A Quantitative Review of Tebuconazole Effect on Fusarium Head Blight and Deoxynivalenol Content in Wheat

Paul, Pierce A.; Lipps, P. E.; Hershman, D. E.; McMullen, Marcia; Draper, Martin A.; Madden, L. V.

doi:10.1094/phyto-97-2-0211

Cited by 97 publications

(56 citation statements)

References 34 publications

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“…The most likely explanation is the very high index values (55 to 98%) in the check plots in Wegulo et al (43), whereas the mean index values in the current investigation ranged from 2 to 65%. Paul et al (27) previously showed an effect of baseline index and DON on percent control but the available data did not allow them to consider baseline index values above 65%.…”

Section: Discussionmentioning

confidence: 99%

“…However, the difference in efficacy between index and DON for moderate resistance-fungicide treatment (MR_TR: 75.7 to 71.0% = 4.7%) or resistance alone (MR_UT: 54.4 to 50.7% = 3.7%) was much smaller than for fungicide treatment alone (S_TR: 52.9 to 38.9% = 14.0%). Paul et al (27,28) also observed greater percent control of index than of DON with fungicide application alone (on susceptible cultivars) and speculated that the inferior efficacy against DON was probably due to DON contamination from late-season primary infections, fungal colonization of spikes late in the season, and possibly even secondary infections well after anthesis, when the single fungicide application would no longer provide protection. DON contamination after the period of greatest fungicide effect may cause the fungicide to appear less effective than it initially is.…”

Section: Discussionmentioning

confidence: 99%

“…Stage two of the analysis was based on Paul et al (27,28). Multivariate random-effects meta-analytical models were fitted to the index and DON data, with log of the response as the dependent variable (7) used to estimate the overall expected index and DON for different cultivar resistance-fungicide treatment management combinations.…”

Section: Checkmentioning

confidence: 99%

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Efficacy and Stability of Integrating Fungicide and Cultivar Resistance to Manage Fusarium Head Blight and Deoxynivalenol in Wheat

et al. 2012

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Willyerd, K. T., Li, C., Madden, L. V., Bradley, C. A., Bergstrom, G. C., Sweets, L. E., McMullen, M., Ransom, J. K., Grybauskas, A., Osborne, L., Wegulo, S. N., Hershman, D. E., Wise, K., Bockus, W. W., Groth, D., Dill-Macky, R., Milus, E., Esker, P. D., Waxman, K. D., Adee, E. A., Ebelhar, S. E., Young, B. G., and Paul, P. A. 2012 [MR_UT]) were used in multivariate meta-analyses, and mean log response ratios across trials were estimated and transformed to estimate mean percent control ( C ) due to the management combinations relative to S_UT. All combinations led to a significant reduction in index and DON (P < 0.001). MR_TR was the most effective combination, with a C of 76% for index and 71% for DON, followed by MS_TR (71 and 58%, respectively), MR_UT (54 and 51%, respectively), S_TR (53 and 39%, respectively), and MS_UT (43 and 30%, respectively). Calculations based on the principle of treatment independence showed that the combination of fungicide application and resistance was additive in terms of percent control for index and DON. Management combinations were ranked based on percent control relative to S_UT within each trial, and nonparametric analyses were performed to determine management combination stability across environments (trials) using the Kendall coefficient of concordance (W). There was a significant concordance of management combinations for both index and DON (P < 0.001), indicating a nonrandom ranking across environments and relatively low variability in the within-environment ranking of management combinations. MR_TR had the highest mean rank (best control relative to S_UT) and was one of the most stable management combinations across environments, with low rank stability variance (0.99 for index and 0.67 for DON). MS_UT had the lowest mean rank (poorest control) but was also one of the most stable management combinations. Based on Piepho's nonparametric rank-based variance homogeneity U test, there was an interaction of management combination and environment for index (P = 0.011) but not for DON (P = 0.147), indicating that the rank ordering for index depended somewhat on environment. In conclusion, although the magnitude of percent control will likely vary among environments, integrating a single tebuconazole + prothioconazole application at anthesis with cultivar resistance will be a more effective and stable management practice for both index and DON than either approach used alone.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Efficacy and Stability of Integrating Fungicide and Cultivar Resistance to Manage Fusarium Head Blight and Deoxynivalenol in Wheat

et al. 2012

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“…The models are simple, in that they contain a few easily calculated predictors, an asset when deploying models at the regional scale. The Risk Assessment Tool's purpose is to provide growers enough advanced notification, if the forecasted risk of an FHB epidemic is high, to optionally apply a fungicide at anthesis (51,52), an effective mitigation strategy, especially when combined with cultivar resistance (2,67,69).…”

mentioning

confidence: 99%

Predicting Fusarium Head Blight Epidemics With Weather-Driven Pre- and Post-Anthesis Logistic Regression Models

Shah¹,

Molineros²,

Paul³

et al. 2013

Phytopathology®

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Shah, D. A., Molineros, J. E., Paul, P. A., Willyerd, K. T., Madden, L. V., and De Wolf, E. D. 2013. Predicting Fusarium head blight epidemics with weather-driven pre-and post-anthesis logistic regression models. Phytopathology 103:906-919.Our objective was to identify weather-based variables in pre-and postanthesis time windows for predicting major Fusarium head blight (FHB) epidemics (defined as FHB severity  10%) in the United States. A binary indicator of major epidemics for 527 unique observations (31% of which were major epidemics) was linked to 380 predictor variables summarizing temperature, relative humidity, and rainfall in 5-, 7-, 10-, 14-, or 15-daylong windows either pre-or post-anthesis. Logistic regression models were built with a training data set (70% of the 527 observations) using the leaps-and-bounds algorithm, coupled with bootstrap variable and model selection methods. Misclassification rates were estimated on the training and remaining (test) data. The predictive performance of models with indicator variables for cultivar resistance, wheat type (spring or winter), and corn residue presence was improved by adding up to four weatherbased predictors. Because weather variables were intercorrelated, no single model or subset of predictor variables was best based on accuracy, model fit, and complexity. Weather-based predictors in the 15 final empirical models selected were all derivatives of relative humidity or temperature, except for one rainfall-based predictor, suggesting that relative humidity was better at characterizing moisture effects on FHB than other variables. The average test misclassification rate of the final models was 19% lower than that of models currently used in a national FHB prediction system.Additional keywords: additive logistic regression, data mining, multiple imputation.In the United States, Fusarium head blight (FHB) of wheat (Triticum aestivum L. em. Thell) is caused primarily by Fusarium graminearum sensu stricto of the F. graminearum species complex (44). Major FHB epidemics have occurred somewhere in the United States in every decade since the disease was formally described by W. G. Smith in 1884 (60) although, in any given location, epidemics tend to occur sporadically. During the last two decades, U.S. wheat experienced large direct production losses because of FHB (35,36) and even larger indirect losses in other sectors of the economy (43), contributing to the characterization of FHB as a reemerging disease of importance (36,53). Increased corn (Zea mays) production in wheat-growing regions, concurrent with wider adoption of reduced tillage for soil conservation, were likely contributory factors to severe epidemics beginning in the latter part of the 19th century (36,60), as pathogen survival in corn residue is an acknowledged FHB risk factor (13,27). FHB epidemiological research includes (i) basic documentation of epidemics and observed weather conditions at the time, a mainly descriptive effort, followed by quantification of optimal (usually controlle...

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“…Anderson [14] found incidence increases as temperature increases from 20˚C to 30˚C, and little or no infection occurred under 15˚C. Humidity is as important as temperature for the occurrence of wheat scab [8,25,29,30]. Infection in different stages has different effects on both yield and quality reduction, and various climatic factors have different effects on infection at different wheat growth stages, which may be responsible for the difficulty in controlling infection.…”

Section: Fhb Development and Prevalancementioning

confidence: 99%

Controlling fusarium head blight of wheat (triticum aestivum L.) with genetics

Zhang¹,

Luo²,

Ren³

et al. 2011

ABB

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Fusarium head blight, one of the most destructive diseases of wheat (Triticum aestivum L.), results in significant economic losses from reduced grain yield and quality. In recent decades, the disease has been frequently recorded, especially under warm and wet climatic conditions. Genetic resistance has engaged plant breeders because the use of resistant cultivars is the most economical, effective, and environmentally friendly method of control. In the present paper, we summarize the research on resistance genetics of Fusarium head blight, suggest a new method for evaluating Fusarium head blight resistance, and recommend strategies for creating and developing new sources of resistance to Fusarium head blight through the use of alien genes and chromosomal segments.

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A Quantitative Review of Tebuconazole Effect on Fusarium Head Blight and Deoxynivalenol Content in Wheat

Cited by 97 publications

References 34 publications

Efficacy and Stability of Integrating Fungicide and Cultivar Resistance to Manage Fusarium Head Blight and Deoxynivalenol in Wheat

Efficacy and Stability of Integrating Fungicide and Cultivar Resistance to Manage Fusarium Head Blight and Deoxynivalenol in Wheat

Predicting Fusarium Head Blight Epidemics With Weather-Driven Pre- and Post-Anthesis Logistic Regression Models

Controlling fusarium head blight of wheat (triticum aestivum L.) with genetics

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