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Registro Completo |
Biblioteca(s): |
Embrapa Pantanal. |
Data corrente: |
12/01/2005 |
Data da última atualização: |
12/01/2005 |
Autoria: |
CRISPIM, S. M. A.; RODRIGUES, C. A. G.; BRANCO, O. D. |
Afiliação: |
Embrapa Pantanal (Corumbá, MS); Embrapa Monitoramento por Satélite (Campinas, SP). |
Título: |
Levantamentos altimétrico e florístico na Fazenda Nhumirim, Pantanal da Nhecolândia-MS. |
Ano de publicação: |
2004 |
Fonte/Imprenta: |
In: SIMPÓSIO SOBRE RECURSOS NATURAIS E SÓCIO-ECONÔMICOS DO PANTANAL, 4., 2004, Corumbá, MS. Sustentabilidade regional: anais. Corumbá: Embrapa Pantanal: UCDB: UFMS: SEBRAE-MS, 2004. (CD-ROM). |
Idioma: |
Português |
Conteúdo: |
No Brasil, o Pantanal está dividido em 11 sub-regiões, sendo a Nhecolândia a segunda
maior em extensão de área. A imensa maioria de seu território está situada dentro do
município de Corumbá. De acordo com dados da literatura, nesta sub-região observa-se
uma sucessiva freqüência de contrastes altimétricos de dois a cinco metros. Esses
contrastes estão entre o topo das parte altas denominadas regionalmente de cordilheiras,
que podem ser formadas por cerradão, cerrado, vindo a seguir as áreas de campo limpo,
os campos alagáveis (baixadas) e os corpos d?água (baías e salinas). Com o objetivo de
verificar essas diferenças altimétricas e o comportamento da vegetação foi feito um
experimento na fazenda Nhumirim, no ano de 2002. |
Palavras-Chave: |
Grass; Native pasture. |
Thesagro: |
Gramínea; Legume; Leguminosa; Pastagem Nativa. |
Categoria do assunto: |
-- |
Marc: |
LEADER 01495naa a2200217 a 4500 001 1811513 005 2005-01-12 008 2004 bl uuuu u00u1 u #d 100 1 $aCRISPIM, S. M. A. 245 $aLevantamentos altimétrico e florístico na Fazenda Nhumirim, Pantanal da Nhecolândia-MS. 260 $c2004 520 $aNo Brasil, o Pantanal está dividido em 11 sub-regiões, sendo a Nhecolândia a segunda maior em extensão de área. A imensa maioria de seu território está situada dentro do município de Corumbá. De acordo com dados da literatura, nesta sub-região observa-se uma sucessiva freqüência de contrastes altimétricos de dois a cinco metros. Esses contrastes estão entre o topo das parte altas denominadas regionalmente de cordilheiras, que podem ser formadas por cerradão, cerrado, vindo a seguir as áreas de campo limpo, os campos alagáveis (baixadas) e os corpos d?água (baías e salinas). Com o objetivo de verificar essas diferenças altimétricas e o comportamento da vegetação foi feito um experimento na fazenda Nhumirim, no ano de 2002. 650 $aGramínea 650 $aLegume 650 $aLeguminosa 650 $aPastagem Nativa 653 $aGrass 653 $aNative pasture 700 1 $aRODRIGUES, C. A. G. 700 1 $aBRANCO, O. D. 773 $tIn: SIMPÓSIO SOBRE RECURSOS NATURAIS E SÓCIO-ECONÔMICOS DO PANTANAL, 4., 2004, Corumbá, MS. Sustentabilidade regional: anais. Corumbá: Embrapa Pantanal: UCDB: UFMS: SEBRAE-MS, 2004. (CD-ROM).
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Embrapa Pantanal (CPAP) |
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Registro Completo
Biblioteca(s): |
Embrapa Café. |
Data corrente: |
16/05/2022 |
Data da última atualização: |
16/05/2022 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
SOUSA, I. C. de; NASCIMENTO, M.; SANT’ANNA, I. de C.; CAIXETA, E. T.; AZEVEDO, C. F.; CRUZ, C. D.; SILVA, F. L. da; ALKIMIM, E. R.; NASCIMENTO, A. C. C.; SERÃO, N. V. L. |
Afiliação: |
ITHALO COELHO DE SOUSA, IOWA STATE UNIVERSITY; MOYSÉS NASCIMENTO, UFV; ISABELA DE CASTRO SANT’ANNA, IAC; EVELINE TEIXEIRA CAIXETA MOURA, CNPCa; CAMILA FERREIRA AZEVEDO, UFV; COSME DAMIÃO CRUZ, UFV; FELIPE LOPES DA SILVA, UFV; EMILLY RUAS ALKIMIM, UFMT; ANA CAROLINA CAMPANA NASCIMENTO, UFV; NICK VERGARA LOPES SERÃO, IOWA STATE UNIVERSITY. |
Título: |
Marker effects and heritability estimates using additive-dominance genomic architectures via artificial neural networks in Coffea canephora. |
Ano de publicação: |
2022 |
Fonte/Imprenta: |
Plos One, v. 17, n.1, e0262055, 2022. |
DOI: |
https://doi.org/10.1371/journal.pone.0262055 |
Idioma: |
Inglês |
Conteúdo: |
Many methodologies are used to predict the genetic merit in animals and plants, but some of them require priori assumptions that may increase the complexity of the model. Artificial neural network (ANN) has advantage to not require priori assumptions about the relationships between inputs and the output allowing great flexibility to handle different types of complex non-additive effects, such as dominance and epistasis. Despite this advantage, the biological interpretability of ANNs is still limited. The aim of this research was to estimate the heritability and markers effects for two traits in Coffea canephora using an additive-dominance architecture ANN and to compare it with genomic best linear unbiased prediction (GBLUP). The data used consists of 51 clones of C. canephora varietal Conilon, 32 of varietal group Robusta and 82 intervarietal hybrids. From this, 165 phenotyped individuals were genotyped for 14,387 SNPs. Due to the high computational cost of ANNs, we used Bagging decision tree to reduce the dimensionality of the data, selecting the markers that accumulated 70% of the total importance. An ANN with three hidden layers was run, each varying from 1 to 40 neurons summing 64,000 neural networks. The network architectures with the best predictive ability were selected. The best architectures were composed by 4, 15, and 33 neurons in the first, second and third hidden layers, respectively, for yield, and by 13, 20, and 24 neurons, respectively for rust resistance. The predictive ability was greater when using ANN with three hidden layers than using one hidden layer and GBLUP, with 0.72 and 0.88 for yield and coffee leaf rust resistance, respectively. The concordance rate (CR) of the 10% larger markers effects among the methods varied between 10% and 13.8%, for additive effects and between 5.4% and 11.9% for dominance effects. The narrow-sense (h2a ) and dominance-only (h2a ) heritability estimates were 0.25 and 0.06, respectively, for yield, and 0.67 and 0.03, respectively for rust resistance. The ANN was able to estimate the heritabilities from an additive-dominance genomic architectures and the ANN with three hidden layers obtained best predictive ability when compared with those obtained from GBLUP and ANN with one hidden layer. MenosMany methodologies are used to predict the genetic merit in animals and plants, but some of them require priori assumptions that may increase the complexity of the model. Artificial neural network (ANN) has advantage to not require priori assumptions about the relationships between inputs and the output allowing great flexibility to handle different types of complex non-additive effects, such as dominance and epistasis. Despite this advantage, the biological interpretability of ANNs is still limited. The aim of this research was to estimate the heritability and markers effects for two traits in Coffea canephora using an additive-dominance architecture ANN and to compare it with genomic best linear unbiased prediction (GBLUP). The data used consists of 51 clones of C. canephora varietal Conilon, 32 of varietal group Robusta and 82 intervarietal hybrids. From this, 165 phenotyped individuals were genotyped for 14,387 SNPs. Due to the high computational cost of ANNs, we used Bagging decision tree to reduce the dimensionality of the data, selecting the markers that accumulated 70% of the total importance. An ANN with three hidden layers was run, each varying from 1 to 40 neurons summing 64,000 neural networks. The network architectures with the best predictive ability were selected. The best architectures were composed by 4, 15, and 33 neurons in the first, second and third hidden layers, respectively, for yield, and by 13, 20, and 24 neurons, respectively for rust resistance. T... Mostrar Tudo |
Palavras-Chave: |
Rede neural artificial. |
Thesagro: |
Coffea Canephora; Marcador Genético. |
Thesaurus NAL: |
Dominance (genetics); Neural networks. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1143026/1/Marker-effects-and-heritability.pdf
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Marc: |
LEADER 03240naa a2200301 a 4500 001 2143026 005 2022-05-16 008 2022 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.1371/journal.pone.0262055$2DOI 100 1 $aSOUSA, I. C. de 245 $aMarker effects and heritability estimates using additive-dominance genomic architectures via artificial neural networks in Coffea canephora.$h[electronic resource] 260 $c2022 520 $aMany methodologies are used to predict the genetic merit in animals and plants, but some of them require priori assumptions that may increase the complexity of the model. Artificial neural network (ANN) has advantage to not require priori assumptions about the relationships between inputs and the output allowing great flexibility to handle different types of complex non-additive effects, such as dominance and epistasis. Despite this advantage, the biological interpretability of ANNs is still limited. The aim of this research was to estimate the heritability and markers effects for two traits in Coffea canephora using an additive-dominance architecture ANN and to compare it with genomic best linear unbiased prediction (GBLUP). The data used consists of 51 clones of C. canephora varietal Conilon, 32 of varietal group Robusta and 82 intervarietal hybrids. From this, 165 phenotyped individuals were genotyped for 14,387 SNPs. Due to the high computational cost of ANNs, we used Bagging decision tree to reduce the dimensionality of the data, selecting the markers that accumulated 70% of the total importance. An ANN with three hidden layers was run, each varying from 1 to 40 neurons summing 64,000 neural networks. The network architectures with the best predictive ability were selected. The best architectures were composed by 4, 15, and 33 neurons in the first, second and third hidden layers, respectively, for yield, and by 13, 20, and 24 neurons, respectively for rust resistance. The predictive ability was greater when using ANN with three hidden layers than using one hidden layer and GBLUP, with 0.72 and 0.88 for yield and coffee leaf rust resistance, respectively. The concordance rate (CR) of the 10% larger markers effects among the methods varied between 10% and 13.8%, for additive effects and between 5.4% and 11.9% for dominance effects. The narrow-sense (h2a ) and dominance-only (h2a ) heritability estimates were 0.25 and 0.06, respectively, for yield, and 0.67 and 0.03, respectively for rust resistance. The ANN was able to estimate the heritabilities from an additive-dominance genomic architectures and the ANN with three hidden layers obtained best predictive ability when compared with those obtained from GBLUP and ANN with one hidden layer. 650 $aDominance (genetics) 650 $aNeural networks 650 $aCoffea Canephora 650 $aMarcador Genético 653 $aRede neural artificial 700 1 $aNASCIMENTO, M. 700 1 $aSANT’ANNA, I. de C. 700 1 $aCAIXETA, E. T. 700 1 $aAZEVEDO, C. F. 700 1 $aCRUZ, C. D. 700 1 $aSILVA, F. L. da 700 1 $aALKIMIM, E. R. 700 1 $aNASCIMENTO, A. C. C. 700 1 $aSERÃO, N. V. L. 773 $tPlos One$gv. 17, n.1, e0262055, 2022.
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