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通过光谱法估算小麦器官中的氮浓度以评估不同灌溉制度下的氮素再转运情况

Spectroscopic Estimation of N Concentration in Wheat Organs for Assessing N Remobilization Under Different Irrigation Regimes.

作者信息

Li Wei, Zhou Xiaonan, Yu Kang, Zhang Zhen, Liu Yang, Hu Naiyue, Liu Ying, Yao Chunsheng, Yang Xiaoguang, Wang Zhimin, Zhang Yinghua

机构信息

College of Agronomy and Biotechnology, China Agricultural University, Beijing, China.

College of Resources and Environmental Sciences, China Agricultural University, Beijing, China.

出版信息

Front Plant Sci. 2021 Apr 9;12:657578. doi: 10.3389/fpls.2021.657578. eCollection 2021.

Abstract

Nitrogen (N) remobilization is a critical process that provides substantial N to winter wheat grains for improving yield productivity. Here, the remobilization of N from anthesis to maturity in two wheat cultivars under three irrigation regimes was measured and its relationship to organ N concentration was examined. Based on spectral data of organ powder samples, partial least squares regression (PLSR) models were calibrated to estimate N concentration ( ) and validated against laboratory-based measurements. Although spectral reflectance could accurately estimate , the PLSR-based -spectra predictive model was found to be organ-specific, organs at the top canopy (chaff and top three leaves) received the best predictions ( > 0.88). In addition, N remobilization efficiency (NRE) in the top two leaves and top third internode was highly correlated with its corresponding N concentration change (Δ ) with an of 0.90. Δ of the top first internode (TIN1) explained 78% variation of the whole-plant NRE. This study provides a proof of concept for estimating N concentration and assessing N remobilization using hyperspectral data of individual organs, which offers a non-chemical and low-cost approach to screen germplasms for an optimal NRE in drought-resistance breeding.

摘要

氮(N)再转运是一个关键过程,它为冬小麦籽粒提供大量氮素以提高产量。在此,测定了三个灌溉制度下两个小麦品种从开花到成熟的氮再转运情况,并研究了其与器官氮浓度的关系。基于器官粉末样品的光谱数据,校准了偏最小二乘回归(PLSR)模型以估计氮浓度( ),并与基于实验室的测量结果进行验证。虽然光谱反射率可以准确估计 ,但发现基于PLSR的 -光谱预测模型具有器官特异性,冠层顶部的器官(颖壳和顶部三片叶子)预测效果最佳( > 0.88)。此外,顶部两片叶子和顶部第三节间的氮再转运效率(NRE)与其相应的氮浓度变化(Δ )高度相关, 为0.90。顶部第一节间(TIN1)的Δ 解释了全株NRE 78%的变异。本研究为利用单个器官的高光谱数据估计氮浓度和评估氮再转运提供了概念验证,为抗旱育种中筛选具有最佳氮再转运效率的种质提供了一种非化学且低成本的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebd6/8062884/0e98647f9124/fpls-12-657578-g001.jpg

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