Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes
The purpose of this work was to evaluate a methodology of adaptability and phenotypic stability of alfalfa genotypes based on the training of an artificial neural network considering the methodology of Eberhart and Russell. Data from an experiment on dry matter production of 92 alfalfa genotypes (Medicago sativa L.) were used. The experimental design constituted of randomized blocks, with two repetitions. The genotypes were submitted to 20 cuttings, in the growing season of November 2004 to June 2006. Each cutting was considered an environment. The artificial neural network was able to satisfactorily classify the genotypes. In addition, the analysis presented high agreement rates, compared with the results obtained by the methodology of Eberhart and Russell.
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Crop Breeding and Applied Biotechnology
2013
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oai:scielo:S1984-703320130002000082013-08-20Artificial neural networks for adaptability and stability evaluation in alfalfa genotypesNascimento,MoysésPeternelli,Luiz AlexandreCruz,Cosme DamiãoNascimento,Ana Carolina CampanaFerreira,Reinaldo de PaulaBhering,Leonardo LopesSalgado,Caio Césio Bioinformatics data simulation Eberhart and Russell The purpose of this work was to evaluate a methodology of adaptability and phenotypic stability of alfalfa genotypes based on the training of an artificial neural network considering the methodology of Eberhart and Russell. Data from an experiment on dry matter production of 92 alfalfa genotypes (Medicago sativa L.) were used. The experimental design constituted of randomized blocks, with two repetitions. The genotypes were submitted to 20 cuttings, in the growing season of November 2004 to June 2006. Each cutting was considered an environment. The artificial neural network was able to satisfactorily classify the genotypes. In addition, the analysis presented high agreement rates, compared with the results obtained by the methodology of Eberhart and Russell.info:eu-repo/semantics/openAccessCrop Breeding and Applied BiotechnologyCrop Breeding and Applied Biotechnology v.13 n.2 20132013-07-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332013000200008en |
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Nascimento,Moysés Peternelli,Luiz Alexandre Cruz,Cosme Damião Nascimento,Ana Carolina Campana Ferreira,Reinaldo de Paula Bhering,Leonardo Lopes Salgado,Caio Césio |
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Nascimento,Moysés Peternelli,Luiz Alexandre Cruz,Cosme Damião Nascimento,Ana Carolina Campana Ferreira,Reinaldo de Paula Bhering,Leonardo Lopes Salgado,Caio Césio Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes |
| author_facet |
Nascimento,Moysés Peternelli,Luiz Alexandre Cruz,Cosme Damião Nascimento,Ana Carolina Campana Ferreira,Reinaldo de Paula Bhering,Leonardo Lopes Salgado,Caio Césio |
| author_sort |
Nascimento,Moysés |
| title |
Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes |
| title_short |
Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes |
| title_full |
Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes |
| title_fullStr |
Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes |
| title_full_unstemmed |
Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes |
| title_sort |
artificial neural networks for adaptability and stability evaluation in alfalfa genotypes |
| description |
The purpose of this work was to evaluate a methodology of adaptability and phenotypic stability of alfalfa genotypes based on the training of an artificial neural network considering the methodology of Eberhart and Russell. Data from an experiment on dry matter production of 92 alfalfa genotypes (Medicago sativa L.) were used. The experimental design constituted of randomized blocks, with two repetitions. The genotypes were submitted to 20 cuttings, in the growing season of November 2004 to June 2006. Each cutting was considered an environment. The artificial neural network was able to satisfactorily classify the genotypes. In addition, the analysis presented high agreement rates, compared with the results obtained by the methodology of Eberhart and Russell. |
| publisher |
Crop Breeding and Applied Biotechnology |
| publishDate |
2013 |
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http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332013000200008 |
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