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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
27/09/2022 |
Actualizado : |
27/09/2022 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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. |
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. |
Título : |
Mapping and characterizing social-ecological land systems of South America. |
Fecha de publicación : |
2022 |
Fuente / Imprenta : |
Ecology and Society, 2022, Volume 27, Issue 2, Article number 27. OPEN ACCESS. doi: https://doi.org/10.5751/ES-13066-270227 |
ISSN : |
1708-3087 |
DOI : |
10.5751/ES-13066-270227 |
Idioma : |
Inglés |
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 |
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 |
Palabras claves : |
Automatization; Hierarchical clustering; Multidisciplinary data; Participatory mapping; Social-ecological mapping. |
Asunto categoría : |
F01 Cultivo |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16772/1/ES-2021-13066.pdf
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Marc : |
LEADER 03737naa a2200457 a 4500 001 1063581 005 2022-09-27 008 2022 bl uuuu u00u1 u #d 022 $a1708-3087 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. 700 1 $aLEVERS, C. 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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INIA Las Brujas (LB) |
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Biblioteca (s) : |
INIA Tacuarembó. |
Fecha actual : |
13/03/2018 |
Actualizado : |
13/03/2018 |
Tipo de producción científica : |
Abstracts/Resúmenes |
Autor : |
TORRES, D.; MORAES, M.L.T. DE |
Afiliación : |
DIEGO GABRIEL TORRES DINI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIO LUIZ TEIXEIRA DE MORAES. |
Título : |
Source to cold resistance in Eucalyptus breeding programs. [Resumen]. |
Fecha de publicación : |
2014 |
Fuente / Imprenta : |
In: IUFRO Forest Tree Breeding Conference, August 25-29, Prague, Czech Republic, 2014. Book of Abstracts. |
Páginas : |
p. 45 |
Idioma : |
Inglés |
Contenido : |
The global area forested with Eucalypt has exceeded 20 million hectares, distributed in more than 90 countries with the most diverse climates. About 50% of these
surfaces are located in low temperature countries with frequent frost. This is risky for the production and can cause death in juvenile trees; and also combined with
other damages like fungal or insects reducing its growth. The correct species and provenance selection for cold resistance phenotypes is a key factor before starting
the tree improvement program. The comparison of native original climates with the exotic regions can be inferred with climate mapping software, associated
with genetics studies and field tests. A wide range of genetic variability of Eucalyptus genus ensures the availability of species and germplasm resistance. The
species E. globulus, E. dunnii, E. nitens, E. viminalis, E. benthamii and E. pauciflora, are originated from the coldest region of Australia. These species have good
frost resistance and optimal growth. In the case of E. Pauciflora is distinguished by its extreme cold resistance, representing optimal model specie for the physiological
basis to cold resistance researches. These species were used in different countries and continents, producing different results on each of the experiences. The aim of
this article is to review different strategies and results obtained from cold resistant breeding around the world |
Thesagro : |
FORESTACIÓN. |
Asunto categoría : |
K10 Producción forestal |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/8911/1/Source-to-cold-resistance-in-Eucaluptus-Breeding-programs.pdf
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Marc : |
LEADER 01915nam a2200145 a 4500 001 1058241 005 2018-03-13 008 2014 bl uuuu u01u1 u #d 100 1 $aTORRES, D. 245 $aSource to cold resistance in Eucalyptus breeding programs. [Resumen].$h[electronic resource] 260 $aIn: IUFRO Forest Tree Breeding Conference, August 25-29, Prague, Czech Republic, 2014. Book of Abstracts.$c2014 300 $ap. 45 520 $aThe global area forested with Eucalypt has exceeded 20 million hectares, distributed in more than 90 countries with the most diverse climates. About 50% of these surfaces are located in low temperature countries with frequent frost. This is risky for the production and can cause death in juvenile trees; and also combined with other damages like fungal or insects reducing its growth. The correct species and provenance selection for cold resistance phenotypes is a key factor before starting the tree improvement program. The comparison of native original climates with the exotic regions can be inferred with climate mapping software, associated with genetics studies and field tests. A wide range of genetic variability of Eucalyptus genus ensures the availability of species and germplasm resistance. The species E. globulus, E. dunnii, E. nitens, E. viminalis, E. benthamii and E. pauciflora, are originated from the coldest region of Australia. These species have good frost resistance and optimal growth. In the case of E. Pauciflora is distinguished by its extreme cold resistance, representing optimal model specie for the physiological basis to cold resistance researches. These species were used in different countries and continents, producing different results on each of the experiences. The aim of this article is to review different strategies and results obtained from cold resistant breeding around the world 650 $aFORESTACIÓN 700 1 $aMORAES, M.L.T. DE
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