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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
Fecha : |
07/05/2021 |
Actualizado : |
07/05/2021 |
Tipo de producción científica : |
Abstracts/Resúmenes |
Autor : |
NÚÑEZ, L.; JAURENA, M.; DIAZ, S.; LATTANZI, F.; BREMM,C. |
Afiliación : |
LAURA NÚÑEZ SUÁREZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay.; MARTIN ALEJANDRO JAURENA BARRIOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SAULO SEBASTIAN DIAZ OLIVERA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO A. LATTANZI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Unidad Federal do Río Grande do Sul (UFRGS), Porto Alegre, Brasil. |
Título : |
Atributos de la estructura del campo natural que explican la ganancia animal. [Structure attributes rangelands related to animal performance]. |
Complemento del título : |
PP 113. |
Fecha de publicación : |
2018 |
Fuente / Imprenta : |
In: Congreso Argentino de Producción Animal, 41°, 16-19 oct. 2018. Mar del Plata, (Argentina): Asociación Argentina de Producción Animal (AAPA). |
Páginas : |
p.281. |
Serie : |
(Revista Argentina de Producción Animal; 2018; 38; Suppl.1). |
Idioma : |
Español |
Contenido : |
Conclusiones:
La caracterización de la proporción de parches de altura de la pastura predice una alta proporción de los cambios en el peso de los animales en pastoreo. Estos primeros resultados evidencian que existen atributos de la estructura del forraje relacionadas con la respuesta animal, a partir de lo cual es necesario analizar más experimentos y series temporales para logar un modelo de predicción robusto que oriente en el manejo de la pastura. |
Palabras claves : |
PASTOREO CONTINUO. |
Thesagro : |
CAMPO NATURAL; MANEJO DE PASTURAS. |
Asunto categoría : |
-- |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/15543/1/Revista-Argentina-de-Produccion-Animal-2018.v.38.supl.1.PP-113..pdf
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Marc : |
LEADER 01288nam a2200217 a 4500 001 1062026 005 2021-05-07 008 2018 bl uuuu u01u1 u #d 100 1 $aNÚÑEZ, L. 245 $aAtributos de la estructura del campo natural que explican la ganancia animal. [Structure attributes rangelands related to animal performance].$h[electronic resource] 260 $aIn: Congreso Argentino de Producción Animal, 41°, 16-19 oct. 2018. Mar del Plata, (Argentina): Asociación Argentina de Producción Animal (AAPA).$c2018 300 $ap.281. 490 $a(Revista Argentina de Producción Animal; 2018; 38; Suppl.1). 520 $aConclusiones: La caracterización de la proporción de parches de altura de la pastura predice una alta proporción de los cambios en el peso de los animales en pastoreo. Estos primeros resultados evidencian que existen atributos de la estructura del forraje relacionadas con la respuesta animal, a partir de lo cual es necesario analizar más experimentos y series temporales para logar un modelo de predicción robusto que oriente en el manejo de la pastura. 650 $aCAMPO NATURAL 650 $aMANEJO DE PASTURAS 653 $aPASTOREO CONTINUO 700 1 $aJAURENA, M. 700 1 $aDIAZ, S. 700 1 $aLATTANZI, F. 700 1 $aBREMM,C.
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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 : |
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