The Dissection of Nitrogen Response Traits Using Drone Phenotyping and Dynamic Phenotypic Analysis to Explore N Responsiveness and Associated Genetic Loci in Wheat.

IF 7.6 1区 农林科学 Q1 AGRONOMY
Plant Phenomics Pub Date : 2023-12-22 eCollection Date: 2023-01-01 DOI:10.34133/plantphenomics.0128
Guohui Ding, Liyan Shen, Jie Dai, Robert Jackson, Shuchen Liu, Mujahid Ali, Li Sun, Mingxing Wen, Jin Xiao, Greg Deakin, Dong Jiang, Xiu-E Wang, Ji Zhou
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引用次数: 0

Abstract

Inefficient nitrogen (N) utilization in agricultural production has led to many negative impacts such as excessive use of N fertilizers, redundant plant growth, greenhouse gases, long-lasting toxicity in ecosystem, and even effect on human health, indicating the importance to optimize N applications in cropping systems. Here, we present a multiseasonal study that focused on measuring phenotypic changes in wheat plants when they were responding to different N treatments under field conditions. Powered by drone-based aerial phenotyping and the AirMeasurer platform, we first quantified 6 N response-related traits as targets using plot-based morphological, spectral, and textural signals collected from 54 winter wheat varieties. Then, we developed dynamic phenotypic analysis using curve fitting to establish profile curves of the traits during the season, which enabled us to compute static phenotypes at key growth stages and dynamic phenotypes (i.e., phenotypic changes) during N response. After that, we combine 12 yield production and N-utilization indices manually measured to produce N efficiency comprehensive scores (NECS), based on which we classified the varieties into 4 N responsiveness (i.e., N-dependent yield increase) groups. The NECS ranking facilitated us to establish a tailored machine learning model for N responsiveness-related varietal classification just using N-response phenotypes with high accuracies. Finally, we employed the Wheat55K SNP Array to map single-nucleotide polymorphisms using N response-related static and dynamic phenotypes, helping us explore genetic components underlying N responsiveness in wheat. In summary, we believe that our work demonstrates valuable advances in N response-related plant research, which could have major implications for improving N sustainability in wheat breeding and production.

利用无人机表型分析和动态表型分析剖析氮响应性状,探索小麦的氮响应性及相关基因位点。
农业生产中氮素(N)的低效利用导致了许多负面影响,如过量使用氮肥、植物生长冗余、温室气体、生态系统中长期毒性,甚至对人类健康造成影响,这表明了在种植系统中优化氮素施用的重要性。在这里,我们介绍了一项多季节研究,重点是测量小麦植物在田间条件下对不同氮处理做出反应时的表型变化。在基于无人机的空中表型分析和 AirMeasurer 平台的支持下,我们首先利用从 54 个冬小麦品种收集到的基于小区的形态、光谱和纹理信号,量化了 6 个与氮响应相关的性状。然后,我们利用曲线拟合技术开发了动态表型分析,建立了这些性状在整个季节的剖面曲线,从而能够计算关键生长阶段的静态表型和氮响应期间的动态表型(即表型变化)。然后,我们将人工测量的 12 个产量和氮利用率指数结合起来,得出氮效率综合评分(NECS),并据此将品种分为 4 个氮响应性(即氮依赖性增产)组。NECS 分级有助于我们建立一个量身定制的机器学习模型,仅利用氮响应表型就能对与氮响应相关的品种进行高准确度的分类。最后,我们利用 Wheat55K SNP 阵列绘制了与氮响应相关的静态和动态表型的单核苷酸多态性图谱,帮助我们探索了小麦氮响应性的遗传成分。总之,我们相信,我们的工作展示了氮响应相关植物研究的宝贵进展,这对提高小麦育种和生产中氮的可持续性具有重大意义。
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来源期刊
Plant Phenomics
Plant Phenomics Multiple-
CiteScore
8.60
自引率
9.20%
发文量
26
审稿时长
14 weeks
期刊介绍: Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics. The scope of the journal covers the latest technologies in plant phenotyping for data acquisition, data management, data interpretation, modeling, and their practical applications for crop cultivation, plant breeding, forestry, horticulture, ecology, and other plant-related domains.
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