AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Plant Phenomics Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Perspective | Open Access

A Combined Genomics and Phenomics Approach is Needed to Boost Breeding in Sugarcane

Ting Luo1, Xiaoyan Liu1( ), Prakash Lakshmanan1,2,3( )
Key Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Sugarcane Research Institute, Guangxi Academy of Agricultural Sciences, Guangxi Key Laboratory of Sugarcane Genetic Improvement, Guangxi Academy of Agricultural Sciences, Nanning 530007, China
Interdisciplinary Research Center for Agriculture Green Development in Yangtze River Basin, College of Resources and Environment, Southwest University, Chongqing 400716, China
Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia 4067, QLD, Australia
Show Author Information

References

1
FAO, FAOSTAT database. 2023. http://www.fao.org/faostat/en/#data/QC.
2

Singels A, Jones MR, Lumsden TG. Potential for sugarcane production under current and future climates in South Africa: Sugar and ethanol yields, and crop water use. Sugar Tech. 2023;25(2):473–481.
Crossref Google Scholar
3

Ram B, Hemaprabha G, Singh BD, Appunu C. History and current status of sugarcane breeding, germplasm development and molecular biology in India. Sugar Tech. 2022;24(1):4–29.
Crossref Google Scholar
4

Jackson P, McRae TA. Selection of sugarcane clones in small plots: Effects of plot size and selection criteria. Crop Sci. 2001;41(2):315–322.
Crossref Google Scholar
5

Natarajan S, Basnayake J, Wei X, Lakshmanan P. High-throughput phenotyping of indirect traits for early-stage selection in sugarcane breeding. Remote Sensing. 2019;11(24):Article 2952.
Crossref Google Scholar
6

Collard BC, Mackill DJ. Marker-assisted selection: An approach for precision plant breeding in the twenty-first century. Philos Trans R Soc Lond Ser B Biol Sci. 2008;363(1491):557–572.
Crossref Google Scholar
7

Varshney RK, Bohra A, Yu J, Graner A, Zhang Q,Sorrells ME. Designing future crops: Genomics-assisted breeding comes of age. Trends Plant Sci. 2021;26(6):631–649.
Crossref Google Scholar
8

Aitken KS. History and development of molecular markers for sugarcane breeding. Sugar Tech. 2022;24(1):341–353.
Crossref Google Scholar
9

Da Silva JA, Sorrells ME, Burnquist WL, Tanksley SD. RFLP linkage map and genome analysis of Saccharum spontaneum. Genome. 1993;36(4):782–791.
Crossref Google Scholar
10

Deomano E, Jackson P, Wei X, Aitken K, Kota R, Pérez-Rodríguez P. Genomic prediction of sugar content and cane yield in sugar cane clones in different stages of selection in a breeding program, with and without pedigree information. Mol Breed. 2020;40(4):Article 38.
Crossref Google Scholar
11

Yadav S, Jackson P, Wei X, Ross EM, Aitken K, Deomano E, Atkin F, Hayes BJ, Voss-Fels KP. Accelerating genetic gain in sugarcane breeding using genomic selection. Agronomy. 2020;10(4):Article 585.
Crossref Google Scholar
12

O'Connell A, Deo J, Deomano E, Wei X, Jackson P, Aitken KS, Manimekalai R, Mohanraj K, Hemaprabha G, Ram B, et al. Combining genomic selection with genome-wide association analysis identified a large-effect QTL and improved selection for red rot resistance in sugarcane. Front Plant Sci. 2022;13:Article 1021182.
Crossref Google Scholar
13

Roitsch T, Cabrera-Bosquet L, Fournier A, Ghamkhar K, Jiménez-Berni J, Pinto F, Ober ES. Review: New sensors and data-driven approaches—A path to next generation phenomics. Plant Sci. 2019;282:2–10.
Crossref Google Scholar
14

Garcia AP, Umezu CK, Moriones Polania EC, Dias Neto AF, Rossetto R, Albiero D. Sensor-based technologies in sugarcane agriculture. Sugar Tech. 2022;24(3):679–698.
Crossref Google Scholar
15

Furbank RT, Jimenez-Berni JA, George-Jaeggli B, Potgieter AB,Deery DM. Field crop phenomics: Enabling breeding for radiation use efficiency and biomass in cereal crops. New Phytol. 2019;223(4):1714–1727.
Crossref Google Scholar
16

Messina CD, Podlich D, Dong Z, Samples M, Cooper M. Yield-trait performance landscapes: From theory to application in breeding maize for drought tolerance. J Exp Bot. 2011;62(3):855–868.
Crossref Google Scholar
17

Deery DM, Jones HG. Field phenomics: Will it enable crop improvement? Plant Phenomics. 2021;2021:Article 9871989.
Crossref Google Scholar
18

Phuphaphud A, Saengprachatanarug K, Posom J, Taira E, Panduangnate L. Prediction and classification of energy content in growing cane stalks for breeding programmes using visible and shortwave near infrared. Sugar Tech. 2022;24(5):1497–1509.
Crossref Google Scholar
19

Basnayake J, Jackson PA, Inman-Bamber NG, Lakshmanan P. Sugarcane for water-limited environments. Variation in stomatal conductance and its genetic correlation with crop productivity. J Exp Bot. 2015;66(13):3945–3958.
Crossref Google Scholar
20

Cooper M, Gho C, Leafgren R, Tang T, Messina C. Breeding drought-tolerant maize hybrids for the US corn-belt: Discovery to product. J Exp Bot. 2014;65(21):6191–6204.
Crossref Google Scholar
Plant Phenomics
Volume 5,
2023
Article number: 0074
DOI: 10.34133/plantphenomics.0074
Cite this article:
Luo T, Liu X, Lakshmanan P. A Combined Genomics and Phenomics Approach is Needed to Boost Breeding in Sugarcane. Plant Phenomics, 2023, 5: 0074. https://doi.org/10.34133/plantphenomics.0074

133

Views

3

Crossref

1

Web of Science

2

Scopus

0

CSCD

Altmetrics
Received: 08 June 2023
Accepted: 29 June 2023
Published: 14 July 2023
© 2023 Ting Luo et al. Exclusive licensee Nanjing Agricultural University. No claim to original U.S. Government Works.

Distributed under a Creative Commons Attribution License (CC BY 4.0).
Return