摘要:为验证作物模型在浙江地区不同茶树品种产量和开采期的适用性，基于FAO推荐的AquaCrop作物模型，在浙江省典型茶树种植区选择‘白叶一号’‘龙井43’以及‘龙井群体种’等3个茶树主栽品种，通过田间试验、数据收集和参数敏感性分析等方式获取AquaCrop模型所需的茶树生长参数，并使用历史数据对模型进行本地化校正，建立了基于AquaCrop模型的安吉县和松阳县茶叶产量预报模型以及3个茶树品种的春茶开采期预报模型。AquaCrop模型模拟2013—2017年松阳县茶叶平均年总产量为1.497 t·hm-2，相对误差为1.98%；2014—2018年安吉县春茶平均年产量为0.164 t·hm-2，相对误差为0.99%；模拟松阳县和安吉县茶叶产量归一化均方根误差（NRMSE）分别为2.20%和1.10%，均方根误差（RMSE）分别为0.0325 t·hm-2和0.0018 t·hm-2；协同指数（d）值分别为0.84和0.88。确定了‘白叶一号’‘龙井43’和‘龙井群体种’茶叶的生长度日预报标准，并通过逐步回归方法获得了3品种茶叶生长度日预测公式；利用AquaCrop分别基于生长度日预报法和逐步回归预报法对3品种开采期进行预测。基于生长度日的3品种开采期预报模型的回代后平均绝对误差（MAE）分别为1.1 d、2.1 d和1.1 d；基于逐步回归的预报模型均通过P < 0.01显著性检验，3品种的MAE分别为0.7 d、0.7 d和0.9 d。结果表明，AquaCrop模型经过校正后对浙江地区不同品种茶叶具有较好的适应性，本地化后的AquaCrop可以用于茶园水分管理，产量潜力等研究。基于生长度日和逐步回归的两种AquaCrop茶树开采期预报模型均具有应用价值，逐步回归预报模型的预报效果更理想，具有实际生产指导作用。
关键词:AquaCrop模型/
茶叶/
产量/
开采期
Abstract:This study aimed to verify the applicability of crop models for the yield and picking data prediction of different tea varieties in Zhejiang Province and to fill the gap in tea simulations of crop models. Three tea varieties ('White Leaf 1' 'Longjing43', and 'Longjingqunti') were selected in the typical tea planting areas of Zhejiang Province (Anji County, Songyang County and Hangzhou City). Based on the AquaCrop model recommendations and the tea plant conservative parameters provided by the Food and Agriculture Organization (FAO), the parameters of the AquaCrop model were obtained from field experiments, data collection, and parameter sensitivity analysis, and the model was localized and corrected using data from previous years. The yield forecast models for Anji and Songyang Counties and prediction models of the spring tea picking data for the three tea varieties were established based on the AquaCrop. The AquaCrop model simulated the average total annual tea output of Songyang County from 2013 to 2017 to be 1.497 t·hm-2, with a relative error of 1.98%. The average annual output of spring tea in Anji County from 2014 to 2018 was 0.164 t·hm-2, with a relative error of 0.99%. The normalized root mean square error of the AquaCrop model for tea yield simulation in Songyang and Anji Counties was 2.20% and 1.10%, respectively; and the root mean square error was 0.0325 t·hm-2and 0.0018 t·hm-2, respectively; the conformity index was 0.84 and 0.88, respectively. The prediction standard of the tea growing degree-days (GDDs) for 'White Leaf 1' 'Longjing43', and 'Longjingqunti' were determined, and the prediction formulas of the three tea GDDs were obtained via the stepwise regression method. The mean absolute errors (MAE) of AquaCrop model based GDDs prediction of three tea varieties were 1.1 d, 2.1 d, and 1.1 d, respectively. The AquaCrop model based on stepwise regression prediction of the tea picking data was significant (P < 0.01), with the simulated MAEs for three tea varieties were 0.7 d, 0.7 d, and 0.9 d, respectively. The results show that the AquaCrop model has good adaptability to different tea varieties in Zhejiang Province after correction. Localized AquaCrop models can be used to study the water management and yield potential of tea gardens. Both prediction models the AquaCrop model of the tea picking data based on GDDs prediction and stepwise regression prediction have applicable value, and the predictions based on the stepwise regression analysis model is more accurate with higher practical value than the GGDs forecasting model.
Key words:AquaCrop Model/
Tea/
Yield/
Picking date
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