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    Ge Huishuo, Song Yuepeng, Su Xuehui, Zhang Deqiang, Zhang Xiaoyu. Optimal growth model of Populus simonii seedling combination based on Logistic and Gompertz models[J]. Journal of Beijing Forestry University, 2020, 42(5): 59-70. DOI: 10.12171/j.1000-1522.20190296
    Citation: Ge Huishuo, Song Yuepeng, Su Xuehui, Zhang Deqiang, Zhang Xiaoyu. Optimal growth model of Populus simonii seedling combination based on Logistic and Gompertz models[J]. Journal of Beijing Forestry University, 2020, 42(5): 59-70. DOI: 10.12171/j.1000-1522.20190296

    Optimal growth model of Populus simonii seedling combination based on Logistic and Gompertz models

    • Objective  Through the research on the growth regularity and growth model of Populus simonii seedlings, it could provide reference for its growth prediction and scientific seedling raising.
      Method  Taking P. simonii seedlings in 16 regions of China as the research object, basic data were obtained by means of experimental field investigation and measurement, and the growth rules of plant height were analyzed from the time dimension. We used existed growth equations to select the optimal basic growth model according to the model goodness of fit and evaluation indexes, and constructed optimal growth models suitable for seedling growth in different regions with the basis of the optimal model.
      Result  (1) The comprehensive optimal basic models of sample plant height in different regions were Logistic equation and Gompertz equation, respectively. The Logistic model’s R2 and prediction accuracy were above 0.847 9 and 92.23%, and the Gompertz model ’s R2 and prediction accuracy were above 0.891 5 and 92.60%, respectively. (2) On this basis, the combinative optimization model constructed showed a larger F value (α = 0.01) and a higher prediction accuracy for the growth of P. simonii seedlings in 7 regions of China, among which the prediction accuracy of samples in Menyuan County increased by 0.90%, and the sample prediction accuracy of samples in Fuping County and Dulan County increased by 0.37% and 0.34%, respectively. (3) Through combinatorial optimization of model parameters, it was found that samples from 16 regions reached the maximum growth rate in the 17.73th day on average.
      Conclusion  The growth of P.  simonii seedlings was affected by geographical location and climate and other conditions. Establishing models suitable for the growth of seedlings of P. simonii in different regions was conducive to improve the accuracy and applicability of the models, which could not only provide a scientific basis for seedling research, but also lay a foundation for further genome-wide association analysis.
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