摘要
【目的】现有关于茶叶产量预测的深入研究比较缺乏,为了分析不同特征或不同茶区对预测结果的影响,以期确定影响茶叶产量的决定性和辅助性因素,并为茶叶生产管理提供指导意见。【方法】本文以浙江省为研究区域,根据1995-2016年浙江省的地面气候资料日值数据、茶叶统计产量数据以及社会发展要素,提取每年、各季节的温湿度、日照、降水量等共94个气象特征和茶叶主产区的经纬度、高程3个空间特征以及茶园面积、农业机械总动力、有效灌溉面积、农村用电量、化肥施用量(折纯)5个社会发展特征,以各茶叶主产区(县级行政区)实际茶叶年产量作为目标变量,通过梯度提升决策树构建多种特征变量组合的茶叶产量预测回归模型,并根据特征重要度分析各个特征变量对产量的影响。【结果】研究结果表明:(1)相较于仅使用社会发展特征或空间气象特征,两者相结合的预测模型效果更佳,其决定系数达到0.90,均方根误差为1492 t,平均绝对误差为1050 t。另外,社会发展特征对茶叶产量预测起决定性作用;(2)相较于夏季和秋季,春季和冬季的茶叶产量预测精度更高,决定系数均达到0.89;(3)除社会发展特征之外,空间气象特征中空间特征、年气象特征、春季气象特征、冬季气象特征对产量影响较大。【结论】本文提出的预测模型在预测茶叶产量以及分析影响茶叶产量的因素方面具有重要的参考价值。
【Objective】Existing in-depth research on tea yield prediction is lacking,so the present paper aimed to analyze the influence of different features or different tea areas on the prediction results,and determine the decisive and auxiliary factors that affect the tea field,thus to provide guidance for the management of tea production.【Method】Zhejiang province was taken as a research area.According to the daily ground observation meteorological data,the tea statistical yield data and social development factors in Zhejiang province from 1995 to 2016,a total of 94 meteorological features such as temperature and humidity,sunshine,precipitation in each year and each season.Three spatial features of the major tea producing areas,latitude,longitude,elevation,the four social development features,tea garden area,total power of agricultural machinery,effective irrigation area,rural electricity consumption were extracted and the actual annual yield of each major tea producing area was taken as the target variable.Then regression models of tea yield prediction by the gradient boosting decision tree with combining multiple feature variables were constructed,and the relationship between each feature and yield was analyzed according to the importance of the feature.【Result】(ⅰ)Compared with using only social development features or space meteorological features,the prediction model combining social development features and space meteorological features was better.Its decision coefficient reached 0.90,root mean square error was 1492 t,mean absolute error was 1050 t.In addition,social development features played a significant role in the tea prediction;(ⅱ)Compared with the summer and autumn,the prediction accuracy of tea yield in spring and winter was higher,and the values of determination coefficient reached 0.89;(ⅲ)In addition to the social development features,among the space meteorological features,the spatial features,annual meteorological features,spring meteorological features,and winter meteorological featur
作者
丁鹏
徐爱俊
周素茵
DING Peng;XU Ai-jun;ZHOU Su-yin(School of Information Engineering,Zhejiang A&F University,Zhejiang Hangzhou 311300,China;Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology,Zhejiang Hangzhou 311300,China)
出处
《西南农业学报》
CSCD
北大核心
2021年第7期1556-1563,共8页
Southwest China Journal of Agricultural Sciences
基金
国家自然科学基金项目(31670641)
浙江省科技重点研发计划(2018C02013)。
关键词
产量预测
多特征
社会发展要素
梯度提升决策树
茶叶
Yield prediction
Multiple features
Social development factors
Gradient boosting decision tree
Tea
