杂交水稻2026，Vol.41Issue(4)：42-52,11.DOI:10.16267/j.cnki.1005-3956.20241217.287
不同产量分离方法在水稻产量预报上的对比分析
Comparative Analysis of Different Yield Separation Methods in Rice Yield Forecasting:a Case Study of Xinyang City
摘要
Abstract
Utilizing meteorological observation data and rice yield and growth stage data of Xinyang City from 1993 to 2021,this study compared and analyzed the applicability of five-year sliding average,three-year sliding average,HP filtering,double exponential smoothing,ARIMA model,and grey prediction model GM(1,1)in forecasting rice yield in Xinyang City.The correlation analysis was performed to determine the key meteorological factors affecting rice yield in Xinyang City.A rice yield forecasting model was established based on the stepwise regression method.The forecasting models were then used to conduct back testing on rice yields from 1993 to 2021 and forecasting tests on rice yields from 2022 to 2023.Two testing methods,namely accuracy rate and Taylor plot,were employed to evaluate and analyze the effects of forecasting equations based on different yield separation methods in rice yield forecasting in Xinyang City.The results showed that the key meteorological factors affecting rice yield in Xinyang City were sunshine duration in late July,average temperature in late July,and precipitation in late July.The multi-year average back-testing accuracy rates of six models ranged from 90.2％to 94.5％.Among them,HP filtering and five-year sliding average demonstrated higher accuracy rates of 94.5％and 94.4％,respectively.The multi-year average forecasting accuracy rates of six models ranged from 89.2％to 98.8％,and the two methods with higher forecasting accuracy rates were double exponential smoothing(98.8％)and ARIMA model(96.5％).关键词
水稻/产量预报/HP滤波/滑动平均/二次指数平滑/ARIMA模型/灰色预测模型Key words
rice/yield forecasting/HP filtering/sliding average/double exponential smoothing/ARIMA model/grey prediction model分类
农业科技引用本文复制引用
方彤,庄若南,胡莉婷..不同产量分离方法在水稻产量预报上的对比分析[J].杂交水稻,2026,41(4):42-52,11.基金项目
信阳市茶叶气象重点实验室科研基金(XYYB-202404) （XYYB-202404）
