Hello Everyone, Re: Fire Blight and models.
I was quite interested in the comment that despite the models indicating high risk, that you generally did not experience much blight this year. Also, that it may have been the abnormally dry conditions that may explain this. This situation occurs more often than not in the Pacific Northwest. The infection process modeled by both MaryBlyt and CougarBlight requires (in this order): First, Open blossoms, something easy to see, but often overlooked post primary bloom, when secondary bloom are often present especially on trees with dwarfing rootstocks. Second, Contamination of the stigma surface by E. a. bacteria, which is difficult to detect rapidly, but there is ongoing research on this issue. “CougarBlight” asks you to adjust your temperature risk thresholds and use your orchard history and judgment here. This contamination is not present on most flowers in most orchards, but if there are cankers nearby, this situation degrades rapidly, and many flowers are contaminated, the closer to the canker source, the higher the risk. I think there are more sources of fire blight bacteria in the general environment in the northeastern USA due to your woodlots and forests (with feral apples and native hosts such as Hawthorne) as contrasted with the treeless conditions around many eastern Washington orchards. Third, Sufficient heat over sufficient time to enable the bacterial colony to reach the numbers necessary to cause flower infection. There is an interaction between this factor and the number two factor. Larger initial colony size will reduce the amount of time and total heat necessary to colony to reach this hundred thousand to 1 million bacteria colony size necessary for infection. IT sounds as if these conditions were met very well in the East this season. Fourth, the sufficiently contaminated flowers must be lightly wetted to allow the bacteria to move from the stigma surface into the nectaries. Then the battle begins. The bacteria need to thrive in the nectary in order to reach numbers sufficient to switch on their virulence. Once this is accomplished you have an infection. In the Pacific Northwest we have plentiful supply of open apple and pear flowers over an extended period of time. Fire blight is relatively rare in any given region most seasons, so most growers can assume little pressure from the bacteria during most seasons. We spent a lot of time emphasizing sanitation because a little fire blight one year can carry over and lead to great deal of fire blight the next year. Because of this documented common lack of contamination of flowers by E. a., we very often experience a series of days of temperatures sufficient to lead to fire blight, but with only scattered subsequent fire blight showing up over the next two or three weeks. We are often able to attribute that scattered blight to dew formation on flowers, as it often occurs in areas that are more prone to dew, such as frost pockets or other low areas in the orchard. When we have a rainy day after a series of warm days, those areas that have been in bloom during those warm days have increased experience with more common and severe fire blight, which in this case depends on the presence or absence of E. amylovora. CougarBlight was developed where blossom wetting is an exceptional event. I try to counsel people using this model in areas where blossom wetting is common to look at the heat risk as primary, and to assume the blossom wetting will occur, often almost every night. Dew and rain are the rule rather than the exception in many areas where apples and pears are grown. This doesn't change the principle of the model that heat is the driver for infection, the blossom wetting is the trigger for the infection event. We can learn a great deal about interpreting models by looking at the weather data around the time that we are fairly certain that isolated infection events occurred. We can also look at when expected infections did not occur. It would be very helpful to me if any of you would share weather data including rainfall, hourly temperature (or daily temps) and especially leaf wetness readings. Please send data that covers days from first bloom to about 3 to 4 weeks after petal fall. Excel files are a real time saver. Thank you. Tim Smith WSU (Emeritus) 4. From: apple-crop-boun...@virtualorchard.net [mailto:apple-crop-boun...@virtualorchard.net] On Behalf Of Daniel Cooley Sent: Wednesday, August 12, 2015 11:22 AM To: Apple-crop discussion list <apple-crop@virtualorchard.net> Subject: Re: [apple-crop] Looking for comments on fire blight management A group of us wrote the article attached for the UMass/Rutgers publication Fruit Notes/New Jersey Horticultural News. http://umassfruitnotes.com/v80n2/Cover802.html<https://urldefense.proofpoint.com/v1/url?u=http://umassfruitnotes.com/v80n2/Cover802.html&k=EWEYHnIvm0nsSxnW5y9VIw%3D%3D%0A&r=VR1vaGJPOzxhk9dUVIL5%2Bg%3D%3D%0A&m=UfabeZAWBFZVVH4RygbVAxxf%2BYztNRlyB0BCPGgl9kE%3D%0A&s=6f16d747d2ee23b3be62b4005b0a579f17fdd4826d89fe8c7fc83eb8b4a9b1a6> The key point is that even in those years when fire blight doesn’t do much, and particularly the following year, people need to stay alert. Yes, fire blight can be kept at non-damaging levels, but it takes regular attention every year. This year in southern New England and the Hudson Valley, the two fire bight models, Maryblyt and CougarBlyt, alone or in NEWA, Ag-Radar and other decision support tools, shot off the charts warning of unprecedented fire bight risk. Growers in the region had experienced a bad year last year, and were generally ready to use strep early and often during bloom. Fire blight never really appeared at problematic levels. Much to our puzzlement, this was true even for trees that didn’t get strep treatments, leading us to wonder what was going on with the models and the disease. The best we have so far is that it was so dry during bloom in most areas that even though epiphytic populations of bacteria were tremendous, they never got washed into flowers to cause infection. Another possibility is that the extremely dry weather suppressed bacterial growth, something not taken into account in the models. Dan
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