Al-Qadisiyah Journal For Agriculture Sciences

A Nondestructive Method for Estimation of Leaf Area in Quercus aegilops L. Tree

Document Type : Research Paper

Author

Department of Forestry, College of Agricultural Engineering Sciences, Salahaddin University-Erbil, Erbil, Kurdistan Region, Iraq

Abstract

Non-destructive techniques for leaf area estimation are much sought after by both forest managers and researchers in the fields of biology and forest ecology. Leaf area (LA) can be determined by developing linear metrics, such as leaf length (L) and width (W). The objective of this study was to create a reliable approach for accurately estimating the leaf area (LA) of Quercus aegilops L. by using data of leaf length, width, or other dimensions. Seven non-destructive models were formulated to accurately estimate the leaf area (LA) of the species under investigation, developing the leaf dimensions of length (L) and width (W). The regression models that utilized a single dimension, such as length or breadth, were shown to be less effective in predicting the leaf area of Q. aegilops L. compared to models that integrated the product of length and width measurements. Among the seven produced models, the equation LA = 0.3256L*W1.016 was chosen as the final equation since it was considered the most suitable among the other equations.

Keywords

Main Subjects

References
  1. Nasser, M. H. (1984). Forests and forestry in Iraq: prospects and limitations. The Commonwealth Forestry Review, 299-304.‏
  2. Zohary, M. (1973). Geobotanical foundations of the Middle East.‏
  3. Shahbaz, S. E., Balo, A. H., & J MT, H. (2005). Phenotypic variation in natural stands of Quercus aegilops (Fagaceae) in Duhok province. Journal of Duhok University, 8, 1-9.‏
  4. Younis, A. and K. Hassan, M. (2019) “Assessing volume of Quercus aegilops L.TREES IN DUHOK GOVERNORATE, KURDISTAN REGION OF IRAQ”, Journal of Duhok University, 22(1), pp. 265-276. doi: 10.26682/avuod.2019.22.1.25.
  5. Ghoreishi, M., Hossini, Y., & Maftoon, M. (2012). Simple models for predicting leaf area of mango (L.). Mangifera indica Journal, 45-53.‏
  6. Öztürk, A., Cemek, B., Demirsoy, H., & Küçüktopcu, E. (2019). Modelling of the leaf area for various pear cultivars using neuro computing approaches. Spanish Journal of Agricultural Research, 17(4), e0206-e0206.‏ https://doi.org/10.5424/sjar/2019174-14675.
  7. Chen, J. M., Rich, P. M., Gower, S. T., Norman, J. M., & Plummer, S. (1997). Leaf area index of boreal forests: Theory, techniques, and measurements. Journal of Geophysical Research: Atmospheres, 102(D24), 29429-29443‏. https://doi.org/10.1029/97JD01107
  8. Sidabras, N., & Augustaitis, A. (2015). Application perspectives of the leaf area index (LAI) estimated by the Hemiview system in forestry. Rural Sustainability Research, 33(1), 26-34. ‏https://doi.org/10.1515/plua-2015-00
  9. Rich, P. M., Chen, J., Sulatycki, S. J., Vashisht, R., & Wachspress, W. S. (1995). Calculation of leaf area index and other canopy indices from gap fraction: a manual for the LAICALC software. Kansas Applied Remote Sensing Program Open File Report. LAICALC (c) copyright, 
  10. Rouphael Y., Mouneimne, A. H., Ismail, A., Mendoza-De Gyves, E., Rivera, C. M., & Colla, G. (2010): "Modeling individual leaf area of rose (Rosa hybrida L.) based on leaf length and width measurement." Photosynthetica 48 9-15.‏ https://doi.org/10.1007/s11099-010-0003-x
  11. Montgomery, E. G. (1911). Correlation studies in corn. Neb. Agric. Exp. Stn. Annu. Rep, 24, 108-159.‏
  12. Beerling, D. J., & Fry, J. C. (1990). A comparison of the accuracy, variability and speed of five different methods for estimating leaf area. Annals of Botany, 65(5), 483-488. https://doi.org/10.1093/oxfordjournals.aob.a087959.
  13. Zellers, C.E., Saunders, M.R., Morrissey, R.C. et al. (2012). Development of allometric leaf area models for intensively managed black walnut (Juglans nigra L.). Annals of Forest Science 69, 907–913 https://doi.org/10.1007/s13595-012-0215-2
  14. Carvalho, L. B., Souza, M. C., Bianco, M. S., & Bianco, S. (2011). Estimativa da área foliar de plantas daninhas de ambiente aquático: Pistia stratiotes. Planta Daninha, 29, 65-68.‏.‏ https://doi.org/10.1590/S0100-83582011000100008
  15. Öztürk, A., Cemek, B., Demirsoy, H., & Küçüktopcu, E. (2019). Modelling of the leaf area for various pear cultivars using neuro computing approaches. Spanish Journal of Agricultural Research, 17(4), e0206-e0206.‏ https://doi.org/10.5424/sjar/2019174-14675
  16. SABR, H. A. (2020) “PREDICTION OF LEAF AREA BY A NON-DESTRUCTIVE METHOD OF Platanus orientalis TREE”, Journal of Duhok University, 23(2), pp. 211-217. doi: 10.26682/ajuod.2020.23.2.24
  17. Boyacı, S., Küçükönder, H. A research on Non-Destructive Leaf Area Estimation Modeling for some Apple Cultivars. Erwerbs-Obstbau 64, 1–7 (2022). https://doi.org/10.1007/s10341-021-00619-w
  18. Soysal, D. (2024). A Non-destructive Leaf Area Prediction Model and Some Physical Leaf Properties in Apples. Applied Fruit Science, 1-6.‏ https://doi.org/10.1007/s10341-024-01103-x
  19. Neter, J., Kutner, M. H., Nachtsheim, C. J., & Wasserman, W. (1996). Applied linear statistical models.‏
  20. Cristofori, V., Fallovo, C., Mendoza-de Gyves, E., Rivera, C. M., Bignami, C., & Rouphael, Y. (2008). Non-destructive, analogue model for leaf area estimation in persimmon (Diospyros kaki L. f.) based on leaf length and width measurement. European Journal of Horticultural Science, 73(5), 216.‏
  21. Olfati, J. A., Peyvast, G., Shabani, H., & Nosrati-Rad, Z. (2010). An estimation of individual leaf area in cabbage and broccoli using non-destructive methods.‏
  22. Tsialtas, J. T., & Maslaris, N. (2005). Leaf area estimation in a sugar beet cultivar by linear models. Photosynthetica, 43(3), 477-479.https://doi.org/10.1007/s11099-005-0077-z
  23. Rouphael, Y., Rivera, C. M., Cardarelli, M., Fanasca, S., & Colla, G. (2006). Leaf area estimation from linear measurements in zucchini plants of different ages. The Journal of Horticultural Science and Biotechnology, 81(2), 238-241.‏ https://doi.org/10.1080/14620316.2006.11512056
  24. Mendoza-de Gyves E, Rouphael Y, Cristofori V, Mira FR. (2007). A non-destructive, simple and accurate model for estimating the individual leaf area of kiwi (Actinidia deliciosa). Fruits.;62(3):171-176. doi:10.1051/fruits:2007012
  25. Tsialtas, J. T., & Maslaris, N. (2008). Leaf shape and its relationship with Leaf Area Index in a sugar beet (Beta vulgaris L.) cultivar. Photosynthetica, 46(1), 48-48.https://doi.org/10.1007/s11099-008-0009-9
  26. Ghoreishi, M., Hossini, Y., & Maftoon, M. (2012). Simple models for predicting leaf area of mango (L.). Mangifera indicaJournal, 45-53.‏
  27. Tsialtas, J. T., & Maslaris, N. (2008). Leaf shape and its relationship with Leaf Area Index in a sugar beet (Beta vulgaris L.) cultivar. Photosynthetica, 46(1), 48-48.https://doi.org/10.1007/s11099-008-0009-9
  28. Leroy, C., Saint-André, L. & Auclair, D. Practical methods for non-destructive measurement of tree leaf area. Agroforest Syst 71, 99–108 (2007). https://doi.org/10.1007/s10457-007-9077-2
  29. Al-Barzinji, I. M. and Amin, B. M. (2016) “Non-destructive Method of Leaf Area Estimation for Oleander (Nerium oleander L.) Cultivated in the Iraqi Kurdistan Region”, ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 4(1), pp. 22-26. doi: 10.14500/aro.10088
  30. Ercanlı, İ., Günlü, A., Şenyurt, M. et al. Artificial neural network models predicting the leaf area index: a case study in pure even-aged Crimean pine forests from Turkey. For. Ecosyst. 5, 29 (2018). https://doi.org/10.1186/s40663-018-0149-8
Al-Qadisiyah Journal For Agriculture Sciences
Volume 14, Issue 2
December 2024
Page 1-9
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History
  • Receive Date: 19 May 2024
  • Revise Date: 05 June 2024
  • Accept Date: 24 June 2024
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