文献类型：期刊文献

英文题名：Spatial Vertical Distribution of the Leaf Nitrogen Concentration in Young Cephalotaxus hainanensis

作者：Shi, Mengmeng[1,2] He, Danni[3,4] Yuan, Ying[5] Chen, Zhulin[1,2] Chen, Shudan[1,2] Chen, Xingjing[1,2] Wang, Tian[6] Wang, Xuefeng[1,2]

第一作者：Shi, Mengmeng

机构：[1] Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, China; [2] Key Laboratory of Forest Management and Growth Modelling, National Forestry and Grassland Administration, Beijing, 100091, China; [3] Institute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou, 450052, China; [4] Key Laboratory of Remote Sensing and Geographic Information Systems in Henan Province, Zhengzhou, 450052, China; [5] Precision Agriculture Lab, School of Life Sciences, Technical University of Munich, Freising, 85354, Germany; [6] Chinese Academy of Forestry, Beijing, 100091, China

年份：2026

卷号：17

期号：2

外文期刊名：Forests

收录：EI(收录号：20260920191373);Scopus(收录号：2-s2.0-105031442213)

语种：英文

外文关键词：Conservation - Correlation methods - Diagnosis - Ecology - Forestry - Nitrogen - Nitrogen fertilizers - Nitrogen removal - Nutrients - Plants (botany) - Regression analysis

摘要：Cephalotaxus hainanensis, a valuable medicinal and endangered conifer, requires scientific conservation and precision management to ensure the sustainable utilization of its genetic and ecological resources. Nitrogen (N) is a key nutrient that regulates plant growth and metabolism; rapid and accurate nitrogen diagnosis is vital for optimizing fertilization, reducing nutrient losses, and promoting healthy plant development. This study employed a combined approach integrating stepwise regression, correlation analysis, and Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify leaf color features strongly correlated with leaf nitrogen content (LNC). A support vector regression (SVR) model, suitable for small-sample datasets, was then employed to accurately estimate LNC across canopy layers. Nine color variables were found to be highly associated with LNC, among which the Green Minus Blue index (GMB) consistently appeared across all correlation methods, demonstrating strong robustness and generality. Color features effectively reflected LNC variations among nitrogen treatments—especially between N1 and N4—and across canopy layers, with the most pronounced contrasts observed between upper and lower leaves. The Spearman-based SVR model revealed that the middle canopy maintained the highest and most stable LNC. However, the lower leaves were most sensitive to nitrogen deficiency, while the upper leaves were more sensitive to nitrogen excess. Comprehensive analysis identified N2 as the optimal nitrogen treatment, representing a balanced nutrient state. Overall, this study confirms the reliability of color features for LNC estimation and highlights the importance of vertical canopy LNC distribution in nitrogen diagnostics, providing a theoretical and methodological foundation for color-based nitrogen diagnosis and precision nutrient management in evergreen conifers. ? 2026 by the authors.