[an error occurred while processing this directive]

Plant Diversity ›› 2014, Vol. 36 ›› Issue (04): 497-504.doi: 10.7677/ynzwyj201413212

• 研究论文 • 上一篇    下一篇

翅果油树叶面积和叶重的预测模型

 卫晶, 毕润成, 苗艳明   

  1. 山西师范大学生命科学学院, 山西 临汾041000
  • 收稿日期:2013-10-15 出版日期:2014-07-25 发布日期:2013-12-20
  • 基金资助:

    山西省留学基金项目 (20081073)

Predicting the Leaf Area and Leaf Dry Weight of Elaeagnus mollis in Shanxi

 卫晶, 毕润成, 苗艳明   

  1. School of Life Science, Shanxi Nomal University, Linfen 041000, China
  • Received:2013-10-15 Online:2014-07-25 Published:2013-12-20
  • Supported by:

    山西省留学基金项目 (20081073)

摘要:

数学模型对于非破坏性地研究和预测植物的生长状况非常方便有效。以山西省翅果油树自然保护区翅果油树(Elaeagnus mollis)叶片为研究对象,利用简单易测的叶长(L)、叶宽(W)和叶绿素含量(SPAD)及其不同的组合作为模型拟合参数,建立了关于叶面积(LA)、叶饱和鲜重(SFW)和叶干重(DW)的预测模型共10个,选择拟合度最好的模型作为LA、SFW和DW的预测模型,这3个模型分别为:LA=3647+0383LW+0001LWS(R=0968),SFW=-0464+0081L+000008LWS(R=0963),DW=-0094+0032W+00001LS(R=0960),并用实测值对模型进行了验证,结果表明LA、SFW和DW的预测值与实测值分别达到了高度一致,能够用于对实际未知叶片LA、SFW和DW的预测。

关键词: 叶长, 叶宽, 模型, 翅果油树, 叶绿素含量

Abstract:

Nondestructive and mathematical approaches of modeling can be very convenient and useful for plant growth estimation. The leaf of Elaeagnus mollis was taken as the object of research. Leaf length、 leaf width、SPAD value and different combinations of these variables were developed models to predict individual leaf area, saturated fresh weight, and dry weight of Elaeagnus mollis. Ten regression equations were compared. Select fitting the best model as a predictive model in leaf area, saturated fresh weight and dry weight. The three models were as follows: individual leaf area LA=3647+0383LW+0001LWS (R=0968), saturated fresh weight SFW=-0464+0081L+000008LWS (R=0963), and dry weight DW=-0094+0032W+00001LS (R=0960). The best prediction model of LA, SFW and DW was validated with the measured value. The results showed that the predicted values and measured values were highly consistent. It could be used to predict the LA, SFW and DW of actual unknown leaves.

Key words: Leaf length, Leaf width, Model, Elaeagnus mollis, SPAD value

中图分类号: 

  • Q 141