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| 2. |  | RODRÍGUEZ, S.; SILVA, P.; BENTANCOR, O.; GAMBA, F.; PRITSCH, C. Diversidad en las respuestas de defensa a Cochliobulus sativus en cuatro genotipos de cebada. In: JORNADA BIANUAL DE FITOPATOLOGÍA, 2., 2012, Montevideo, UY. Impacto de la intensificación de los cultivos en el desarrollo de las enfermedades: pósters inéditos. Montevideo: SUFIT, 2012. p. 31.| Biblioteca(s): INIA La Estanzuela. |
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| 5. | | ROSS, S.; PECHI, E.; SCALTRITTI, J.; RODRÍGUEZ, S.; CASTILLO, A.; SPERONI, G.; VIGNALE, B.; CABRERA, D.; SPERANZA, P. Estudios sobre rizogénesis en Guayabo del País. In: INIA (Instituto Nacional de Investigación Agropecuaria), Programa Nacional Producción Frutícola. 7° Encuentro Nacional sobre Frutos Nativos. Colonia (UY): INIA, 2015. p. 15 (INIA Serie Actividades de Difusión; 745)| Biblioteca(s): INIA Las Brujas. |
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| 6. | | VIGNALE, B.; PECHI, E.; SCALTRITTI, J.P.; RODRÍGUEZ, S.; SPERONI, G.; SPERANZA, P.; ROSS, S.; CABRERA, D.; CASTILLO, A. "Estudios sobre rizogénesis en ?Guayabo del país". [Presentación oral] In: UNIVERSIDAD DE LA REPÚBLICA (UDELAR)/ FACULTAD DE AGRONOMÍA; MGAP/ DGF (DIRECCIÓN GENERAL FORESTAL); INIA, PROGRAMA NACIONAL PRODUCCIÓN FRUTÍCOLA. 7° Encuentro Nacional sobre Frutos Nativos. Colonia: Estación Experimental "Alberto Boerger", INIA La Estanzuela, 9 y 10 de abril de 2015.| Biblioteca(s): INIA Las Brujas. |
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| 10. | | MEDEROS, A.; MONTOSSI, F.; DE BARBIERI, I.; GALLINAL, M.; RISSO, D.; LUZARDO, S.; SAN JULIÁN, R.; RODRIGUEZ, S.; ROVIRA, F.; BARRETTO, J.; LEVRATTO, J.; GONZALEZ, H.; SILVA, L. Evaluación del efecto de la especie forrajera y los taninos condensados en el control de los parásitos gastrointestinales, la producción y la calidad de carne ovina y lana de corderos pesados en engorde. ln: INIA TACUAREMBÓ. ESTACIÓN EXPERIMENTAL GLENCOE. Día de campo. Producción animal, pasturas y forestal. Estación Experimental Glencoe, Paysandú, octubre, 2005. Tacuarembó (Uruguay): INIA, 2005. p. 28-31 (INIA Serie Actividades de Difusión ; 431)| Biblioteca(s): INIA Tacuarembó. |
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| 12. | | ROSS, S.; PECHI, E.; SCALTRITTI, J.P.; RODRÍGUEZ, S.; SPERONI, G.; VIGNALE, B.; CABRERA, D.; SPERANZA, P. Formación de raíces adventicias en "Guayabo del país". In: INIA (INSTITUTO NACIONAL DE INVESTIGACIÓN AGROPECUARIA); PROGRAMA NACIONAL PRODUCCIÓN FRUTÍCOLA. 8° Encuentro Nacional sobre Frutos Nativos. Las Brujas, Canelones (Uruguay): INIA, 2017. p. 14 (Actividades de Difusión; 772)| Biblioteca(s): INIA Las Brujas. |
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| 13. | | MEDEROS, A.; MONTOSSI, F.; CUADRO, W.; DE BARBIERI, I.; GALLINAL, M.M.; RISSO, D.; RODRIGUEZ, S.; IGLESIAS, P.; RAMOS, N. Manejo de la nutrición y uso de forrajes bioactivos en el control de los parásitos gastrointestinales de los ovinos. In: BERRETTA, E.; MONTOSSI, F.; BRITO, G. (Ed.). Alternativas tecnológicas para los sistemas ganaderos del basalto. Montevideo, UY: INIA, 2014. p. 451-473 (Serie Técnica; 217)| Biblioteca(s): INIA Tacuarembó; INIA Treinta y Tres. |
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| 16. | | MEDEROS, A.; MONTOSSI, F.; BANCHERO, G.; CUADRO, R.; LARA, S.; DE BARBIERI, I.; GALLINAL, M.; RODRIGUEZ, S.; RISSO, D. Parásitos gastrointestinales en ovinos: resistencia antihelmíntica y métodos alternativos de control con énfasis en alimentación y forrajes bioactivos. [Presentación oral]. En: Seminario de Actualización Técnica: Alternativas Tecnológicas para los Sistemas Ganaderos de Basalto. ( 2015, Salto Grande, Uruguay) Salto Grande, UY, INIA Tacuarembó; INIA Salto Grande.| Biblioteca(s): INIA Tacuarembó. |
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| 17. | | RODRÍGUEZ S., N. Rol de las praderas en los sistemas de producción en Chile Revista INIA de Investigaciones Agronómicas, 1992, no.1:2, p.227-240. 227-240 Revista INIA de Investigaciones Agronómicas; v.1 (2) ln: Simposio Sustentabilidad de las Rotaciones Cultivo-Pasturas en el Cono Sur, 1991 may 27-29 : Montevideo [Memorias]. Montevideo (Uruguay): INIA, 1992.| Biblioteca(s): INIA La Estanzuela. |
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| 18. | | SILVA, P.; TORRES-PUYO, C.; RODRÍGUEZ, S.; PEREYRA, S.; GAMBA, F.; PRITSCH, C. Variabilidad patogénica, diversidad genética y estructura poblacional de Cochliobulus sativus (anamorfo Bipolaris sorokiniana) en Uruguay. In: JORNADA BIANUAL DE FITOPATOLOGÍA, 2., 2012, Montevideo, UY. Impacto de la intensificación de los cultivos en el desarrollo de las enfermedades. Montevideo: SUFIT, 2012. p. 14.| Biblioteca(s): INIA La Estanzuela. |
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| Registros recuperados : 18 | |
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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. |
|
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. |
|
Asunto categoría : |
A50 Investigación agraria |
|
Marc : |
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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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