Crop Breeding and Applied Biotechnology, Volume: 21, Issue: spe, Published: 2021
  • Genomics of grain quality in cereals ARTICLE

    Henry, Robert J

    Abstract in English:

    Abstract: Rapid advances in genomics are providing the tools to determine the genetic basis of quality (both nutritional and functional) in cereals. This promises to allow increased rates of genetic gain in breeding by reducing the need for extensive end-product testing of new varieties. Many quality traits are the result of relatively recent human selection and are thus likely to be controlled by only a few major genes. This makes identification of these genes for use in breeding selection an attractive target for breeders. Examples of the discovery of genes that are major contributors to key grain quality attributes include, fragrance and cooking temperature in rice (identified by re-sequencing) and loaf volume and milling yield in wheat (identified by transcriptome analysis). Extension of genomic tools to an analysis of the wider gene pool including wild relatives will enable the identification of alleles that may contribute to improved or novel grain quality in the future and may be critical to ensuring quality is retained in a changed climate. Completely new cereal species might be produced.
  • Technical nuances of machine learning: implementation and validation of supervised methods for genomic prediction in plant breeding ARTICLE

    Xavier, Alencar

    Abstract in English:

    Abstract The decision-making process in plant breeding is driven by data. The machine learning framework has powerful tools that can extract useful information from data. However, there is still a lack of understanding about the underlying algorithms of these methods, their strengths, and pitfalls. Machine learning has two main branches: supervised and unsupervised learning. In plant breeding, supervised learning is used for genomic prediction, where phenotypic traits are modeled as a function of molecular markers. The key supervised learning algorithms for genomic prediction are linear methods, kernel methods, neural networks, and tree ensembles. This manuscript provides an insight into the implementation of these algorithms and how cross-validations can be used to compare methods. Examples for genomic prediction come from plant breeding.
  • Plant breeding in Brazil: Retrospective of the past 50 years ARTICLE

    Ramalho, Magno Antonio Patto; Marques, Thaís Lima; Lemos, Roxane do Carmo

    Abstract in English:

    Abstract: The importance of plant breeding in Brazilian agriculture has grown a lot in the last 50 years. This occurred mainly because of the: increase in graduate programs, which qualified hundreds of professionals; creation of EMBRAPA and other research institutes or state companies, with an emphasis on the production of new cultivars and; promulgation of the cultivar protection law, which stimulates investments in seed production. The retrospective of what happened, enabling the country to move from being an importer of grains, fruits, and fibers to one of the largest exporters of these products worldwide, was the focus of this work. Taking as reference some agricultural products, this article highlights the significant contribution of plant breeding in recent years. Also, some of the enormous challenges that still have to be overcome, in which the participation of Brazilian breeders will be fundamental to continue the progress of agriculture in the coming years.
Crop Breeding and Applied Biotechnology Universidade Federal de Viçosa, Departamento de Fitotecnia, 36570-000 Viçosa - Minas Gerais/Brasil, Tel.: (55 31)3899-2611, Fax: (55 31)3899-2611 - Viçosa - MG - Brazil
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