Spatiotemporal Dynamic Relationship of Meteorological Factors and Sugar Content of Sugarcane by Vector Autoregression Model

Abstract

Sugarcane is a globally significant economic crop, and sugar content is a key determinant of its financial and industrial value. This study utilized sugar content information from spring-planted and ratoon sugarcane in six research regions across Guangxi, China from 2008 to 2023 along with concurrent meteorological data. By conducting statistical tests, the critical meteorological factors influencing the sugar content of sugarcane (effective cumulative temperature and rainfall) were identified. These factors were then used as independent variables to construct a vector autoregression (VAR) model, which was employed to analyze the spatiotemporal dynamic relationships between sugar content and meteorological variables across different planting periods. The empirical results demonstrated that the influence of effective cumulative temperature on sugar content across various regions and planting periods shifted from positive to negative in the short-term, eventually reverting to a positive effect after a period of alternating influences. The impact of rainfall mirrored effective cumulative temperature, though it was relatively less pronounced. The sugarcane in Nanning and Baise was less influenced by effective cumulative temperature and rainfall, with the short-term impact changing from positive to negative and diminishing over time. Our findings provide scientific insights for guiding the ecosystem management of sugarcane in China.

Keywords

• effective cumulative temperature
• rainfall
• internal connection
• statistical analysis
• effect