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4. | | PARODI, P.; BAZZANO, V.; ARMÚA-FERNÁNDEZ, M.T.; FÉLIX, M.L.; CARVALHO, L.A.; FREIRE, J.; VENZAL, J.M. Molecular survey of Piroplasmida, Hepatozoon spp. and Anaplasmataceae in anemic and thrombocytopenic dogs from Uruguay. Original article. Veterinary Parasitology: Regional Studies and Reports. 2024, Volume 51, 101027. https://doi.org/10.1016/j.vprsr.2024.101027 Article history: Received 28 February 2024, Revised 8 April 2024, Accepted 17 April 2024, Available online 21 April 2024, Version of Record 23 April 2024. -- Corresponding author at: Rivera 1350, CP 50000 Salto, Uruguay. E-mail...Biblioteca(s): INIA Las Brujas. |
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6. | | VERA, G.; CONDON, F.; VÁZQUEZ, D. Soybean germplasm characterization for human consumption aptitude in Uruguay. [Caracterização do germoplasma de soja com aptidão ao consumo humano no Uruguai.] Original article. Brazilian Journal of Food Technology. 2024, Volume 27, Pages 1-17. https://doi.org/10.1590/1981-6723.04823 -- OPEN ACCESS. Article history: Received 19 Apr 2023, Accepted 21 Nov 2023, Publication in this collection 02 Feb 2024, Date of issue 2024. -- Correspondence: Vázquez, D.; Instituto Nacional de Investigación Agropecuaria (INIA), Agroalimentos, Ruta 50,...Biblioteca(s): INIA Las Brujas. |
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7. | | GIANNITTI, F.; DORSCH, M.; SCHILD, C.; CAFFARENA, D.; SVERLOW, K.; ARMIÉN , A.; RIET-CORREA, F. O-053 Pathological and immunohistochemical evidence of a possible Francisellaceae family member causing ovine abortion in Uruguay: Should we be concerned about tularemia in South America?. [conference abstract]. Animal - science proceedings, March 2023, Volume 14, Issue 1, Page 94. doi: https://doi.org/10.1016/j.anscip.2023.01.129 Article history: Available online 13 March 2023, Version of Record 13 March 2023. -- Corresponding author: Corresponding author: Federico Giannitti. E-mail: fgiannitti@inia.org.uy -- Acknowledgements and funding: INIA's grant PL_27. --...Biblioteca(s): INIA Las Brujas. |
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8. | | CASAUX, M.L.; NETO, W.S.; SCHILD, C.; COSTA, R.A. DA; MACÍAS-RIOSECO, M.; CAFFARENA, D.; SILVEIRA, C.S.; ARÁOZ, V.; DONCEL, B.; GIANNITTI, F.; FRAGA, M. Epidemiological and clinicopathological findings in 15 fatal outbreaks of salmonellosis in dairy calves and virulence genes in the causative Salmonella enterica Typhimurium and Dublin strains. Veterinary Microbiology - Research Paper. Brazilian Journal of Microbiology, 2023, volume 54, isuue 1, pp. 475-490. doi: https://doi.org/10.1007/s42770-022-00898-9 Article history: Received 06 December 2021; Accepted 20 December 2022; Published 05 January 2023. -- Correspondence author: Fraga, M.; Plataforma de Investigación en Salud Animal, Instituto Nacional de Investigación Agropecuaria (INIA),...Biblioteca(s): INIA Las Brujas. |
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11. | | MACHADO, M.; MARTÍNEZ, R.; ANDRES, S.; SUMARAH, M.W.; RENAUD, J.B.; ARMIÉN, A.G.; BARROS, C.S.L.; RIET-CORREA, F.; MENCHACA, A.; SCHILD, C. Poisoning by Baccharis coridifolia in early-weaned beef calves: pathological study and new macrocyclic trichothecene identification. Toxins. 2023; 15(12):681. https://doi.org/10.3390/toxins15120681 -- OPEN ACCESS. Article history: Submission received: 19 October 2023; Revised: 20 November 2023; Accepted: 27 November 2023; Published: 1 December 2023. -- Supplementary Materials: The following supporting information can be downloaded at:...Biblioteca(s): INIA Las Brujas. |
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13. | | REBOLLO, I.; SCHEFFEL, S.; BLANCO, P.H.; MOLINA, F.; MARTÍNEZ, S.; CARRACELAS, G.; PÉREZ DE VIDA, F.; ROSAS, J.E. Instituto Nacional de Investigación Agropecuaria (INIA) Rice Breeding Program Historical Dataset. [Dataset]. DRYAD Dataset, 2024. https://doi.org/10.5061/dryad.x69p8czn8 Correspondence author: Juan E. Rosas, email: jrosas@inia.org.uy -- Publication date: February 16, 2024. -- This dataset is embargoed and will be released when the associated article is published. Lists of files and downloads will become...Biblioteca(s): INIA Las Brujas. |
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14. | | MUSSIO, P.; MARTÍNEZ, I.; LUZARDO, S.; NAVARRO, A.; LEOTTA, G.; VARELA, G. Phenotypic and genotypic characterization of Shiga toxin-producing Escherichia coli strains recovered from bovine carcasses in Uruguay. Original research. Frontiers in Microbiology, 2023, volume 14, article 1130170. OPEN ACCESS. doi: https://doi.org/10.3389/fmicb.2023.1130170 Article history: Received 22 December 2022; Accepted 13 February 2023; Published 06 March 2023. -- Correspondence authors: Paula Mussio, email: paumussio@gmail.com ; Gustavo Varela, email: gvarela@higiene.edu.uy -- Edited by: Vinicius...Biblioteca(s): INIA Las Brujas. |
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15. | | SCHILD, C.; BOABAID, F.M.; OLIVEIRA, L.G.S.; ARMENDANO, J.I.; SARAVIA, A.; CUSTODIO, A.; ALGORTA, J.; ALVAREZ, C.; JAURENA, M.; DIXON, R.M.; RIET-CORREA, F. Response of cows with osteomalacia grazing sub-tropical native pastures to phosphorus supplementation with loose mineral mix or feed blocks. Veterinary Journal. 2023, Volume 298-299, 106013. https://doi.org/10.1016/j.tvjl.2023.106013 Article history: Accepted 20 June 2023; Available online 22 June 2023. -- Correspondence author: Riet-Correa, F.; Programa de pós-graduação em Ciência Animal nos Trópicos, Escola de Medicina Veterinária e Zootecnia, Universidade Federal...Biblioteca(s): INIA Las Brujas. |
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Registro completo
|
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 percep... Presentar Todo |
Palabras claves : |
Agroecological transitions; ANPP; Artificial neural networks; Grasslands; Remote sensing; Uruguay. |
Asunto categoría : |
-- |
Marc : |
LEADER 04176naa 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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