Journal of Forestry Research

, Volume 28, Issue 5, pp 975–982

Allometric models for leaf area and leaf mass predictions across different growing seasons of elm tree (Ulmus japonica)

Original Paper


Convenient and effective methods to determine seasonal changes in individual leaf area (LA) and leaf mass (LM) of plants are useful in research on plant physiology and forest ecology. However, practical methods for estimating LA and LM of elm (Ulmus japonica) leaves in different periods have rarely been reported. We collected sample elm leaves in June, July and September. Then, we developed allometric models relating LA, LM and leaf parameters, such as leaf length (L) and width (W) or the product of L and W (LW). Our objective was to find optimal allometric models for conveniently and effectively estimating LA and LM of elm leaves in different periods. LA and LM were significantly correlated with leaf parameters (P < 0.05), and allometric models with LW as an independent variable were best for estimating LA and LM in each period. A linear model was separately developed to predict LA of elm leaves in June, July and September, and it yielded high accuracies of 93, 96 and 96%, respectively. Similarly, a specific allometric model for predicting LM was developed separately in three periods, and the optimal model form in both June and July was a power model, but the linear model was optimal for September. The accuracies of the allometric models in predicting LM were 88, 83 and 84% for June, July and September, respectively. The error caused by ignoring seasonal variation of allometric models in predicting LA and LM in the three periods were 1–4 and 16–59%, respectively.


Leaf length Leaf width Linear model Power model Non-destructive method 


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Copyright information

© Northeast Forestry University and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Center for Ecological ResearchNortheast Forestry UniversityHarbinPeople’s Republic of China
  2. 2.School of ForestryNortheast Forestry UniversityHarbinPeople’s Republic of China

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