{"id":701,"date":"2019-06-05T09:30:54","date_gmt":"2019-06-05T12:30:54","guid":{"rendered":"http:\/\/pressreleases.scielo.org\/en\/?p=701"},"modified":"2023-03-28T13:39:46","modified_gmt":"2023-03-28T16:39:46","slug":"what-is-the-behavior-of-hypocotyl-of-soybean-cultivars-over-several-planting-seasons","status":"publish","type":"post","link":"https:\/\/pressreleases.scielo.org\/en\/2019\/06\/05\/what-is-the-behavior-of-hypocotyl-of-soybean-cultivars-over-several-planting-seasons\/","title":{"rendered":"What is the behavior of hypocotyl of soybean cultivars over several planting seasons?"},"content":{"rendered":"

By \u00c9der Matsuo, Professor and researcher, Instituto de Ci\u00eancias Exatas e Tecnol\u00f3gicas, Universidade Federal de Vi\u00e7osa, Campus<\/em> Rio Parana\u00edba, Rio Parana\u00edba, MG, Brazil<\/strong>\"\"<\/a><\/p>\n

What is the behavior of hypocotyl over several soybean planting times? Motivated by this question, researchers from the Universidade Federal de Vi\u00e7osa<\/em>, in Minas Gerais, Brazil, developed a study to identify soybean cultivars with patterns of behavior as to the length of the hypocotyl. The results of the study are described in the article “Stability of the hypocotyl length of soybean cultivars using neural networks and traditional methods”, in Ci\u00eancia Rural<\/em> journal (vol. 49, no. 3).<\/p>\n

The researchers used neural network techniques and traditional methods to identify soybean cultivars with predictable and stable behavior. They analyzed 16 soybean cultivars in six planting seasons under greenhouse conditions. Some methodologies and analysis techniques used were analysis of variance and Tukey’s test, the technique of Plaisted and Peterson (1959), Eberhart and Russel (1966) and neural networks proposed by Barroso, et al<\/em>. (2013).<\/p>\n

The cultivars BRS810C, BRSMG760SRR, TMG1175RR and BMX Tornado RR showed the highest stability and adaptability of hypocotyl length.<\/p>\n

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Image: mari_lazaro<\/a>.<\/i><\/p><\/div>\n

For the researcher \u00c9der Matsuo, the study may contribute to soybean genetic improvement by providing more detailed information on the potential descriptor, the length of the hypocotyl. “Studies of refinement of the knowledge already acquired as to the length of the hypocotyl, become important for this factor to be, in the future, considered a descriptor for the soybean crop in the analysis of registration and protection of cultivars with the Ministry of Agriculture, Livestock and Supply (MAP)\/National System of Protection of Cultivars (SNPC) “, points Matsuo. Information about hypocotyl behavior was still lacking over several planting periods.<\/p>\n

The study is part of a research field interested in increasing knowledge about potential descriptors of cultivars, such as those by Nogueira, et al<\/em>. (2008), Matsuo, et al<\/em>. (2012) and Machado J\u00fanior, et al<\/em>. (2018) and the neural networks in agriculture as those of Carvalho, et al<\/em>. (2018) and Nascimento, et al<\/em>. (2013) that focused, respectively, on the classification of genotypes and descriptors of cotton and alfalfa crops.<\/p>\n

References<\/h3>\n

BARROSO, L.M.A., et al<\/em>. The use of Eberhart and Russell method as a priori information for application of artificial neural networks and analysis discriminant for evaluate the phenotypic adaptability and stability of alfafa (Medicago sativa) genotypes. Revista Brasileira de Biometria.<\/em> 2013, vol. 31, no. 2, pp. 176-188, e-ISSN: 1983-0823 [viewed 5 June 2019]. Available from: http:\/\/jaguar.fcav.unesp.br\/RME\/fasciculos\/v31\/v31_n2\/A1_Lais_FAbiano.pdf<\/a><\/p>\n

CARVALHO, L.P., et al<\/em>. Artificial neural networks classify cotton genotypes for fiber length. Crop Breed. Appl. Biotechnol<\/em>. [online]. 2018, vol. 18, no. 2, pp. 200-204, ISSN: 1518-7853 [viewed 5 June 2019]. DOI: 10.1590\/1984-70332018v18n2n28<\/a>. Available from: http:\/\/ref.scielo.org\/wrxxpn<\/a><\/p>\n

EBERHART, S.A. and RUSSEL, W.A. Stability parameters for comparing varieties. Crop Science<\/em> [online]. 1966, vol. 6, no. 1, pp. 36-40, e-ISSN: 1435-0653 [viewed 5 June 2019]. DOI: 10.2135\/cropsci1966.0011183X000600010011x<\/a>. Available from: https:\/\/dl.sciencesocieties.org\/publications\/cs\/abstracts\/6\/1\/CS0060010036<\/a><\/p>\n

MACHADO JUNIOR, R., et al<\/em>. Use of millimeter ruler as an alternative tool in the phenotyping of potential descriptors of soybean. African Journal of Agricultural Research<\/em> [online]. 2018, vol. 13, no. 28, pp. 1425-1429, e-ISSN: 1991-637X [viewed 5 June 2019]. DOI: 10.5897\/AJAR2018.13248<\/a>. Available from: https:\/\/academicjournals.org\/journal\/AJAR\/article-abstract\/ED7D0B157761<\/a><\/p>\n

MATSUO, \u00c9., et al<\/em>. Estimates of the genetic parameters, optimum sample size and conversion of quantitative data in multiple categories for soybean genotypes. Acta Sci., Agron<\/em>. [online]. 2012, vol. 34, no. 3, pp. 265-273, ISSN: 1807-8621 [viewed 5 June 2019]. DOI: 10.1590\/S1807-86212012000300006<\/a>. Available from: http:\/\/ref.scielo.org\/vmyc88<\/a><\/p>\n

NASCIMENTO, M., et al<\/em>. Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes. Crop Breed. Appl. Biotechnol<\/em>. [online]. 2013, vol. 13, no. 2, pp. 152-156, ISSN: 1984-7033 [viewed 5 June 2019]. Available from: http:\/\/ref.scielo.org\/tcncjj<\/a><\/p>\n

NOGUEIRA, A.P.O., et al<\/em>. Novas caracter\u00edsticas para diferencia\u00e7\u00e3o de cultivares de soja pela an\u00e1lise discriminante. Cienc. Rural<\/em> [online]. 2008, vol. 38, no. 9, pp. 2427-2433, ISSN: 0103-8478 [viewed 5 June 2019]. DOI: 10.1590\/S0103-84782008005000025<\/a>. Available from: http:\/\/ref.scielo.org\/r99sj6<\/a><\/p>\n

PLAISTED, R.L. and PETERSON, L.C. A technique for evaluating the ability of selections to yield consistently in different locations and seasons. American Potato Journal<\/em> [online]. 1959, vol. 36, no. 11, pp. 381-385, e-ISSN: 1874-9380 [viewed 5 June 2019]. DOI: 10.1007\/BF02852735<\/a>. Available from: https:\/\/link.springer.com\/article\/10.1007\/BF02852735<\/a><\/p>\n

To read the article, access it<\/h3>\n

ALVES, G.F., et al<\/em>. Stability of the hypocotyl length of soybean cultivars using neural networks and traditional methods. Cienc. Rural<\/em> [online]. 2019, vol. 49, no. 3, e20180300, ISSN: 0103-8478 [viewed 5 June 2019]. DOI: 10.1590\/0103-8478cr20180300<\/a>. Available from: http:\/\/ref.scielo.org\/vxjwkh<\/a><\/p>\n

External links<\/h3>\n

Ci\u00eancia Rural \u2013 CR: <http:\/\/www.scielo.br\/cr<\/a>><\/p>\n

Laborat\u00f3rio de Bioestat\u00edstica <http:\/\/bioestatistica.crp.ufv.br\/<\/a>><\/p>\n","protected":false},"excerpt":{"rendered":"

In soybean culture, the length of the hypocotyl is one of the factors that evidences the genetic variability of these cultivars. This study evaluated the length of the hypocotyl of soybean crop over several planting seasons, identifying cultivars of predictable and stable behavior in relation to this factor. …<\/span> Read More →<\/span><\/a><\/span><\/p>\n","protected":false},"author":299,"featured_media":703,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[3,20,12],"tags":[192,21],"class_list":["post-701","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-agricultural-sciences","category-cr","category-press-releases","tag-agronomy","tag-ciencia-rural"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/posts\/701","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/users\/299"}],"replies":[{"embeddable":true,"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/comments?post=701"}],"version-history":[{"count":4,"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/posts\/701\/revisions"}],"predecessor-version":[{"id":706,"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/posts\/701\/revisions\/706"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/media\/703"}],"wp:attachment":[{"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/media?parent=701"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/categories?post=701"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/tags?post=701"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}