西湖龙井茶开采期影响因子及预报模型
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摘要
基于西湖龙井茶主栽品种(龙井43和龙井群体种)开采期及气象资料,应用积温和逐步回归方法,分别构建西湖龙井茶的积温预报模型和逐步回归预报模型,并利用多元线性回归方法,对两个模型的预报结果进行集成,构建集合预报模型。结果表明:积温预报模型、逐步回归预报模型、集成预报模型均通过0.01水平的显著性检验;龙井43和龙井群体种的积温预报模型回代检验平均绝对误差(MAE)分别为3.6d和2.8d,2a试预报MAE分别为2.5d和1.0d;逐步回归预报模型的回代检验MAE分别为0.9d和1.4d,2a试预报MAE分别为1.6d和0.8d;集成预报模型的回代检验MAE分别为0.7d和1.1d,2a试预报MAE分别为1.3d和0.8d。3种预报模型对西湖龙井茶开采期预报均具有应用价值,集成预报模型较积温预报模型和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。
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 3.6 d and 2.8 d, while the prediction MAE of 2-year test prediction were 2.5 d and 1.0 d for Longjing43 and Longjingqunti respectively. In addition, the simulated MAE of stepwise regression analysis were 0.9 d and 1.4 d, the prediction MAE of 2-year test prediction were 1.6 d and 0.8 d for Longjing43 and Longjingqunti separately. The prediction of multiple regression ensemble method was more accurate than single method with the simulated MAE value were 0.7 d and 1.1 d,while the prediction MAE of 2-year test prediction were 1.3 d and 0.8 d, 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.
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 3.6 d and 2.8 d, while the prediction MAE of 2-year test prediction were 2.5 d and 1.0 d for Longjing43 and Longjingqunti respectively. In addition, the simulated MAE of stepwise regression analysis were 0.9 d and 1.4 d, the prediction MAE of 2-year test prediction were 1.6 d and 0.8 d for Longjing43 and Longjingqunti separately. The prediction of multiple regression ensemble method was more accurate than single method with the simulated MAE value were 0.7 d and 1.1 d,while the prediction MAE of 2-year test prediction were 1.3 d and 0.8 d, 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.
引文
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[9]姜燕敏,金志凤,李松平,等.浙南春茶开采前后气象条件分析及开采期预报[J].中国农业气象,2015,36(2):212-219.Jiang Y M,Jin Z F,Li S P,et al.Meteorological conditions analysis and prediction model establishment on spring tea first plucking date in south Zhejiang[J].Chinese Journal of Agrometeorology,2015,36(2):212-219.(in Chinese)
[10]郭建平,庄立伟,陈玥熤.东北玉米热量指数预测方法研究(Ⅱ):逐步回归预测方法[J].灾害学,2009,24(4):11-14.Guo J P,Zhuang L W,Chen Y Y.Study on forecasting method of corn heat index in northeastern China(Ⅱ):stepwise regression model[J].Journal of Catastrophology,2009,24(4):11-14.(in Chinese)
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[19]杨亚军,郑雷英,王新超等.冷驯化和ABA对茶树抗寒力及其体内脯氨酸含量的影响[J].茶叶科学,2004,24(3):177-182.Yang Y J,Zheng L Y,Wang X C,et al.Effect of cold acclimation and ABA on cold hardiness,contents of proline in tea plants[J].Journal of Tea Science,2004,24(3):177-182.(in Chinese)
[20]唐湖,郝心愿,王璐,等.茶树越冬芽在休眠与萌发时期的物质交流变化及其分子调控[J].作物学报,2017,43(5):669-677.Tang H,Hao X Y,Wang L,et al.Molecular regulation and substance exchange dynamics at dormancy and budbreak stages in overwintering buds of tea plant[J].Acta Agronomica Sinica,2017,43(5):669-677.(in Chinese)
[21]杨亚军,郑雷英,王新超.低温对茶树叶片膜脂脂肪酸和蛋白质的影响[J].亚热带植物科学,2005,34(1):5-9.Yang Y J,Zheng L Y,Wang X C.Changes of membrane fatty acid composition and protein in tea leaves at low temperature[J].Subtropical Plant Science,2005,34(1):5-9.(in Chinese)
[22]李湘阁,闵庆文,余卫东.南京地区茶树生长气候适应性研究[J].南京气象学院学报,1995,18(4):572-577.Li X G,Min Q W,Yu W D.Study of climate adaptability of tea plants in Nanjing Area[J].Journal of Nanjing Institute of Meteorology,1995,18(4):572-577.(in Chinese)
[23]肖静,李楠,姜会飞.作物发育期积温计算方法及其稳定性[J].气象研究与应用,2010,31(2):64-67.Xiao J,Li N,Jiang H F.Calculation and stability of accumulated temperatures in the growing season of winter wheat[J].Journal of Meteorological Research and Application,2010,31(2):64-67.(in Chinese)
[24]Vinocur M G,Ritchie J T.Maize leaf development biases caused by Air-Apex temperature differences[J].Agronomy Journal,2001,93:767-772.
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