Geostatistical analysis of the spatiotemporal dynamics of powdery mildew and leaf rust in wheat.
Plant diseases are dynamic systems that progress or regress in spatial and temporal dimensions. Site-specific or temporally optimized disease control requires profound knowledge about the development of each stressor. The spatiotemporal dynamics of leaf rust (Puccinia recondite f. sp. tritici) and powdery mildew (Blumeria graminis f. sp. tritici) in wheat was analyzed in order to evaluate typical species-dependent characteristics of disease spread. During two growing seasons, severity data and other relevant plant growth parameters were collected in wheat fields. Spatial characteristics of both diseases were assessed by cluster analyses using spatial analysis by distance indices, whereas the temporal epidemic trends were assessed using statistical parameters. Multivariate statistics were used to identify parameters suitable for characterizing disease trends into four classes of temporal dynamics. The results of the spatial analysis showed that both diseases generally occurred in patches but a differentiation between the diseases by their spatial patterns and spread was not possible. In contrast, temporal characteristics allowed for a differentiation of the diseases, due to the fact that a typical trend was found for leaf rust which differed from the trend of powdery mildew. Therefore, these trends suggested a high potential for temporally optimized disease control. Precise powdery mildew control would be more complicated due to the observed high variability in spatial and temporal dynamics. The general results suggest that, in spite of the high variability in spatiotemporal dynamics, disease control that is optimized in space and time is generally possible but requires consideration of disease- and case-dependent characteristics.
Franke
Jonas
J
Center for Remote Sensing of Land Surfaces, University of Bonn, Bonn, Germany. jonasfranke@uni-bonn.de
Gebhardt
Steffen
S
Menz
Gunter
G
Helfrich
Hans-Peter
HP
D001203:Ascomycota / Q000502:physiology*; D016000:Cluster Analysis; D010935:Plant Diseases / Q000382:microbiology*; D011157:Population Dynamics; D013997:Time Factors; D014908:Triticum / Q000382:microbiology*