%0 Journal Article
%A YU Lei
%A ZOU Zhi-ke
%A LIU Feng-li
%A LUO Wen-bing
%A WANG Wen-juan
%T Variation Features and Estimation Model for Meteorological Yield of Mid-season Rice in Zhanghe Irrigated Area of Hubei Province
%D 2024
%R 10.11988/ckyyb.20221141
%J Journal of Yangtze River Scientific Research Institute
%P 82-90
%V 41
%N 2
%X Precise estimation of meteorological yield is a premise of accurately assessing the impact of meteorological conditions on rice yield. This study delves into the time series variation of meteorological yield for single-season rice in the irrigated areas of Zhanghe, Hubei Province. Four methods, in specific, three-point moving average method, HP filtering method, single exponential smoothing method, and quadratic exponential smoothing method, were applied to decompose the rice yield data from 1975 to 2020 into trend yield and meteorological yield. Through correlation analysis, eight meteorological factors associated with rice growth stages were identified and used to construct a rice prediction model alongside the separated meteorological yield. The findings indicate that the four methods effectively capture the regional consistency between meteorological yield series and productivity level in Hubei Province. The annual average meteorological yield accounted for approximately 3.39% of the total output, and after 2008 the figure exceeded 10.1%. Via correlation analysis, the key factors influencing meteorological yield were identified as follows: minimum temperature at heading and flowering stage, maximum temperature at jointing and booting stage, average temperature at late tiller stage, minimum temperature at returning-green stage, evaporation at milk grain stage, and minimum temperature at seedling raising stage. During calibration period (1976-2014) and validation period (2015-2020), the model exhibited relative errors less than 5%, and a determination coefficient (<i>R</i><sup>2</sup>) reaching 0.994. The proposed model holds potential for facilitating the study of regional rice production under future climate change scenarios.
%U http://ckyyb.crsri.cn/EN/10.11988/ckyyb.20221141