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    葛会硕, 宋跃朋, 苏雪辉, 张德强, 张晓宇. 基于Logistic和Gompertz模型的小叶杨幼苗生长组合优化模型[J]. 北京林业大学学报, 2020, 42(5): 59-70. DOI: 10.12171/j.1000-1522.20190296
    引用本文: 葛会硕, 宋跃朋, 苏雪辉, 张德强, 张晓宇. 基于Logistic和Gompertz模型的小叶杨幼苗生长组合优化模型[J]. 北京林业大学学报, 2020, 42(5): 59-70. DOI: 10.12171/j.1000-1522.20190296
    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

    基于Logistic和Gompertz模型的小叶杨幼苗生长组合优化模型

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

    • 摘要:
      目的  通过对我国小叶杨幼苗的株高生长规律及生长模型的研究,为其生长预估及科学育苗提供参考。
      方法  以来自我国16个产区的小叶杨幼苗作为研究对象,通过试验地调查及测量等方法获取基础数据,以时间维度建立生长模型分析小叶杨幼苗株高的生长规律。选取具有生物学意义的生长方程,根据模型拟合优度与评价指标选取最优基础生长模型,并在最优模型的基础上构建适合不同产区幼苗生长的组合优化生长模型。
      结果  (1)不同产区的样本株高的综合最优基础模型分别为Logistic方程和Gompertz方程,其中Logistic模型的R2和预测精度分别在0.847 9和92.23%以上,Gompertz模型的R2和预测精度分别在0.891 5和92.60%以上。(2)在此基础上构建的组合优化模型对我国7个产区的小叶杨幼苗生长呈现出较大的F值(α = 0.01)和较高的预测精度,其中门源回族自治县样本的预测精度提高了0.90%,富平县和都兰县的样本预测精度分别提高了0.37%和0.34%,且模型显著相关。(3)通过组合优化模型参数发现16个产区样本平均在17.73 d达到生长速率最大点。
      结论  小叶杨幼苗的生长受地理位置和气候等条件的影响,通过建立符合不同产区小叶杨幼苗生长的模型,有利于提高生长模型的精度和适用性,不仅能为幼苗研究提供科学基础,也为进一步的全基因组关联分析奠定基础。

       

      Abstract:
      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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