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Development and validation of a weather-based warning system to advise fungicide applications to control dollar spot on turfgrass.

ABSTRACT: Dollar spot is one of the most common diseases of golf course turfgrass and numerous fungicide applications are often required to provide adequate control. Weather-based disease warning systems have been developed to more accurately time fungicide applications; however, they tend to be ineffective and are not currently in widespread use. The primary objective of this research was to develop a new weather-based disease warning system to more accurately advise fungicide applications to control dollar spot activity across a broad geographic and climactic range. The new dollar spot warning system was developed from data collected at field sites in Madison, WI and Stillwater, OK in 2008 and warning system validation sites were established in Madison, WI, Stillwater, OK, Knoxville, TN, State College, PA, Starkville, MS, and Storrs, CT between 2011 and 2016. A meta-analysis of all site-years was conducted and the most effective warning system for dollar spot development consisted of a five-day moving average of relative humidity and average daily temperature. Using this model the highest effective probability that provided dollar spot control similar to that of a calendar-based program across the numerous sites and years was 20%. Additional analysis found that the 20% spray threshold provided comparable control to the calendar-based program while reducing fungicide usage by up to 30%, though further refinement may be needed as practitioners implement this warning system in a range of environments not tested here. The weather-based dollar spot warning system presented here will likely become an important tool for implementing precision disease management strategies for future turfgrass managers, especially as financial and regulatory pressures increase the need to reduce pesticide usage on golf course turfgrass.

SUBMITTER: Smith DL

PROVIDER: S-EPMC5844563 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Development and validation of a weather-based warning system to advise fungicide applications to control dollar spot on turfgrass.

Smith D L DL Kerns J P JP Walker N R NR Payne A F AF Horvath B B Inguagiato J C JC Kaminski J E JE Tomaso-Peterson M M Koch P L PL

PloS one 20180309 3

Dollar spot is one of the most common diseases of golf course turfgrass and numerous fungicide applications are often required to provide adequate control. Weather-based disease warning systems have been developed to more accurately time fungicide applications; however, they tend to be ineffective and are not currently in widespread use. The primary objective of this research was to develop a new weather-based disease warning system to more accurately advise fungicide applications to control dol ...[more]

PMID: 29522560

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Project description:Dollar spot, caused by the fungal pathogen Clarireedia spp., is an economically important foliar disease of amenity turfgrass in temperate climates worldwide. This disease often occurs in a highly variable manner, even on a local scale with relatively uniform environmental conditions. The objective of this study was to investigate mechanisms behind this local variation, focusing on contributions of the soil and rhizosphere microbiome. Turfgrass, rhizosphere, and bulk soil samples were collected from within a 256-m2 area of healthy turfgrass, transported to a controlled environment chamber, and inoculated with Clarireedia jacksonii Bacterial communities were profiled by targeting the 16S rRNA gene, and 16 different soil chemical properties were assessed. Despite their initial uniform appearance, the samples differentiated into highly susceptible and moderately susceptible groups following inoculation in the controlled environment chamber. The highly susceptible samples harbored a unique rhizosphere microbiome with suggestively lower relative abundance of putative antibiotic-producing bacterial taxa and higher predicted abundance of genes associated with xenobiotic biodegradation pathways. In addition, stepwise regression revealed that bulk soil iron content was the only significant soil characteristic that positively regressed with decreased dollar spot susceptibility during the peak disease development stage. These findings suggest that localized variation in soil iron induces the plant to select for a particular rhizosphere microbiome that alters the disease outcome. More broadly, further research in this area may indicate how plot-scale variability in soil properties can drive variable plant disease development through alterations in the rhizosphere microbiome.IMPORTANCE Dollar spot is the most economically important disease of amenity turfgrass, and more fungicides are applied targeting dollar spot than any other turfgrass disease. Dollar spot symptoms are small (3 to 5 cm), circular patches that develop in a highly variable manner within plot scale even under seemingly uniform conditions. The mechanism behind this variable development is unknown. This study observed that differences in dollar spot development over a 256-m2 area were associated with differences in bulk soil iron concentration and correlated with a particular rhizosphere microbiome. These findings provide interesting avenues for future research to further characterize the mechanisms behind the highly variable development of dollar spot, which may inform innovative control strategies. Additionally, these results suggest that small changes in soil properties can alter plant activity and hence the plant-associated microbial community, which has important implications for a broad array of agricultural and horticultural plant pathosystems.

Project description:Dollar spot caused by Clarireedia spp. (formerly Sclerotinia homoeocarpa) is an economically destructive fungal disease of turfgrass that can significantly compromise turf quality, playability, and aesthetic value. Fungicides are frequently used to manage the disease but are costly and potentially unfavorable to the environment. Repeated use of some active ingredients has resulted in reduced efficacy on C. jacksonii causing dollar spot in cool-season turfgrasses in the US. Experiments were conducted to study fungicide sensitivity of Clarireedia spp. as well as to develop alternatives to fungicides against dollar spot on warm-season turfgrass in Georgia. First, 79 isolates of Clarireedia spp. collected across the state were tested on fungicide-amended agar plates for their sensitivity to thiophanate-methyl (benzimidazole) and propiconazole (dimethyl inhibitor). Seventy-seven isolates (97.5%) were sensitive (0.001 to 0.654 μg/mL) and two isolates (2.5%) were found resistant (>1000 μg/mL) to thiophanate-methyl. However, in the case of propiconazole, 27 isolates (34.2%) were sensitive (0.005 to 0.098 μg/mL) while 52 isolates (65.8%) were resistant (0.101 to 3.820 μg/mL). Next, the efficacy of three bio- and six synthetic fungicides and ten different combinations were tested in vitro against C. monteithiana. Seven bio- and synthetic fungicide spray programs comprising Bacillus subtilis QST713 and propiconazole were further tested, either alone or in a tank mix in a reduced rate, on dollar spot infected bermudagrass 'TifTuf' in growth chamber and field environments. These fungicides were selected as they were found to significantly reduce pathogen growth up to 100% on in vitro assays. The most effective spray program in growth chamber assays was 100% B. subtilis QST713 in rotation with 75% B. subtilis QST713 + 25% propiconazole tank mix applied every 14 days. However, the stand-alone application of the biofungicide B. subtilis QST713 every seven days was an effective alternative and equally efficacious as propiconazole, suppressing dollar spot severity and AUDPC up to 75%, while resulting in acceptable turf quality (>7.0) in field experiments. Our study suggests that increased resistance of Clarireedia spp. to benzimidazoles and dimethyl inhibitors warrants continuous surveillance and that biofungicides hold promise to complement synthetic fungicides in an efficacious and environmentally friendly disease management program.

Project description:BackgroundThe use of obstetric early-warning-systems (EWS) has been recommended to improve timely recognition, management and early referral of women who have or are developing a critical illness. Development of such prediction models should involve a statistical combination of predictor clinical observations into a multivariable model which should be validated. No obstetric EWS has been developed and validated for low resource settings. We report on the development and validation of a simple prediction model for obstetric morbidity and mortality in resource-limited settings.MethodsWe performed a multivariate logistic regression analysis using a retrospective case-control analysis of secondary data with clinical indices predictive of severe maternal outcome (SMO). Cases for design and validation were randomly selected (n = 500) from 4360 women diagnosed with SMO in 42 Nigerian tertiary-hospitals between June 2012 and mid-August 2013. Controls were 1000 obstetric admissions without SMO diagnosis. We used clinical observations collected within 24 h of SMO occurrence for cases, and normal births for controls. We created a combined dataset with two controls per case, split randomly into development (n = 600) and validation (n = 900) datasets. We assessed the model's validity using sensitivity and specificity measures and its overall performance in predicting SMO using receiver operator characteristic (ROC) curves. We then fitted the final developmental model on the validation dataset and assessed its performance. Using the reference range proposed in the United Kingdom Confidential-Enquiry-into-Maternal-and-Child-Health 2007-report, we converted the model into a simple score-based obstetric EWS algorithm.ResultsThe final developmental model comprised abnormal systolic blood pressure-(SBP > 140 mmHg or < 90 mmHg), high diastolic blood pressure-(DBP > 90 mmHg), respiratory rate-(RR > 40/min), temperature-(> 38 °C), pulse rate-(PR > 120/min), caesarean-birth, and the number of previous caesarean-births. The model was 86% (95% CI 81-90) sensitive and 92%- (95% CI 89-94) specific in predicting SMO with area under ROC of 92% (95% CI 90-95%). All parameters were significant in the validation model except DBP. The model maintained good discriminatory power in the validation (n = 900) dataset (AUC 92, 95% CI 88-94%) and had good screening characteristics. Low urine output (300mls/24 h) and conscious level (prolonged unconsciousness-GCS < 8/15) were strong predictors of SMO in the univariate analysis.ConclusionWe developed and validated statistical models that performed well in predicting SMO using data from a low resource settings. Based on these, we proposed a simple score based obstetric EWS algorithm with RR, temperature, systolic BP, pulse rate, consciousness level, urinary output and mode of birth that has a potential for clinical use in low-resource settings..

Dataset Information

Development and validation of a weather-based warning system to advise fungicide applications to control dollar spot on turfgrass.

Publications

Development and validation of a weather-based warning system to advise fungicide applications to control dollar spot on turfgrass.

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