Objective Establishing different regions and different types of phenology model aims to provide theoretical basis for tourism and business activity management.
Method We choose the three genetic types of the Dadongliu Nursery Garden in Beijing, i.e., the white flower; the new varieties as ‘Jinguanxiapei’ and ‘Junguanjinxia’ as the research objects and performed the observation of flowering phenotypes in 2017. Referring to the observed data of the early flowering stage, the full flowering stage and late flowering stage of the white yellowhorn in 15 different locations of 8 provinces in main distribution area of Xanthoceras sorbifolium, we used the sharing meteorological data from the Chinese meteorological data website to analyze floral traits and three flowering phenology periods in different space-time scales.
Result (1) The flowering sequences of three different genetic types of yellowhorn, i.e., the white flower, " Jinguanxiapei” and " Junguanjinxia” as well as the differences in phenological period were significant or extremely significant. In addition, the changes of inflorescence growth with 0, 3, 5, 7, and 10 ℃ accumulative temperature fit the Logistic growth model better and the number of flowers changed with time and accumulative temperature fit the quadratic polynomials better. (2) The required accumulated temperature for each phenology in different locations with same accumulated temperature index showed no difference, the required accumulated temperature in different accumulated temperature index showed significant difference. However, both accumulated temperature index and phenology showed significant influence on required accumulated temperature and there was a significant difference on interaction between them. (3) The 5 ℃ cumulative temperature index (the warmth index) was highly correlated with the phenological date and could be used for flowering prediction. (4) The 5 ℃ accumulated temperature date of three flowering phenologies of white flowers showed an extremely significant multivariable regression relationship with longitude, latitude and altitude. The one-way ANOVO and simulated values at different observation sites confirmed that this regression model could be used for flowering prediction. (5) The Krisking model can be used to draw the space-time distribution maps of the three flowering phenologies of the white flower yellowhorn.
Conclusion The flowering phenological model of Xanthoceras sorbifolium based on 5 ℃ cumulative temperature index (the warmth index) can be used to predict flowering period.