• 论文 •

### 西湖龙井茶开采期影响因子及预报模型

1. 1．杭州市气象局，杭州 310051；2．浙江省气候中心，杭州 310017
• 收稿日期:2018-09-13 出版日期:2019-03-20 发布日期:2019-03-16
• 作者简介:朱兰娟（1969-），女，高级工程师，主要从事农业气象服务研究工作。E-mail: 463339804@qq.com
• 基金资助:
浙江省气象科技计划项目（2015C02048；2017ZD10）；杭州市气象科技计划项目（QX201503）

### Research on the Factors of Xihulongjing Tea Picking Date and Its Prediction Model

ZHU Lan-juan, JIN Zhi-feng, ZHANG Yu-jing, WANG Zhi-hai, LIU Min, FAN Liao-sheng

1. 1. Hangzhou Meteorological Bureau, Hangzhou 310051, China；2. Zhejiang Climate Center, Hangzhou 310017
• Received:2018-09-13 Online:2019-03-20 Published:2019-03-16

Abstract: Based on the picking date of the main species of Xihulongjing Tea (Longjing43 and Longjingqunti) and its meteorological data, the accumulated temperature prediction model and the stepwise regression prediction model of Xihulongjing tea were constructed by using accumulated temperature and stepwise regression method, also the prediction of multiple regression ensemble method was integrated by these two prediction results, using the multiple linear regression method. The results showed that the accumulated temperature prediction model, stepwise regression prediction model and integrated prediction model all passed the significance test of P<0.01. The simulated mean absolute error（MAE） of accumulated temperature prediction model were 3.6d and 2.8d, while the prediction MAE of 2-year test prediction were 2.5d and 1.0d for Longjing43 and Longjingqunti respectively. In addition, the simulated MAE of stepwise regression analysis were 0.9d and 1.4d, the prediction MAE of 2-year test prediction were 1.6d and 0.8d for Longjing43 and Longjingqunti separately. The prediction of multiple regression ensemble method was more accurate than single method with the simulated MAE value were 0.7d and 1.1d ,while the prediction MAE of 2-year test prediction were 1.3d and 0.8d, the prediction of multiple regression ensemble method would provide more scientific support for guiding tea production. These three forecasting models are of practical value for the prediction of the picking up period of Xihulongjing tea. The prediction of multiple regression ensemble method is more ideal and with more practical application value than accumulated temperature forecasting model and stepwise regression analysis model.