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Regionale Milchmengenprognose: Regressionsmodelle und Maschinelles Lernen im Vergleich

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  • Baaken, Dominik
  • Hess, Sebastian

Abstract

Prognose-Instrumente für das regionale Milchaufkommen werden seit dem Wegfall der europäischen Milchquoten im Jahr 2015 seitens der Marktteilnehmer vermehrt nachgefragt, aber existieren bisher nicht. Bestehende Ansätze zur Milchmengenprognose basieren meist auf der Vorhersage der Laktationskurve einzelner Milchkühe. In der vorliegenden Arbeit werden sechs Modellansätze aus verschiedenen Bereichen des Maschinellen Lernens (ML) und der linearen Regression (OLS) miteinander verglichen. Für die Ansätze werden unterschiedliche Variablenblöcke miteinander kombiniert, um zeitliche Trends, direkte und indirekte Wettereffekte, sowie das Preisgeschehen in die Vorhersage mit einzubeziehen. Für einen Vorhersagezeitraum von 21 Monaten kann für die einbezogenen landwirtschaftlichen Betriebe aus Niedersachsen ein maximales Bestimmtheitsmaß von 0,92 durch ML-Methoden und maximal 0,77 durch OLS Regressionen erzielt werden. Im Vergleich der ML-Algorithmen untereinander tritt ein Unterschied der Modelle vor allem im Hinblick auf die Trainingsgeschwindigkeiten zu Tage.

Suggested Citation

  • Baaken, Dominik & Hess, Sebastian, 2021. "Regionale Milchmengenprognose: Regressionsmodelle und Maschinelles Lernen im Vergleich," 61st Annual Conference, Berlin, Germany, September 22-24, 2021 317056, German Association of Agricultural Economists (GEWISOLA).
    • Handle: RePEc:ags:gewi21:317056
    DOI: 10.22004/ag.econ.317056
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    References listed on IDEAS

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    Keywords

    Farm Management; Research Methods / Statistical Methods;

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