Modeling tree leaf area of Chinese fir plantations

IF 1.7 3区 农林科学 Q2 FORESTRY Canadian Journal of Forest Research Pub Date : 2023-10-03 DOI:10.1139/cjfr-2023-0127
Yancheng Qu, Yihang Jiang, Hanyue Chen, Yuxin Hu, Quang V. Cao, Anli Luo, Jian-guo Zhang, Xiongqing Zhang
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Abstract

Leaf area is an important ecophysiological variable for quantifying the potential production of trees, since it is closely related to tree growth. However, it is difficult to measure the leaf area completely because of the large number of leaves, so it is particularly important to develop accurate species-specific leaf area models. In this study, using 144 parse trees from 48 plots of different climate zones and ages of Chinese fir, tree leaf area models were developed based on sapwood area at breast height (SABH), diameter at breast height (DBH), and diameter at crown base (DCB), respectively. The results showed that the population-averaged levels of nonlinear mixed-effects (NLME) models were better than the plot-levels and base models, and the leaf area models based on DCB performed the best. Finally, the NLME model (16) based on DCB was used as the final model for tree leaf area of Chinese fir plantations, which was consistent with the pipe model theory. All the variables had certain biological and statistical significance and were easy to obtain in the field work (nondestructive). In addition, this study can also provide a reference for other tree species in predicting tree leaf area.
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杉木人工林叶面积建模
叶面积与树木生长密切相关,是量化树木潜在产量的重要生理生态变量。然而,由于叶片数量众多,很难完全测量叶面积,因此建立准确的物种特异性叶面积模型就显得尤为重要。本研究以杉木不同气候带和年龄的48个样地144棵分析树为研究对象,分别基于胸高边材面积(SABH)、胸高径(DBH)和冠底径(DCB)建立了杉木叶面积模型。结果表明,非线性混合效应(NLME)模型的种群平均水平优于样地水平和基准模型,其中基于DCB的叶面积模型表现最好。最后,采用基于DCB的NLME模型(16)作为杉木人工林叶面积的最终模型,该模型与管道模型理论一致。所有变量均具有一定的生物学和统计学意义,易于在野外(无损)工作中获得。此外,本研究也可为其他树种的叶面积预测提供参考。
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来源期刊
CiteScore
4.20
自引率
9.10%
发文量
109
审稿时长
3 months
期刊介绍: Published since 1971, the Canadian Journal of Forest Research is a monthly journal that features articles, reviews, notes and concept papers on a broad spectrum of forest sciences, including biometrics, conservation, disturbances, ecology, economics, entomology, genetics, hydrology, management, nutrient cycling, pathology, physiology, remote sensing, silviculture, social sciences, soils, stand dynamics, and wood science, all in relation to the understanding or management of ecosystem services. It also publishes special issues dedicated to a topic of current interest.
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