Abstract: In this study, the field observation data of spring maize varieties including “Danyu 13”, “Longdan 13” and “Sidan 19” in Northeast China were used to fit three nonlinear accumulated temperature models proposed by Shen Guoquan, Gao Liangzhi and Yin Xinyou respectively, and to obtain the model parameters. Precision and stability of the simulation results were assessed, and compared with that of the commonly used accumulatedtemperature method. The results showed that the simulation result of Gao Liangzhi model was the best during the period from emergence to maturity. The three nonlinear accumulated temperature models had the similar results in fitting effective accumulated temperature during the period from emergence to jointing. During the jointingheading and headingmaturity stages, Gao Liangzhi model performed best, followed by Yin Xinyou model and Shen Guoquan model. On the analysis of variation coefficient and relatively extreme value of the effective accumulated temperature calculated by the four methods, the stability of the three nonlinear accumulated temperature models was consistently superior to the commonly used method during the whole growth period. During the emergencejointing stage, Shen Guoquan model had better stability. During the jointingheading stage, the stability of Shen Guoquan and Yin Xinyou models was the best. The stability of the four methods was similar during the period from heading to maturity. However, a parameter validity problem was found in the process of fitting the three accumulated temperature nonlinear models. In conclusion, Shen Guoquan model could be selected during the period from emergence to jointing. From jointing to heading stage, Yin Xinyou model was the best. Gao Liangzhi model was suitable for the whole growth period. Our results can provide theoretical basis and technical support for spring maize development and yield prediction in Northeast China.

Key words: genetic structure, Rhopilema esculentum, COI gene, genetic characterization, life-cycle.