Abstract: The wolfberry industry is a labor-intensive industry. It is very important for fruit picking and management to accurate simulation wolfberry ripening stage. The meteorological factors significantly related to the maturity stage of wolfberry were determined by correlation analysis method. The development stage closely related to maturity were determined by grey correlation analysis. Then, linear regression equations were established respectively. Based on the simulated day sequence of two models, the integrated simulation model was established. All models had been validated. The results showed that (1) there was no multicollinearity between the independent variables for all models established by three methods, while the equation was statistically significant and passed the significance test. (2) In the simulation model based on meteorological factors, the best simulation effect was found in the starting ripening stage and the common ripening stage of Laoyan branch. RA was 63.16% and 90.00%, RMSE was 2.70 and 2.08, RE was 1.39% and 1.00% by the back substitution test, respectively. MAE of those was 2.31d and 1.35d by the simulation test. However, the results of the starting ripening stage and the common ripening stage of summer fruit branch did not have good performance. (3) In the simulation model based on other phenology, the starting ripening stage and the common ripening stage of summer fruit branch had the best simulation effect. RA of those was 89.47% and 90.00%, RMSE was 1.65 and 1.60, RE was 0.75% and 0.69%, by the back substitution test. MAE of those was 1.38d and 1.00d by the simulation test. (4) The limitations of the two models were overcome by the integrated simulation method. The accuracy of simulation results was improved, and the simulation errors of 6 models were less than 3%. It was concluded that the integrated simulation method had the highest accuracy, while suitable for the prediction of maturity stage of wolfberry.

Key words: Qaidam wolfberry, Maturity stage, Meteorological factors, Grey correlation analysis, Integration model