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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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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
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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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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Registro original : |
INIA Las Brujas (LB) |
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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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Registro completo
|
|
Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
23/04/2015 |
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Actualizado : |
23/10/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
A - 1 |
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Autor : |
LADO, J.; ALÓS, E.; RODRIGO, M.J.; ZACARÍAS, L. |
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Afiliación : |
JOANNA LADO LINDNER, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ENRIQUETA ALÓS, CSIC/ IAS (Instituto de Agricultura Sostenible); MARÍA JESÚS RODRIGO, IATA (Instituto de Agroquímica y Tecnología de Alimentos); LORENZO ZACARÍAS, IATA (Instituto de Agroquímica y Tecnología de Alimentos). |
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Título : |
Light avoidance reduces ascorbic acid accumulation in the peel of Citrus fruit. |
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Fecha de publicación : |
2015 |
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Fuente / Imprenta : |
Plant Science, 2015, v.231, p.138-147. |
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DOI : |
10.1016/j.plantsci.2014.12.002 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 2 October 2014 / Received in revised form 26 November 2014 / Accepted 1 December 2014 / Available online 5 December 2014. |
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Contenido : |
ABSTRACT.
Citrus fruits are highly consumed worldwide and represent one of the most important sources of ascorbic acid (AsA). However, information about the molecular mechanisms regulating AsA accumulation in Citrus fruit and the effects of environmental factors is scarce. In this study we have investigated the effect of fruit shading on AsA content and the expression of AsA biosynthetic, degrading and recycling genes in fruits of different Citrus species. Immature-green fruits were covered at the end of the cell enlargement phase and AsA concentration in the flavedo declined and remained at low levels as compared with light-exposed fruits. Fruit shading marginally altered the expression of genes from the l-galactose pathway and this effect was variable in the four Citrus species. However, specific isoforms (GalUR8 or GalUR12) from the l-galacturonic acid pathway were significantly repressed paralleling the reduction in AsA concentration. No significant effect of shading was detected in transcription of genes of the myo-inositol and l-gulose pathways as well as recycling and degradation. Collectively, results indicate that light avoidance inhibited accumulation of AsA in the flavedo of Citrus fruits and suggest that the l-galacturonic acid pathway has a relevant contribution to AsA content in this tissue. |
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Palabras claves : |
VITAMINA C. |
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Thesagro : |
ANTIOXIDANTES; CITRUS; EXPRESION GENICA; VITAMINAS. |
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Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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Marc : |
LEADER 02127naa a2200241 a 4500
001 1052574
005 2019-10-23
008 2015 bl uuuu u00u1 u #d
024 7 $a10.1016/j.plantsci.2014.12.002$2DOI
100 1 $aLADO, J.
245 $aLight avoidance reduces ascorbic acid accumulation in the peel of Citrus fruit.$h[electronic resource]
260 $c2015
500 $aArticle history: Received 2 October 2014 / Received in revised form 26 November 2014 / Accepted 1 December 2014 / Available online 5 December 2014.
520 $aABSTRACT. Citrus fruits are highly consumed worldwide and represent one of the most important sources of ascorbic acid (AsA). However, information about the molecular mechanisms regulating AsA accumulation in Citrus fruit and the effects of environmental factors is scarce. In this study we have investigated the effect of fruit shading on AsA content and the expression of AsA biosynthetic, degrading and recycling genes in fruits of different Citrus species. Immature-green fruits were covered at the end of the cell enlargement phase and AsA concentration in the flavedo declined and remained at low levels as compared with light-exposed fruits. Fruit shading marginally altered the expression of genes from the l-galactose pathway and this effect was variable in the four Citrus species. However, specific isoforms (GalUR8 or GalUR12) from the l-galacturonic acid pathway were significantly repressed paralleling the reduction in AsA concentration. No significant effect of shading was detected in transcription of genes of the myo-inositol and l-gulose pathways as well as recycling and degradation. Collectively, results indicate that light avoidance inhibited accumulation of AsA in the flavedo of Citrus fruits and suggest that the l-galacturonic acid pathway has a relevant contribution to AsA content in this tissue.
650 $aANTIOXIDANTES
650 $aCITRUS
650 $aEXPRESION GENICA
650 $aVITAMINAS
653 $aVITAMINA C
700 1 $aALÓS, E.
700 1 $aRODRIGO, M.J.
700 1 $aZACARÍAS, L.
773 $tPlant Science, 2015$gv.231, p.138-147.
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