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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
25/02/1999 |
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Data da última atualização: |
19/12/2007 |
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Autoria: |
KUHN, E.; LUDWIG, T. |
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Título: |
VIP-MDBS: a logic multidatabase system. |
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Ano de publicação: |
1994 |
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Fonte/Imprenta: |
In: HURSON, A. R.; BRIGHT, M. W.; PAKZAD, S. H. (Ed.). Multidatabase systems: an advanced solution for global information sharing. Los Alamitos: IEEE Computer Society Press,1994. |
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Páginas: |
p.280-291. |
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Idioma: |
Inglês |
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Conteúdo: |
We present a multidatabase management system built in Viena Integrated Prolog(VIP) for cooperative management of autonomous databases. Data in different databases may differ with respect to naming, structures and value types. VIP-MDBS(VIP multidatabase system) allows the ability to manipulate them jointly and in a non-procedural way. Its features are similar to those of the relational multidatabase language MSQL, but adapted to logic programming. We introduce the concept of so-called semantic relations, a concept which stems from the extension of global views by deductiveness. VIP-MDBS allows for representation of intentional data and formulation of recursive multiple queries. |
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Palavras-Chave: |
Banco de dados distribuidos; Distributed databases. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01258naa a2200169 a 4500
001 1007125
005 2007-12-19
008 1994 bl uuuu u00u1 u #d
100 1 $aKUHN, E.
245 $aVIP-MDBS$ba logic multidatabase system.
260 $c1994
300 $ap.280-291.
520 $aWe present a multidatabase management system built in Viena Integrated Prolog(VIP) for cooperative management of autonomous databases. Data in different databases may differ with respect to naming, structures and value types. VIP-MDBS(VIP multidatabase system) allows the ability to manipulate them jointly and in a non-procedural way. Its features are similar to those of the relational multidatabase language MSQL, but adapted to logic programming. We introduce the concept of so-called semantic relations, a concept which stems from the extension of global views by deductiveness. VIP-MDBS allows for representation of intentional data and formulation of recursive multiple queries.
653 $aBanco de dados distribuidos
653 $aDistributed databases
700 1 $aLUDWIG, T.
773 $tIn: HURSON, A. R.; BRIGHT, M. W.; PAKZAD, S. H. (Ed.). Multidatabase systems: an advanced solution for global information sharing. Los Alamitos: IEEE Computer Society Press,1994.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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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: |
LEADER 03114naa a2200517 a 4500
001 2121007
005 2020-04-20
008 2020 bl uuuu u00u1 u #d
022 $a0011-183X
024 7 $a10.1002/csc2.20048$2DOI
100 1 $aRAMIREZ-VILLEGAS, J.
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
650 $aClimate change
650 $aCrops
650 $aFood security
650 $aPlant adaptation
650 $aSimulation models
650 $aClima
653 $aCrop improvement
653 $aCrop modeling
700 1 $aMOLERO MILAN, A.
700 1 $aALEXANDROV, N.
700 1 $aASSENG, S.
700 1 $aCHALLINOR, A. J.
700 1 $aCROSSA, J.
700 1 $aVAN EEUWIJK, F.
700 1 $aGHANEM, M. E.
700 1 $aGRENIER, C.
700 1 $aHEINEMANN, A. B.
700 1 $aWANG, J.
700 1 $aJULIANA, P.
700 1 $aKEHEL, Z.
700 1 $aKHOLOVA, J
700 1 $aKOO, J.
700 1 $aPEQUENO, D.
700 1 $aQUIROZ, R.
700 1 $aREBOLLEDO, M. C.
700 1 $aSUKUMARAN, S.
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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