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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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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
18/04/2023 |
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Actualizado : |
18/04/2023 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
MOSCIARO, M.J.; SEGHEZZO, L.; TEXEIRA, M.; PARUELO, J.; VOLANTE, J. |
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Afiliación : |
MARÍA JESÚS MOSCIARO, Estación Experimental Salta, Instituto Nacional de Tecnología Agropecuaria (INTA), Ruta Nacional 68 km 172 (A4403AGE), Cerrillos, Salta, Argentina; LUCAS SEGHEZZO, Instituto de Investigaciones en Energía No Convencional (INENCO), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Salta (UNSa), Salta, Argentina; MARCOS TEXEIRA, Laboratorio de Análisis Regional y Teledetección. IFEVA, Depto. Métodos Cuantitativos y Sistemas de Información, Facultad de Agronomía, UBA and CONICET, Av. San Martín 4453, Buenos Aires, 1417, Argentina; JOSÉ PARUELO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Dpto. Métodos Cuantitativos y Sistemas de Información, Fac. Agronomía, LART IFEVA, Univ. Bs.As., CONICET, Bs.As. Argentina; Fac. Ciencias, IECA, Univ. de la República, Montevideo, Uruguay.; JOSÉ VOLANTE, Estación Experimental Salta, Instituto Nacional de Tecnología Agropecuaria (INTA), Ruta Nacional 68 km 172 (A4403AGE), Cerrillos, Salta, Argentina. |
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Título : |
Where did the forest go? Post-deforestation land use dynamics in the Dry Chaco region in Northwestern Argentina. |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
Land Use Policy, June 2023, Volume 129, article 106650. doi: https://doi.org/10.1016/j.landusepol.2023.106650 |
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ISSN : |
0264-8377 |
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DOI : |
10.1016/j.landusepol.2023.106650 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 21 December 2021; Received in revised form 27 January 2023; Accepted 20 March 2023; Available online 23 March 2023. -- Correspondence author: Mosciaro, M.J.; Estación Experimental Salta, Instituto Nacional de Tecnología Agropecuaria (INTA), Ruta Nacional 68 km 172 (A4403AGE), Cerrillos, Salta, Argentina; email:mosciaro.maria@inta.gob.ar -- |
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Contenido : |
Land transformation is a major component of global change, directly altering habitat composition and spatial configuration, biodiversity, and ecosystem functioning. Over the last decades, the Dry Chaco region in Northwestern Argentina has become one of the regions most heavily transformed worldwide due to the expansion of its agricultural frontier. Many questions remain unanswered about how this process of change occurred. In this study, a parcel-scale database was used to assess the conversion of natural landscapes to different agroecosystems. The magnitude and direction of land use transitions during the last 20 years (2001-2019) were analyzed. Ranching is the main proximate cause of deforestation, accounting for more than 63% of the area cleared annually, though the land use expansion pattern has varied in space and time. Trajectories of land use transitions revealed a spatial arrangement where croplands have displaced ranching to drier areas. The analysis of the intensity of these transitions has shown that the trajectory of post-deforestation land use dynamics has followed a permanent systematic spatio-temporal pattern of change: (1) Dry Forest to Pastures; (2) Pastures to Single Crops; and (3) Single Cropping and Double Cropping systems, where processes of expansion, replacement, and intensification have been identified. Information on transition patterns has allowed us to develop a deeper understanding of land transformation processes, essential in the design of effective land use management strategies. © 2023 Elsevier Ltd MenosLand transformation is a major component of global change, directly altering habitat composition and spatial configuration, biodiversity, and ecosystem functioning. Over the last decades, the Dry Chaco region in Northwestern Argentina has become one of the regions most heavily transformed worldwide due to the expansion of its agricultural frontier. Many questions remain unanswered about how this process of change occurred. In this study, a parcel-scale database was used to assess the conversion of natural landscapes to different agroecosystems. The magnitude and direction of land use transitions during the last 20 years (2001-2019) were analyzed. Ranching is the main proximate cause of deforestation, accounting for more than 63% of the area cleared annually, though the land use expansion pattern has varied in space and time. Trajectories of land use transitions revealed a spatial arrangement where croplands have displaced ranching to drier areas. The analysis of the intensity of these transitions has shown that the trajectory of post-deforestation land use dynamics has followed a permanent systematic spatio-temporal pattern of change: (1) Dry Forest to Pastures; (2) Pastures to Single Crops; and (3) Single Cropping and Double Cropping systems, where processes of expansion, replacement, and intensification have been identified. Information on transition patterns has allowed us to develop a deeper understanding of land transformation processes, essential in the design of effec... Presentar Todo |
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Palabras claves : |
Dry Chaco; Land use change; Land use trajectories; Natural landscapes; Patterns of change; Proximate causes of deforestation. |
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Asunto categoría : |
P01 Conservación de la naturaleza y recursos de La tierra |
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Marc : |
LEADER 02820naa a2200277 a 4500
001 1064038
005 2023-04-18
008 2023 bl uuuu u00u1 u #d
022 $a0264-8377
024 7 $a10.1016/j.landusepol.2023.106650$2DOI
100 1 $aMOSCIARO, M.J.
245 $aWhere did the forest go? Post-deforestation land use dynamics in the Dry Chaco region in Northwestern Argentina.$h[electronic resource]
260 $c2023
500 $aArticle history: Received 21 December 2021; Received in revised form 27 January 2023; Accepted 20 March 2023; Available online 23 March 2023. -- Correspondence author: Mosciaro, M.J.; Estación Experimental Salta, Instituto Nacional de Tecnología Agropecuaria (INTA), Ruta Nacional 68 km 172 (A4403AGE), Cerrillos, Salta, Argentina; email:mosciaro.maria@inta.gob.ar --
520 $aLand transformation is a major component of global change, directly altering habitat composition and spatial configuration, biodiversity, and ecosystem functioning. Over the last decades, the Dry Chaco region in Northwestern Argentina has become one of the regions most heavily transformed worldwide due to the expansion of its agricultural frontier. Many questions remain unanswered about how this process of change occurred. In this study, a parcel-scale database was used to assess the conversion of natural landscapes to different agroecosystems. The magnitude and direction of land use transitions during the last 20 years (2001-2019) were analyzed. Ranching is the main proximate cause of deforestation, accounting for more than 63% of the area cleared annually, though the land use expansion pattern has varied in space and time. Trajectories of land use transitions revealed a spatial arrangement where croplands have displaced ranching to drier areas. The analysis of the intensity of these transitions has shown that the trajectory of post-deforestation land use dynamics has followed a permanent systematic spatio-temporal pattern of change: (1) Dry Forest to Pastures; (2) Pastures to Single Crops; and (3) Single Cropping and Double Cropping systems, where processes of expansion, replacement, and intensification have been identified. Information on transition patterns has allowed us to develop a deeper understanding of land transformation processes, essential in the design of effective land use management strategies. © 2023 Elsevier Ltd
653 $aDry Chaco
653 $aLand use change
653 $aLand use trajectories
653 $aNatural landscapes
653 $aPatterns of change
653 $aProximate causes of deforestation
700 1 $aSEGHEZZO, L.
700 1 $aTEXEIRA, M.
700 1 $aPARUELO, J.
700 1 $aVOLANTE, J.
773 $tLand Use Policy, June 2023, Volume 129, article 106650. doi: https://doi.org/10.1016/j.landusepol.2023.106650
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INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
23/02/2024 |
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Actualizado : |
23/02/2024 |
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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 : |
PARUELO, J.; TEXEIRA, M.; TOMASEL, F. |
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Afiliación : |
JOSÉ PARUELO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; IFEVA, Universidad de Buenos Aires, CONICET, Facultad de Agronomía, Buenos Aires, Argentina; IECA, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay; MARCOS TEXEIRA, IFEVA, Universidad de Buenos Aires, CONICET, Facultad de Agronomía, Buenos Aires, Argentina; FERNANDO TOMASEL, Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, United States. |
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Título : |
Hybrid modeling for grassland productivity prediction: A parametric and machine learning technique for grazing management with applicability to digital twin decision systems. |
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Fecha de publicación : |
2024 |
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Fuente / Imprenta : |
Agricultural Systems. 2024. Volume 214, article 103847. https://doi.org/10.1016/j.agsy.2023.103847 |
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ISSN : |
0308-521X |
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DOI : |
10.1016/j.agsy.2023.103847 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 1 August 2023; Received in revised form 5 December 2023; Accepted 18 December 2023; Available online 28 December 2023. -- Correspondence: Paruelo, J.M.; Instituto Nacional de Investigación Agropecuaria, INIA, La Estanzuela, Ruta 50 km 11, Colonia, Uruguay; email:jparuelo@inia.org.uy -- Funding: This work was supported by grants from ANII (Uruguay. FSDA_1_2018_1_154773 and IA_2021_1_04 and IA_2021_1_1010784), CSIC-Universidad de la República - Uruguay (Programa I + D Grupos 2018-433), Universidad de Buenos Aires (Argentina) and CONICET (2021-2024. PIP-2021. 11220200100956CO01). -- Supplementary data: https://doi.org/10.1016/j.agsy.2023.103847 -- |
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Contenido : |
ABSTRACT.- CONTEXT: Monitoring Aboveground Net Primary Production (ANPP) is critical to assess not only the current ecosystem status but also its long-term dynamics. In rangelands, the seasonal dynamics of ANPP determines forage availability, stock density, and livestock productivity. OBJECTIVE: To develop a hybrid model to be used as a prediction engine for ANPP in the native grasslands of Uruguay. The model combines a parametric component based on the seasonal dynamics of ANPP, and an artificial neural network (ANN) component used to model the remaining non-linearities, which are mainly related to precipitation and temperature variability. The output of hybrid model is proposed as the "virtual entity" of a digital twin support decision system where the "physical entity" is characterized by a collection of bi-weekly (fortnight) ANPP estimates. METHODS: Fortnight ANPP data were calculated from MODIS EVI for the 2001-2020 period. A sigmoidal functional response, having three parameters with an explicit biological interpretation, was fitted to the accumulated ANPP as a function of time. Forecasts were generated by extrapolating the sigmoidal functional response fit up to four fortnights ahead. From these fits, we obtained the fortnight ANPP values by differentiating the accumulated fortnight ANPP. Predictions (up to four fortnights) were generated for each fortnight and year. The residuals from these fits were modeled using a multilayer perceptron trained by backpropagation using climate variables as independent variables. RESULTS AND CONCLUSIONS: The sigmoidal functional response model fit was highly significant for the accumulated ANPP profile. This model also had a high explanatory power for the accumulated ANPP curve. The median of the percentage absolute residuals for forecasts made 1 to 4 fortnights ahead ranged from 17% to 18%. The ANN significantly reduced this unexplained variability in ANPP, showing a median reduction in residuals of 35%, 31%, 30%, and 30% for 1 to 4 fortnights ahead forecasts, respectively, when compared to predictions from the sigmoidal functional response fit. SIGNIFICANCE: By integrating both parametric and machine learning techniques, the hybrid model developed can make accurate predictions in a way that is both efficient and dependable. The hybrid model not only represents an advantage in terms of predictive power, but it also allows for a deeper understanding of the basic ecological processes involved in forage production. © 2023 MenosABSTRACT.- CONTEXT: Monitoring Aboveground Net Primary Production (ANPP) is critical to assess not only the current ecosystem status but also its long-term dynamics. In rangelands, the seasonal dynamics of ANPP determines forage availability, stock density, and livestock productivity. OBJECTIVE: To develop a hybrid model to be used as a prediction engine for ANPP in the native grasslands of Uruguay. The model combines a parametric component based on the seasonal dynamics of ANPP, and an artificial neural network (ANN) component used to model the remaining non-linearities, which are mainly related to precipitation and temperature variability. The output of hybrid model is proposed as the "virtual entity" of a digital twin support decision system where the "physical entity" is characterized by a collection of bi-weekly (fortnight) ANPP estimates. METHODS: Fortnight ANPP data were calculated from MODIS EVI for the 2001-2020 period. A sigmoidal functional response, having three parameters with an explicit biological interpretation, was fitted to the accumulated ANPP as a function of time. Forecasts were generated by extrapolating the sigmoidal functional response fit up to four fortnights ahead. From these fits, we obtained the fortnight ANPP values by differentiating the accumulated fortnight ANPP. Predictions (up to four fortnights) were generated for each fortnight and year. The residuals from these fits were modeled using a multilayer perceptron trained by backpropagation us... Presentar Todo |
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Palabras claves : |
Agroecological transitions; ANPP; Artificial neural networks; Grasslands; Remote sensing; Uruguay. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 04040naa a2200253 a 4500
001 1064472
005 2024-02-23
008 2024 bl uuuu u00u1 u #d
022 $a0308-521X
024 7 $a10.1016/j.agsy.2023.103847$2DOI
100 1 $aPARUELO, J.
245 $aHybrid modeling for grassland productivity prediction$bA parametric and machine learning technique for grazing management with applicability to digital twin decision systems.$h[electronic resource]
260 $c2024
500 $aArticle history: Received 1 August 2023; Received in revised form 5 December 2023; Accepted 18 December 2023; Available online 28 December 2023. -- Correspondence: Paruelo, J.M.; Instituto Nacional de Investigación Agropecuaria, INIA, La Estanzuela, Ruta 50 km 11, Colonia, Uruguay; email:jparuelo@inia.org.uy -- Funding: This work was supported by grants from ANII (Uruguay. FSDA_1_2018_1_154773 and IA_2021_1_04 and IA_2021_1_1010784), CSIC-Universidad de la República - Uruguay (Programa I + D Grupos 2018-433), Universidad de Buenos Aires (Argentina) and CONICET (2021-2024. PIP-2021. 11220200100956CO01). -- Supplementary data: https://doi.org/10.1016/j.agsy.2023.103847 --
520 $aABSTRACT.- CONTEXT: Monitoring Aboveground Net Primary Production (ANPP) is critical to assess not only the current ecosystem status but also its long-term dynamics. In rangelands, the seasonal dynamics of ANPP determines forage availability, stock density, and livestock productivity. OBJECTIVE: To develop a hybrid model to be used as a prediction engine for ANPP in the native grasslands of Uruguay. The model combines a parametric component based on the seasonal dynamics of ANPP, and an artificial neural network (ANN) component used to model the remaining non-linearities, which are mainly related to precipitation and temperature variability. The output of hybrid model is proposed as the "virtual entity" of a digital twin support decision system where the "physical entity" is characterized by a collection of bi-weekly (fortnight) ANPP estimates. METHODS: Fortnight ANPP data were calculated from MODIS EVI for the 2001-2020 period. A sigmoidal functional response, having three parameters with an explicit biological interpretation, was fitted to the accumulated ANPP as a function of time. Forecasts were generated by extrapolating the sigmoidal functional response fit up to four fortnights ahead. From these fits, we obtained the fortnight ANPP values by differentiating the accumulated fortnight ANPP. Predictions (up to four fortnights) were generated for each fortnight and year. The residuals from these fits were modeled using a multilayer perceptron trained by backpropagation using climate variables as independent variables. RESULTS AND CONCLUSIONS: The sigmoidal functional response model fit was highly significant for the accumulated ANPP profile. This model also had a high explanatory power for the accumulated ANPP curve. The median of the percentage absolute residuals for forecasts made 1 to 4 fortnights ahead ranged from 17% to 18%. The ANN significantly reduced this unexplained variability in ANPP, showing a median reduction in residuals of 35%, 31%, 30%, and 30% for 1 to 4 fortnights ahead forecasts, respectively, when compared to predictions from the sigmoidal functional response fit. SIGNIFICANCE: By integrating both parametric and machine learning techniques, the hybrid model developed can make accurate predictions in a way that is both efficient and dependable. The hybrid model not only represents an advantage in terms of predictive power, but it also allows for a deeper understanding of the basic ecological processes involved in forage production. © 2023
653 $aAgroecological transitions
653 $aANPP
653 $aArtificial neural networks
653 $aGrasslands
653 $aRemote sensing
653 $aUruguay
700 1 $aTEXEIRA, M.
700 1 $aTOMASEL, F.
773 $tAgricultural Systems. 2024. Volume 214, article 103847. https://doi.org/10.1016/j.agsy.2023.103847
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