Abstract: Statistical analysis was used to analyze the influences of meteorological factors such as temperature and precipitation on the sugar content of sugarcane. Random forest and forward stepwise selection algorithms were utilized to screen the key meteorological factors. Based on the generalized additivity model and so on, six sugar content simulation models were constructed to simulate the sugar content of sugarcane at the maturity stage with the key meteorological factors as independent variables. Evaluate the simulation effects and quantify the influences of key meteorological factors on the sugar content of sugarcane by comparing and analyzing the simulated and measured values. The results showed that the meteorological factors in different regions and time scales affected the sugar content of sugarcane. The key meteorological factors include diurnal variation of temperature, minimum ground temperature, sunshine hours, maximum temperature, average air pressure, precipitation, and average relative humidity. The influence of diurnal variation of temperature on the sugar content of sugarcane showed a nearly stable linear trend, while the influences of the other key meteorological factors on the sugar content of sugarcane showed a complex fluctuation trend. Based on the key meteorological factors, six models were established to simulate the sugar content of sugarcane. The generalized additive model had the best simulation effect, with the fit R² up to 0.97, the absolute error was 0.12 percent points, the relative error was 0.86%, and the root mean square error was 0.15 in the model validation. It indicates that the simulated values have good consistency and correlation with the measured values, and the model can realize the prediction of the sugar content of sugarcane in Guangxi. Meanwhile, quantifying the effects of key meteorological factors on the sugar content of sugarcane also provides a basis for regulating the ecosystem.

Key words: Sugar content of sugarcane, Meteorological factors, Random forest, Regression model, Simulation