Tracking states and transitions in semiarid rangelands: A spatiotemporal archetypal analysis of productivity dynamics using wavelets

Autores
Bruzzone, Octavio Augusto; Hurtado, Santiago Ignacio; Perri, Daiana Vanesa; Maddio, Rafael Adrián; Sello, Mario Eugenio; Easdale, Marcos Horacio
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Climate change poses challenges in classifying ecosystem dynamics, as they are influenced by shifting dynamics resulting from changes in climate forces and meteorological variables, including temperature and water availability. To address this, our study presents a novel approach using Continuous Wavelet Transform (CWT) and power spectrum analysis to classify vegetation dynamics, considering the time-dependent variability of ecosystem frequencies. We applied our method to centred and standardized MODIS NDVI time series for the period 2000–2021, using an experimental field station in northern Patagonia as a case study. By performing a continuous wavelet transform on the data for each pixel, we obtained instantaneous power spectra, capturing variability across different dates and pixels. These spectrums were then consolidated into a comprehensive database, and subsequently classified using archetypal analysis. We identified a convex combination of archetypal spectrums that best represented the entire power spectrum database. Mapping the resulting archetypes and their weights in both space and time allowed us to explore pixels' variations in archetype weights in relation to factors such as time, topography, and climate. In addition, to examine the potential relationship between the NDVI time series and climate drivers, we computed the Average Cross-Wavelet Power Spectrum (ACWPS) to different climatic indices. Three archetypes were sufficient to explain the majority of power spectrum variability in the study area. These archetypes exhibited distinctive characteristics: 1) medium-frequency variability (2–4 years), 2) low-frequency variability (>4 years), and 3) an annual (i.e. seasonal) cycle with low-frequency variability. Spatially, the first two archetypes were predominantly observed in highland steppes, while the third archetype prevailed in lowland areas associated with meadows. At the beginning of the studied period, Archetypes 1 and 3 dominated, but after the Puyehue-Cordón Caulle Volcanic Complex eruption in 2011 their prominence diminished, and Archetype 2 became more prevalent in the whole study area. Finally, all three NDVI series representative of archetypes showed a relative peak at approximately four years, which could be linked to the Indian Ocean Dipole variability. These results highlight an abrupt shift in the system's behaviour, primarily related to changes in variability distribution rather than mean values. This disturbance-induced transition aligns with the theory of state and transitions in ecological system dynamics. We propose that the states in this model are not fixed but represent alternative dynamic behaviours, akin to different types of limit cycles. Consequently, employing a wavelet analysis-based classification method provides a robust means of studying and understanding such variability and transitions, thereby offering clarity and comprehension of ecosystem states. Notably, this methodology proves particularly effective for large databases of detailed time series.
EEA Bariloche
Fil: Bruzzone, Octavio Augusto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche (IFAB); Argentina
Fil: Bruzzone, Octavio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB); Argentina
Fil: Hurtado, Santiago Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche (IFAB); Argentina
Fil: Hurtado, Santiago Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB); Argentina
Fil: Perri, Daiana Vanesa. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche (IFAB); Argentina
Fil: Perri, Daiana Vanesa. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB); Argentina
Fil: Maddio, Rafael Adrián. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche (IFAB); Argentina
Fil: Maddio, Rafael Adrián. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB); Argentina
Fil: Sello, Mario Eugenio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche (IFAB); Argentina
Fil: Sello, Mario Eugenio. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB); Argentina
Fil: Easdale, Marcos Horacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche (IFAB); Argentina
Fil: Easdale, Marcos Horacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB); Argentina
Fuente
Remote Sensing of Environment 308 : 114203. (July 2024)
Materia
Tierras de Pastos
Zona Semiárida
Cambio Climático
Medio Ambiente
Estepas
Indice Normalizado Diferencial de la Vegetación
Rangelands
Semiarid Zones
Climate Change
Environment
Steppes
Normalized Difference Vegetation Index
NDVI
Nivel de accesibilidad
acceso restringido
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/18890
Acceder
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network_name_str INTA Digital (INTA)
spelling Tracking states and transitions in semiarid rangelands: A spatiotemporal archetypal analysis of productivity dynamics using waveletsBruzzone, Octavio AugustoHurtado, Santiago IgnacioPerri, Daiana VanesaMaddio, Rafael AdriánSello, Mario EugenioEasdale, Marcos HoracioTierras de PastosZona SemiáridaCambio ClimáticoMedio AmbienteEstepasIndice Normalizado Diferencial de la VegetaciónRangelandsSemiarid ZonesClimate ChangeEnvironmentSteppesNormalized Difference Vegetation IndexNDVIClimate change poses challenges in classifying ecosystem dynamics, as they are influenced by shifting dynamics resulting from changes in climate forces and meteorological variables, including temperature and water availability. To address this, our study presents a novel approach using Continuous Wavelet Transform (CWT) and power spectrum analysis to classify vegetation dynamics, considering the time-dependent variability of ecosystem frequencies. We applied our method to centred and standardized MODIS NDVI time series for the period 2000–2021, using an experimental field station in northern Patagonia as a case study. By performing a continuous wavelet transform on the data for each pixel, we obtained instantaneous power spectra, capturing variability across different dates and pixels. These spectrums were then consolidated into a comprehensive database, and subsequently classified using archetypal analysis. We identified a convex combination of archetypal spectrums that best represented the entire power spectrum database. Mapping the resulting archetypes and their weights in both space and time allowed us to explore pixels' variations in archetype weights in relation to factors such as time, topography, and climate. In addition, to examine the potential relationship between the NDVI time series and climate drivers, we computed the Average Cross-Wavelet Power Spectrum (ACWPS) to different climatic indices. Three archetypes were sufficient to explain the majority of power spectrum variability in the study area. These archetypes exhibited distinctive characteristics: 1) medium-frequency variability (2–4 years), 2) low-frequency variability (>4 years), and 3) an annual (i.e. seasonal) cycle with low-frequency variability. Spatially, the first two archetypes were predominantly observed in highland steppes, while the third archetype prevailed in lowland areas associated with meadows. At the beginning of the studied period, Archetypes 1 and 3 dominated, but after the Puyehue-Cordón Caulle Volcanic Complex eruption in 2011 their prominence diminished, and Archetype 2 became more prevalent in the whole study area. Finally, all three NDVI series representative of archetypes showed a relative peak at approximately four years, which could be linked to the Indian Ocean Dipole variability. These results highlight an abrupt shift in the system's behaviour, primarily related to changes in variability distribution rather than mean values. This disturbance-induced transition aligns with the theory of state and transitions in ecological system dynamics. We propose that the states in this model are not fixed but represent alternative dynamic behaviours, akin to different types of limit cycles. Consequently, employing a wavelet analysis-based classification method provides a robust means of studying and understanding such variability and transitions, thereby offering clarity and comprehension of ecosystem states. Notably, this methodology proves particularly effective for large databases of detailed time series.EEA BarilocheFil: Bruzzone, Octavio Augusto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche (IFAB); ArgentinaFil: Bruzzone, Octavio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB); ArgentinaFil: Hurtado, Santiago Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche (IFAB); ArgentinaFil: Hurtado, Santiago Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB); ArgentinaFil: Perri, Daiana Vanesa. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche (IFAB); ArgentinaFil: Perri, Daiana Vanesa. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB); ArgentinaFil: Maddio, Rafael Adrián. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche (IFAB); ArgentinaFil: Maddio, Rafael Adrián. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB); ArgentinaFil: Sello, Mario Eugenio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche (IFAB); ArgentinaFil: Sello, Mario Eugenio. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB); ArgentinaFil: Easdale, Marcos Horacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche (IFAB); ArgentinaFil: Easdale, Marcos Horacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB); ArgentinaElsevier2024-08-09T14:48:45Z2024-08-09T14:48:45Z2024-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/18890https://www.sciencedirect.com/science/article/abs/pii/S00344257240022190034-42571879-0704https://doi.org/10.1016/j.rse.2024.114203Remote Sensing of Environment 308 : 114203. (July 2024)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-09-29T13:46:43Zoai:localhost:20.500.12123/18890instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-29 13:46:45.227INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Tracking states and transitions in semiarid rangelands: A spatiotemporal archetypal analysis of productivity dynamics using wavelets
title Tracking states and transitions in semiarid rangelands: A spatiotemporal archetypal analysis of productivity dynamics using wavelets
spellingShingle Tracking states and transitions in semiarid rangelands: A spatiotemporal archetypal analysis of productivity dynamics using wavelets
Bruzzone, Octavio Augusto
Tierras de Pastos
Zona Semiárida
Cambio Climático
Medio Ambiente
Estepas
Indice Normalizado Diferencial de la Vegetación
Rangelands
Semiarid Zones
Climate Change
Environment
Steppes
Normalized Difference Vegetation Index
NDVI
title_short Tracking states and transitions in semiarid rangelands: A spatiotemporal archetypal analysis of productivity dynamics using wavelets
title_full Tracking states and transitions in semiarid rangelands: A spatiotemporal archetypal analysis of productivity dynamics using wavelets
title_fullStr Tracking states and transitions in semiarid rangelands: A spatiotemporal archetypal analysis of productivity dynamics using wavelets
title_full_unstemmed Tracking states and transitions in semiarid rangelands: A spatiotemporal archetypal analysis of productivity dynamics using wavelets
title_sort Tracking states and transitions in semiarid rangelands: A spatiotemporal archetypal analysis of productivity dynamics using wavelets
dc.creator.none.fl_str_mv Bruzzone, Octavio Augusto
Hurtado, Santiago Ignacio
Perri, Daiana Vanesa
Maddio, Rafael Adrián
Sello, Mario Eugenio
Easdale, Marcos Horacio
author Bruzzone, Octavio Augusto
author_facet Bruzzone, Octavio Augusto
Hurtado, Santiago Ignacio
Perri, Daiana Vanesa
Maddio, Rafael Adrián
Sello, Mario Eugenio
Easdale, Marcos Horacio
author_role author
author2 Hurtado, Santiago Ignacio
Perri, Daiana Vanesa
Maddio, Rafael Adrián
Sello, Mario Eugenio
Easdale, Marcos Horacio
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Tierras de Pastos
Zona Semiárida
Cambio Climático
Medio Ambiente
Estepas
Indice Normalizado Diferencial de la Vegetación
Rangelands
Semiarid Zones
Climate Change
Environment
Steppes
Normalized Difference Vegetation Index
NDVI
topic Tierras de Pastos
Zona Semiárida
Cambio Climático
Medio Ambiente
Estepas
Indice Normalizado Diferencial de la Vegetación
Rangelands
Semiarid Zones
Climate Change
Environment
Steppes
Normalized Difference Vegetation Index
NDVI
dc.description.none.fl_txt_mv Climate change poses challenges in classifying ecosystem dynamics, as they are influenced by shifting dynamics resulting from changes in climate forces and meteorological variables, including temperature and water availability. To address this, our study presents a novel approach using Continuous Wavelet Transform (CWT) and power spectrum analysis to classify vegetation dynamics, considering the time-dependent variability of ecosystem frequencies. We applied our method to centred and standardized MODIS NDVI time series for the period 2000–2021, using an experimental field station in northern Patagonia as a case study. By performing a continuous wavelet transform on the data for each pixel, we obtained instantaneous power spectra, capturing variability across different dates and pixels. These spectrums were then consolidated into a comprehensive database, and subsequently classified using archetypal analysis. We identified a convex combination of archetypal spectrums that best represented the entire power spectrum database. Mapping the resulting archetypes and their weights in both space and time allowed us to explore pixels' variations in archetype weights in relation to factors such as time, topography, and climate. In addition, to examine the potential relationship between the NDVI time series and climate drivers, we computed the Average Cross-Wavelet Power Spectrum (ACWPS) to different climatic indices. Three archetypes were sufficient to explain the majority of power spectrum variability in the study area. These archetypes exhibited distinctive characteristics: 1) medium-frequency variability (2–4 years), 2) low-frequency variability (>4 years), and 3) an annual (i.e. seasonal) cycle with low-frequency variability. Spatially, the first two archetypes were predominantly observed in highland steppes, while the third archetype prevailed in lowland areas associated with meadows. At the beginning of the studied period, Archetypes 1 and 3 dominated, but after the Puyehue-Cordón Caulle Volcanic Complex eruption in 2011 their prominence diminished, and Archetype 2 became more prevalent in the whole study area. Finally, all three NDVI series representative of archetypes showed a relative peak at approximately four years, which could be linked to the Indian Ocean Dipole variability. These results highlight an abrupt shift in the system's behaviour, primarily related to changes in variability distribution rather than mean values. This disturbance-induced transition aligns with the theory of state and transitions in ecological system dynamics. We propose that the states in this model are not fixed but represent alternative dynamic behaviours, akin to different types of limit cycles. Consequently, employing a wavelet analysis-based classification method provides a robust means of studying and understanding such variability and transitions, thereby offering clarity and comprehension of ecosystem states. Notably, this methodology proves particularly effective for large databases of detailed time series.
EEA Bariloche
Fil: Bruzzone, Octavio Augusto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche (IFAB); Argentina
Fil: Bruzzone, Octavio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB); Argentina
Fil: Hurtado, Santiago Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche (IFAB); Argentina
Fil: Hurtado, Santiago Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB); Argentina
Fil: Perri, Daiana Vanesa. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche (IFAB); Argentina
Fil: Perri, Daiana Vanesa. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB); Argentina
Fil: Maddio, Rafael Adrián. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche (IFAB); Argentina
Fil: Maddio, Rafael Adrián. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB); Argentina
Fil: Sello, Mario Eugenio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche (IFAB); Argentina
Fil: Sello, Mario Eugenio. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB); Argentina
Fil: Easdale, Marcos Horacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche (IFAB); Argentina
Fil: Easdale, Marcos Horacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB); Argentina
description Climate change poses challenges in classifying ecosystem dynamics, as they are influenced by shifting dynamics resulting from changes in climate forces and meteorological variables, including temperature and water availability. To address this, our study presents a novel approach using Continuous Wavelet Transform (CWT) and power spectrum analysis to classify vegetation dynamics, considering the time-dependent variability of ecosystem frequencies. We applied our method to centred and standardized MODIS NDVI time series for the period 2000–2021, using an experimental field station in northern Patagonia as a case study. By performing a continuous wavelet transform on the data for each pixel, we obtained instantaneous power spectra, capturing variability across different dates and pixels. These spectrums were then consolidated into a comprehensive database, and subsequently classified using archetypal analysis. We identified a convex combination of archetypal spectrums that best represented the entire power spectrum database. Mapping the resulting archetypes and their weights in both space and time allowed us to explore pixels' variations in archetype weights in relation to factors such as time, topography, and climate. In addition, to examine the potential relationship between the NDVI time series and climate drivers, we computed the Average Cross-Wavelet Power Spectrum (ACWPS) to different climatic indices. Three archetypes were sufficient to explain the majority of power spectrum variability in the study area. These archetypes exhibited distinctive characteristics: 1) medium-frequency variability (2–4 years), 2) low-frequency variability (>4 years), and 3) an annual (i.e. seasonal) cycle with low-frequency variability. Spatially, the first two archetypes were predominantly observed in highland steppes, while the third archetype prevailed in lowland areas associated with meadows. At the beginning of the studied period, Archetypes 1 and 3 dominated, but after the Puyehue-Cordón Caulle Volcanic Complex eruption in 2011 their prominence diminished, and Archetype 2 became more prevalent in the whole study area. Finally, all three NDVI series representative of archetypes showed a relative peak at approximately four years, which could be linked to the Indian Ocean Dipole variability. These results highlight an abrupt shift in the system's behaviour, primarily related to changes in variability distribution rather than mean values. This disturbance-induced transition aligns with the theory of state and transitions in ecological system dynamics. We propose that the states in this model are not fixed but represent alternative dynamic behaviours, akin to different types of limit cycles. Consequently, employing a wavelet analysis-based classification method provides a robust means of studying and understanding such variability and transitions, thereby offering clarity and comprehension of ecosystem states. Notably, this methodology proves particularly effective for large databases of detailed time series.
publishDate 2024
dc.date.none.fl_str_mv 2024-08-09T14:48:45Z
2024-08-09T14:48:45Z
2024-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/18890
https://www.sciencedirect.com/science/article/abs/pii/S0034425724002219
0034-4257
1879-0704
https://doi.org/10.1016/j.rse.2024.114203
url http://hdl.handle.net/20.500.12123/18890
https://www.sciencedirect.com/science/article/abs/pii/S0034425724002219
https://doi.org/10.1016/j.rse.2024.114203
identifier_str_mv 0034-4257
1879-0704
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv restrictedAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Remote Sensing of Environment 308 : 114203. (July 2024)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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