This research demonstrates the role of Markov chains in predicting humidity impacts on Vitis vinifera varieties, suggesting implications for crop protection forecasting.
The aim of our research is to contribute to the modeling of relative humidity with Markov chains and its use in crop protection forecasting in the Tokaj-Hegyalja wine region. The two most common wine grape varieties in this region are Vitis vinifera cv. Furmint and V. vinifera cv. Hárslevelű. For noble rot on these varieties, humidity in the optimum range is an essential environmental condition. Using 10-min and 60-min resolution humidity data sets from the Hungarian Meteorological Service (HungaroMet) for the period 2002−2020, we estimated the probability that a random humidity measurement will fall into a given result (five humidity categories) in the future. The mathematical model used for this research is the theory of Markov chains, a new approach to statistical analysis of relative humidity data. It is pointed out that the Markov process of relative humidity variation and the results of a 60-min resolution data series across 19 years of study can be used to model humidity.
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Szám et al. (2025) studied this question.
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