Resumen
Rapeseed is one of China?s major oil crops, and accurate yield forecasting is crucial to the growth of the rapeseed industry and the country?s food security. In this study, the data on natural and socio-economic factors from 2001 to 2020 and the yield of rapeseed in China were used as the data basis. The Pearson correlation coefficient was used to analyze the relationship between the influencing factors and the yield of rapeseed, and the prediction effect of four machine learning models (linear regression (LR), decision tree (DTR), random forest (RF), and support vector machine (SVM)) on the yield of rapeseed was compared in China?s main rapeseed-producing area. The results demonstrate that the yield of rapeseed in China showed an increasing trend, but fluctuated greatly. Rural electricity consumption, gross agricultural production, the net amount of agricultural fertilizer application, effective irrigation area, total power of agricultural machinery, and consumption of agricultural plastic film had a positive effect on the increase in rapeseed yield. However, due to the impact of climate change and disasters, the yield of rapeseed has had significant fluctuations. A Pearson correlation analysis showed that socio-economic factors (rural electricity consumption, gross agricultural production, effective irrigation area, total power of agricultural machinery, consumption of agricultural plastic film, etc.) played a dominant role in rapeseed yield changes. The RF model had a good prediction effect on rapeseed yield, and natural factors and socio-economic factors had different effects on spring rapeseed and winter rapeseed. Winter rapeseed yield was mainly affected by socio-economic factors, accounting for as high as 89% of the importance. Among them, the sown area of rapeseed and the effective irrigation area had the greatest impact. The effects of natural factors and socio-economic factors on spring rapeseed yield were similar, accounting for 47% and 53%, respectively, and the mean annual precipitation, sunshine duration, and sown area of rapeseed were the most influential variables.