文献类型：期刊文献

英文题名：Spectrometric Prediction of Nitrogen Content in Different Tissues of Slash Pine Trees

作者：Li, Yanjie[1] Sun, Honggang[1] Tomasetto, Federico[2] Jiang, Jingmin[1] Luan, Qifu[1]

第一作者：李彦杰

通信作者：Luan, QF[1]

机构：[1]Chinese Acad Forestry, Res Inst Subtrop Forestry, Hangzhou 311400, Zhejiang, Peoples R China;[2]AgResearch Ltd, Christchurch 8140, New Zealand

年份：2022

卷号：2022

外文期刊名：PLANT PHENOMICS

收录：;EI(收录号：20220611598358);Scopus(收录号：2-s2.0-85124069729);WOS:【SCI-EXPANDED(收录号:WOS:000774289800006)】；

基金：This work was funded by Fundamental Research Funds of CAF (CAFYBB2020SY008) and National Key R&D Program of China (2017YFD0600502-2).

语种：英文

外文关键词：Digital storage - Forestry - Histology - Infrared devices - Least squares approximations - Needles - Nitrogen - Tissue - Trees (mathematics)

摘要：The internal cycling of nitrogen (N) storage and consumption in trees is an important physiological mechanism associated with tree growth. Here, we examined the capability of near-infrared spectroscopy (NIR) to quantify the N concentration across tissue types (needle, trunk, branch, and root) without time and cost-consuming. The NIR spectral data of different tissues from slash pine trees were collected, and the N concentration in each tissue was determined using standard analytical method in laboratory. Partial least squares regression (PLSR) models were performed on a set of training data randomly selected. The full-length spectra and the significant multivariate correlation (sMC) variable selected spectra were used for model calibration. Branch, needle, and trunk PLSR models performed well for the N concentration using both full length and sMC selected NIR spectra. The generic model preformatted a reliable accuracy with R-C(2) and R-CV(2) of 0.62 and 0.66 using the full-length spectra, and 0.61 and 0.65 using sMC-selected spectra, respectively. Individual tissue models did not perform well when being used in other tissues. Five significantly important regions, i.e., 1480, 1650, 1744, 2170, and 2390 nm, were found highly related to the N content in plant tissues. This study evaluates a rapid and efficient method for the estimation of N content in different tissues that can help to serve as a tool for tree N storage and recompilation study.