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| 2. |  | RESENDE, M. D. V. de. Estatística espacial, séries temporais e competição (interação social). In: RESENDE, M. D. V. de; SILVA, F. F. e; AZEVEDO, C. F. Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão, aleatória, seleção genômica, QTL, GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa, MG: UFV, 2014. p. 504-558.| Biblioteca(s): Embrapa Florestas. |
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| 3. | | RESENDE, M. D. V. de. Estatística. In: RESENDE, M. D. V. de; SILVA, F. F. e; AZEVEDO, C. F. Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão, aleatória, seleção genômica, QTL, GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa, MG: UFV, 2014. p. 56-129.| Biblioteca(s): Embrapa Florestas. |
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| 10. | | RESENDE, M. D. V. de. Matemática. In: RESENDE, M. D. V. de; SILVA, F. F. e; AZEVEDO, C. F. Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão, aleatória, seleção genômica, QTL, GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa, MG: UFV, 2014. p. 7-55.| Biblioteca(s): Embrapa Florestas. |
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| 11. | | RESENDE, M. D. V. de. Modelos BLUP univariados multiefeitos. In: RESENDE, M. D. V. de; SILVA, F. F. e; AZEVEDO, C. F. Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão, aleatória, seleção genômica, QTL, GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa, MG: UFV, 2014. p. 226-256.| Biblioteca(s): Embrapa Florestas. |
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| 12. | | RESENDE, M. D. V. de. Modelos computacionais BLUP. In: RESENDE, M. D. V. de; SILVA, F. F. e; AZEVEDO, C. F. Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão, aleatória, seleção genômica, QTL, GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa, MG: UFV, 2014. p. 206-225.| Biblioteca(s): Embrapa Florestas. |
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| 14. | | RESENDE, M. D. V. de. Modelos lineares. In: RESENDE, M. D. V. de; SILVA, F. F. e; AZEVEDO, C. F. Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão, aleatória, seleção genômica, QTL, GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa, MG: UFV, 2014. p. 130-205.| Biblioteca(s): Embrapa Florestas. |
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| 16. | | RESENDE, M. D. V. de. Modelos multivariados. In: RESENDE, M. D. V. de; SILVA, F. F. e; AZEVEDO, C. F. Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão, aleatória, seleção genômica, QTL, GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa, MG: UFV, 2014. p. 257-311.| Biblioteca(s): Embrapa Florestas. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Café. Para informações adicionais entre em contato com biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
08/12/2023 |
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Data da última atualização: |
08/12/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
FERREIRA, F. M.; CHAVES, S. F. S.; BHERING, L. L.; ALVES, R. S.; TAKAHASHI, E. K.; SOUSA, J. E.; RESENDE, M. D. V. de; LEITE, F. P.; GEZAN, S. A.; VIANA, J. M. S.; FERNANDES, S. B.; DIAS, K. O. G. |
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Afiliação: |
FILIPE M. FERREIRA, UNIVERSIDADE FEDERAL DE VIÇOSA; SAULO F. S. CHAVES, UNIVERSIDADE FEDERAL DE VIÇOSA; LEONARDO L. BHERING, UNIVERSIDADE FEDERAL DE VIÇOSA; RODRIGO S. ALVES, CELULOSE NIPO BRASILEIRA S.A; ELIZABETE K. TAKAHASHI, CELULOSE NIPO BRASILEIRA S.A; JOÃO E. SOUSA, CELULOSE NIPO BRASILEIRA S.A; MARCOS DEON VILELA DE RESENDE, CNPCa; FERNANDO P. LEITE, CELULOSE NIPO BRASILEIRA S.A; SALVADOR A. GEZAN, VSN INTERNATIONAL; JOSÉ M. S. VIANA, UNIVERSIDADE FEDERAL DE VIÇOSA; SAMUEL B. FERNANDES, UNIVERSITY OF ARKANSAS; KAIO O. G. DIAS, UNIVERSIDADE FEDERAL DE VIÇOSA. |
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Título: |
A novel strategy to predict clonal composites by jointly modeling spatial variation and genetic competition. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Forest Ecology and Management, v. 548, Article 121393, 2023. |
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Páginas: |
10 p. |
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DOI: |
https://doi.org/10.1016/j.foreco.2023.121393 |
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Idioma: |
Inglês |
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Conteúdo: |
Biotic and abiotic stresses have been making it difficult to maintain the rates of genetic gains in forest tree breeding. Planting a mixture of genotypes or clonal composites can be an alternative to increase the security of forest plantations. The formation of clonal composites is complex due to inter-genotypic competition and indirect genotypic effects that can affect the efficiency of genetic selection. This study aims to understand how indirect genotypic effects can impact the response to selection and how the stand composition can be used to explore these effects and enhance forest yield. We used two clonal trials of eucalyptus hybrids implanted in a randomized complete block design with 24 replications, containing a single tree per plot evaluated for mean annual increment at 3 and 6 years. We focused on partitioning the genotypic variation into direct and indirect genotypic effects based on competition intensity factors. We identified aggressive, homeostatic, and sensitive clones based on the magnitude of indirect genotypic effects. Considering indirect genotypic effects, for mean annual increment, the total heritability decreased from 0.25 to 0.10 for 3 years and from 0.30 to 0.14 for 6 years, respectively. We proposed a workflow that uses the direct and indirect genotypic effects to predict the mean values for clonal composite combinations and to select the one with the highest yield. Our methodology considered spatial variation and interplot competition that can affect the total heritable estimates and response to selection in forest trials. The clones were classified as aggressive, homeostatic, and sensitive, according to their deviation from the indirect genotypic effects means. Our methodology enabled the extraction of useful information and predict many clonal composites' performance, which can be used in recommending genotypes to be planted in mixtures. MenosBiotic and abiotic stresses have been making it difficult to maintain the rates of genetic gains in forest tree breeding. Planting a mixture of genotypes or clonal composites can be an alternative to increase the security of forest plantations. The formation of clonal composites is complex due to inter-genotypic competition and indirect genotypic effects that can affect the efficiency of genetic selection. This study aims to understand how indirect genotypic effects can impact the response to selection and how the stand composition can be used to explore these effects and enhance forest yield. We used two clonal trials of eucalyptus hybrids implanted in a randomized complete block design with 24 replications, containing a single tree per plot evaluated for mean annual increment at 3 and 6 years. We focused on partitioning the genotypic variation into direct and indirect genotypic effects based on competition intensity factors. We identified aggressive, homeostatic, and sensitive clones based on the magnitude of indirect genotypic effects. Considering indirect genotypic effects, for mean annual increment, the total heritability decreased from 0.25 to 0.10 for 3 years and from 0.30 to 0.14 for 6 years, respectively. We proposed a workflow that uses the direct and indirect genotypic effects to predict the mean values for clonal composite combinations and to select the one with the highest yield. Our methodology considered spatial variation and interplot competition that can aff... Mostrar Tudo |
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Thesaurus NAL: |
Clonal variation; Eucalyptus; Forest trees; Plant breeding; Plant competition; Quantitative genetics. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02942naa a2200349 a 4500
001 2159376
005 2023-12-08
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.foreco.2023.121393$2DOI
100 1 $aFERREIRA, F. M.
245 $aA novel strategy to predict clonal composites by jointly modeling spatial variation and genetic competition.$h[electronic resource]
260 $c2023
300 $a10 p.
520 $aBiotic and abiotic stresses have been making it difficult to maintain the rates of genetic gains in forest tree breeding. Planting a mixture of genotypes or clonal composites can be an alternative to increase the security of forest plantations. The formation of clonal composites is complex due to inter-genotypic competition and indirect genotypic effects that can affect the efficiency of genetic selection. This study aims to understand how indirect genotypic effects can impact the response to selection and how the stand composition can be used to explore these effects and enhance forest yield. We used two clonal trials of eucalyptus hybrids implanted in a randomized complete block design with 24 replications, containing a single tree per plot evaluated for mean annual increment at 3 and 6 years. We focused on partitioning the genotypic variation into direct and indirect genotypic effects based on competition intensity factors. We identified aggressive, homeostatic, and sensitive clones based on the magnitude of indirect genotypic effects. Considering indirect genotypic effects, for mean annual increment, the total heritability decreased from 0.25 to 0.10 for 3 years and from 0.30 to 0.14 for 6 years, respectively. We proposed a workflow that uses the direct and indirect genotypic effects to predict the mean values for clonal composite combinations and to select the one with the highest yield. Our methodology considered spatial variation and interplot competition that can affect the total heritable estimates and response to selection in forest trials. The clones were classified as aggressive, homeostatic, and sensitive, according to their deviation from the indirect genotypic effects means. Our methodology enabled the extraction of useful information and predict many clonal composites' performance, which can be used in recommending genotypes to be planted in mixtures.
650 $aClonal variation
650 $aEucalyptus
650 $aForest trees
650 $aPlant breeding
650 $aPlant competition
650 $aQuantitative genetics
700 1 $aCHAVES, S. F. S.
700 1 $aBHERING, L. L.
700 1 $aALVES, R. S.
700 1 $aTAKAHASHI, E. K.
700 1 $aSOUSA, J. E.
700 1 $aRESENDE, M. D. V. de
700 1 $aLEITE, F. P.
700 1 $aGEZAN, S. A.
700 1 $aVIANA, J. M. S.
700 1 $aFERNANDES, S. B.
700 1 $aDIAS, K. O. G.
773 $tForest Ecology and Management$gv. 548, Article 121393, 2023.
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