The Dissection of Nitrogen Response Traits Using Drone Phenotyping and Dynamic Phenotypic Analysis to Explore N Responsiveness and Associated Genetic Loci in Wheat.
The Dissection of Nitrogen Response Traits Using Drone Phenotyping and Dynamic Phenotypic Analysis to Explore N Responsiveness and Associated Genetic Loci in Wheat.
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DOI：
10.34133/plantphenomics.0128
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Zhou J
中科院分区：
文献类型：
--
作者：
Ding G;Shen L;Dai J;Jackson R;Liu S;Ali M;Sun L;Wen M;Xiao J;Deakin G;Jiang D;Wang XE;Zhou J
在农业生产中，氮素利用效率低下导致了氮肥的过量使用、植物生长冗余、温室气体排放、生态系统的长期毒性甚至对人体健康的影响等诸多负面影响，表明优化氮素在种植系统中的重要性。在这里，我们提出了一项多季节研究，重点研究了小麦植株在田间条件下对不同氮处理的响应时的表型变化。在无人机空中表型分析和AirMeasurer平台的支持下，我们首先利用从54个冬小麦品种收集的基于图的形态、光谱和纹理信号，量化了6个氮响应相关性状作为目标。然后，我们利用曲线拟合的方法进行动态表型分析，建立了各性状在季节中的剖面曲线，从而使我们能够计算出关键生育期的静态表型和氮响应期间的动态表型（即表型变化）。在此基础上，将12个产量和氮素利用指标结合起来，得到氮素效率综合评分（NECS），并将各品种分为4个氮素响应性（即依赖于氮素的增产）组。NECS排序帮助我们建立了一个针对N响应性相关品种分类的量身定制的机器学习模型，该模型仅使用N响应表型，具有较高的准确性。最后，我们利用Wheat55K SNP阵列利用与氮响应相关的静态和动态表型来绘制单核苷酸多态性，帮助我们探索小麦氮响应性的遗传成分。综上所述，我们认为我们的工作表明了与氮素响应相关的植物研究取得了有价值的进展，这可能对提高小麦育种和生产中的氮素可持续性具有重要意义。
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.
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