Abstract:

To construct a local peanut yield forecasting model based on the influence of meteorological factors in Anhui, and provide reference for exploring the economic benefits of peanut and coping with the risk of meteorological disasters, grey correlation analysis on peanut meteorological output and meteorological factors from 2000 to 2017 of all cities in Anhui was conducted. The meteorological factors with greater correlation were screened out and a yield prediction model based on stepwise regression was established. The results showed that the correlation between Anhui peanut production and meteorological factors during growth period was followed the order of average temperature in May > light hours in July > light hours in May > light hours in June > the average temperature in July > light hours in August > the average temperature in August > the average temperature in June > precipitation in August > precipitation in July > precipitation in May > precipitation in June. Furthermore, peanut production over the years was tested based on the peanut per unit yield prediction model, revealing that the root mean square error between the predicted value and actual value was 815 kg/hm² and the fitting index was 0.81. The prediction model is proved to have certain application value.

Key words: peanut, yield, meteorology, gray correlation, stepwise regression