Carbendazim-resistance of Gibberella zeae associated with fusarium head blight and its management in Jiangsu Province, China

Chen, Hongzhou; Wu, Qinyan; Zhang, Guo; Wu, Jiawen; Zhu, Fei; Yang, Hongfu; YiQing, Zhuang

doi:10.1016/j.cropro.2019.104866

Cited by 22 publications

(14 citation statements)

References 35 publications

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“…Fungicides commonly used to control FHB, not all are equally effective [ 303 , 304 , 305 ], include carbendazim, triazoles, e.g., metconazole, tebuconazole and prothioconazole, and strobilurins, e.g., azoxystrobin, or combinations of two or more of these compounds [ 21 , 306 ]. In particular, azoxystrobin alone should be avoided as its use can increase the amount of DON produced [ 305 , 307 ]. In China, carbendazim has been the primary fungicide since the 1970s, and >60% of the fungal population in Jiangsu Province was carbendazim resistant in 2016 [ 307 ].…”

Section: Pre-harvestmentioning

confidence: 99%

“…In particular, azoxystrobin alone should be avoided as its use can increase the amount of DON produced [ 305 , 307 ]. In China, carbendazim has been the primary fungicide since the 1970s, and >60% of the fungal population in Jiangsu Province was carbendazim resistant in 2016 [ 307 ]. Tebuconazole is now the primary fungicide with DON levels lowered by 38–79%, and no resistant isolates recovered from the field as of 2014 [ 308 ].…”

Section: Pre-harvestmentioning

confidence: 99%

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Key Global Actions for Mycotoxin Management in Wheat and Other Small Grains

Leslie

Moretti

Mesterházy

et al. 2021

Toxins

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Mycotoxins in small grains are a significant and long-standing problem. These contaminants may be produced by members of several fungal genera, including Alternaria, Aspergillus, Fusarium, Claviceps, and Penicillium. Interventions that limit contamination can be made both pre-harvest and post-harvest. Many problems and strategies to control them and the toxins they produce are similar regardless of the location at which they are employed, while others are more common in some areas than in others. Increased knowledge of host-plant resistance, better agronomic methods, improved fungicide management, and better storage strategies all have application on a global basis. We summarize the major pre- and post-harvest control strategies currently in use. In the area of pre-harvest, these include resistant host lines, fungicides and their application guided by epidemiological models, and multiple cultural practices. In the area of post-harvest, drying, storage, cleaning and sorting, and some end-product processes were the most important at the global level. We also employed the Nominal Group discussion technique to identify and prioritize potential steps forward and to reduce problems associated with human and animal consumption of these grains. Identifying existing and potentially novel mechanisms to effectively manage mycotoxin problems in these grains is essential to ensure the safety of humans and domesticated animals that consume these grains.

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Section: Pre-harvestmentioning

confidence: 99%

Section: Pre-harvestmentioning

confidence: 99%

Key Global Actions for Mycotoxin Management in Wheat and Other Small Grains

Leslie

Moretti

Mesterházy

et al. 2021

Toxins

View full text Add to dashboard Cite

show abstract

“…Models with similar Brier scores clustered together, indicating that they had the tendency to make the same errors. We postulated that little would have been gained from combining models within the same cluster, as model averaging performs best when done over dissimilar models [28]. In what amounts to essentially a subsampling and reweighting from the full set of PLOS COMPUTATIONAL BIOLOGY logistic regression models [29], the dendrogram was split into groups under the assumption that models in the same group were too similar but models in different groups were less likely to be so.…”

Section: Discussionmentioning

confidence: 99%

Accuracy in the prediction of disease epidemics when ensembling simple but highly correlated models

et al. 2021

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Ensembling combines the predictions made by individual component base models with the goal of achieving a predictive accuracy that is better than that of any one of the constituent member models. Diversity among the base models in terms of predictions is a crucial criterion in ensembling. However, there are practical instances when the available base models produce highly correlated predictions, because they may have been developed within the same research group or may have been built from the same underlying algorithm. We investigated, via a case study on Fusarium head blight (FHB) on wheat in the U.S., whether ensembles of simple yet highly correlated models for predicting the risk of FHB epidemics, all generated from logistic regression, provided any benefit to predictive performance, despite relatively low levels of base model diversity. Three ensembling methods were explored: soft voting, weighted averaging of smaller subsets of the base models, and penalized regression as a stacking algorithm. Soft voting and weighted model averages were generally better at classification than the base models, though not universally so. The performances of stacked regressions were superior to those of the other two ensembling methods we analyzed in this study. Ensembling simple yet correlated models is computationally feasible and is therefore worth pursuing for models of epidemic risk.

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“…In recent years, benzimidazole fungicide carbendazim‐resistant isolates have developed in the FGSC, which results in a decline in the efficacy of fungicides and the difficulty in controlling FHB (Chen, Wang, Luo, Yuan, & Zhou, ; Duan et al, ; Zhou et al, ). In Jiangsu Province, located in the Yangtze River Basin in southern China, the resistance frequency of Gibberella zeae isolates to carbendazim was more than 50% during 2016 to 2018 (Chen et al, ) and its resistance frequency had been only 15.3% in 2010 (Shao et al, ). In the Anhui Province, located in the Huaihe River Basin of southern China, the resistance frequency of F. asiaticum isolates to carbendazim was 8.2% in 2010 and 2012 (Chen et al, ).…”

Section: Discussionmentioning

confidence: 99%

Baseline sensitivity of Fusarium graminearum from wheat fields in Henan, China, to metconazole and analysis of cross resistance with carbendazim and phenamacril

Liu

et al. 2019

Journal of Phytopathology

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The filamentous plant pathogenic fungus Fusarium graminearum is one of the most important pathogens causing Fusarium head blight (FHB) in wheat in the Henan Province of China. Metconazole is among the demethylation inhibitor (DMI) fungicides with a higher inhibitory activity on the mycelial growth of F. graminearum. In 2016 and 2017, 119 single spore isolates of F. graminearum, prior to being exposed to metconazole, were recovered from 52 wheat fields near 11 cities in Henan Province. The inhibitory activity of metconazole on the mycelia of the Henan F. graminearum population was determined, and EC50 values were calculated. The range of EC50 values of the Henan F. graminearum population to metconazole was 0.0103 to 0.0775 μg/ml with an average EC50 value of 0.0293 ± 0.0114 μg/ml. The sensitivity frequency distribution curve presented a single peak in a narrow range. No cross‐resistance was found between the DMI fungicide metconazole and the benzimidazole fungicide carbendazim or the cyanoacrylate fungicide phenamacril. Therefore, these sensitivity data could be used as the baseline of F. graminearum susceptibility to metconazole in the Henan Province and provide the basis for monitoring metconazole resistance in this area.

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Carbendazim-resistance of Gibberella zeae associated with fusarium head blight and its management in Jiangsu Province, China

Cited by 22 publications

References 35 publications

Key Global Actions for Mycotoxin Management in Wheat and Other Small Grains

Key Global Actions for Mycotoxin Management in Wheat and Other Small Grains

Accuracy in the prediction of disease epidemics when ensembling simple but highly correlated models

Baseline sensitivity of Fusarium graminearum from wheat fields in Henan, China, to metconazole and analysis of cross resistance with carbendazim and phenamacril

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