A Study of Forecasting Cabbage Yields using a Mixed Data Sampling Model

Jineon Moon

Journal of Rural Development/Nongchon-Gyeongje, 2021, vol. 44, issue 4

Abstract: This paper explores the forecasting model of the Korean cabbage yield using the mixed data sampling (MIDAS) model based on high-frequency weather variables and growth measurement data. The principal objective of our research is to improve the predictive accuracy of crop production by incorporating the nonlinear climatic effect by growth stage. A mixed data sampling model is employed to reflect the relationship between climate and the nonlinear effect on the cabbage yield, especially at each growth stage. Our analysis includes the meteorological variables of extreme climatic phenomena such as heat waves and Typhoons and the growth measurement data such as plant heights and number of leaves. By applying the mixed data sampling model, this study obtains the result of improving the prediction accuracy for cabbage yield. In particular, when using a mixed data sampling model including high-frequency temperature data, it achieves an in-sample forecasting accuracy improvement of about 12%~14% and an out-of-sample forecasting accuracy of 15%~27%.

Keywords: Crop Production/Industries; Environmental Economics and Policy (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:ags:jordng:330828

DOI: 10.22004/ag.econ.330828

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