Adaptation of Fire Blight Forecasting to Optimize the Use of Biological Controls
We investigated adaptation of fire blight forecasting concepts to incorporate and optimize the use of biological agents for disease suppression. The effect of temperature on growth of the bacterial antagonists, Pseudomonas fluorescens A506 and Pantoea agglomerans C9-1S, and of the pathogen Erwinia a...
| Veröffentlicht in: | Plant disease. - 1997. - 88(2004), 1 vom: 20. Jan., Seite 41-48 |
|---|---|
| 1. Verfasser: | |
| Weitere Verfasser: | , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2004
|
| Zugriff auf das übergeordnete Werk: | Plant disease |
| Schlagworte: | Journal Article |
| Zusammenfassung: | We investigated adaptation of fire blight forecasting concepts to incorporate and optimize the use of biological agents for disease suppression. The effect of temperature on growth of the bacterial antagonists, Pseudomonas fluorescens A506 and Pantoea agglomerans C9-1S, and of the pathogen Erwinia amylovora153N, on pear and apple blossoms was evaluated in growth chamber and screenhouse experiments. New blossoms were inoculated with the strains and subsequent growth was measured over 96 h. Bacterial growth rates on blossoms were described as functions of temperature. A degree hour-based "bacterial growth index" (96-h moving total of degree hours >10°C) was created to assess conduciveness of orchard environments for antagonist colonization. A comparison of this index to a disease risk index indicated that biocon-trol treatments could be timed such that the antagonists could be expected to grow to an effective population size before the disease index shifted from "low" to "moderate" risk. For six pear- and apple-production areas of Oregon and Washington, regression of actual values of the bacterial growth and disease risk indices on index values derived from 4-day temperature forecasts resulted in coefficients of determination that averaged 0.75. The "bacterial growth index" and its estimation via temperature forecasts were incorporated into a decision matrix designed to guide optimal treatment timing |
|---|---|
| Beschreibung: | Date Revised 20.11.2019 published: Print Citation Status PubMed-not-MEDLINE |
| ISSN: | 0191-2917 |
| DOI: | 10.1094/PDIS.2004.88.1.41 |