 77, pp. 1021-1027, (1987)
[10]  
Jeger M.J., The relation between total, infectious, and postinfectious diseased plant tissue, Phytopathology, 72, pp. 1185-1189, (1982)