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

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          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.

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps

            Recent advances in molecular genetic techniques will make dense marker maps available and genotyping many individuals for these markers feasible. Here we attempted to estimate the effects of ∼50,000 marker haplotypes simultaneously from a limited number of phenotypic records. A genome of 1000 cM was simulated with a marker spacing of 1 cM. The markers surrounding every 1-cM region were combined into marker haplotypes. Due to finite population size (Ne = 100), the marker haplotypes were in linkage disequilibrium with the QTL located between the markers. Using least squares, all haplotype effects could not be estimated simultaneously. When only the biggest effects were included, they were overestimated and the accuracy of predicting genetic values of the offspring of the recorded animals was only 0.32. Best linear unbiased prediction of haplotype effects assumed equal variances associated to each 1-cM chromosomal segment, which yielded an accuracy of 0.73, although this assumption was far from true. Bayesian methods that assumed a prior distribution of the variance associated with each chromosome segment increased this accuracy to 0.85, even when the prior was not correct. It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.
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              Deep learning in agriculture: A survey

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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                13 May 2022
                2022
                : 13
                : 904131
                Affiliations
                [1] 1CSIRO Agriculture and Food , Narrabri, NSW, Australia
                [2] 2CSIRO Agriculture and Food , Canberra, ACT, Australia
                [3] 3Cotton Seed Distributors Ltd. , Wee Waa, NSW, Australia
                [4] 4Hawkesbury Institute for the Environment, Western Sydney University , Richmond, NSW, Australia
                Author notes

                Edited by: Linghe Zeng, United States Department of Agriculture (USDA), United States

                Reviewed by: Don Jones, Cotton Incorporated, United States; Jenny Koebernick, Auburn University, United States; Johnie Jenkins, USDA Crop Science Research Lab, United States

                *Correspondence: Warren C. Conaty, Warren.Conaty@ 123456csiro.au

                These authors share senior authorship

                This article was submitted to Plant Breeding, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2022.904131
                9136452
                35646011
                844f9cde-0a2e-4109-b2b3-d4b8a7cdd4db
                Copyright © 2022 Conaty, Broughton, Egan, Li, Li, Liu, Llewellyn, MacMillan, Moncuquet, Rolland, Ross, Sargent, Zhu, Pettolino and Stiller.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 25 March 2022
                : 08 April 2022
                Page count
                Figures: 8, Tables: 0, Equations: 0, References: 129, Pages: 18, Words: 14612
                Funding
                Funded by: Cotton Breeding Australia, doi 10.13039/501100020381;
                Categories
                Plant Science
                Review

                Plant science & Botany
                cotton,plant breeding,genomic selection (gs),gene editing,phenomics,gm traits,panomics,gene based breeding

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