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
  • 33 Downloads

Abstract

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.

Keywords

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

References

  1. Baille M, Baille A, Delmon D (1994) Microclimate and transpiration of greenhouse rose crops. Agric For Meteorol 71(s1–2):83–97CrossRefGoogle Scholar
  2. Bell A (1991) Plant form: an illustrated guide to flowering plant morphology. Oxford University Press, LondonGoogle Scholar
  3. Buttaro D, Rouphael Y, Rivera CM, Colla G, Gonnella M (2015) Simple and accurate allometric model for leaf area estimation in Vitis vinifera L. genotypes. Photosynthetica 53(3):342–348CrossRefGoogle Scholar
  4. Cristofori V, Rouphael Y, Mendoza-de Gyves E, Bignami C (2007) A simple model for estimating leaf area of hazelnut from linear measurements. Sci Hortic 113(2):221–225CrossRefGoogle Scholar
  5. Daughtry CST (1990) Direct measurements of canopy structure. Remote Sens Rev 5(1):45–60CrossRefGoogle Scholar
  6. Demirsoy H, Demirsoy L, Uzun S, Ersoy B (2004) Non-destructive leaf area estimation in peach. Eur J Hortic Sci 69(4):144–146Google Scholar
  7. Demirsoy H, Demirsoy L, Öztürk A (2005) Improved model for the non-destructive estimation of strawberry leaf area. Fruits 60(1):69–73CrossRefGoogle Scholar
  8. Fallovo C, Cristofori V, de-Gyves EM, Rivera CM, Rea R, Fanasca S, Bignami C, Sassine Y, Rouphael Y (2008) Leaf area estimation model for small fruits from linear measurements. HortScience 43(7):2263–2267Google Scholar
  9. Gao M, Van der Heijden G, Vos J, Eveleens B, Marcelis L (2012) Estimation of leaf area for large scale phenotyping and modeling of rose genotypes. Sci Hortic 138:227–234CrossRefGoogle Scholar
  10. Gebauer R, Vanbeveren SPP, Volařík D, Plichta R, Ceulemans R (2016) Petiole and leaf traits of poplar in relation to parentage and biomass yield. For Ecol Manag 362:1–9CrossRefGoogle Scholar
  11. Gill J (1986) Outliers, residuals, and influence in multiple regression. J Anim Breed Genet 103(1–5):161–175CrossRefGoogle Scholar
  12. Katsoulas N, Baille A, Kittas C (2001) Effect of misting on transpiration and conductances of a greenhouse rose canopy. Agric For Meteorol 106(3):233–247CrossRefGoogle Scholar
  13. Keramatlou I, Sharifani M, Sabouri H, Alizadeh M, Kamkar B (2015) A simple linear model for leaf area estimation in Persian walnut (Juglans regia L.). Sci Hortic 184:36–39CrossRefGoogle Scholar
  14. Liu ZL, Wang XC, Chen JM, Wang CK, Jin GZ (2015a) On improving the accuracy of digital hemispherical photography measurements of seasonal leaf area index variation in deciduous broadleaf forests. Can J For Res 45:721–731CrossRefGoogle Scholar
  15. Liu ZL, Wang CK, Chen JM, Wang XC, Jin GZ (2015b) Empirical models for tracing seasonal changes in leaf area index in deciduous broadleaf forests by digital hemispherical photography. For Ecol Manag 351:67–77CrossRefGoogle Scholar
  16. Marquard RD (1987) Influence of leaf to fruit ratio on nut quality, shoot carbohydrates, and photosynthesis of pecan. HortScience 22:256–257Google Scholar
  17. Marquardt DW (1970) Generalized inverse, ridge regression and biased linear estimation. Technometrics 12:591–612CrossRefGoogle Scholar
  18. Meng F, Zhang G, Li X, Niklas KJ, Sun S (2015) Growth synchrony between leaves and stems during twig development differs among plant functional types of subtropical rainforest woody species. Tree Physiol 35(6):621–631CrossRefPubMedGoogle Scholar
  19. Milla R, Reich PB (2007) The scaling of leaf area and mass: the cost of light interception increases with leaf size. Proc R Soci Lond B Biol Sci 274(1622):2109–2114CrossRefGoogle Scholar
  20. Montero F, De Juan J, Cuesta A, Brasa A (2000) Nondestructive methods to estimate leaf area in Vitis vinifera L. HortScience 35(4):696–698Google Scholar
  21. Olfati JA, Peyvast G, Sanavi M, Salehi M, Mahdipour M, Nosratie-Rad Z (2009) Comparisons of leaf area estimation from linear measurements of red cabbage. Int J Veg Sci 15(2):185–192CrossRefGoogle Scholar
  22. Peksen E (2007) Non-destructive leaf area estimation model for faba bean (Vicia faba L.). Sci Hortic 113(4):322–328CrossRefGoogle Scholar
  23. Rivera CM, Rouphael Y, Cardarelli M, Colla G (2007) A simple and accurate equation for estimating individual leaf area of eggplant from linear measurements. Eur J Hortic Sci 72(5):228–230Google Scholar
  24. Rouphael Y, Colla G, Fanasca S, Karam F (2007) Leaf area estimation of sunflower leaves from simple linear measurements. Photosynthetica 45(2):306–308CrossRefGoogle Scholar
  25. Swart EAMD, Groenwold R, Kanne HJ, Stam P, Marcelis LFM, Voorrips RE (2004) Non-destructive estimation of leaf area for different plant ages and accessions of Capsicum annuum L. J Hortic Sci Biotechnol 79(5):764–770CrossRefGoogle Scholar
  26. Tondjo K, Brancheriau L, Sabatier S-A, Kokutse A, Akossou A, Kokou K, Fourcaud T (2015) Non-destructive measurement of leaf area and dry biomass in Tectona grandis. Trees 29(5):1625–1631CrossRefGoogle Scholar
  27. Weraduwage SM, Chen J, Anozie FC, Morales A, Weise SE, Sharkey TD (2015) The relationship between leaf area growth and biomass accumulation in Arabidopsis thaliana. Front Plant Sci 6:167CrossRefPubMedPubMedCentralGoogle Scholar
  28. Williams L, Martinson TE (2003) Nondestructive leaf area estimation of ‘Niagara’ and ‘DeChaunac’ grapevines. Sci Hortic 98(4):493–498CrossRefGoogle Scholar
  29. Wright IJ, Reich PB, Westoby M, Ackerly DD, Baruch Z, Bongers F, Cavender-Bares J, Chapin T, Cornelissen JHC, Diemer M, Flexas J, Garnier E, Groom PK, Gulias J, Hikosaka K, Lamont BB, Lee T, Lee W, Lusk C, Midgley JJ, Navas M-L, Niinemets U, Oleksyn J, Osada N, Poorter H, Poot P, Prior L, Pyankov VI, Roumet C, Thomas SC, Tjoelker MG, Veneklaas EJ, Villar R (2004) The worldwide leaf economics spectrum. Nature 428(6985):821–827CrossRefPubMedGoogle Scholar

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

Personalised recommendations