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| Registros recuperados : 45 | |
| 4. |  | MASUDA, Y.; AGUILAR, I.; TSURUTA, S.; MISZTAL, I. Acceleration of computations in AI REML for single-step GBLUP models. Volume Methods and Tools: Statistical methods - linear and nonlinear models (Posters), 703. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.703.| Biblioteca(s): INIA Las Brujas. |
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| 6. | | AGUILAR, I.; MISZTAL, I.; LEGARRA, A.; TSURUTA, S. Efficient computations of genomic relationship matrix and other matrices used in the single-step evaluation. Volume Methods and tools: Software and bioinformatics - Lecture Sessions, 0768. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 9., Leipzig, Germany, August 1-6, 2010. p. 0768. Acknowledgments: This study was partially funded by the Holstein Association USA Inc. and by AFRI grants 2009-65205-05665 and 2010-65205-20366 from the USDA NIFA Animal Genome Program. The authors thank P.M. VanRaden from Animal...| Biblioteca(s): INIA Las Brujas. |
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| 8. | | TSURUTA, S.; MISZTAL, I.; AGUILAR, I.; LAWLOR, T. J. Genome wide association study on cow mortality in three US regions. Volume Species Breeding: Dairy cattle (Posters), 805. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.805.| Biblioteca(s): INIA Las Brujas. |
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| 9. | | TSURUTA, S.; MISZTAL, I.; AGUILAR, I.; LAWLOR, T.J. Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. Journal of Dairy Science, 2011, v.94, no.8, p.4198-4204. OPEN ACCESS. Article history: Received February 9, 2011. / Accepted April 8, 2011.| Biblioteca(s): INIA Las Brujas. |
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| 10. | | AGUILAR, I.; MISZTAL, I.; TSURUTA, S.; LEGARRA, A.; WANG, H. PREGSF90 - POSTGSF90: Computational tools for the implementation of single-step genomic selection and genome-wide association with ungenotyped individuals in BLUPF90 programs. Volume Methods and Tools: Statistical and genomic tools for mapping QTL and genes (Posters), 680. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.680.| Biblioteca(s): INIA Las Brujas. |
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| 11. | | MASUDA, Y.; AGUILAR, I.; TSURUTA, S.; MISZTAL, I. Technical note: Acceleration of sparse operations for average-information REML analyses with supernodal methods and sparse-storage refinements. Journal of Animal Science, 2015, v. 93, p. 4670 - 4674. Published October 9, 2015 Article history: Received June 8, 2015.; Accepted August 7, 2015.
1. We acknowledge the work by François Guillaume in programming a hash function. We greatly appreciate the work of the two anonymous reviewers.
2. The AIREMLF90 program...| Biblioteca(s): INIA Las Brujas. |
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| 13. | | LOURENCO, D; MISZTAL, I.; TSURUTA, S.; AGUILAR, I.; LAWLOR, T. J.; WELLER, J. I. Are evaluations on young genotyped dairy bulls benefiting from the past generations? [conference paper]. Volume Species Breeding: Dairy cattle, 297. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.297.| Biblioteca(s): INIA Las Brujas. |
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| 14. | | GARCÍA, A.; AGUILAR, I.; LEGARRA, A.; MILLER, S.; TSURUTA, S.; MISZTAL, I.; LOURENCO, D. Accuracy of indirect predictions for large datasets based on prediction error covariance of SNP effects from single-step GBLUP. [abstract 22]. Issue Section: Animal Breeding and Genetics. Journal of Animal Science, 2020, Volume 98, Issue Supplement 4, Pages 6-7. doi: https://doi.org/10.1093/jas/skaa278.012 Article history: 30 November 2020.
ASAS Annual 2020 Meeting Abstracts.| Biblioteca(s): INIA Las Brujas. |
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| 15. | | AGUILAR, I.; TSURUTA, S.; MASUDA, Y.; LOURENCO, D.A.L.; LEGARRA, A.; MISZTAL, I. BLUPF90 suite of programs for animal breeding with focus on genomics. Volume Methods and Tools - Software, p. 751. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 11., Aotea Centre Auckland, New Zealand: WCGALP, ICAR, 11-16 feb 2018. 6 p.| Biblioteca(s): INIA Las Brujas. |
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| 16. | | GARCÍA, A.; AGUILAR, I.; LEGARRA, A.; TSURUTA, S.; MISZTAL, I.; LOURENCO, D. Correction: Theoretical accuracy for indirect predictions based on SNP effects from single-step GBLUP (Genetics, selection, evolution : GSE (2022) 54:1 (66)). Genetics, Selection, Evolution : GSE, 2023, Volume 55, Issue 1, Pages 26. OPEN ACCESS. https://doi.org/10.1186/s12711-023-00799-x Article history: Published online 17 April 2023. -- Document: Erratum - Gold Open Access. -- The original article can be found online at https://doi.org/10.1186/s12711-022-00752-4| Biblioteca(s): INIA Las Brujas. |
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| 17. | | LOURENÇO, D. A. L.; MISZTAL, I.; TSURUTA, S.; FRAGOMENI, B.; AGUILAR, I.; MASUDA, Y.; MOSER, D. Direct and indirect genomic evaluations in beef cattle. Interbull Bulletin, 2015, v. 49, p.80 - 84.| Biblioteca(s): INIA Las Brujas. |
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| 18. | | AGUILAR, I.; MISZTAL, I.; JOHNSON, D.L.; LEGARRA, A.; TSURUTA, S.; LAWLOR, T.J. Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science, 2010, v. 93, no. 2, p. 743-752. OPEN ACCESS Article history: Received September 14, 2009 / Accepted November 10, 2009 / Published in issue: February 2010.| Biblioteca(s): INIA Las Brujas. |
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| 19. | | MISZTAL, I.; LOURENCO, D.; TSURUTA, S.; AGUILAR, I.; MASUDA, Y.; BERMANN, M.; CESARANI, A.; LEGARRA, A. How ssGBLUP became suitable for national dairy cattle evaluations. [668]. Part 37 - Bovine dairy - genetic evaluation methods. In: Proceedings of the World Congress on Genetics Applied to Livestock Production (WCGALP), 12., Rotterdam, the Netherlands, 3-8 July 2022. doi: https://doi.org/10.3920/978-90-8686-940-4_668 2757-2760. Article history: Published online: February 9, 2023 -- Corresponding author: I. Misztal, email: ignacy@uga.edu| Biblioteca(s): INIA Las Brujas. |
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| 20. | | TSURUTA, S.; AGUILAR, I.; MISZTAL, I.; LEGARRA, A.; LAWLOR, T. J. Multiple trait genetic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. Volume Genetic improvement programmes: Selection using molecular information - Poster Sessions, 0489. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 9., Leipzig, Germany, August 1-6, 2010. p. 0489.| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 45 | |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
27/09/2022 |
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Actualizado : |
27/09/2022 |
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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 : |
ZARBÁ, L.; PIQUER-RODRÍGUEZ, M.; BOILLAT, S.; LEVERS, C.; GASPARRI, I.; AIDE, T. M.; ÁLVAREZ-BERRÍOS, N. L.; ANDERSON, L. O.; ARAOZ, E.; ARIMA, E.; BATISTELLA, M.; CALDERÓN-LOOR, M.; ECHEVERRÍA, C.; GONZALEZ-ROGLICH, M.; JOBBÁGY, E. G.; MATHEZ-STIEFEL, S.-L.; RAMIREZ-REYES, C-; PACHECHO, A.; VALLEJOS, M.; YOUNG, K. R.; GRAU, R. |
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Afiliación : |
LUCÍA ZARBÁ, Instituto de Ecología Regional (IER), Universidad Nacional de Tucumán (UNT) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina.; MARÍA PIQUER-RODRÍGUEZ, Instituto Ecología Regional (IER), Univ. Nacional de Tucumán (UNT). Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina; Lateinamerika-Institut, Freie Universität Berlin, Germany; Geography Department, Humbold, Germany; SÉBASTIEN BOILLAT, Institute of Geography, University of Bern, Bern, Switzerland; CHRISTIAN LEVERS, Depart. Environmental Geography, Inst. for Environmental Studies, Vrije Univ. Amsterdam, Netherlands; Inst. for Resources, Environment and Sustainability, Univ. of British Columbia, Vancouver, BC, Canada; School of Public Policy and Global Affairs, Univ.; IGNACIO GASPARRI, Instituto de Ecología Regional (IER), Universidad Nacional de Tucumán (UNT) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina; T. MITCHELL AIDE, Department of Biology, University of Puerto Rico-Rio Piedras, Puerto Rico; NORA L. ÁLVAREZ-BERRÍOS, USDA Forest Service, International Institute of Tropical Forestry, Río Piedras, Puerto Rico; LIANA O. ANDERSON, National Center for Monitoring and Early Warning of Natural Disasters-CEMADEN, Ministry of Science, Technology and Innovation-MCTI, Brazil; EZEQUIEL ARAOZ, Instituto de Ecología Regional (IER), Universidad Nacional de Tucumán (UNT) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina; EUGENIO ARIMA, Department of Geography and the Environment, University of Texas at Austin, United States; MATEUS BATISTELLA, Brazilian Agricultural Research Corporation (Embrapa Agricultural Informatics) State University of Campinas (Unicamp), Brazil; MARCO CALDERÓN-LOOR, Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Melbourne, Australia;Grupo de Investigación de Biodiversidad, Medio Ambiente y Salud-BIOMAS, Universidad de las Américas (UDLA), Quito, Ecuador; CRISTIAN ECHEVERRÍA, Landscape Ecology Laboratory, Facultad de Ciencias Forestales, Universidad de Concepción, Chile; Millennium Nucleus Center for the Socioeconomic Impact of Environmental Policies (CESIEP), Santiago de Chile, Chile; MARIANO GONZALEZ-ROGLICH, Wildlife Conservation Society, Buenos Aires, Argentina; ESTEBAN G. JOBBÁGY, Grupo de Estudios Ambientales, IMASL-CONICET and Universidad Nacional de San Luis, San Luis, Argentina; South American Institute for Resilience and Sustainability Studies (SARAS), Maldonado, Uruguay; SARAH-LAN MATHEZ-STIEFEL, Centre for Development and Environment, University of Bern, Switzerland; Wyss Academy for Nature at the University of Bern, Switzerland; CARLOS RAMIREZ-REYES, Quantitative Ecology & Spatial Technologies Laboratory, Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, United States; ANDREA PACHECO, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Germany; MARÍA VALLEJOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Departamento de Métodos Cuantitativos y Sistemas de Información, Facultad de Agronomía, Universidad de Buenos Aires, Argentina; KENNETH R. YOUNG, Department of Geography and the Environment, University of Texas at Austin, United States; RICARDO GRAU, Instituto de Ecología Regional (IER), Universidad Nacional de Tucumán (UNT) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina. |
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Título : |
Mapping and characterizing social-ecological land systems of South America. |
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Fecha de publicación : |
2022 |
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Fuente / Imprenta : |
Ecology and Society, 2022, Volume 27, Issue 2, Article number 27. OPEN ACCESS. doi: https://doi.org/10.5751/ES-13066-270227 |
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ISSN : |
1708-3087 |
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DOI : |
10.5751/ES-13066-270227 |
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Idioma : |
Inglés |
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Notas : |
Article: Gold Open Access, Green Open Access. -- Erratum: On 6 June 2022 the abstract was edited. See online for more detail: https://ecologyandsociety.org/vol27/iss2/art27/#dataarchive_stmt --
LICENSE: Published under license by The Resilience Alliance. This article is under a Creative Commons Attribution 4.0 International License. You may share and adapt the work provided the original author and source are credited, you indicate whether any changes were made, and you include a link to the license. -- Article metrics: https://plu.mx/plum/a/?doi=10.5751/ES-13066-270227&theme=plum-bigben-theme |
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Contenido : |
ABSTRACT.- Humans place strong pressure on land and have modified around 75% of Earth's terrestrial surface. In this context, ecoregions and biomes, merely defined on the basis of their biophysical features, are incomplete characterizations of the territory. Land system science requires classification schemes that incorporate both social and biophysical dimensions. In this study, we generated spatially explicit social-ecological land system (SELS) typologies for South America with a hybrid methodology that combined data-driven spatial analysis with a knowledge-based evaluation by an interdisciplinary group of regional specialists. Our approach embraced a holistic consideration of the social-ecological land systems, gathering a dataset of 26 variables spanning across 7 dimensions: physical, biological, land cover, economic, demographic, political, and cultural. We identified 13 SELS nested in 5 larger social-ecological regions (SER). Each SELS was discussed and described by specific groups of specialists. Although 4 environmental and 1 socioeconomic variable explained most of the distribution of the coarse SER classification, a diversity of 15 other variables were shown to be essential for defining several SELS, highlighting specific features that differentiate them. The SELS spatial classification presented is a systematic and operative characterization of South American social-ecological land systems. We propose its use can contribute as a reference framework for a wide range of applications such as analyzing observations within larger contexts, designing system-specific solutions for sustainable development, and structuring hypothesis testing and comparisons across space. Similar efforts could be done elsewhere in the world. Copyright © 2022 by the author(s). MenosABSTRACT.- Humans place strong pressure on land and have modified around 75% of Earth's terrestrial surface. In this context, ecoregions and biomes, merely defined on the basis of their biophysical features, are incomplete characterizations of the territory. Land system science requires classification schemes that incorporate both social and biophysical dimensions. In this study, we generated spatially explicit social-ecological land system (SELS) typologies for South America with a hybrid methodology that combined data-driven spatial analysis with a knowledge-based evaluation by an interdisciplinary group of regional specialists. Our approach embraced a holistic consideration of the social-ecological land systems, gathering a dataset of 26 variables spanning across 7 dimensions: physical, biological, land cover, economic, demographic, political, and cultural. We identified 13 SELS nested in 5 larger social-ecological regions (SER). Each SELS was discussed and described by specific groups of specialists. Although 4 environmental and 1 socioeconomic variable explained most of the distribution of the coarse SER classification, a diversity of 15 other variables were shown to be essential for defining several SELS, highlighting specific features that differentiate them. The SELS spatial classification presented is a systematic and operative characterization of South American social-ecological land systems. We propose its use can contribute as a reference framework for a wide ran... Presentar Todo |
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Palabras claves : |
Automatization; Hierarchical clustering; Multidisciplinary data; Participatory mapping; Social-ecological mapping. |
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Asunto categoría : |
F01 Cultivo |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16772/1/ES-2021-13066.pdf
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Marc : |
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024 7 $a10.5751/ES-13066-270227$2DOI
100 1 $aZARBÁ, L.
245 $aMapping and characterizing social-ecological land systems of South America.$h[electronic resource]
260 $c2022
500 $aArticle: Gold Open Access, Green Open Access. -- Erratum: On 6 June 2022 the abstract was edited. See online for more detail: https://ecologyandsociety.org/vol27/iss2/art27/#dataarchive_stmt -- LICENSE: Published under license by The Resilience Alliance. This article is under a Creative Commons Attribution 4.0 International License. You may share and adapt the work provided the original author and source are credited, you indicate whether any changes were made, and you include a link to the license. -- Article metrics: https://plu.mx/plum/a/?doi=10.5751/ES-13066-270227&theme=plum-bigben-theme
520 $aABSTRACT.- Humans place strong pressure on land and have modified around 75% of Earth's terrestrial surface. In this context, ecoregions and biomes, merely defined on the basis of their biophysical features, are incomplete characterizations of the territory. Land system science requires classification schemes that incorporate both social and biophysical dimensions. In this study, we generated spatially explicit social-ecological land system (SELS) typologies for South America with a hybrid methodology that combined data-driven spatial analysis with a knowledge-based evaluation by an interdisciplinary group of regional specialists. Our approach embraced a holistic consideration of the social-ecological land systems, gathering a dataset of 26 variables spanning across 7 dimensions: physical, biological, land cover, economic, demographic, political, and cultural. We identified 13 SELS nested in 5 larger social-ecological regions (SER). Each SELS was discussed and described by specific groups of specialists. Although 4 environmental and 1 socioeconomic variable explained most of the distribution of the coarse SER classification, a diversity of 15 other variables were shown to be essential for defining several SELS, highlighting specific features that differentiate them. The SELS spatial classification presented is a systematic and operative characterization of South American social-ecological land systems. We propose its use can contribute as a reference framework for a wide range of applications such as analyzing observations within larger contexts, designing system-specific solutions for sustainable development, and structuring hypothesis testing and comparisons across space. Similar efforts could be done elsewhere in the world. Copyright © 2022 by the author(s).
653 $aAutomatization
653 $aHierarchical clustering
653 $aMultidisciplinary data
653 $aParticipatory mapping
653 $aSocial-ecological mapping
700 1 $aPIQUER-RODRÍGUEZ, M.
700 1 $aBOILLAT, S.
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700 1 $aGASPARRI, I.
700 1 $aAIDE, T. M.
700 1 $aÁLVAREZ-BERRÍOS, N. L.
700 1 $aANDERSON, L. O.
700 1 $aARAOZ, E.
700 1 $aARIMA, E.
700 1 $aBATISTELLA, M.
700 1 $aCALDERÓN-LOOR, M.
700 1 $aECHEVERRÍA, C.
700 1 $aGONZALEZ-ROGLICH, M.
700 1 $aJOBBÁGY, E. G.
700 1 $aMATHEZ-STIEFEL, S.-L.
700 1 $aRAMIREZ-REYES, C-
700 1 $aPACHECHO, A.
700 1 $aVALLEJOS, M.
700 1 $aYOUNG, K. R.
700 1 $aGRAU, R.
773 $tEcology and Society, 2022, Volume 27, Issue 2, Article number 27. OPEN ACCESS. doi: https://doi.org/10.5751/ES-13066-270227
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