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| 5. | | SCHILD, C.; ALBORNOZ, A.; DIAZ, S.; SILVEIRA, J.; RIET-CORREA, F.; JAURENA, M. Condición corporal de vacas primíparas en función de la estructura del campo natural y la concentración de fósforo en el suelo. [Resumen]. In: CONGRESO ASOCIACIÓN URUGUAYA DE PRODUCCIÓN ANIMAL (6º, Marzo, 2018, Tacuarembó, Uruguay). Resúmenes. Tacuarembó: AUPA, 2018. p. 79| Biblioteca(s): INIA Tacuarembó. |
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| 8. | | PERAZA, P.; RINCÓN, G.; SOTELO-SILVEIRA, J.; CIAPPESONI, G.; ISLAS-TREJO, A.; MEDRANO, J.F. Estudio mediante RNA-SEQ del transcriptoma de diferentes tejidos de ovinos resistentes y susceptibles a parásitos gastrointestinales. GGM 16 - COMUNICACIONES LIBRES - GGM. GENÓMICA Y GENÉTICA MOLECULAR In: JOURNAL OF BASIC & APPLIED GENETICS, 2016, Vol.27, Iss. 1 (Supp.). XVI LATIN AMERICAN CONGRESS OF GENETICS, IV CONGRESS OF THE URUGUAYAN SOCIETY OF GENETICS, XLIX ANNUAL MEETING OF THE GENETICS SOCIETY OF CHILE, XLV ARGENTINE CONGRESS OF GENETICS, 9-12 October 2016. PROCEEDINGS. Montevideo (Uruguay): SAG, 2016. p. 262| Biblioteca(s): INIA Las Brujas. |
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| 10. | | MENDINA, P.; SALDAÑA, J.; SILVEIRA, J.; MEDEROS, A.; MANTA, E.; DOMÍNGUEZ, L. Optimización de nuevos antihelmínticos en desarrollo: aproximación molecular. 2a. ed INIA TACUAREMBÓ. Seminario de Actualización, 19 de agosto, Tacuarembó, 2004. Parasitosis Gastrointestinales en Ovinos y Bovinos. Tacuarembó (Uruguay): INIA, 2004. p. 32-35 (INIA Serie Actividades de Difusión ; 369)| Biblioteca(s): INIA Tacuarembó. |
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| 11. | | JAURENA, M.; GIORELLO, D.; ANTUNEZ, J.; DIAZ, S.; SOSA, M.; ZAGO, R.; SILVEIRA, J.; SUAREZ, M.; ALBORNOZ, A. Efectos de la fertilización NP en la producción de forraje del campo natural en condiciones de riego y secano. In: INIA TACUAREMBÓ. JORNADA DE DIVULGACIÓN, 6 DE FEBRERO, 2015, TAMBORES, URUGUAY. Manejo de la fertilización de pasturas, forrajes y campo natural bajo riego suplementario. Tacuarembó (Uruguay): INIA, 2015. p. 9-16 (Serie Actividades de Difusión; 742)| Biblioteca(s): INIA Tacuarembó. |
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| 12. | | SIERRA, M.; PAZ, A.; NIETO, A.; DÍAZ, L.; SAAVEDRA, C.; SILVEIRA, J.; LÓPEZ, T.; DE GIUDA, M. El modelo KIM y su aplicación en Uruguay. Innovación abierta y transferencia de tecnología: los mercados globales y la lógica de las relaciones colaborativas. INNOTEC Gestión, 2011, ene-dic., no.3, p.15-23.| Biblioteca(s): INIA Las Brujas. |
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| 13. | | Gama, E.E.G.; Resende, M.; Silveira, J.S.M.; Santos, M.X.dos; Parentoni, S.N.; Magalhaes, P.C.; Pacheco, C.A.P.; Guimaraes, P.E.deO. Avaliaçao das populaçoes CMS 54 e CMS 04 sob condiçao de encharcamento [s.l.]: [s.n.], [s.f.]. v. 6, p. 200 EMPRAPA CNPMS Relatorio Técnico Anual ; 1992-1993| Biblioteca(s): INIA La Estanzuela. |
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| 14. | | PÉREZ GOMAR, E.; SILVEIRA, J.; MONTOSSI, F.; SILVEIRA, C.; LUZARDO, S.; CIAPPESONI, G.; DE BARBIERI, I.; CACERES, J.; CORREA, L.; SANCRISTOBAL, L. Producción intensiva de corderos pesados pastoreando una pastura de trébol rojo bajo riego en sistemas ganaderos del basalto. ln: INIA Tacuarembó. Unidad Experimental Glencoe. Efectos de la sequía: acciones realizadas y propuestas otoño-invernales. Tacuarembó (Uruguay): INIA, 2009. p. 8-11| Biblioteca(s): INIA Tacuarembó. |
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| 15. | | DE BARBIERI, I.; MONTOSSI, F.; BURJEL, F.; LUZARDO, S.; SOARES DE LIMA, J.M.; MARTÍNEZ, H.; FRUGONI, J.C.; SILVEIRA, J.; LEVRATTO, J.; PLATERO, P.; BOTTERO, D.; ROVIRA, F.; BENTANCURT, M.; CUADRO, P. Acondicionamiento diferencial de la esquila: Una herramienta en evaluación para potenciar el proceso de diferenciación y agregado de valor en lanas superfinas y extrafinas. In: INIA Tacuarembó. Unidad Experimental Glencoe. Avances obtenidos en el Proyecto Merino Fino del Uruguay: núcleo fundacional Unidad Experimental Glencoe, 1999-2006. Paysandú, 19 diciembre, 2006. Tacuarembó (Uruguay): INIA, 2006. p. 86-93 (INIA Serie Actividades de Difusión ; 475)| Biblioteca(s): INIA Tacuarembó. |
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| 16. | | LEITE, F.F.G.D.; ALVES, B.J.R.; NÓBREGA, G.N.; CORDEIRO, R.C.; CESÁRIO, F.V.; CAMBARERI, G.S.; FAVERIN, C.; DA SILVEIRA, J.G.; CIGANDA, V.; ARMACOLO, N.M.; ALECRIM, F.B.; RODRIGUES, R.D.A.R. Checking the progress of using the static chamber method for the measurement of greenhouse gases in Latin America. Carbon Management, 2021. doi: https://doi.org/10.1080/17583004.2021.1995503 Aragão Article history: Published online: 27 Oct 2021.
Correspondence author: T Renato de Aragão Ribeiro Rodrigues, e-mail: renato.rodrigues@embrapa.br
Additional information: The authors thank the scholarship supported by the CGIAR Research...| Biblioteca(s): INIA Las Brujas. |
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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas; INIA Treinta y Tres. |
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Fecha actual : |
12/11/2015 |
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Actualizado : |
09/10/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
MARCAIDA, M.; ASSENG, S.; EWERT, F.; BASSU, S.; DURAND, J.L.; LI, T.; MARTRE, P.; ADAM, M.; AGGARWAL, P.K.; ANGULO, C.; BARON, C.; BASSO, B.; BERTUZZI, P.; BIERNATH, C.; BOOGAARD, H.; BOOTE, K.J.; BOUMAN, B.; BREGAGLIO, S.; BRISSON, N.; BUIS, S.; CAMMARANO, D.; CHALLINOR, A.J.; CONFALONIERI, R.; CONIJN, J.G.; CORBEELS, M.; DERYNG, D.; DE SANCTIS, G.; DOLTRA, J.; FUMOTO, T.; GAYDON, D.; GAYLER, S.; GOLDBERG, R.; GRANT, R.F.; GRASSINI, P.; HATFIELD, J.L.; HASEGAWA, T.; HENG, L.; HOEK, S.; HOOKER, J.; HUNT, L.A.; INGWERSEN, J.; IZAURRALDE, R.C.; JONGSCHAAP, R.E.E.; JONES, J.W.; KEMANIAN, R.A.; KERSEBAUM, K.C.; KIM, S.-H.; LIZASO, J.; MÜLLER, C.; NAKAGAWA, H.; NARESH KUMAR, S.; NENDEL, C.; O'LEARY, G.J.; OLESEN, J.E.; ORIOL, P.; OSBORNE, T.M.; PALOSUO, T.; PRAVIA, V.; PRIESACK, E.; RIPOCHE, D.; ROSENZWEIG, C.; RUANE, A.C.; RUGET, F.; SAU, F.; SEMENOV, M.A.; SHCHERBAK, I.; SINGH, B.; SINGH, U.; SOO, H.K.; STEDUTO, P.; STÖCKLE, C.; STRATONOVITCH, P.; STRECK, T.; SUPIT, I.; TANG, L.; TAO, F.; TEIXEIRA, E.I.; THORBURN, P.; TIMLIN, D.; TRAVASSO, M.; RÖTTER, R.P.; WAHA, K.; WALLACH, D.; WHITE, J.W.; WILKENS, P.; WILLIAMS, J.R.; WOLF, J.; YIN, X.; YOSHIDA, H.; ZHANG, Z.; ZHU, Y. |
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Afiliación : |
MARIA VIRGINIA PRAVIA NIN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration. |
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Fecha de publicación : |
2015 |
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Fuente / Imprenta : |
Agricultural and Forest Meteorology, 2015, v.214-215, p. 483-493. |
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ISSN : |
0168-1923 |
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DOI : |
10.1016/j.agrformet.2015.09.013 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 6 March 2015 / Received in revised form 29 July 2015 / Accepted 20 September 2015 / Available online 1 October 2015. |
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Contenido : |
ABSTRACT.
Ensembles of process-based crop models are increasingly used to simulate crop growth for scenariosof temperature and/or precipitation changes corresponding to different projections of atmospheric CO2concentrations. This approach generates large datasets with thousands of simulated crop yield data. Suchdatasets potentially provide new information but it is difficult to summarize them in a useful way due totheir structural complexities. An associated issue is that it is not straightforward to compare crops and tointerpolate the results to alternative climate scenarios not initially included in the simulation protocols.Here we demonstrate that statistical models based on random-coefficient regressions are able to emulateensembles of process-based crop models. An important advantage of the proposed statistical models isthat they can interpolate between temperature levels and between CO2concentration levels, and canthus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulatedby 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to thesedatasets, and are then used to analyze the variability of the yield response to [CO2] and temperature.Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effectof a temperature increase of +2◦C in the considered sites. Compared to wheat, required levels of [CO2]increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulatingclimate change impacts increase more with temperature than with elevated [CO2].
© 2015 Elsevier B.V. All rights reserved. MenosABSTRACT.
Ensembles of process-based crop models are increasingly used to simulate crop growth for scenariosof temperature and/or precipitation changes corresponding to different projections of atmospheric CO2concentrations. This approach generates large datasets with thousands of simulated crop yield data. Suchdatasets potentially provide new information but it is difficult to summarize them in a useful way due totheir structural complexities. An associated issue is that it is not straightforward to compare crops and tointerpolate the results to alternative climate scenarios not initially included in the simulation protocols.Here we demonstrate that statistical models based on random-coefficient regressions are able to emulateensembles of process-based crop models. An important advantage of the proposed statistical models isthat they can interpolate between temperature levels and between CO2concentration levels, and canthus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulatedby 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to thesedatasets, and are then used to analyze the variability of the yield response to [CO2] and temperature.Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effectof a temperature increase of +2◦C in... Presentar Todo |
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Palabras claves : |
Climate change; CROP MODEL; Emulator; MAIZE; Meta-model; MODELIZACIÓN DE LOS CULTIVOS; RICE; Statistical model; WHEAT; Yield. |
|
Thesagro : |
ARROZ; CAMBIO CLIMÁTICO; MAÍZ; MODELOS ESTADISTICOS; TRIGO. |
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Asunto categoría : |
A50 Investigación agraria |
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Marc : |
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008 2015 bl uuuu u00u1 u #d
022 $a0168-1923
024 7 $a10.1016/j.agrformet.2015.09.013$2DOI
100 1 $aMARCAIDA, M.
245 $aA statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration.
260 $c2015
500 $aArticle history: Received 6 March 2015 / Received in revised form 29 July 2015 / Accepted 20 September 2015 / Available online 1 October 2015.
520 $aABSTRACT. Ensembles of process-based crop models are increasingly used to simulate crop growth for scenariosof temperature and/or precipitation changes corresponding to different projections of atmospheric CO2concentrations. This approach generates large datasets with thousands of simulated crop yield data. Suchdatasets potentially provide new information but it is difficult to summarize them in a useful way due totheir structural complexities. An associated issue is that it is not straightforward to compare crops and tointerpolate the results to alternative climate scenarios not initially included in the simulation protocols.Here we demonstrate that statistical models based on random-coefficient regressions are able to emulateensembles of process-based crop models. An important advantage of the proposed statistical models isthat they can interpolate between temperature levels and between CO2concentration levels, and canthus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulatedby 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to thesedatasets, and are then used to analyze the variability of the yield response to [CO2] and temperature.Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effectof a temperature increase of +2◦C in the considered sites. Compared to wheat, required levels of [CO2]increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulatingclimate change impacts increase more with temperature than with elevated [CO2]. © 2015 Elsevier B.V. All rights reserved.
650 $aARROZ
650 $aCAMBIO CLIMÁTICO
650 $aMAÍZ
650 $aMODELOS ESTADISTICOS
650 $aTRIGO
653 $aClimate change
653 $aCROP MODEL
653 $aEmulator
653 $aMAIZE
653 $aMeta-model
653 $aMODELIZACIÓN DE LOS CULTIVOS
653 $aRICE
653 $aStatistical model
653 $aWHEAT
653 $aYield
700 1 $aASSENG, S.
700 1 $aEWERT, F.
700 1 $aBASSU, S.
700 1 $aDURAND, J.L.
700 1 $aLI, T.
700 1 $aMARTRE, P.
700 1 $aADAM, M.
700 1 $aAGGARWAL, P.K.
700 1 $aANGULO, C.
700 1 $aBARON, C.
700 1 $aBASSO, B.
700 1 $aBERTUZZI, P.
700 1 $aBIERNATH, C.
700 1 $aBOOGAARD, H.
700 1 $aBOOTE, K.J.
700 1 $aBOUMAN, B.
700 1 $aBREGAGLIO, S.
700 1 $aBRISSON, N.
700 1 $aBUIS, S.
700 1 $aCAMMARANO, D.
700 1 $aCHALLINOR, A.J.
700 1 $aCONFALONIERI, R.
700 1 $aCONIJN, J.G.
700 1 $aCORBEELS, M.
700 1 $aDERYNG, D.
700 1 $aDE SANCTIS, G.
700 1 $aDOLTRA, J.
700 1 $aFUMOTO, T.
700 1 $aGAYDON, D.
700 1 $aGAYLER, S.
700 1 $aGOLDBERG, R.
700 1 $aGRANT, R.F.
700 1 $aGRASSINI, P.
700 1 $aHATFIELD, J.L.
700 1 $aHASEGAWA, T.
700 1 $aHENG, L.
700 1 $aHOEK, S.
700 1 $aHOOKER, J.
700 1 $aHUNT, L.A.
700 1 $aINGWERSEN, J.
700 1 $aIZAURRALDE, R.C.
700 1 $aJONGSCHAAP, R.E.E.
700 1 $aJONES, J.W.
700 1 $aKEMANIAN, R.A.
700 1 $aKERSEBAUM, K.C.
700 1 $aKIM, S.-H.
700 1 $aLIZASO, J.
700 1 $aMÜLLER, C.
700 1 $aNAKAGAWA, H.
700 1 $aNARESH KUMAR, S.
700 1 $aNENDEL, C.
700 1 $aO'LEARY, G.J.
700 1 $aOLESEN, J.E.
700 1 $aORIOL, P.
700 1 $aOSBORNE, T.M.
700 1 $aPALOSUO, T.
700 1 $aPRAVIA, V.
700 1 $aPRIESACK, E.
700 1 $aRIPOCHE, D.
700 1 $aROSENZWEIG, C.
700 1 $aRUANE, A.C.
700 1 $aRUGET, F.
700 1 $aSAU, F.
700 1 $aSEMENOV, M.A.
700 1 $aSHCHERBAK, I.
700 1 $aSINGH, B.
700 1 $aSINGH, U.
700 1 $aSOO, H.K.
700 1 $aSTEDUTO, P.
700 1 $aSTÖCKLE, C.
700 1 $aSTRATONOVITCH, P.
700 1 $aSTRECK, T.
700 1 $aSUPIT, I.
700 1 $aTANG, L.
700 1 $aTAO, F.
700 1 $aTEIXEIRA, E.I.
700 1 $aTHORBURN, P.
700 1 $aTIMLIN, D.
700 1 $aTRAVASSO, M.
700 1 $aRÖTTER, R.P.
700 1 $aWAHA, K.
700 1 $aWALLACH, D.
700 1 $aWHITE, J.W.
700 1 $aWILKENS, P.
700 1 $aWILLIAMS, J.R.
700 1 $aWOLF, J.
700 1 $aYIN, X.
700 1 $aYOSHIDA, H.
700 1 $aZHANG, Z.
700 1 $aZHU, Y.
773 $tAgricultural and Forest Meteorology, 2015$gv.214-215, p. 483-493.
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