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Bentgrass susceptibility, disease forecasting, and fungicide timing effects on dollar spot
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Hempfling, James Warren. Bentgrass susceptibility, disease forecasting, and fungicide timing effects on dollar spot. Retrieved from https://doi.org/doi:10.7282/t3-8tz0-a004

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TitleBentgrass susceptibility, disease forecasting, and fungicide timing effects on dollar spot
NameHempfling, James Warren (author); Murphy, James A (chair); Clarke, Bruce B (co-chair); Lalancette, Norman (internal member); Koch, Paul L (outside member); Rutgers University; School of Graduate Studies
Date Created2019
Other Date2019-05 (degree)
SubjectBentgrass, Plant Biology, Agrostis -- Diseases and pests -- Control
Extent1 online resource (xxiii, 222 pages) : illustrations
DescriptionSusceptibility 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.
NotePh.D.
NoteIncludes bibliographical references
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/t3-8tz0-a004
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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