WANG W M, LIN D Y, HONG X Y, SU K. Research on the construction of tea yield model and planting management strategies based on climatic factors in phenological periods: A case study of Fuding City, Fujian Province[J]. Chinese Journal of Eco-Agriculture, 2025, 33(12): 1−14. DOI: 10.12357/cjea.20240846
Citation: WANG W M, LIN D Y, HONG X Y, SU K. Research on the construction of tea yield model and planting management strategies based on climatic factors in phenological periods: A case study of Fuding City, Fujian Province[J]. Chinese Journal of Eco-Agriculture, 2025, 33(12): 1−14. DOI: 10.12357/cjea.20240846

Research on the construction of tea yield model and planting management strategies based on climatic factors in phenological periods: A case study of Fuding City, Fujian Province

  • This article focuses on the complex relationship between tea yield and phenological climatic factors that conform to the growth rhythm of tea plants. By using climatic indicators of the phenological periods of tea plants as factors, it explores the establishment of a tea yield model, and formulates tea planting and management strategies adapted to regional climate based on the main climatic factors identified by the model. Based on the meteorological data and tea yield data of Fuding City from 1990 to 2022, the linear trend method is adopted to study the trend of climate change. Three methods, namely the multiple linear regression method, the principal component regression method, and the neural network method, are used to construct the tea yield model of Fuding City. The research results show that from 1990 to 2022, the annual average temperature in Fuding City increased significantly at a rate of 0.436 ℃∙(10a)−1, The annual sunshine hours and annual precipitation increased at rates of 4.078 h∙(10a)−1 and 31.105 mm∙(10a)−1, respectively, during irregular fluctuations, and climate change affects the stability of tea yield. Among the 168 phenological climatic elements formed by the climatic indicators of different tea plant phenological periods, 14 climatic factors were screened out. The model evaluation results of the the neural network yield model constructed based on these key factors show that the root mean square error (RMSE) is 7.5956, the normalized root mean square error (NRMSE) is 0.0807, the coefficient of determination (R²) is 0.9351, and the average fitting accuracy rate(P) is 93.13%, making it the optimal yield model. Finally, focusing on the three high-importance factors in the optimal model, namely the total number of precipitation days during the full phenological period, the average air pressure during the spring tea picking season, and the accumulated temperature above 10 °C during the second growth period, as well as the five moderate-importance factors, namely the evaporation during the bud germination period, the evaporation during the dormancy period, the average wind speed during the dormancy period, the average wind speed during the second growth period, and the average daily minimum temperature during the second growth period, combined with the local climate warming trend, an analysis and discussion are carried out. Tea planting strategies adapted to the climatic characteristics of Fuding City are proposed, such as optimizing water resources, soil moisture conservation, ventilating to reduce humidity, preventing meteorological disasters, and dispersing climate risks.
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