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| Registros recuperados : 17 | |
| 6. |  | SEGNINI, A; POSADAS, A.; QUIROZ, R.; MILORI, D. M. B. P.; VAZ, C. M. P.; MARTIN NETO, L. Soil carbon stocks and stability across an altitudinal gradient in southern Peru. Journal of Soil and water conservation, Ankeny, v. 66, n. 4, p. 213-220, 2011.| Biblioteca(s): Embrapa Instrumentação. |
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| 9. | | MILORI, D. M. B. P.; SEGNINI, A.; SILVA, W. T. L. da; POSADAS, A.; MARES, V.; QUIROZ, R.; MARTIN NETO, L. Emerging techniques for soil carbon measurements. In: WOLLENBERG, E.; NIHART, A.; TAPIO-BISTROM, M.-L.; GRIEG-GRAN, M. (Ed.). Climate change mitigation and agriculture. London; New York: Earthscan, 2012. p.252-262| Biblioteca(s): Embrapa Instrumentação. |
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| 10. | | SEGNINI, A.; POSADAS, A.; QUIROZ, R.; MILORI, D. M. B. P.; SAAB, S. C.; VAZ, C. M. P.; MARTIN NETO, L. Avaliação de sistema fotônico portátil para estudos de sequestro de carbono de solos: um estudo de caso nos andes peruanos. In: CONGRESSO BRASILEIRO DE CIENCIA DO SOLO, 32., 2009, Fortaleza, CE. O solo e a produção de bioenergia: perspectivas e desafios. [S. l.]: SBCS, 2009. 1 CD-ROM.| Biblioteca(s): Embrapa Instrumentação. |
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| 11. | | SEGNINI, A.; POSADAS, A.; SOUZA, A. A.; NOVOTNY, E. H.; QUIROZ, R.; BONAGAMBA, T. J.; MILORI, D. M. B. P.; SILVA, W. T. L. da. Caracterização de turfas dos Andes Peruanos por espectroscopia de RMN 13C. In: JORNADA CIENTÍFICA - EMBRAPA SÃO CARLOS, 2., 2010, São Carlos, SP. Anais... São Carlos: Embrapa Instrumentação Agropecuária: Embrapa Pecuária Sudeste, 2010. p. 52. (Embrapa Instrumentação Agropecuária. Documentos, 50).| Biblioteca(s): Embrapa Instrumentação. |
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| 12. | | SEGNINI, A.; SOUZA, A. A.; NOVOTNY, E. H.; BONAGAMBA, T. J.; POSADAS, A.; QUIROZ, R.; MILORI, D. M. B. P.; MARTIN NETO, L. Characterization of peatland soils from the high andes by 13C NMR spectroscopy. In: MEETING OF THE INTERNATIONAL HUMIC SUBSTANCES SOCIETY, 15., 2010, Puerto de la Cruz, ES. Advances in natural organic matter and humic substances research 2008-2010: proceedings. [S. l.: s. n.], 2010. v. 2. p. 352-355. J. A. González-Pérez, F. J. González-Vila, G. Almendros, Eds.| Biblioteca(s): Embrapa Instrumentação. |
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| 13. | | SEGNINI, A.; SOUZA, A. A. de; NOVOTNY, E. H.; MILORI, D. M. B. P.; SILVA, W. T. L. da; BONAGAMBA, T. J.; POSADAS, A.; QUIROZ, R. Characterization of peatland soils from the high andes through 13C nuclear magnetic resonance spectroscopy. Soil Science Society of America Journal, v. 77, n. 2, p. 673-679, Mar. 2013.| Biblioteca(s): Embrapa Solos. |
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| 14. | | SERGINI, A.; SOUZA, A. A.; NOVOTNY, E. H.; MILORI, D. M. B. P.; SILVA, W. T. L. da; BONAGAMBA, T. J.; POSADAS, A.; QUIROZ, R. Characterization of peatland soils from the high Andes through ¹³C magnetic resonance spectroscopy. Soil Science Society of America Journal, [S.l.] , v. 77, n. 2, p. 673-679, 2013. doi: 10.2136/ssaj2012.0291.| Biblioteca(s): Embrapa Instrumentação. |
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| 15. | | SEGNINI, A.; POSADAS, A.; QUIROZ, R.; MILORI, D. M. B. P.; SAAB, S. C.; MARTIN NETO, L.; VAZ, C. M. P. Spectroscopic assessment of soil organic matter in wetlands from the high andes. Soil Science Society of America journal, Madison, v. 74, n. 6, p. 2246-2253, 2010.| Biblioteca(s): Embrapa Instrumentação. |
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| 16. | | DIXON, J.; XIAOYUN, L.; MSANGI, S.; DIMARANAN, B.; AMEDE, T.; BOSSIO, D.; CEBALLOS, H.; OSPINA, B.; HOWELER, R.; REDDY, B. V. S.; ABAIDOO, R.; TIMSINA, J.; CRISSMAN, C.; MARES, V.; QUIROZ, R.; LEON-VELARDE, C.; HERRERO, M.; PETERS, M.; WHITE, D.; SZONYI, J. Feed, food and fuel: competition and potential impacts on small crop-livestock-energy farming systems. [S.l]: CGIAR, 2008. 74 p. A study commissioned by the CGIAR Systemwide Livestock Program.| Biblioteca(s): Embrapa Agroenergia. |
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| 17. | | RAMIREZ-VILLEGAS, J.; MOLERO MILAN, A.; ALEXANDROV, N.; ASSENG, S.; CHALLINOR, A. J.; CROSSA, J.; VAN EEUWIJK, F.; GHANEM, M. E.; GRENIER, C.; HEINEMANN, A. B.; WANG, J.; JULIANA, P.; KEHEL, Z.; KHOLOVA, J; KOO, J.; PEQUENO, D.; QUIROZ, R.; REBOLLEDO, M. C.; SUKUMARAN, S.; VADEZ, V.; WHITE, J. W.; REYNOLDS, M. CGIAR modeling approaches for resource-constrained scenarios: I. Accelerating crop breeding for a changing climate. Crop Science, 2020. Online Version of Record before inclusion in an issue.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| Registros recuperados : 17 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
06/03/2020 |
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Data da última atualização: |
20/04/2020 |
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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: |
RAMIREZ-VILLEGAS, J.; MOLERO MILAN, A.; ALEXANDROV, N.; ASSENG, S.; CHALLINOR, A. J.; CROSSA, J.; VAN EEUWIJK, F.; GHANEM, M. E.; GRENIER, C.; HEINEMANN, A. B.; WANG, J.; JULIANA, P.; KEHEL, Z.; KHOLOVA, J; KOO, J.; PEQUENO, D.; QUIROZ, R.; REBOLLEDO, M. C.; SUKUMARAN, S.; VADEZ, V.; WHITE, J. W.; REYNOLDS, M. |
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Afiliação: |
JULIAN RAMIREZ-VILLEGAS, CIAT; ANABEL MOLERO MILAN, CIMMYT; NICKOLAI ALEXANDROV, IRRI; SENTHOLD ASSENG, UNIVERSITY OF FLORIDA, Gainesville-FL; ANDREW J. CHALLINOR, UNIVERSITY OF LEEDS, Leeds-UK; JOSE CROSSA, CIMMYT; FREED VAN EEUWIJK, WAGENINGEN UNIVERSITY, The Netherlands; MICHEL EDMOND GHANEM, ICARDA; CECILE GRENIER, CIAT; ALEXANDRE BRYAN HEINEMANN, CNPAF; JIANKANG WANG, INSTITUTE OF CROP SCIENCES, Beijing; PHILOMIN JULIANA, CIMMYT; ZAKARIA KEHEL, ICARDA; JANA KHOLOVA, ICRISAT; JAWOO KOO, IFPRI; DIEGO PEQUENO, CIMMYT; ROBERTO QUIROZ, CIP; MARIA C. REBOLLEDO, CIAT; SIVAKUMAR SUKUMARAN, CIMMYT; VINCENT VADEZ, ICRISAT; JEFFREY W. WHITE, USDA-ARS; MATTHEW REYNOLDS, CIMMYT. |
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Título: |
CGIAR modeling approaches for resource-constrained scenarios: I. Accelerating crop breeding for a changing climate. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Crop Science, 2020. |
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ISSN: |
0011-183X |
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DOI: |
10.1002/csc2.20048 |
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Idioma: |
Inglês |
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Notas: |
Online Version of Record before inclusion in an issue. |
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Conteúdo: |
Crop improvement efforts aiming at increasing crop production (quantity, quality) and adapting to climate change have been subject of active research over the past years. But, the question remains 'to what extent can breeding gains be achieved under a changing climate, at a pace sufficient to usefully contribute to climate adaptation, mitigation and food security?'. Here, we address this question by critically reviewing how model-based approaches can be used to assist breeding activities, with particular focus on all CGIAR (formerly the Consultative Group on International Agricultural Research but now known simply as CGIAR) breeding programs. Crop modeling can underpin breeding efforts in many different ways, including assessing genotypic adaptability and stability, characterizing and identifying target breeding environments, identifying tradeoffs among traits for such environments, and making predictions of the likely breeding value of the genotypes. Crop modeling science within the CGIAR has contributed to all of these. However, much progress remains to be done if modeling is to effectively contribute to more targeted and impactful breeding programs under changing climates. In a period in which CGIAR breeding programs are undergoing a major modernization process, crop modelers will need to be part of crop improvement teams, with a common understanding of breeding pipelines and model capabilities and limitations, and common data standards and protocols, to ensure they follow and deliver according to clearly defined breeding products. This will, in turn, enable more rapid and better-targeted crop modeling activities, thus directly contributing to accelerated and more impactful breeding efforts. MenosCrop improvement efforts aiming at increasing crop production (quantity, quality) and adapting to climate change have been subject of active research over the past years. But, the question remains 'to what extent can breeding gains be achieved under a changing climate, at a pace sufficient to usefully contribute to climate adaptation, mitigation and food security?'. Here, we address this question by critically reviewing how model-based approaches can be used to assist breeding activities, with particular focus on all CGIAR (formerly the Consultative Group on International Agricultural Research but now known simply as CGIAR) breeding programs. Crop modeling can underpin breeding efforts in many different ways, including assessing genotypic adaptability and stability, characterizing and identifying target breeding environments, identifying tradeoffs among traits for such environments, and making predictions of the likely breeding value of the genotypes. Crop modeling science within the CGIAR has contributed to all of these. However, much progress remains to be done if modeling is to effectively contribute to more targeted and impactful breeding programs under changing climates. In a period in which CGIAR breeding programs are undergoing a major modernization process, crop modelers will need to be part of crop improvement teams, with a common understanding of breeding pipelines and model capabilities and limitations, and common data standards and protocols, to ensure they follo... Mostrar Tudo |
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Palavras-Chave: |
Crop improvement; Crop modeling. |
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Thesagro: |
Clima. |
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Thesaurus NAL: |
Breeding; Climate change; Crops; Food security; Plant adaptation; Simulation models. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/211586/1/CNPAF-2020-cs.pdf
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Marc: |
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245 $aCGIAR modeling approaches for resource-constrained scenarios$bI. Accelerating crop breeding for a changing climate.$h[electronic resource]
260 $c2020
500 $aOnline Version of Record before inclusion in an issue.
520 $aCrop improvement efforts aiming at increasing crop production (quantity, quality) and adapting to climate change have been subject of active research over the past years. But, the question remains 'to what extent can breeding gains be achieved under a changing climate, at a pace sufficient to usefully contribute to climate adaptation, mitigation and food security?'. Here, we address this question by critically reviewing how model-based approaches can be used to assist breeding activities, with particular focus on all CGIAR (formerly the Consultative Group on International Agricultural Research but now known simply as CGIAR) breeding programs. Crop modeling can underpin breeding efforts in many different ways, including assessing genotypic adaptability and stability, characterizing and identifying target breeding environments, identifying tradeoffs among traits for such environments, and making predictions of the likely breeding value of the genotypes. Crop modeling science within the CGIAR has contributed to all of these. However, much progress remains to be done if modeling is to effectively contribute to more targeted and impactful breeding programs under changing climates. In a period in which CGIAR breeding programs are undergoing a major modernization process, crop modelers will need to be part of crop improvement teams, with a common understanding of breeding pipelines and model capabilities and limitations, and common data standards and protocols, to ensure they follow and deliver according to clearly defined breeding products. This will, in turn, enable more rapid and better-targeted crop modeling activities, thus directly contributing to accelerated and more impactful breeding efforts.
650 $aBreeding
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700 1 $aVADEZ, V.
700 1 $aWHITE, J. W.
700 1 $aREYNOLDS, M.
773 $tCrop Science, 2020.
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Embrapa Arroz e Feijão (CNPAF) |
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