Evidence map›Paper›PMID 35646011›Full record

ReviewFrontiers in plant science2022

Cotton Breeding in Australia: Meeting the Challenges of the 21st Century.

Warren C Conaty, Katrina J Broughton, Lucy M Egan, Xiaoqing Li, Zitong Li, Shiming Liu, Danny J Llewellyn, Colleen P MacMillan, Philippe Moncuquet, Vivien Rolland and 5 more

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
5.7field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 29 citations in OpenAlex.

  1. Review
  2. Speed breeding in upland cotton (Frontiers in plant science · 2026
    Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
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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

15 authors at 3 institutions in 1 country.

Warren C ConatyCSIRO Agriculture and Food, Narrabri, NSW, Australia.
Katrina J BroughtonCSIRO Agriculture and Food, Narrabri, NSW, Australia.
Lucy M EganCSIRO Agriculture and Food, Narrabri, NSW, Australia.
Xiaoqing LiCSIRO Agriculture and Food, Canberra, ACT, Australia.
Zitong LiCSIRO Agriculture and Food, Canberra, ACT, Australia.
Shiming LiuCSIRO Agriculture and Food, Narrabri, NSW, Australia.
Danny J LlewellynCSIRO Agriculture and Food, Canberra, ACT, Australia.
Colleen P MacMillanCSIRO Agriculture and Food, Canberra, ACT, Australia.
Philippe MoncuquetCSIRO Agriculture and Food, Canberra, ACT, Australia.
Vivien RollandCSIRO Agriculture and Food, Canberra, ACT, Australia.
Brett RossCotton Seed Distributors Ltd., Wee Waa, NSW, Australia.
Demi SargentCSIRO Agriculture and Food, Narrabri, NSW, Australia.
Qian-Hao ZhuCSIRO Agriculture and Food, Canberra, ACT, Australia.
Filomena A PettolinoCSIRO Agriculture and Food, Canberra, ACT, Australia.
Warwick N StillerCSIRO Agriculture and Food, Narrabri, NSW, Australia.
Commonwealth Scientific and Industrial Research Organisation · AUAgriculture and Food · AUCotton Research and Development Corporation · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program is the sole breeding effort for cotton in Australia, developing high performing cultivars for the local industry which is worth∼AU$3 billion per annum. The program is supported by Cotton Breeding Australia, a Joint Venture between CSIRO and the program's commercial partner, Cotton Seed Distributors Ltd. (CSD). While the Australian industry is the focus, CSIRO cultivars have global impact in North America, South America, and Europe. The program is unique compared with many other public and commercial breeding programs because it focuses on diverse and integrated research with commercial outcomes. It represents the full research pipeline, supporting extensive long-term fundamental molecular research; native and genetically modified (GM) trait development; germplasm enhancement focused on yield and fiber quality improvements; integration of third-party GM traits; all culminating in the release of new commercial cultivars. This review presents evidence of past breeding successes and outlines current breeding efforts, in the areas of yield and fiber quality improvement, as well as the development of germplasm that is resistant to pests, diseases and abiotic stressors. The success of the program is based on the development of superior germplasm largely through field phenotyping, together with strong commercial partnerships with CSD and Bayer CropScience. These relationships assist in having a shared focus and ensuring commercial impact is maintained, while also providing access to markets, traits, and technology. The historical successes, current foci and future requirements of the CSIRO cotton breeding program have been used to develop a framework designed to augment our breeding system for the future. This will focus on utilizing emerging technologies from the genome to phenome, as well as a panomics approach with data management and integration to develop, test and incorporate new technologies into a breeding program. In addition to streamlining the breeding pipeline for increased genetic gain, this technology will increase the speed of trait and marker identification for use in genome editing, genomic selection and molecular assisted breeding, ultimately producing novel germplasm that will meet the coming challenges of the 21st Century.

Indexed as

cottongene based breedinggene editinggenomic selection (GS)GM traitspanomicsphenomicsplant breeding

Identifiers

PMID35646011
PMCPMC9136452
OpenAlexW4280645353

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.