Abstract：
Brassica juncea depends heavily on nitrogen (N) fertilizers for growth and accumulation of seed protein. However, it is an inefficient mobilizer of applied N which leads to accumulation of excess N in the soil, posing environmental risks. Hence, it is imperative to systematically examine spatial-temporal pattern of crop N to efficiently manage N application. The Kjeldahl method is commonly used to estimate N status of crops but it is a destructive method that entails the use of perilous and expensive chemicals. Near-infrared reflectance spectroscopy (NIRS) offers a safe, accurate, and non-destructive alternative for large-scale screening of seed metabolites. Currently, no NIRS model exists to quickly estimate N content in shoots and roots from large germplasm sets in any rapeseed-mustard crop. Developing such a model is essential to breed for enhanced nitrogen use efficiency (NUE). We used 738 shoot and 346 root samples from a B. juncea diversity set to construct the NIRS models. A diverse range of genetic variation in N content was recorded in the stem (0.21-6.61%) and root (0.15-3.04%) tissues of the crop raised on two different N levels (N0 and N100). Modified partial least squares (MPLS) method was employed to establish a regression equation linking reference N values with spectral changes. The developed models exhibited strong associations with reference values, with RSQ values of 0.884 for stem and 0.645 for roots. Furthermore, external validation confirms the reliability of the developed models. The developed models have strong predictive capabilities for rapid and reliable N estimation in various tissues of B. juncea plants.
摘要：
芥菜在很大程度上依赖于氮（N）肥料来生长和积累种子蛋白质。然而,它是一种低效的施氮动员剂，导致土壤中过量的氮积累，造成环境风险。因此，必须系统地研究作物氮素的时空格局，以有效地管理氮素的应用。Kjeldahl方法通常用于估计作物的氮素状况，但它是一种破坏性方法，需要使用危险且昂贵的化学物质。近红外反射光谱(NIRS)提供了一种安全、准确,和非破坏性替代大规模筛选种子代谢物。目前,不存在NIRS模型来快速估算任何油菜芥菜作物中大型种质集的芽和根中的N含量。开发这样的模型对于繁殖以提高氮利用效率（NUE）至关重要。我们使用了来自芽孢杆菌多样性集的738个芽和346个根样本来构建NIRS模型。在两个不同的N水平（N0和N100）上生长的作物的茎（0.21-6.61％）和根（0.15-3.04％）组织中，记录了不同范围的N含量遗传变异。采用改进的偏最小二乘（MPLS）方法建立了将参考N值与光谱变化联系起来的回归方程。开发的模型与参考值表现出很强的相关性，茎的RSQ值为0.884，根的RSQ值为0.645。此外，外部验证证实了所开发模型的可靠性。开发的模型具有强大的预测能力，可以快速可靠地估算芥菜植物的各种组织中的N。
