Prediction of Deoxynivalenol Content in Dutch Winter Wheat

E. Franz, K. Booij, H.J. van der Fels-Klerx

    Research output: Contribution to journalArticleAcademicpeer-review

    25 Citations (Scopus)

    Abstract

    Predictive models for the deoxynivalenol (DON) content in wheat can be a useful tool for control authorities and the industry to avoid or limit potential food and/or feed safety problems. The objective of this study was to develop a predictive model for DON in mature Dutch winter wheat. From 2001 to 2007, the concentration of DON was measured in winter wheat samples taken just before harvest from 264 fields throughout The Netherlands. Agronomic and climatic variables were obtained for each field for a 48-day period, centered on the heading date. Multiple regression was used to determine the most important variables and to construct the predictive model. The first model (model 1) was based on 24-day pre- and postheading periods, while the second model (model 2) was based on eight time blocks of 6 days around the heading date. Although both models showed good statistical evaluations and predictive performance, model 1 showed the highest performance (R2 of 0.59 between observed and predicted values, fraction samples correctly below or above the 1,250 µg/kg threshold of 92%, and sensitivity of 63%). With both models, the predicted DON level increased with a higher average temperature, increased precipitation, and higher relative humidity, but decreased with increased number of hours with the temperature above 25°C. We observed a strong regional effect on the levels of DON, which could not be explained by differences in the recorded agronomic and climatic variables. It is suggested that future model improvement might be realized by indentifying and quantifying the mechanism underlying the region effect.
    Original languageEnglish
    Pages (from-to)2170-2177
    JournalJournal of Food Protection
    Volume72
    Issue number10
    Publication statusPublished - 2009

    Fingerprint

    deoxynivalenol
    Triticum
    winter wheat
    prediction
    Temperature
    Humidity
    Netherlands
    heading
    Industry
    Safety
    Food
    relative humidity
    temperature
    industry
    sampling
    wheat

    Keywords

    • fusarium head blight
    • small-grain cereals
    • gibberella-zeae
    • previous crop
    • mycotoxins
    • models
    • inoculum
    • plots
    • graminearum
    • netherlands

    Cite this

    @article{4340e1135b6a45f8a9354cb74614eaa9,
    title = "Prediction of Deoxynivalenol Content in Dutch Winter Wheat",
    abstract = "Predictive models for the deoxynivalenol (DON) content in wheat can be a useful tool for control authorities and the industry to avoid or limit potential food and/or feed safety problems. The objective of this study was to develop a predictive model for DON in mature Dutch winter wheat. From 2001 to 2007, the concentration of DON was measured in winter wheat samples taken just before harvest from 264 fields throughout The Netherlands. Agronomic and climatic variables were obtained for each field for a 48-day period, centered on the heading date. Multiple regression was used to determine the most important variables and to construct the predictive model. The first model (model 1) was based on 24-day pre- and postheading periods, while the second model (model 2) was based on eight time blocks of 6 days around the heading date. Although both models showed good statistical evaluations and predictive performance, model 1 showed the highest performance (R2 of 0.59 between observed and predicted values, fraction samples correctly below or above the 1,250 µg/kg threshold of 92{\%}, and sensitivity of 63{\%}). With both models, the predicted DON level increased with a higher average temperature, increased precipitation, and higher relative humidity, but decreased with increased number of hours with the temperature above 25°C. We observed a strong regional effect on the levels of DON, which could not be explained by differences in the recorded agronomic and climatic variables. It is suggested that future model improvement might be realized by indentifying and quantifying the mechanism underlying the region effect.",
    keywords = "fusarium head blight, small-grain cereals, gibberella-zeae, previous crop, mycotoxins, models, inoculum, plots, graminearum, netherlands",
    author = "E. Franz and K. Booij and {van der Fels-Klerx}, H.J.",
    year = "2009",
    language = "English",
    volume = "72",
    pages = "2170--2177",
    journal = "Journal of Food Protection",
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    publisher = "International Association for Food Protection",
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    }

    Prediction of Deoxynivalenol Content in Dutch Winter Wheat. / Franz, E.; Booij, K.; van der Fels-Klerx, H.J.

    In: Journal of Food Protection, Vol. 72, No. 10, 2009, p. 2170-2177.

    Research output: Contribution to journalArticleAcademicpeer-review

    TY - JOUR

    T1 - Prediction of Deoxynivalenol Content in Dutch Winter Wheat

    AU - Franz, E.

    AU - Booij, K.

    AU - van der Fels-Klerx, H.J.

    PY - 2009

    Y1 - 2009

    N2 - Predictive models for the deoxynivalenol (DON) content in wheat can be a useful tool for control authorities and the industry to avoid or limit potential food and/or feed safety problems. The objective of this study was to develop a predictive model for DON in mature Dutch winter wheat. From 2001 to 2007, the concentration of DON was measured in winter wheat samples taken just before harvest from 264 fields throughout The Netherlands. Agronomic and climatic variables were obtained for each field for a 48-day period, centered on the heading date. Multiple regression was used to determine the most important variables and to construct the predictive model. The first model (model 1) was based on 24-day pre- and postheading periods, while the second model (model 2) was based on eight time blocks of 6 days around the heading date. Although both models showed good statistical evaluations and predictive performance, model 1 showed the highest performance (R2 of 0.59 between observed and predicted values, fraction samples correctly below or above the 1,250 µg/kg threshold of 92%, and sensitivity of 63%). With both models, the predicted DON level increased with a higher average temperature, increased precipitation, and higher relative humidity, but decreased with increased number of hours with the temperature above 25°C. We observed a strong regional effect on the levels of DON, which could not be explained by differences in the recorded agronomic and climatic variables. It is suggested that future model improvement might be realized by indentifying and quantifying the mechanism underlying the region effect.

    AB - Predictive models for the deoxynivalenol (DON) content in wheat can be a useful tool for control authorities and the industry to avoid or limit potential food and/or feed safety problems. The objective of this study was to develop a predictive model for DON in mature Dutch winter wheat. From 2001 to 2007, the concentration of DON was measured in winter wheat samples taken just before harvest from 264 fields throughout The Netherlands. Agronomic and climatic variables were obtained for each field for a 48-day period, centered on the heading date. Multiple regression was used to determine the most important variables and to construct the predictive model. The first model (model 1) was based on 24-day pre- and postheading periods, while the second model (model 2) was based on eight time blocks of 6 days around the heading date. Although both models showed good statistical evaluations and predictive performance, model 1 showed the highest performance (R2 of 0.59 between observed and predicted values, fraction samples correctly below or above the 1,250 µg/kg threshold of 92%, and sensitivity of 63%). With both models, the predicted DON level increased with a higher average temperature, increased precipitation, and higher relative humidity, but decreased with increased number of hours with the temperature above 25°C. We observed a strong regional effect on the levels of DON, which could not be explained by differences in the recorded agronomic and climatic variables. It is suggested that future model improvement might be realized by indentifying and quantifying the mechanism underlying the region effect.

    KW - fusarium head blight

    KW - small-grain cereals

    KW - gibberella-zeae

    KW - previous crop

    KW - mycotoxins

    KW - models

    KW - inoculum

    KW - plots

    KW - graminearum

    KW - netherlands

    M3 - Article

    VL - 72

    SP - 2170

    EP - 2177

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    JF - Journal of Food Protection

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    ER -