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 | Acesso ao texto completo restrito à biblioteca da Embrapa Cerrados. Para informações adicionais entre em contato com cpac.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Agrossilvipastoril; Embrapa Cerrados; Embrapa Gado de Leite; Embrapa Pecuária Sul; Embrapa Tabuleiros Costeiros. |
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Data corrente: |
16/08/2021 |
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Data da última atualização: |
23/08/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
FERREIRA, F. M.; ROCHA, J. R. do A. S. de C.; BHERING, L. L.; FERNANDES, F. D.; LEDO, F. J. da S.; RANGEL, J. H. de A.; KOPP, M. M.; CAMARA, T. M. M.; SILVA, V. Q. R. da; MACHADO, J. C. |
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Afiliação: |
FILIPE MANOEL FERREIRA, Universidade Federal de Viçosa; JOÃO ROMERO DO AMARAL SANTOS DE CARVALHO ROCHA, Universidade Federal de Viçosa; LEONARDO LOPES BHERING, Universidade Federal de Viçosa; FRANCISCO DUARTE FERNANDES, CPAC; FRANCISCO JOSE DA SILVA LEDO, CNPGL; JOSE HENRIQUE DE ALBUQUERQUE RANGEL, CPATC; MAURICIO MARINI KOPP, CPPSUL; TASSIANO MAXWELL MARINHO CAMARA, CPATC; VANESSA QUITETE RIBEIRO DA SILVA, CPAMT; JUAREZ CAMPOLINA MACHADO, CNPGL. |
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Título: |
Optimal harvest number and genotypic evaluation of total dry biomass, stability, and adaptability of elephant grass clones for bioenergy purposes. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Biomass and Bioenergy, v. 149, 106104, 2021. |
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Idioma: |
Inglês |
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Conteúdo: |
For elephant grass breeding for bioenergy purposes, selection should be based on repeated harvests to maximize the selection efficiency. Selecting genotypes with high total dry biomass, associated with high adaptability and stability, is extremely important. Thus, the objective of this study was to estimate the minimum number of harvests required for total dry biomass to enable an accurate selection of elephant grass clones in multiple environmental trials. Additionally, the relative performance of genetic values (HMRPGV) and centroid methods were used to cluster and rank the best clones on the basis of their total dry biomass, adaptability, and stability. Nineteen clones and two checks were evaluated in six environments. Two or four harvests were performed in each environment. A mixed model methodology (REML/BLUP) was used to estimate the variance components and predict the genotypic values. The repeatability coefficient for total dry biomass was 0.20. The accuracies of performing two and four harvests were 59% and 72%, respectively. The increase in selection efficiency by performing two, four, and seven harvests instead of just one was 29%, 57%, and 76%, respectively. Finally, the clones were clustered into four classes according to their total dry biomass, adaptability, and stability. We conclude that at least seven harvests are required to achieve an accuracy of 80%, and that the HMRPGV and centroid methods are practical, visual, and reliable for simultaneously clustering, ranking, and selection of clones based on their adaptability, stability, and total dry biomass production. MenosFor elephant grass breeding for bioenergy purposes, selection should be based on repeated harvests to maximize the selection efficiency. Selecting genotypes with high total dry biomass, associated with high adaptability and stability, is extremely important. Thus, the objective of this study was to estimate the minimum number of harvests required for total dry biomass to enable an accurate selection of elephant grass clones in multiple environmental trials. Additionally, the relative performance of genetic values (HMRPGV) and centroid methods were used to cluster and rank the best clones on the basis of their total dry biomass, adaptability, and stability. Nineteen clones and two checks were evaluated in six environments. Two or four harvests were performed in each environment. A mixed model methodology (REML/BLUP) was used to estimate the variance components and predict the genotypic values. The repeatability coefficient for total dry biomass was 0.20. The accuracies of performing two and four harvests were 59% and 72%, respectively. The increase in selection efficiency by performing two, four, and seven harvests instead of just one was 29%, 57%, and 76%, respectively. Finally, the clones were clustered into four classes according to their total dry biomass, adaptability, and stability. We conclude that at least seven harvests are required to achieve an accuracy of 80%, and that the HMRPGV and centroid methods are practical, visual, and reliable for simultaneously clusterin... Mostrar Tudo |
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Palavras-Chave: |
Cenchrus purpureus; Mixed model; Permanent environment. |
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Thesagro: |
Bioenergia; Biomassa; Capim Elefante; Gramínea Forrageira. |
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Thesaurus Nal: |
Energy crops; Genotype-environment interaction; Models. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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Marc: |
LEADER 02674naa a2200349 a 4500
001 2133810
005 2021-08-23
008 2021 bl uuuu u00u1 u #d
100 1 $aFERREIRA, F. M.
245 $aOptimal harvest number and genotypic evaluation of total dry biomass, stability, and adaptability of elephant grass clones for bioenergy purposes.$h[electronic resource]
260 $c2021
520 $aFor elephant grass breeding for bioenergy purposes, selection should be based on repeated harvests to maximize the selection efficiency. Selecting genotypes with high total dry biomass, associated with high adaptability and stability, is extremely important. Thus, the objective of this study was to estimate the minimum number of harvests required for total dry biomass to enable an accurate selection of elephant grass clones in multiple environmental trials. Additionally, the relative performance of genetic values (HMRPGV) and centroid methods were used to cluster and rank the best clones on the basis of their total dry biomass, adaptability, and stability. Nineteen clones and two checks were evaluated in six environments. Two or four harvests were performed in each environment. A mixed model methodology (REML/BLUP) was used to estimate the variance components and predict the genotypic values. The repeatability coefficient for total dry biomass was 0.20. The accuracies of performing two and four harvests were 59% and 72%, respectively. The increase in selection efficiency by performing two, four, and seven harvests instead of just one was 29%, 57%, and 76%, respectively. Finally, the clones were clustered into four classes according to their total dry biomass, adaptability, and stability. We conclude that at least seven harvests are required to achieve an accuracy of 80%, and that the HMRPGV and centroid methods are practical, visual, and reliable for simultaneously clustering, ranking, and selection of clones based on their adaptability, stability, and total dry biomass production.
650 $aEnergy crops
650 $aGenotype-environment interaction
650 $aModels
650 $aBioenergia
650 $aBiomassa
650 $aCapim Elefante
650 $aGramínea Forrageira
653 $aCenchrus purpureus
653 $aMixed model
653 $aPermanent environment
700 1 $aROCHA, J. R. do A. S. de C.
700 1 $aBHERING, L. L.
700 1 $aFERNANDES, F. D.
700 1 $aLEDO, F. J. da S.
700 1 $aRANGEL, J. H. de A.
700 1 $aKOPP, M. M.
700 1 $aCAMARA, T. M. M.
700 1 $aSILVA, V. Q. R. da
700 1 $aMACHADO, J. C.
773 $tBiomass and Bioenergy$gv. 149, 106104, 2021.
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Registro original: |
Embrapa Cerrados (CPAC) |
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Biblioteca |
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Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
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| Acesso ao texto completo restrito à biblioteca da Embrapa Pecuária Sudeste. Para informações adicionais entre em contato com cppse.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Gado de Leite; Embrapa Pecuária Sudeste. |
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Data corrente: |
17/11/2022 |
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Data da última atualização: |
29/11/2022 |
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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: |
CONGIO, G. F. S.; BANNINK, A.; MAYORGA, O. L.; RODRIGUES, J. P. P.; BOUGOUIN, A.; KEBREAB, E.; CARVALHO, P. C. F.; BERCHIELLI, T. T.; MERCADANTE, M. E. Z.; VALADARES-FILHO, S. C.; BORGES, A. L. C. C.; BERNDT, A.; RODRIGUES, P. H. M.; KU-VERA, J. C.; MOLINA-BOTERO, I. C.; ARANGO, J.; REIS, R. A.; POSADA-OCHOA , S. L.; TOMICH, T. R.; CASTELÁN-ORTEGA, O. A.; MARCONDES, M. I.; GÓMEZ, C.; RIBEIRO-FILHO, H. M. N.; GERE, J. I.; ARIZA-NIETO, C.; GIRALDO, L. A.; GONDA, H.; CERÓN-CUCCHI, M. E.; HERNÁNDEZ, O.; RICCI, P.; HRISTOV, A. N. |
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Afiliação: |
GUILHERMO F. S. CONGIO, Escola Superior de Agricultura Luiz de Queiroz; ANDRÉ BANNINK, Wageningen University & Research; OLGA L. MAYORGA, Colombian Corporation for Agricultural Research; JOÃO P. P. RODRIGUES, Universidade Federal Rural do Rio de Janeiro; ADELINE BOUGOUIN, University of California; ERMIAS KEBREAB, University of California; PAULO C. F. CARVALHO, Universidade Federal do Rio de Janeiro; TELMA T. BERCHIELLI, Universidade Estadual Paulista; MARIA E. Z. MERCADANTE, Agência Paulista de Tecnologia do Agronegócio; SEBASTIÃO C. VALADARES-FILHO, Universidade Federal de Viçosa; ANA L. C. C. BORGES, Universidade Federal de Minas Gerais; ALEXANDRE BERNDT, CPPSE; PAULO H. M. RODRIGUES, Universidade de São Paulo; JUAN C. KU-VERA, University of Yucatan; ISABEL C. MOLINA-BOTERO, National Agrarian University La Molina; JACOBO ARANGO, International Center for Tropical Agriculture; RICARDO A. REIS, Universidade Estadual Paulista; SANDRA L. POSADA-OCHOA, University of Antioquia; THIERRY RIBEIRO TOMICH, CNPGL; OCTAVIO A. CASTELÁN-ORTEGA, Autonomous University of the State of Mexico; MARCOS I. MARCONDES, Washington State University; CARLOS GÓMEZ, National Agrarian University La Molina; HENRIQUE M. N. RIBEIRO-FILHO, Universidade do Estado de Santa Catarina; JOSÉ I. GERE, National Technological University; CLAUDIA ARIZA-NIETO, Colombian Corporation for Agricultural Research; LUIS A. GIRALDO, National University of Colombia; HORACIO GONDA, Swedish University of Agricultural Sciences; MARÍA E. CERÓN-CUCCHI, National Institute of Agricultural Technology; OLEGARIO HERNÁNDEZ, National Institute of Agricultural Technology; PATRICIA RICCI, National Institute of Agricultural Technology; ALEXANDER N. HRISTOV, The Pennsylvania State University. |
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Título: |
Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Science of the Total Environment, v. 856, 159128, 2023. |
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DOI: |
http://dx.doi.org/10.1016/j.scitotenv.2022.159128 |
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Idioma: |
Inglês |
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Conteúdo: |
On-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended prac-tices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the pre-dictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (similar to 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % >= DFC >= 54 %), and low-forage (50 % >= DFC) diets. Feed intake and average daily gain (ADG) were the main pre-dictors of CH4 emission (g d-1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg-1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Sim-ple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equa-tions to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data. MenosOn-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended prac-tices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the pre-dictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (similar to 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % >= DFC >= 54 %), and low-forage (50 % >= DFC) diets. Feed intake and average daily gain (ADG) were the main pre-dictors of CH4 emission (g d-1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg-1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Sim-ple and multiple regression models including ADG were accurate and a feasible option to predict CH4 ... Mostrar Tudo |
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Thesagro: |
Bovino; Efeito Estufa; Gado de Corte; Gás; Metano; Regressão Linear. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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Marc: |
LEADER 03486naa a2200565 a 4500
001 2148394
005 2022-11-29
008 2023 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org/10.1016/j.scitotenv.2022.159128$2DOI
100 1 $aCONGIO, G. F. S.
245 $aImproving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries.$h[electronic resource]
260 $c2023
520 $aOn-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended prac-tices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the pre-dictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (similar to 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % >= DFC >= 54 %), and low-forage (50 % >= DFC) diets. Feed intake and average daily gain (ADG) were the main pre-dictors of CH4 emission (g d-1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg-1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Sim-ple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equa-tions to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data.
650 $aBovino
650 $aEfeito Estufa
650 $aGado de Corte
650 $aGás
650 $aMetano
650 $aRegressão Linear
700 1 $aBANNINK, A.
700 1 $aMAYORGA, O. L.
700 1 $aRODRIGUES, J. P. P.
700 1 $aBOUGOUIN, A.
700 1 $aKEBREAB, E.
700 1 $aCARVALHO, P. C. F.
700 1 $aBERCHIELLI, T. T.
700 1 $aMERCADANTE, M. E. Z.
700 1 $aVALADARES-FILHO, S. C.
700 1 $aBORGES, A. L. C. C.
700 1 $aBERNDT, A.
700 1 $aRODRIGUES, P. H. M.
700 1 $aKU-VERA, J. C.
700 1 $aMOLINA-BOTERO, I. C.
700 1 $aARANGO, J.
700 1 $aREIS, R. A.
700 1 $aPOSADA-OCHOA , S. L.
700 1 $aTOMICH, T. R.
700 1 $aCASTELÁN-ORTEGA, O. A.
700 1 $aMARCONDES, M. I.
700 1 $aGÓMEZ, C.
700 1 $aRIBEIRO-FILHO, H. M. N.
700 1 $aGERE, J. I.
700 1 $aARIZA-NIETO, C.
700 1 $aGIRALDO, L. A.
700 1 $aGONDA, H.
700 1 $aCERÓN-CUCCHI, M. E.
700 1 $aHERNÁNDEZ, O.
700 1 $aRICCI, P.
700 1 $aHRISTOV, A. N.
773 $tScience of the Total Environment$gv. 856, 159128, 2023.
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