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Biblioteca (s) : |
INIA La Estanzuela. |
Fecha : |
20/05/2015 |
Actualizado : |
20/05/2015 |
Tipo de producción científica : |
Artículos en Revistas Agropecuarias |
Autor : |
BANCHERO, G.; GANZÁBAL, A.; FERNÁNDEZ, M.E.; ARES, VIRGINIA; VAZ MARTINS, D. |
Afiliación : |
GEORGGET ELIZABETH BANCHERO HUNZIKER, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANDRES RICARDO GANZABAL PLANINICH, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIA EUGENIA FERNANDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; VIRGINIA ARES, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; DANIEL VAZ MARTINS GIGENA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Bajas ganacias en otoño: una brecha importante para el ciclo de engorde de corderos. |
Fecha de publicación : |
2006 |
Fuente / Imprenta : |
Revista del Plan Agropecuario, n. 117, p. 34-37, 2006. |
ISSN : |
0797-3047 |
Idioma : |
Español |
Palabras claves : |
CALIDAD DE FORRAJE; DISPONIBILIDAD DE FORRAJE; EFECTO OTOÑAL; EFICIENCIA DE CONVERSIÓN DE ALIMENTO; PÉRDIDA DE PESO OTOÑAL; TASA DE CRECIMIENTO OTOÑAL; TRASTORNOS METABÓLICOS. |
Thesagro : |
COMPORTAMIENTO ANIMAL; CORDEROS; ENGORDE; PASTOREO; SUPLEMENTACIÓN. |
Asunto categoría : |
L02 Alimentación animal |
Marc : |
LEADER 00928naa a2200313 a 4500 001 1052660 005 2015-05-20 008 2006 bl uuuu u00u1 u #d 022 $a0797-3047 100 1 $aBANCHERO, G. 245 $aBajas ganacias en otoño$buna brecha importante para el ciclo de engorde de corderos. 260 $c2006 650 $aCOMPORTAMIENTO ANIMAL 650 $aCORDEROS 650 $aENGORDE 650 $aPASTOREO 650 $aSUPLEMENTACIÓN 653 $aCALIDAD DE FORRAJE 653 $aDISPONIBILIDAD DE FORRAJE 653 $aEFECTO OTOÑAL 653 $aEFICIENCIA DE CONVERSIÓN DE ALIMENTO 653 $aPÉRDIDA DE PESO OTOÑAL 653 $aTASA DE CRECIMIENTO OTOÑAL 653 $aTRASTORNOS METABÓLICOS 700 1 $aGANZÁBAL, A. 700 1 $aFERNÁNDEZ, M.E. 700 1 $aARES, VIRGINIA 700 1 $aVAZ MARTINS, D. 773 $tRevista del Plan Agropecuario$gn. 117, p. 34-37, 2006.
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| Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
Registro completo
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Biblioteca (s) : |
INIA Las Brujas; INIA Treinta y Tres. |
Fecha actual : |
12/11/2015 |
Actualizado : |
09/10/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
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. |
Afiliación : |
MARIA VIRGINIA PRAVIA NIN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration. |
Fecha de publicación : |
2015 |
Fuente / Imprenta : |
Agricultural and Forest Meteorology, 2015, v.214-215, p. 483-493. |
ISSN : |
0168-1923 |
DOI : |
10.1016/j.agrformet.2015.09.013 |
Idioma : |
Inglés |
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 |
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 : |
LEADER 05363naa a2201417 a 4500 001 1053856 005 2019-10-09 008 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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