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Bentgrass susceptibility, disease forecasting, and fungicide timing effects on dollar spot

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TitleInfo
Title
Bentgrass susceptibility, disease forecasting, and fungicide timing effects on dollar spot
Name (type = personal)
NamePart (type = family)
Hempfling
NamePart (type = given)
James Warren
NamePart (type = date)
1984-
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James Warren Hempfling
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RoleTerm (authority = RULIB)
author
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NamePart (type = family)
Murphy
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James A
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James A Murphy
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Advisory Committee
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chair
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Clarke
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Bruce B
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Bruce B Clarke
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Advisory Committee
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co-chair
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Lalancette
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Norman
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Norman Lalancette
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Advisory Committee
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internal member
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Koch
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Paul L
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Paul L Koch
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Advisory Committee
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outside member
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Rutgers University
Role
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degree grantor
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School of Graduate Studies
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school
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Text
Genre (authority = marcgt)
theses
OriginInfo
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2019
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2019-05
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2019
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
Susceptibility to dollar spot, caused by Clarireedia jacksonii, varies among bentgrass (Agrostis spp.) species and cultivars. Two field trials managed as fairway turf were conducted from 2015 to 2017. The objective of the first trial was to assess the reliability of existing dollar spot disease forecasting models on bentgrass species and cultivars that range in susceptibility to this disease. Creeping bentgrass (A. stolonifera) cultivar susceptibility ranked as ‘Independence’ = ‘Penncross’ > ‘Shark’ > ‘007’ > ‘Declaration’ = ‘Capri’ (A. capillaris). A growing degree day (GDD, base 15°C and biofix 1 April) model was evaluated for predicting disease onset and a logistic regression model (a 5-day moving averages of hourly relative humidity and hourly air temperature) was evaluated for predicting disease onset and progress. A 20% risk index threshold for the logistic regression model was first reached 10- to 52-d before initial disease onset; measuring the accumulation of hours that the risk index was above the threshold more accurately predicted onset. In contrast, the GDD model was highly variable and not reliable for predicting the initial onset of disease. The 20% risk index threshold accurately forecasted season-long disease progress up to 66% of the time on high susceptibility cultivars and as low as 48% on low susceptibility cultivars; inaccuracies were largely over-predictions while under-predictions occurred 2 to 6% of the time. Model interpretations that used alternate risk index thresholds, the change in the risk index over time (slope), or a combination of a threshold and slope improved model accuracy. Increasing the risk index threshold above 20% for low susceptibility cultivars dramatically improved accuracy; however, the risk of under-predicting disease greatly increased. For high susceptibility cultivars, there was no substantial improvement in accuracy from altering the risk index. Using slope of the risk index also improved accuracy for low and moderate susceptibility cultivars compared to the 20% risk index threshold interpretation. Slope was not as accurate as increasing the risk index threshold but reduced the number of under-predictions. Combining the 20% risk index threshold provided the best improvement in accuracy with the fewest under-predictions for low and moderate susceptibility cultivars. This combination slightly improved accuracy for high susceptibility cultivars but produced the greatest number of under-predictions. Further improvements in accuracy were observed with other combinations of risk index thresholds and slope but research is needed to determine whether under-predictions are problematic when using these interpretations to schedule fungicides.
In the second trial, the factors of bentgrass cultivar, initial fungicide timing, and subsequent fungicide timing were assessed for effects on fungicide inputs and efficacy of disease control. The timing of fungicide applications was based on the GDD model, the logistic model 20% risk index, or a disease damage threshold. Cultivar susceptibility and subsequent application timing (threshold- or logistic model-based) significantly influenced disease severity (area under disease progress curve [AUDPC]) each year; whereas, initial fungicide timing did not. While initial fungicide timing did not affect season-long AUDPC, early initial timings increased fungicide inputs by one to three applications per year compared to later initial timings. Fungicide savings were evident in the cultivar and subsequent timing factors. The logistic model-based subsequent fungicide timing made eight applications per year compared to nine applications with the calendar-based program. Threshold-based subsequent timing resulted in 1 to 5 or 4 to 9 applications per year on the low or high susceptibility cultivars, respectively. Acceptable disease control was observed under logistic regression model- and disease threshold-based applications except for threshold-based applications to the high susceptibility cultivar. This research clearly indicates that fungicide savings and acceptable disease control can be achieved when using logistic model-based timing or a threshold-based timing with less susceptible cultivars. The most substantial reduction in fungicide inputs was observed when threshold-based applications were made to bentgrass cultivars with low susceptibility to dollar spot.
Subject (authority = local)
Topic
Bentgrass
Subject (authority = RUETD)
Topic
Plant Biology
Subject (authority = LCSH)
Topic
Agrostis -- Diseases and pests -- Control
RelatedItem (type = host)
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Title
Rutgers University Electronic Theses and Dissertations
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ETD_9707
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Extent
1 online resource (xxiii, 222 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
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School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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Identifier (type = doi)
doi:10.7282/t3-8tz0-a004
Genre (authority = ExL-Esploro)
ETD doctoral
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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Hempfling
GivenName
James
MiddleName
Warren
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Permission or license
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2019-04-07 18:09:19
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Name
James Hempfling
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Affiliation
Rutgers University. School of Graduate Studies
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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2019-05-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2021-05-30
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Access to this PDF has been restricted at the author's request. It will be publicly available after May 30th, 2021.
Copyright
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Copyright protected
Availability
Status
Open
Reason
Permission or license
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