基于物候期气候因素的茶叶产量模型构建与种植管理策略研究以福建福鼎市为例

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

  • 摘要: 本文聚焦于茶叶产量与符合茶树生长节律的物候期气候因素之间的复杂关系, 采用茶树物候期气候指标作为因子, 探索建立茶叶产量模型, 并基于模型主要气候因子制定具有区域气候适应性的茶树种植管理策略。基于福鼎市1990—2022年的气象观测资料和茶叶产量资料, 采用线性趋势法研究气候变化趋势, 运用多元线性回归法、主成分回归法及神经网络法3种方法构建福鼎市茶叶产量模型。研究结果表明: 1990—2022年福鼎市年平均温度以0.436 ℃∙(10a)−1的速率快速提升, 年日照时数与年降水量在不规律波动中分别以4.078 h∙(10a)−1和31.105 mm∙(10a)−1的速率提高, 气候变化影响茶叶产量的稳定性; 由不同茶树物候期气候指标形成的168个物候期气候要素中, 共筛选出14个关键气候因子, 基于这些关键因子构建的神经网络产量模型的模型评价结果显示: 均方根误差(RMSE)为7.595 6, 归一化均方根误差(NRMSE)为0.080 7, 决定系数(R²)为0.935 1, 平均拟合准确率(P)为93.13%, 为最优产量模型。最后, 围绕最优模型中全物候期降水日数、春茶采摘期平均气压和第2次生长期≥10 ℃积温3个较高水平重要性因子, 新梢萌芽期蒸发量、休眠期蒸发量、休眠期平均风速、第2次生长期平均风速和第2次生长期平均日最低气温5个中等程度重要性水平的因子, 结合当地气候变暖趋势, 进行分析探讨, 提出优化水分、土壤保墒、通风降湿、气象灾害防治和分散气候风险等契合福鼎市气候特征的茶树种植策略。

     

    Abstract: 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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