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Biblioteca(s): |
Embrapa Solos. |
Data corrente: |
22/11/2002 |
Data da última atualização: |
22/11/2002 |
Autoria: |
SALDARRIAGA, J. G.; UHL, C. |
Título: |
Recovery of forest vegetation following slash-and-burn agriculture in the Upper Rio Negro |
Ano de publicação: |
1991 |
Fonte/Imprenta: |
In: GOMEZ-POMPA, A.; WHITMORE, T.C.; HADLEY, M. Rain forest regeneration and management. Paris: UNESCO/Parthenon Publishing, 1991. (Man and the biophere series, v.6). |
Páginas: |
p.303-312 |
Idioma: |
Inglês |
Conteúdo: |
Changes in species composition, forest structure and biomass have been studied at 24 tropical forest sites along the Upper Rio Negro region of Colombia and Venezuela. Stands were selected from the tierra firme forests (non-flooded) to represent a chronosequence of succession following slash-and-burn agricultural practices. After abandonment, the number of species increases from early successional to mature forests. The speciescomposition of the mature forests depends on a small fraction of primary species that survive from early stages of succession and on the introduction of many primary species at later stages of succession. Small areas disturbed by slash-and-burn agriculture recover their original species composition, but the time required varies, dependingon the intensity and frequency of disturbance in the area. On a large scale, the forest is a mosaic of different aged patches and structural characteristics, with high variability among stands, depending on soils, micro-relief, species composition, and disturbance dynamics. Approximately 140-200 years is required for an abandoned farm to attain the biomass values comparable to those of a mature forest. Recovery is thus five to seven times longer in the Upper Rio Negro than in other tropical areas in South America. |
Thesagro: |
Conservação; Ecologia; Floresta Tropical; Manejo; Silvicultura. |
Categoria do assunto: |
-- |
Marc: |
LEADER 01964naa a2200205 a 4500 001 1327859 005 2002-11-22 008 1991 bl uuuu u00u1 u #d 100 1 $aSALDARRIAGA, J. G. 245 $aRecovery of forest vegetation following slash-and-burn agriculture in the Upper Rio Negro 260 $c1991 300 $ap.303-312 520 $aChanges in species composition, forest structure and biomass have been studied at 24 tropical forest sites along the Upper Rio Negro region of Colombia and Venezuela. Stands were selected from the tierra firme forests (non-flooded) to represent a chronosequence of succession following slash-and-burn agricultural practices. After abandonment, the number of species increases from early successional to mature forests. The speciescomposition of the mature forests depends on a small fraction of primary species that survive from early stages of succession and on the introduction of many primary species at later stages of succession. Small areas disturbed by slash-and-burn agriculture recover their original species composition, but the time required varies, dependingon the intensity and frequency of disturbance in the area. On a large scale, the forest is a mosaic of different aged patches and structural characteristics, with high variability among stands, depending on soils, micro-relief, species composition, and disturbance dynamics. Approximately 140-200 years is required for an abandoned farm to attain the biomass values comparable to those of a mature forest. Recovery is thus five to seven times longer in the Upper Rio Negro than in other tropical areas in South America. 650 $aConservação 650 $aEcologia 650 $aFloresta Tropical 650 $aManejo 650 $aSilvicultura 700 1 $aUHL, C. 773 $tIn: GOMEZ-POMPA, A.; WHITMORE, T.C.; HADLEY, M. Rain forest regeneration and management. Paris: UNESCO/Parthenon Publishing, 1991. (Man and the biophere series$gv.6).
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Embrapa Solos (CNPS) |
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Registro Completo
Biblioteca(s): |
Embrapa Milho e Sorgo. |
Data corrente: |
25/03/2024 |
Data da última atualização: |
25/03/2024 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
BARRETO, C. A. V.; DIAS, K. O. das G.; SOUSA, I. C. de; AZEVEDO, C. F.; NASCIMENTO, A. C. C.; GUIMARAES, L. J. M.; GUIMARÃES, C. T.; PASTINA, M. M.; NASCIMENTO, M. |
Afiliação: |
CYNTHIA APARECIDA VALIATI BARRETO, UNIVERSIDADE FEDERAL DE VIÇOSA; KAIO OLIMPIO DAS GRAÇAS DIAS, UNIVERSIDADE FEDERAL DE VIÇOSA; ITHALO COELHO DE SOUSA, UNIVERSIDADE FEDERAL DE RONDÔNIA; CAMILA FERREIRA AZEVEDO, UNIVERSIDADE FEDERAL DE VIÇOSA; ANA CAROLINA CAMPANA NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA; LAURO JOSE MOREIRA GUIMARAES, CNPMS; CLAUDIA TEIXEIRA GUIMARAES, CNPMS; MARIA MARTA PASTINA, CNPMS; MOYSÉS NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA. |
Título: |
Genomic prediction in multi-environment trials in maize using statistical and machine learning methods. |
Ano de publicação: |
2024 |
Fonte/Imprenta: |
Scientific Reports, v. 14, 1062, 2024. |
DOI: |
https://doi.org/10.1038/s41598-024-51792-3 |
Idioma: |
Inglês |
Conteúdo: |
In the context of multi-environment trials (MET), genomic prediction is proposed as a tool that allows the prediction of the phenotype of single cross hybrids that were not tested in field trials. This approach saves time and costs compared to traditional breeding methods. Thus, this study aimed to evaluate the genomic prediction of single cross maize hybrids not tested in MET, grain yield and female flowering time. We also aimed to propose an application of machine learning methodologies in MET in the prediction of hybrids and compare their performance with Genomic best linear unbiased prediction (GBLUP) with non-additive effects. Our results highlight that both methodologies are efficient and can be used in maize breeding programs to accurately predict the performance of hybrids in specific environments. The best methodology is case-dependent, specifically, to explore the potential of GBLUP, it is important to perform accurate modeling of the variance components to optimize the prediction of new hybrids. On the other hand, machine learning methodologies can capture non-additive effects without making any assumptions at the outset of the model. Overall, predicting the performance of new hybrids that were not evaluated in any field trials was more challenging than predicting hybrids in sparse test designs. |
Palavras-Chave: |
Predição genômica. |
Thesagro: |
Hibrido; Milho; Produtividade. |
Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1163114/1/Genomic-prediction-in-multi-environment-trials-in-maize.pdf
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Marc: |
LEADER 02163naa a2200277 a 4500 001 2163114 005 2024-03-25 008 2024 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.1038/s41598-024-51792-3$2DOI 100 1 $aBARRETO, C. A. V. 245 $aGenomic prediction in multi-environment trials in maize using statistical and machine learning methods.$h[electronic resource] 260 $c2024 520 $aIn the context of multi-environment trials (MET), genomic prediction is proposed as a tool that allows the prediction of the phenotype of single cross hybrids that were not tested in field trials. This approach saves time and costs compared to traditional breeding methods. Thus, this study aimed to evaluate the genomic prediction of single cross maize hybrids not tested in MET, grain yield and female flowering time. We also aimed to propose an application of machine learning methodologies in MET in the prediction of hybrids and compare their performance with Genomic best linear unbiased prediction (GBLUP) with non-additive effects. Our results highlight that both methodologies are efficient and can be used in maize breeding programs to accurately predict the performance of hybrids in specific environments. The best methodology is case-dependent, specifically, to explore the potential of GBLUP, it is important to perform accurate modeling of the variance components to optimize the prediction of new hybrids. On the other hand, machine learning methodologies can capture non-additive effects without making any assumptions at the outset of the model. Overall, predicting the performance of new hybrids that were not evaluated in any field trials was more challenging than predicting hybrids in sparse test designs. 650 $aHibrido 650 $aMilho 650 $aProdutividade 653 $aPredição genômica 700 1 $aDIAS, K. O. das G. 700 1 $aSOUSA, I. C. de 700 1 $aAZEVEDO, C. F. 700 1 $aNASCIMENTO, A. C. C. 700 1 $aGUIMARAES, L. J. M. 700 1 $aGUIMARÃES, C. T. 700 1 $aPASTINA, M. M. 700 1 $aNASCIMENTO, M. 773 $tScientific Reports$gv. 14, 1062, 2024.
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