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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
26/04/2017 |
Actualizado : |
13/12/2022 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
BAETHGEN, W.; BERTERRETCHE, M.; GIMÉNEZ, A. |
Afiliación : |
WALTER E. BAETHGEN, IRI, The Earth Institute, Columbia University, USA.; MERCEDES BERTERRETCHE, SNIA, Ministry of Agriculture and Fisheries, Uruguay; AGUSTIN EDUARDO GIMÉNEZ FUREST, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Informing decisions and policy: the national agricultural information system of Uruguay. [Informando decisões e políticas: o sistema nacional de informação
agrícola do Uruguai]. |
Fecha de publicación : |
2016 |
Fuente / Imprenta : |
Agrometeoros, Passo Fundo, v.24, n.1, p.97-112, 2016. |
Idioma : |
Inglés |
Notas : |
Número Temático em Agrometeorologia Operacional. Article history: Received 11 August 2015 // Accepted 17 August 2015 |
Contenido : |
ABSTRACT.
Agricultural production systems confront the challenge of achieving sustainable intensification, i.e., increase productivity without affecting the environment in ways that could compromise the development of future generations. In many developing countries the availability of information for assisting public and private stakeholders to achieve this goal is often not a critical limitation. Moving the relevant information from research institutes to extension agents, policy makers, agribusinesses and farmers has been a more frequent limitation. New approaches, tools and information systems are needed to effectively embed the knowledge generated in the research systems into actual decisions and in the elaboration of public policy. These new approaches and tools must consider, understand and map the existing knowledge networks through which the information flows and reaches public and private. The new systems should incorporate the scientific and technological advances achieved in the different
disciplines and generate integrated knowledge for effectively assisting actual decisions and policies. This article describes the process that led to the establishment of an information and decision support system in Uruguay (SNIA) that is being led by the IRI in collaboration with INIA and the Ministry of Agriculture. The article discusses the
general approach and goals that the IRI and its partners are using for establishing the SNIA, and introduces some of its characteristics, the components and capabilities.
Finally, the article contains examples of the information, products and tools that are being included in the SNIA.
© 2016 SBAgro. All rights reserved
.-.-.-.-.-.-.-.-.-.-.-.-.-.-.
RESUMO.
Os sistemas de produção agrícola enfrentam o desafio de alcançar a intensificação sustentável, ou seja, aumentar a produtividade sem afetar o ambiente de uma forma
que possa comprometer o desenvolvimento das gerações futuras. Em muitos países em desenvolvimento a disponibilidade de informações para auxiliar os interessados,
tanto os agentes públicos quanto os privados, para atingir esse objetivo, muitas vezes, não é uma limitação crítica. Levar as informações relevantes de institutos de
pesquisa para os agentes de extensão, agricultores, agroindústrias e formuladores de política tem sido uma limitação mais frequente. Novos enfoques, ferramentas e
sistemas de informação são necessários para efetivamente incorporar o conhecimento gerado nos sistemas de pesquisa em decisões reais e na elaboração de políticas
públicas. Estas novas abordagens e ferramentas devem levara em consideração, entender e mapear as redes de conhecimento existentes, através das quais a informação
flui e atinge os agentes públicos e privados. Os novos sistemas devem incorporar os avanços científicos e tecnológicos alcançados nas diferentes disciplinas e
gerar conhecimento integrado para efetivamente auxiliar políticas e decisões reais. Este artigo descreve o processo que levou ao estabelecimento de um sistema de informação de apoio à tomada de decisão no Uruguai (SNIA) que está sendo liderado pelo IRI em colaboração com INIA e o Ministério da Agricultura. O artigo discute a
abordagem geral e os objetivos que o IRI e seus parceiros estão usando para estabelecer SNIA e introduz algumas das suas características, componentes e recursos.
Por último, o artigo apresenta exemplos de informações, produtos e ferramentas que estão sendo incluídas no SNIA.
© 2016 SBAgro. Todos os direitos reservados MenosABSTRACT.
Agricultural production systems confront the challenge of achieving sustainable intensification, i.e., increase productivity without affecting the environment in ways that could compromise the development of future generations. In many developing countries the availability of information for assisting public and private stakeholders to achieve this goal is often not a critical limitation. Moving the relevant information from research institutes to extension agents, policy makers, agribusinesses and farmers has been a more frequent limitation. New approaches, tools and information systems are needed to effectively embed the knowledge generated in the research systems into actual decisions and in the elaboration of public policy. These new approaches and tools must consider, understand and map the existing knowledge networks through which the information flows and reaches public and private. The new systems should incorporate the scientific and technological advances achieved in the different
disciplines and generate integrated knowledge for effectively assisting actual decisions and policies. This article describes the process that led to the establishment of an information and decision support system in Uruguay (SNIA) that is being led by the IRI in collaboration with INIA and the Ministry of Agriculture. The article discusses the
general approach and goals that the IRI and its partners are using for establishing the SNIA, and introduces some of its characteristics... Presentar Todo |
Palabras claves : |
CLIMATE CHANGE SCENARIOS; DECISION SUPPORT SYSTEM; EXPERT SYSTEMS; REMOTE SENSING; SEASONAL CLIMATE FORECASTS; SENSORAMIENTO REMOTO; SISTEMA DE INFORMACION GEOGRAFICO. |
Thesagro : |
CAMBIO CLIMÁTICO; INIA; IRI; SENSORES REMOTOS; SISTEMA DE INFORMACION GEOGRAFICO; SNIA. |
Asunto categoría : |
-- |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/6730/1/Agrometeoros.-201724885-114679-1-SM-Baethgen-W.-et-al.pdf
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Marc : |
LEADER 04705naa a2200313 a 4500 001 1057141 005 2022-12-13 008 2016 bl uuuu u00u1 u #d 100 1 $aBAETHGEN, W. 245 $aInforming decisions and policy$bthe national agricultural information system of Uruguay. [Informando decisões e políticas: o sistema nacional de informação agrícola do Uruguai].$h[electronic resource] 260 $c2016 500 $aNúmero Temático em Agrometeorologia Operacional. Article history: Received 11 August 2015 // Accepted 17 August 2015 520 $aABSTRACT. Agricultural production systems confront the challenge of achieving sustainable intensification, i.e., increase productivity without affecting the environment in ways that could compromise the development of future generations. In many developing countries the availability of information for assisting public and private stakeholders to achieve this goal is often not a critical limitation. Moving the relevant information from research institutes to extension agents, policy makers, agribusinesses and farmers has been a more frequent limitation. New approaches, tools and information systems are needed to effectively embed the knowledge generated in the research systems into actual decisions and in the elaboration of public policy. These new approaches and tools must consider, understand and map the existing knowledge networks through which the information flows and reaches public and private. The new systems should incorporate the scientific and technological advances achieved in the different disciplines and generate integrated knowledge for effectively assisting actual decisions and policies. This article describes the process that led to the establishment of an information and decision support system in Uruguay (SNIA) that is being led by the IRI in collaboration with INIA and the Ministry of Agriculture. The article discusses the general approach and goals that the IRI and its partners are using for establishing the SNIA, and introduces some of its characteristics, the components and capabilities. Finally, the article contains examples of the information, products and tools that are being included in the SNIA. © 2016 SBAgro. All rights reserved .-.-.-.-.-.-.-.-.-.-.-.-.-.-. RESUMO. Os sistemas de produção agrícola enfrentam o desafio de alcançar a intensificação sustentável, ou seja, aumentar a produtividade sem afetar o ambiente de uma forma que possa comprometer o desenvolvimento das gerações futuras. Em muitos países em desenvolvimento a disponibilidade de informações para auxiliar os interessados, tanto os agentes públicos quanto os privados, para atingir esse objetivo, muitas vezes, não é uma limitação crítica. Levar as informações relevantes de institutos de pesquisa para os agentes de extensão, agricultores, agroindústrias e formuladores de política tem sido uma limitação mais frequente. Novos enfoques, ferramentas e sistemas de informação são necessários para efetivamente incorporar o conhecimento gerado nos sistemas de pesquisa em decisões reais e na elaboração de políticas públicas. Estas novas abordagens e ferramentas devem levara em consideração, entender e mapear as redes de conhecimento existentes, através das quais a informação flui e atinge os agentes públicos e privados. Os novos sistemas devem incorporar os avanços científicos e tecnológicos alcançados nas diferentes disciplinas e gerar conhecimento integrado para efetivamente auxiliar políticas e decisões reais. Este artigo descreve o processo que levou ao estabelecimento de um sistema de informação de apoio à tomada de decisão no Uruguai (SNIA) que está sendo liderado pelo IRI em colaboração com INIA e o Ministério da Agricultura. O artigo discute a abordagem geral e os objetivos que o IRI e seus parceiros estão usando para estabelecer SNIA e introduz algumas das suas características, componentes e recursos. Por último, o artigo apresenta exemplos de informações, produtos e ferramentas que estão sendo incluídas no SNIA. © 2016 SBAgro. Todos os direitos reservados 650 $aCAMBIO CLIMÁTICO 650 $aINIA 650 $aIRI 650 $aSENSORES REMOTOS 650 $aSISTEMA DE INFORMACION GEOGRAFICO 650 $aSNIA 653 $aCLIMATE CHANGE SCENARIOS 653 $aDECISION SUPPORT SYSTEM 653 $aEXPERT SYSTEMS 653 $aREMOTE SENSING 653 $aSEASONAL CLIMATE FORECASTS 653 $aSENSORAMIENTO REMOTO 653 $aSISTEMA DE INFORMACION GEOGRAFICO 700 1 $aBERTERRETCHE, M. 700 1 $aGIMÉNEZ, A. 773 $tAgrometeoros, Passo Fundo$gv.24, n.1, p.97-112, 2016.
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INIA Las Brujas (LB) |
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| Acceso al texto completo restringido a Biblioteca INIA Treinta y Tres. Por información adicional contacte bibliott@inia.org.uy. |
Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
Fecha actual : |
28/03/2016 |
Actualizado : |
24/09/2018 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
A - 1 |
Autor : |
BASSU, S.; BRISSON, N.; DURAND, J.L.; BOOTE, K.; LIZASO, J.; JONES, J.W.; ROSENZWEIG, C.; RUANE, A.C.; ADAM, M.; BARON, C.; BASSO, B.; BIERNATH, C.; BOOGAARD, H.; CONIJN, S.; CORBEELS, M.L; DERYNG, D.; SANTIS, G. DE; GAYLER, S.; GRASSINI, P.; HATFIELD, J.; HOEK, S.; IZAURRALDE, C.; JONGSCHAAP, R.; KEMANIAN, A.R.; KERSEBAUM, C.KIM, S-H.; KUMAR, N.; MAKOWSKI, D.; MÜLLER, C.; NENDEL, C.; PRIESACK, E.; PRAVIA, V.; SAU, F.; SHCHERBAK, I.; TAO, F.; TEXEIRA, E.; TIMLIN, D.; WAHA, K. |
Afiliación : |
MARIA VIRGINIA PRAVIA NIN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Plant Science, The Pennsylvania State University, USA. |
Título : |
How do various maize crop models vary in their responses to climate change factors? |
Fecha de publicación : |
2014 |
Fuente / Imprenta : |
Global Change Biology, 2014, v.20(7), p. 2301-2320. |
DOI : |
10.1111/gcb.12520 |
Idioma : |
Inglés |
Notas : |
Article history: Received 7 June 2013 and accepted 2 December 2013, published 2014. |
Contenido : |
Abstract:
Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania).
While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data forcalibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha1 per °C. Doubling [CO2] from 360 to 720 lmol mol1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information. MenosAbstract:
Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania).
While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data forcalibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha1 per °C. Doubling [CO2] from 360 to 720 lmol mol1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2]... Presentar Todo |
Palabras claves : |
AGMIP; CARBON DIOXIDE; CLIMATE; CO2; GRAIN YIELD; MAIZE; MODEL INTERCOMPARISON; MODELIZACIÓN DE CULTIVOS; SIMULATION MODELS; TEMPERATURE. |
Thesagro : |
CLIMA; DIOXIDO DE CARBONO; INCERTIDUMBRE; MAÍZ; MODELOS DE SIMULACIÓN; TEMPERATURA. |
Asunto categoría : |
U10 Métodos matemáticos y estadísticos |
Marc : |
LEADER 03684naa a2200769 a 4500 001 1054517 005 2018-09-24 008 2014 bl uuuu u00u1 u #d 024 7 $a10.1111/gcb.12520$2DOI 100 1 $aBASSU, S. 245 $aHow do various maize crop models vary in their responses to climate change factors?$h[electronic resource] 260 $c2014 500 $aArticle history: Received 7 June 2013 and accepted 2 December 2013, published 2014. 520 $aAbstract: Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data forcalibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha1 per °C. Doubling [CO2] from 360 to 720 lmol mol1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information. 650 $aCLIMA 650 $aDIOXIDO DE CARBONO 650 $aINCERTIDUMBRE 650 $aMAÍZ 650 $aMODELOS DE SIMULACIÓN 650 $aTEMPERATURA 653 $aAGMIP 653 $aCARBON DIOXIDE 653 $aCLIMATE 653 $aCO2 653 $aGRAIN YIELD 653 $aMAIZE 653 $aMODEL INTERCOMPARISON 653 $aMODELIZACIÓN DE CULTIVOS 653 $aSIMULATION MODELS 653 $aTEMPERATURE 700 1 $aBRISSON, N. 700 1 $aDURAND, J.L. 700 1 $aBOOTE, K. 700 1 $aLIZASO, J. 700 1 $aJONES, J.W. 700 1 $aROSENZWEIG, C. 700 1 $aRUANE, A.C. 700 1 $aADAM, M. 700 1 $aBARON, C. 700 1 $aBASSO, B. 700 1 $aBIERNATH, C. 700 1 $aBOOGAARD, H. 700 1 $aCONIJN, S. 700 1 $aCORBEELS, M.L 700 1 $aDERYNG, D. 700 1 $aSANTIS, G. DE 700 1 $aGAYLER, S. 700 1 $aGRASSINI, P. 700 1 $aHATFIELD, J. 700 1 $aHOEK, S. 700 1 $aIZAURRALDE, C. 700 1 $aJONGSCHAAP, R. 700 1 $aKEMANIAN, A.R. 700 1 $aKERSEBAUM, C.KIM, S-H. 700 1 $aKUMAR, N. 700 1 $aMAKOWSKI, D. 700 1 $aMÜLLER, C. 700 1 $aNENDEL, C. 700 1 $aPRIESACK, E. 700 1 $aPRAVIA, V. 700 1 $aSAU, F. 700 1 $aSHCHERBAK, I. 700 1 $aTAO, F. 700 1 $aTEXEIRA, E. 700 1 $aTIMLIN, D. 700 1 $aWAHA, K. 773 $tGlobal Change Biology, 2014$gv.20(7), p. 2301-2320.
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