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DIRT/3D:大田种植玉米(Zea mays)的三维根系表型分析。

DIRT/3D: 3D root phenotyping for field-grown maize (Zea mays).

机构信息

Department of Plant Biology, University of Georgia, Athens, Georgia 30602, USA.

Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia 30602, USA.

出版信息

Plant Physiol. 2021 Oct 5;187(2):739-757. doi: 10.1093/plphys/kiab311.

Abstract

The development of crops with deeper roots holds substantial promise to mitigate the consequences of climate change. Deeper roots are an essential factor to improve water uptake as a way to enhance crop resilience to drought, to increase nitrogen capture, to reduce fertilizer inputs, and to increase carbon sequestration from the atmosphere to improve soil organic fertility. A major bottleneck to achieving these improvements is high-throughput phenotyping to quantify root phenotypes of field-grown roots. We address this bottleneck with Digital Imaging of Root Traits (DIRT)/3D, an image-based 3D root phenotyping platform, which measures 18 architecture traits from mature field-grown maize (Zea mays) root crowns (RCs) excavated with the Shovelomics technique. DIRT/3D reliably computed all 18 traits, including distance between whorls and the number, angles, and diameters of nodal roots, on a test panel of 12 contrasting maize genotypes. The computed results were validated through comparison with manual measurements. Overall, we observed a coefficient of determination of r2>0.84 and a high broad-sense heritability of Hmean2> 0.6 for all but one trait. The average values of the 18 traits and a developed descriptor to characterize complete root architecture distinguished all genotypes. DIRT/3D is a step toward automated quantification of highly occluded maize RCs. Therefore, DIRT/3D supports breeders and root biologists in improving carbon sequestration and food security in the face of the adverse effects of climate change.

摘要

培育深根作物有很大的希望来减轻气候变化的影响。深根是提高水分吸收能力的一个重要因素,这是提高作物抗旱能力、增加氮捕获量、减少化肥投入和增加从大气中固碳以提高土壤有机肥力的一种方法。实现这些改进的一个主要瓶颈是高通量表型分析,以量化田间生长的根系的根表型。我们使用基于数字成像的根系特性(DIRT)/3D 来解决这个瓶颈问题,这是一个基于图像的 3D 根系表型平台,它可以测量从用 Shovelomics 技术挖掘的成熟田间生长的玉米(Zea mays)根冠(RC)中测量 18 个结构特性。DIRT/3D 可靠地计算了 12 个对比玉米基因型测试面板上的所有 18 个特性,包括轮间距离以及节点根的数量、角度和直径。通过与手动测量的比较验证了计算结果。总的来说,我们观察到所有特性的决定系数 r2>0.84,平均广义遗传力 Hmean2>0.6,除了一个特性。18 个特性的平均值和一个开发的描述符可以描述完整的根系结构,区分了所有基因型。DIRT/3D 是实现自动量化高度封闭的玉米 RC 的一步。因此,DIRT/3D 支持育种家和根系生物学家在面对气候变化的不利影响时提高碳固存和粮食安全。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b91d/8491025/ef8e48c2d65c/kiab311f1.jpg

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