Application of Trend Impact Analysis for predicting future fruit consumption

    Research output: Contribution to journalArticleAcademicpeer-review

    Abstract

    Knowledge of those aspects that motivate consumers towards more fruit consumption is necessary in order to implement policies to stimulate fruit consumption. To predict future fruit consumption based on such knowledge from experts, and based on historic consumption data, the method of Trend Impact Analysis (TIA) was applied to four countries. From the historic data, a trend or baseline has been estimated. TIA combines this information with expert knowledge to forecast future consumption. A Monte Carlo simulation was used to handle uncertainty in the TIA model thus developed. Despite the disadvantages of TIA, the method is suitable to explain a trend by means of impact factors, and offers an opportunity to deal with uncertain prognoses by experts. TIA is a relatively unknown method and requires more research and experience to judge if it is widely applicable, and if it provides a good alternative to more traditional forecasting methods.
    Original languageEnglish
    Pages (from-to)18-21
    JournalJournal of Horticultural Science and Biotechnology
    Volume84
    Issue number6 Isafruit Suppl
    Publication statusPublished - 2009

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    fruit consumption
    Fruit
    expert opinion
    methodology
    prognosis
    Uncertainty
    uncertainty
    Research

    Cite this

    @article{b4d807f89d934b36913c3752833f0873,
    title = "Application of Trend Impact Analysis for predicting future fruit consumption",
    abstract = "Knowledge of those aspects that motivate consumers towards more fruit consumption is necessary in order to implement policies to stimulate fruit consumption. To predict future fruit consumption based on such knowledge from experts, and based on historic consumption data, the method of Trend Impact Analysis (TIA) was applied to four countries. From the historic data, a trend or baseline has been estimated. TIA combines this information with expert knowledge to forecast future consumption. A Monte Carlo simulation was used to handle uncertainty in the TIA model thus developed. Despite the disadvantages of TIA, the method is suitable to explain a trend by means of impact factors, and offers an opportunity to deal with uncertain prognoses by experts. TIA is a relatively unknown method and requires more research and experience to judge if it is widely applicable, and if it provides a good alternative to more traditional forecasting methods.",
    author = "W.H.G.J. Hennen and J. Benninga",
    year = "2009",
    language = "English",
    volume = "84",
    pages = "18--21",
    journal = "Journal of Horticultural Science and Biotechnology",
    issn = "1462-0316",
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    }

    Application of Trend Impact Analysis for predicting future fruit consumption. / Hennen, W.H.G.J.; Benninga, J.

    In: Journal of Horticultural Science and Biotechnology, Vol. 84, No. 6 Isafruit Suppl, 2009, p. 18-21.

    Research output: Contribution to journalArticleAcademicpeer-review

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    AU - Hennen, W.H.G.J.

    AU - Benninga, J.

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    AB - Knowledge of those aspects that motivate consumers towards more fruit consumption is necessary in order to implement policies to stimulate fruit consumption. To predict future fruit consumption based on such knowledge from experts, and based on historic consumption data, the method of Trend Impact Analysis (TIA) was applied to four countries. From the historic data, a trend or baseline has been estimated. TIA combines this information with expert knowledge to forecast future consumption. A Monte Carlo simulation was used to handle uncertainty in the TIA model thus developed. Despite the disadvantages of TIA, the method is suitable to explain a trend by means of impact factors, and offers an opportunity to deal with uncertain prognoses by experts. TIA is a relatively unknown method and requires more research and experience to judge if it is widely applicable, and if it provides a good alternative to more traditional forecasting methods.

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