Abstract
Data from 4 counties of Hainan Province of China from 1991-2012 was used to determine the weather impact on rice yields in both early and late rice seasons with multiple regression models. The results show there is normal weather environment for rice in the heading stage for early season rice in May and the milking stage for late season rice in November. For early season rice, more rain in April and June is better for rice to boot and milk, the average temperature has negative effect for the season rice yield ; for late season rice, the average temperature have positive effect for the difference between rice yield and the mean of total years but in seedling and booting stage; the rice yield difference between double season is compared and analyzed through the difference of meteorological factors, the results show that the precipitation gap in tillering stage has positive effect to rice yield increasing, but against in booting stage. The relative results should be use to forecast rice yield, and further provide the rice production guiding.
Highlights
Scientists conducted research explaining relationship between climate and crop yield forecasting (Mikhail A.Semenov, et al, 2012) [1]
The article is organized in the following parts: first part is introduction; 2nd part is about data and its description; 3rd part is the method, multiple regression models; part 4 is estimation analysis based on model results; and the last part is about conclusion and discussion
In order to estimate the relationship between rice yield and meteorological factors, the model considers precipitation, rain frequency per month, wind speed, the days of strong wind, average temperature, low temperature, sunshine and technological trend, which is denoted as T
Summary
Scientists conducted research explaining relationship between climate and crop yield forecasting (Mikhail A.Semenov, et al, 2012) [1]. Scientists conducted research explaining relationship between climate and crop yield forecasting Some scientists worked on the relationship between crop yield and climate N., 1997; David B Lobell and Gregory P Asner, 2003; Peng, S.,etc., 2004; Xu Shiwei, Yu Wen and etc., 2013; Xu S., Yu W., and etc., 2013) [2]-[6]; and these articles referred above did not include more climate factors and disasters for regression estimation. In addition, the monthly data help estimation the weather contribution in detail. The yield change contributions by meteorological factors, and the coefficients help crop yield changing forecasting. The article is organized in the following parts: first part is introduction; 2nd part is about data and its description; 3rd part is the method, multiple regression models; part 4 is estimation analysis based on model results; and the last part is about conclusion and discussion
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