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Registros recuperados : 10 | |
5. | | IRISARRI, J.G.N.; TEXEIRA, M.; OESTERHELD, M.; VERÓN, S.R.; DELLA NAVE, F.; PARUELO, J. Discriminating the biophysical signal from human-induced effects on long-term primary production dynamics. The case of Patagonia. Global Change Biology, 2021, volume 27, Issue 18, Pages 4381 - 4391. Doi: https://doi.org/10.1111/gcb.15733 Article history: Received 20 January 2021, Accepted 13 May 2021, To be published September 2021.
Supplementary material.
Corresponging author: Irisarri, J.G.N.; Cátedra de Forrajicultura, Departamento de Producción Animal, Facultad de...Biblioteca(s): INIA Las Brujas. |
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6. | | TEXEIRA, M.; VERON, S.; IRISARRIA, G.; OYARZABAL, M.; STAIANO, L.; BAEZA, S.; PARUELO, J. Functional syndromes as indicators of ecosystem change in temperate grasslands. Ecological Indicators, 2019, v. 96, p.600-610.Doi: https://doi.10.1016/j.ecolind.2018.09.046 Article history: Received 27 December 2017// Received in revised form 21 August 2018// Accepted 24 September 2018. Acknowledgments:
This research was supported by CONICET (Argentina), Universidad de Buenos Aires (Argentina), and FONCyT...Biblioteca(s): INIA La Estanzuela. |
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7. | | STAIANO, L.; CAMBA SANS, G.H.; BALDASSINI, P.; GALLEGO .F.; TEXEIRA, M.A.; PARUELO, J. Putting the Ecosystem Services idea at work: Applications on impact assessment and territorial planning. Environmental Development, Volume 38, June 2021, 100570. Open Access. Doi: https://doi.org/10.1016/j.envdev.2020.100570 Article history: Received 20 November 2019/ Revised 12 July 2020, Accepted 12 September 2020, Available online 18 September 2020.Biblioteca(s): INIA La Estanzuela. |
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9. | | MOSCIARO, M.J.; SEGHEZZO, L.; TEXEIRA, M.; PARUELO, J.; VOLANTE, J. Where did the forest go? Post-deforestation land use dynamics in the Dry Chaco region in Northwestern Argentina. Land Use Policy, June 2023, Volume 129, article 106650. doi: https://doi.org/10.1016/j.landusepol.2023.106650 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...Biblioteca(s): INIA Las Brujas. |
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10. | | AGUIAR, S.; MASTRANGELO, M.E.; GARCÍA COLLAZO, M.A.; CAMBA SANS, G.H.; MOSSO, C.E.; CIUFFOLI, L.; SCHMIDT, M.; VALLEJOS, M.; LANGBEHN, L.; CÁCERES, D.; MERLINSKY, G.; PARUELO, J.; SEGHEZZO, L.; STAIANO, L.; TEXEIRA, M.; VOLANTE, J.N.; VERÓN, S.R. Cuál es la situación de la Ley de Bosques en la Región Chaqueña a diez años de su sanción? Revisar su pasado para discutir su futuro. [What is the status of the forest law in the chaco region ten years after its enaction? Reviewing its past to discuss its future?]. Ecologia Austral, August 2018, vol.28, no.2, p. 400-417. OPEN ACCESS. EID: 2-s2.0-85052534872 Article history: Received 23 November 2016. / Accepted 6 March 2018.
Este trabajo fue financiado por el proyecto PICTo 2014-0046 "Bosques nativos, servicios ecosistémicos y bienestar humano en el Norte del Chaco Seco argentino: un...Biblioteca(s): INIA Las Brujas. |
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Registros recuperados : 10 | |
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| Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
23/02/2024 |
Actualizado : |
23/02/2024 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
PARUELO, J.; TEXEIRA, M.; TOMASEL, F. |
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. |
Título : |
Hybrid modeling for grassland productivity prediction: A parametric and machine learning technique for grazing management with applicability to digital twin decision systems. |
Fecha de publicación : |
2024 |
Fuente / Imprenta : |
Agricultural Systems. 2024. Volume 214, article 103847. https://doi.org/10.1016/j.agsy.2023.103847 |
ISSN : |
0308-521X |
DOI : |
10.1016/j.agsy.2023.103847 |
Idioma : |
Inglés |
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 -- |
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 |
Palabras claves : |
Agroecological transitions; ANPP; Artificial neural networks; Grasslands; Remote sensing; Uruguay. |
Asunto categoría : |
-- |
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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