Site-Specific Septoria Leaf Blotch Risk Assessment in Winter Wheat Using Weather-Radar Rainfall Estimates
The Septoria leaf blotch prediction model PROCULTURE was used to assess the impact on simulated infection rates when using rainfall estimated by radar instead of rain gauge measurements. When comparing infection events simulated by PROCULTURE using radar- and gauge-derived data, the simulated probab...
| Veröffentlicht in: | Plant disease. - 1997. - 95(2011), 4 vom: 31. Apr., Seite 384-393 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2011
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| Zugriff auf das übergeordnete Werk: | Plant disease |
| Schlagworte: | Journal Article |
| Zusammenfassung: | The Septoria leaf blotch prediction model PROCULTURE was used to assess the impact on simulated infection rates when using rainfall estimated by radar instead of rain gauge measurements. When comparing infection events simulated by PROCULTURE using radar- and gauge-derived data, the simulated probability of detection (POD) of infection events was high (0.83 on average), and the simulated false alarm ratio (FAR) of infection events was not negligible (0.24 on average). For most stations, simulation-observed FAR decreased to 0 and simulation-observed POD increased (0.85 on average) when the model outputs for both datasets were compared against visual observations of disease symptoms. An analysis of 148 infection events over 3 years at four locations showed no significant difference in the number of infection events of simulations using either dataset, indicating that, for a given location, radar estimates were as reliable as rain gauges for predicting infection events. Radar also provided better estimates of rainfall occurrence over a continuous space than weather station networks. The high spatial resolution provides radar with an important advantage that could significantly improve existing warning systems |
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| Beschreibung: | Date Revised 20.11.2019 published: Print Citation Status PubMed-not-MEDLINE |
| ISSN: | 0191-2917 |
| DOI: | 10.1094/PDIS-07-10-0482 |