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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; INIA Tacuarembó. |
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Fecha : |
04/03/2020 |
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Actualizado : |
29/09/2020 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
RESQUÍN, F.; NAVARRO-CERRILLO, R. M.; CARRASCO-LETELIER, L.; RACHID, C.; BENTANCOR, L. |
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Afiliación : |
JOSE FERNANDO RESQUIN PEREZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; RAFAEL M. NAVARRO-CERRILLO, E.T.S.I.A.M.-Department of Forestry, School of Agriculture and Forestry, University of Córdoba, Córdoba, Spain; LEONIDAS CARRASCO-LETELIER, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANA CECILIA RACHID CASNATI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUIS BENTANCOR, University of the Republic, College of Agronomy, Soil and Water Department, Montevideo, Uruguay. |
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Título : |
Evaluation of the nutrient content in biomass of Eucalyptus species from short rotation plantations in Uruguay. (Research paper) |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
Biomass and Bioenergy, March 2020, Volume 134, Article number 105502. Doi: https://doi.org/10.1016/j.biombioe.2020.105502 |
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ISSN : |
0961-9534 |
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DOI : |
10.1016/j.biombioe.2020.105502 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 1 August 2019 / Revised 27 December 2019 / Accepted 31 January 2020 / Available online 10 February 2020.
Corresponding author: Fernando Resquín - emai: nando@inia.org.uy.
Funding text: This study was funded by the National Institute of Agricultural Research (INIA) and the National Agency of Research and Innovation (ANII) through the grant FSE 1 2011 15615 (Evaluación productiva y ambiental de plantaciones forestales para la generación de Bioenergía). The authors thank the Forestal Oriental company for providing the logistical support for the installation of the experiments. |
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Contenido : |
ABSTRACT.
Forest biomass is an interesting option for bioenergy generation, however sustainability aspects should be carefully considered. In this context, the nutrient exportation is a key factor for reach a sustainable biomass production by an adequate harvest decision about what tree's fractions will export less nutrients of farms. In this work biomass production per fraction and nutrient export for three eucalypt species (Eucalyptus benthamii, E.dunnii and E.grandis) planted at four planting densities: 2220, 3330, 4440 and 6660 trees per hectare, in two different sites, were evaluated at the age of 76 months. The assessed nutrients were: P, Ca, Mg, K and N in bark, branches, leaves, and stemwood fractions. Nutrients use efficiency and the balance between available cations in the soil (Ca, Mg and K) vs their export in the different tree components were assessed. The effect of the interaction (species x planting density) for almost all the variables evaluated was not significant in both sites. The nutrients concentration is slightly affected by the planting density while the nutrient export is significantly related to this parameter in both sites. Bark is the fraction with the higher Ca and Mg concentration values, whereas the higher concentrations of N and K were found on the leaves, meanwhile debarked stemwood is the fraction with the lower nutrient concentration. In the short term, K would be the nutrient that will exhaust first, although the use of stemwood allows an important reduction in the use of cations. Even with low planting densities it is possible to obtain high biomass yields and similar to those achieved with the lowest spacing in particular at the Paysandú site.
© 2020 MenosABSTRACT.
Forest biomass is an interesting option for bioenergy generation, however sustainability aspects should be carefully considered. In this context, the nutrient exportation is a key factor for reach a sustainable biomass production by an adequate harvest decision about what tree's fractions will export less nutrients of farms. In this work biomass production per fraction and nutrient export for three eucalypt species (Eucalyptus benthamii, E.dunnii and E.grandis) planted at four planting densities: 2220, 3330, 4440 and 6660 trees per hectare, in two different sites, were evaluated at the age of 76 months. The assessed nutrients were: P, Ca, Mg, K and N in bark, branches, leaves, and stemwood fractions. Nutrients use efficiency and the balance between available cations in the soil (Ca, Mg and K) vs their export in the different tree components were assessed. The effect of the interaction (species x planting density) for almost all the variables evaluated was not significant in both sites. The nutrients concentration is slightly affected by the planting density while the nutrient export is significantly related to this parameter in both sites. Bark is the fraction with the higher Ca and Mg concentration values, whereas the higher concentrations of N and K were found on the leaves, meanwhile debarked stemwood is the fraction with the lower nutrient concentration. In the short term, K would be the nutrient that will exhaust first, although the use of stemwood allows an i... Presentar Todo |
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Palabras claves : |
Biomass; NUE; Nutrients; Planting density; Short rotation forestry. |
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Thesagro : |
EUCALYPTUS. |
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Asunto categoría : |
K10 Producción forestal |
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Marc : |
LEADER 03223naa a2200277 a 4500
001 1060887
005 2020-09-29
008 2020 bl uuuu u00u1 u #d
022 $a0961-9534
024 7 $a10.1016/j.biombioe.2020.105502$2DOI
100 1 $aRESQUÍN, F.
245 $aEvaluation of the nutrient content in biomass of Eucalyptus species from short rotation plantations in Uruguay. (Research paper)$h[electronic resource]
260 $c2020
500 $aArticle history: Received 1 August 2019 / Revised 27 December 2019 / Accepted 31 January 2020 / Available online 10 February 2020. Corresponding author: Fernando Resquín - emai: nando@inia.org.uy. Funding text: This study was funded by the National Institute of Agricultural Research (INIA) and the National Agency of Research and Innovation (ANII) through the grant FSE 1 2011 15615 (Evaluación productiva y ambiental de plantaciones forestales para la generación de Bioenergía). The authors thank the Forestal Oriental company for providing the logistical support for the installation of the experiments.
520 $aABSTRACT. Forest biomass is an interesting option for bioenergy generation, however sustainability aspects should be carefully considered. In this context, the nutrient exportation is a key factor for reach a sustainable biomass production by an adequate harvest decision about what tree's fractions will export less nutrients of farms. In this work biomass production per fraction and nutrient export for three eucalypt species (Eucalyptus benthamii, E.dunnii and E.grandis) planted at four planting densities: 2220, 3330, 4440 and 6660 trees per hectare, in two different sites, were evaluated at the age of 76 months. The assessed nutrients were: P, Ca, Mg, K and N in bark, branches, leaves, and stemwood fractions. Nutrients use efficiency and the balance between available cations in the soil (Ca, Mg and K) vs their export in the different tree components were assessed. The effect of the interaction (species x planting density) for almost all the variables evaluated was not significant in both sites. The nutrients concentration is slightly affected by the planting density while the nutrient export is significantly related to this parameter in both sites. Bark is the fraction with the higher Ca and Mg concentration values, whereas the higher concentrations of N and K were found on the leaves, meanwhile debarked stemwood is the fraction with the lower nutrient concentration. In the short term, K would be the nutrient that will exhaust first, although the use of stemwood allows an important reduction in the use of cations. Even with low planting densities it is possible to obtain high biomass yields and similar to those achieved with the lowest spacing in particular at the Paysandú site. © 2020
650 $aEUCALYPTUS
653 $aBiomass
653 $aNUE
653 $aNutrients
653 $aPlanting density
653 $aShort rotation forestry
700 1 $aNAVARRO-CERRILLO, R. M.
700 1 $aCARRASCO-LETELIER, L.
700 1 $aRACHID, C.
700 1 $aBENTANCOR, L.
773 $tBiomass and Bioenergy, March 2020, Volume 134, Article number 105502. Doi: https://doi.org/10.1016/j.biombioe.2020.105502
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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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