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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
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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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 : |
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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 | Acceso al texto completo restringido a Biblioteca INIA Treinta y Tres. Por información adicional contacte bibliott@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha actual : |
16/10/2018 |
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Actualizado : |
11/02/2019 |
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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 : |
BORGES, A.; GONZÁLEZ-REYMUNDEZ, A.; ERNST, O.; CADENAZZI, M.; TERRA, J.A.; GUTIÉRREZ, L. |
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Afiliación : |
ALEJANDRA BORGES, Departamento de Estadística. Facultad de Agronomía, UdelaR.; AGUSTÍN GONZÁLEZ-REYMUNDEZ, Departamento de Estadística. Facultad de Agronomía, UdelaR.; OSVALDO, ERNST, Departamento de Producción de Cultivos. EEMAC, Facultad de Agronomía, UdelaR.; MÓNICA CADENAZZI, Departamento de Estadística. Facultad de Agronomía, UdelaR.; JOSÉ ALFREDO TERRA FERNÁNDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCÍA GUTIÉRREZ, Department of Agronomy, University of Wisconsin. |
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Título : |
Can spatial modeling substitute experimental design in agricultural experiments? |
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Fecha de publicación : |
2018 |
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Fuente / Imprenta : |
Crop Science, 2018, v. 59, no. 1, p. 1-10. |
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DOI : |
10.2135/cropsci2018.03.0177 |
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Idioma : |
Inglés |
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Notas : |
Article history: Accepted paper, posted 10/05/18. Published online December, 13. 2018. |
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Contenido : |
Abstract:
One of the most critical aspects of agricultural experimentation is the proper choice of experimental design to control field heterogeneity, especially for large experiments. However, even with complex experimental designs, spatial variability may not be properly controlled if it occurs at scales smaller than blocks. Therefore, modeling spatial variability can be beneficial and some studies even propose spatial modeling instead of experimental design. Our goal was to evaluate the effect of experimental design, spatial modeling, and a combination of both under real field conditions using GIS and simulating experiments. Yield data from cultivars was simulated using real spatial variability from a large uniformity trial of one hundred independent locations and different sizes of experiments for four experimental designs: completely randomized design (CRD), randomized complete block design (RCBD), alpha-lattice incomplete block design (ALPHA), and partially replicated design (PREP). Each realization was analyzed using different levels of spatial correction. Models were compared by precision, accuracy, and the recovery of superior genotypes. For moderate and large experiment sizes, ALPHA was the best experimental design in terms of precision and accuracy. In most situations, models that included spatial correlation were better than models with no spatial correlation but they did not outperformed better experimental designs. Therefore, spatial modeling is not a substitute for good experimental design. MenosAbstract:
One of the most critical aspects of agricultural experimentation is the proper choice of experimental design to control field heterogeneity, especially for large experiments. However, even with complex experimental designs, spatial variability may not be properly controlled if it occurs at scales smaller than blocks. Therefore, modeling spatial variability can be beneficial and some studies even propose spatial modeling instead of experimental design. Our goal was to evaluate the effect of experimental design, spatial modeling, and a combination of both under real field conditions using GIS and simulating experiments. Yield data from cultivars was simulated using real spatial variability from a large uniformity trial of one hundred independent locations and different sizes of experiments for four experimental designs: completely randomized design (CRD), randomized complete block design (RCBD), alpha-lattice incomplete block design (ALPHA), and partially replicated design (PREP). Each realization was analyzed using different levels of spatial correction. Models were compared by precision, accuracy, and the recovery of superior genotypes. For moderate and large experiment sizes, ALPHA was the best experimental design in terms of precision and accuracy. In most situations, models that included spatial correlation were better than models with no spatial correlation but they did not outperformed better experimental designs. Therefore, spatial modeling is not a substitut... Presentar Todo |
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Palabras claves : |
EFFICIENCY STATISTICS; EXPERIMENTAL DESIGN; FIELD VARIABILITY; SPATIAL MODELS; UNIFORMITY TRIAL. |
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Thesagro : |
DISENO ESTADISTICO; DISENO EXPERIMENTAL; MODELOS ESTADISTICOS; VARIABILIDAD. |
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Asunto categoría : |
U30 Métodos de investigación |
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Marc : |
LEADER 02512naa a2200313 a 4500
001 1059193
005 2019-02-11
008 2018 bl uuuu u00u1 u #d
024 7 $a10.2135/cropsci2018.03.0177$2DOI
100 1 $aBORGES, A.
245 $aCan spatial modeling substitute experimental design in agricultural experiments?$h[electronic resource]
260 $c2018
500 $aArticle history: Accepted paper, posted 10/05/18. Published online December, 13. 2018.
520 $aAbstract: One of the most critical aspects of agricultural experimentation is the proper choice of experimental design to control field heterogeneity, especially for large experiments. However, even with complex experimental designs, spatial variability may not be properly controlled if it occurs at scales smaller than blocks. Therefore, modeling spatial variability can be beneficial and some studies even propose spatial modeling instead of experimental design. Our goal was to evaluate the effect of experimental design, spatial modeling, and a combination of both under real field conditions using GIS and simulating experiments. Yield data from cultivars was simulated using real spatial variability from a large uniformity trial of one hundred independent locations and different sizes of experiments for four experimental designs: completely randomized design (CRD), randomized complete block design (RCBD), alpha-lattice incomplete block design (ALPHA), and partially replicated design (PREP). Each realization was analyzed using different levels of spatial correction. Models were compared by precision, accuracy, and the recovery of superior genotypes. For moderate and large experiment sizes, ALPHA was the best experimental design in terms of precision and accuracy. In most situations, models that included spatial correlation were better than models with no spatial correlation but they did not outperformed better experimental designs. Therefore, spatial modeling is not a substitute for good experimental design.
650 $aDISENO ESTADISTICO
650 $aDISENO EXPERIMENTAL
650 $aMODELOS ESTADISTICOS
650 $aVARIABILIDAD
653 $aEFFICIENCY STATISTICS
653 $aEXPERIMENTAL DESIGN
653 $aFIELD VARIABILITY
653 $aSPATIAL MODELS
653 $aUNIFORMITY TRIAL
700 1 $aGONZÁLEZ-REYMUNDEZ, A.
700 1 $aERNST, O.
700 1 $aCADENAZZI, M.
700 1 $aTERRA, J.A.
700 1 $aGUTIÉRREZ, L.
773 $tCrop Science, 2018$gv. 59, no. 1, p. 1-10.
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INIA Treinta y Tres (TT) |
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