Evidence map›Paper›PMID 30368254›Full record

ArticleBiological research2018

High-throughput micro-phenotyping measurements applied to assess stalk lodging in maize (Zea mays L.).

Ying Zhang, Jianjun Du, Jinglu Wang, Liming Ma, Xianju Lu, Xiaodi Pan, Xinyu Guo, Chunjiang Zhao

Abstract read
In one paragraph

Article in Biological research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

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0cells of the map it votes in
11citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ying ZhangBeijing Key Lab of Digital Plant, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Shuguang Huayuan Middle Road, Haidian District, No. 11, Beijing, 100097, People's Republic of China.
Jianjun DuBeijing Key Lab of Digital Plant, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Shuguang Huayuan Middle Road, Haidian District, No. 11, Beijing, 100097, People's Republic of China.
Jinglu WangBeijing Key Lab of Digital Plant, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Shuguang Huayuan Middle Road, Haidian District, No. 11, Beijing, 100097, People's Republic of China.
Liming MaBeijing Key Lab of Digital Plant, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Shuguang Huayuan Middle Road, Haidian District, No. 11, Beijing, 100097, People's Republic of China.
Xianju LuBeijing Key Lab of Digital Plant, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Shuguang Huayuan Middle Road, Haidian District, No. 11, Beijing, 100097, People's Republic of China.
Xiaodi PanBeijing Key Lab of Digital Plant, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Shuguang Huayuan Middle Road, Haidian District, No. 11, Beijing, 100097, People's Republic of China.
Xinyu GuoBeijing Key Lab of Digital Plant, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Shuguang Huayuan Middle Road, Haidian District, No. 11, Beijing, 100097, People's Republic of China. guoxy@nercita.org.cn.
Chunjiang ZhaoBeijing Key Lab of Digital Plant, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Shuguang Huayuan Middle Road, Haidian District, No. 11, Beijing, 100097, People's Republic of China. zhaocj@nercita.org.cn.

Funding

Beijing Municipal Natural Science Foundation 5174033Beijing Postdoctoral Research Foundation 2016 ZZ-66National Nature Science Foundation of China 31671577Scientific and Technological Innovation Capacity Construction Project of Beijing Academy of Agricultural and Forestry Sciences KJCX20170404Scientific and Technological Innovation Team of Beijing Academy of Agricultural and Forestry Sciences JNKYT201604
6 · The paper itself

Abstract

backgroundThe biomechanical properties of maize stalks largely determine their lodging resistance, which affects crop yield per unit area. However, the quantitative and qualitative relationship between micro-phenotypes and the biomechanics of maize stalks is still under examined. In particular, the roles of the number, geometry, and distribution of vascular bundles of stalks in maize lodging resistance remain unclear. Research on these biomechanical properties will benefit from high-resolution micro-phenotypic image acquisition capabilities, which have been improved by modern X-ray imaging devices such as micro-CT and the development of micro-phenotyping analysis software. Hence, high-throughput image analysis and accurate quantification of anatomical phenotypes of stalks are necessary.

resultsWe have updated VesselParser version 1.0 to version 2.0 and have improved its performance, accuracy, and computation strategies. Anatomical characteristics of the second and third stalk internodes of the cultivars 'Jingke968' and 'Jingdan38' were analyzed using VesselParser 2.0. The relationships between lodging resistance and anatomical phenotypes of stalks between the two different maize varieties were investigated. The total area of vascular bundles in the peripheral layer, auxiliary axis diameter, and total area of vascular bundles were revealed to have the highest correlation with mechanical properties, and anatomical phenotypes of maize stalk were better predictors of mechanical properties than macro features observed optically from direct measurement, such as diameter and perimeter.

conclusionsThis study demonstrates the utility of VesselParser 2.0 in assessing stalk mechanical properties. The combination of anatomical phenotypes and mechanical behavior research provides unique insights into the problem of stalk lodging, showing that micro phenotypes of vascular bundles are good predictors of maize stalk mechanical properties that may be important indices for the evaluation and identification of the biomechanical properties to improve lodging resistance of future maize varieties.

Indexed as

PhenotypeHigh-Throughput Screening AssaysImage Processing, Computer-AssistedPlant StemsPlant Vascular BundleReference ValuesReproducibility of ResultsX-Ray MicrotomographyZea maysAnatomical phenotypesMaize stalkStalk lodgingVascular bundleVesselParser 2.0

Identifiers

PMID30368254
PMCPMC6203980

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.