Abstract:

Accurate prediction of the tea-picking period can scientifically guide spring production, preserve tea quality, standardize the market, reduce market fluctuations, and promote the healthy and sustainable development of the entire tea industry chain. In this study, to identify the key meteorological factors affecting the picking period of Xinyang Maojian tea, Pearson correlation analysis was first applied to calculate the correlation coefficients between historical picking dates and multiple concurrent and antecedent meteorological factors (including mean temperature, precipitation, and soil temperatures at different depths), so as to quantify the strength and direction of the linear relationships between these factors and the picking date. Significant correlated factors were then incorporated into the stepwise regression analysis to establish a prediction model. The results revealed significant correlations between the spring tea-picking date and four key meteorological indicators in mid-January: a significant positive correlation with precipitation (r = 0.939, P< 0.05), and significant negative correlations with mean temperature (r = -0.966, P < 0.1), 0 cm ground temperature (r = -0.949, P < 0.05), and 10 cm ground temperature (r = -0.913, P < 0.05). The prediction model based on stepwise regression analysis of screening variables (R² = 0.99, P < 0.01) can achieve accurate prediction of 50-60 days in advance, with a mean absolute error of 0.3 days and prediction errors within ±2 days. The application of this model enables the staggered allocation of picking labour force 60 days ahead, effectively avoiding quality degradation of fresh leaves caused by mistimed harvest. Moreover, the phenological predictions generated by the model can provide a quantitative basis for precise pesticide application in biological control, as well as for setting early-warning thresholds for extreme weather events such as frost and drought. This study offers scientific and technological support for the modern agricultural model of “on-demand production”.

Key words: Xinyang Maojian tea, picking period, forecast model, meteorological factor, Shihe District, Xinyang City