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| 1. |  | BELANCHE, A.; HRISTOV, A.; VAN LINGEN, H.; DENMAN, S. E.; KEBREAB, E.; SCHWARM, A.; KREUZER, M.; NIU, M.; EUGÈNE, M.; NIDERKORN, V.; MARTIN, C.; ARCHIMÈDE, H.; MCGEE, M.; REYNOLDS, C. K.; CROMPTON, L. A.; BAYAT, A. R.; YU, Z.; BANNINK, A.; DIJKSTRA, J.; CHAVES, A. V.; CLARK, H.; MUETZEL, S.; LIND, V.; MOORBY, J. M.; ROOKE, J. A.; AUBRY, A.; ANTEZANA, W.; WANG, M.; HEGARTY, R.; HUTTON O. V.; HILL, J.; VERCOE, P. E.; SAVIAN, J.V.; ABDALLA, A. L.; SOLTAN, Y. A.; GOMES MONTEIRO, A. L.; KU-VERA, J. C.; JAURENA, G.; GÓMEZ-BRAVO, C. A.; MAYORGA, O. L.; CONGIO, G. F. S.; YÁÑEZ-RUIZ, D. R. Prediction of enteric methane emissions by sheep using an intercontinental database. Journal of Cleaner Production, 15 January 2023, Volume 384, 135523. OPEN ACCESS. doi: https://doi.org/10.1016/j.jclepro.2022.135523 Article history: Received 24 May 2022; Received in revised form 11 November 2022; Accepted 3 December 2022; Available online 9 December 2022.
Corresponding author: Belanche, A.; Department of Animal Production and Food Sciences, IA2,...| Biblioteca(s): INIA Las Brujas; INIA Treinta y Tres. |
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| 2. | | SOUZA CONGIO, G. F. DE; BANNINK, A.; MAYORGA, MOGOLLÓN, O. L.; NICOLOV HRISTOV, A.; JAURENA, G.; GONDA, H.; GERE, J. I.; CERÓN-CUCCHI, M.E.; ORTIZ-CHURA, A.; TIERI, M.P.; HERNÁNDEZ, O.; RICCI, P.; JULIARENA, M.P.; LOMBARDI, B.; ABDALLA, A.L.; ABDALLA-FILHO, A.L.; BERNDT, A.; ANCHAO OLIVEIRA, P. P.; HENRIQUE, F. L.; GOMEZ MONTEIRO, A.L.; BORGES, L. I.; RIBEIRO-FILHO, H.M.N.; RIBEIRO PEREIRA, L.G.; RIBEIRO TOMICH, T.; MAGALHAES CAMPOS, M.; SAMARINI MACHADO, F.; MARCONDES, M. I.; ZERLOTTI MERCADANTE, M. E.; SANNOMIYA SAKAMOTO, L.; GALVAO ALBUQUERQUE, L.; FACCIO CARVALHO, P. C. DE; ROSSETTO, J.; SAVIAN, J.V.; MAZZA RODRIGUES, P. H.; PERNA JÚNIOR, F.; MOREIRA, T.S.; MAURÍCIO, R. M.; PACHECO RODRIGUES, J.P.; CRUZ BORGES, A.L. DA C.; REIS E SILVA, R.; FERREIRA LAGE, H.; ANDRADE REIS, R.; RUGGIERI, A.C.; CARDOSO, A. DA SILVA; SILVA, S. CARNEIRO DA; BARBOSA CHIAVEGATO, M.; VALADARES-FILHO, S. DE CAMPOS; SILVA, F. A. DE SALES; ZANETTI, D.; BERCHIELLI, T.T.; DUARTE MESSANA, J.; MUÑOZ, C.; ARIZA-NIETO, C.J.; SIERRA-ALARCÓN, L.I.; GUALDRÓN-DUARTE, L.B.; MESTRA-VARGAS, L.I.; MOLINA-BOTERO, I. C.; BARAHONA-ROSALES, R.; ARANGO, J.; GAVIRIA-URIBE, X.; GIRALDO VALDERRAMA, L.A.; ROSERO-NOGUERA, J.R.; POSADA-OCHOA, S.L.; ABARCA-MONGE, SERGIO; SOTO-BLANCO, R.; KU-VERA, J.C.; JIMÉNEZ-OCAMPO, R.; FLORES-SANTIAGO, E. DEL J.; CASTELÁN-ORTEGA, O.A.; VÁZQUEZ-CARRILLO, M.F.; BENAOUDA, M.; GÓMEZ-BRAVO, C. A.; ALVARADO BOLOVICH, V.I.; DÍAZ CÉSPEDES, M.A.; ASTIGARRAGA, L. Enteric methane mitigation strategies for ruminant livestock systems in the Latin America and Caribbean region: a meta-analysis. Journal of Cleaner Production. 2021. v. 312, art. 127693, 13 p. Doi: https://doi.org/10.1016/j.jclepro.2021.127693 Article history: Receiived 22 December 2020; Received in revised form 24 April 2021; Accepted 25 May 2021. Available online 30 May 2021.
Latin America Methane Project Collaborators está integrada por 71 investigadores en este artículo.| Biblioteca(s): INIA Treinta y Tres. |
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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. |
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Fecha actual : |
05/02/2020 |
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Actualizado : |
05/02/2020 |
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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 : |
FORT, H.; DIEGUEZ, F.; HALTY, V.; SOARES DE LIMA, J.M. |
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Afiliación : |
HUGO FORT, Institute of Physics, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay; FRANCISCO DIEGUEZ, Instituto Plan Agropecuario, Montevideo, Uruguay; Departamento de Sistemas Ambientales, Facultad de Agronomía, UdelaR, Montevideo, Uruguay; VIRGINIA HALTY, Institute of Physics, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay; JUAN MANUEL SOARES DE LIMA LAPETINA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Two examples of application of ecological modeling to agricultural production: Extensive livestock farming and overyielding in grassland mixtures. |
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Fecha de publicación : |
2017 |
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Fuente / Imprenta : |
Ecological Modelling, 10 August 2017, Volume 357, Pages 23-34. Doi: http://dx.doi.org/10.1016/j.ecolmodel.2017.03.023 |
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ISSN : |
0304-3800 |
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DOI : |
10.1016/j.ecolmodel.2017.03.023 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 7 October 2016 / Received in revised form 8 March 2017 / Accepted 8 March 2017 / Available online 14 May 2017.
Corresponding author: Hugo Fort - email: hugo@fisica.edu.uy |
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Contenido : |
ABSTRACT.
Livestock production plays an important role in guaranteeing food security worldwide and has an important contribution to the economy of many countries. Precision livestock production (PLP), or the manipulation of livestock activity taking into account the different components of agroecosystems −pastures, cattle and pasture-cattle interactions−, has acquired growing importance in recent years to optimize productivity. Regarding the pasture component, multi-species pasture mixtures are commonly used worldwide to increase primary productivity and thereby secondary productivity. Here we present two examples of how the combination of experimental research and quantitative modelling can help to the development of protocols for food production optimization. The first example is based on a model we are currently developing for the whole agroecosystem in terms of a predator-prey dynamical system. The second example focuses on the pasture component and is based on a generalized Lotka-Volterra model we recently proposed for describing the mixture of herbaceous species in the pasture. We discuss the usefulness of both models, whose parameters were estimated from experiments or field work, as quantitative predicting tools. Population dynamic models are the common thread in this paper.
© 2017 Elsevier B.V. |
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Palabras claves : |
Agriculture optimization; Lotka-Volterra models. |
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Asunto categoría : |
L01 Ganadería |
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Marc : |
LEADER 02305naa a2200217 a 4500
001 1060771
005 2020-02-05
008 2017 bl uuuu u00u1 u #d
022 $a0304-3800
024 7 $a10.1016/j.ecolmodel.2017.03.023$2DOI
100 1 $aFORT, H.
245 $aTwo examples of application of ecological modeling to agricultural production$bExtensive livestock farming and overyielding in grassland mixtures.$h[electronic resource]
260 $c2017
500 $aArticle history: Received 7 October 2016 / Received in revised form 8 March 2017 / Accepted 8 March 2017 / Available online 14 May 2017. Corresponding author: Hugo Fort - email: hugo@fisica.edu.uy
520 $aABSTRACT. Livestock production plays an important role in guaranteeing food security worldwide and has an important contribution to the economy of many countries. Precision livestock production (PLP), or the manipulation of livestock activity taking into account the different components of agroecosystems −pastures, cattle and pasture-cattle interactions−, has acquired growing importance in recent years to optimize productivity. Regarding the pasture component, multi-species pasture mixtures are commonly used worldwide to increase primary productivity and thereby secondary productivity. Here we present two examples of how the combination of experimental research and quantitative modelling can help to the development of protocols for food production optimization. The first example is based on a model we are currently developing for the whole agroecosystem in terms of a predator-prey dynamical system. The second example focuses on the pasture component and is based on a generalized Lotka-Volterra model we recently proposed for describing the mixture of herbaceous species in the pasture. We discuss the usefulness of both models, whose parameters were estimated from experiments or field work, as quantitative predicting tools. Population dynamic models are the common thread in this paper. © 2017 Elsevier B.V.
653 $aAgriculture optimization
653 $aLotka-Volterra models
700 1 $aDIEGUEZ, F.
700 1 $aHALTY, V.
700 1 $aSOARES DE LIMA, J.M.
773 $tEcological Modelling, 10 August 2017, Volume 357, Pages 23-34. Doi: http://dx.doi.org/10.1016/j.ecolmodel.2017.03.023
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