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Biblioteca (s) : |
INIA Tacuarembó. |
Fecha : |
21/02/2014 |
Actualizado : |
24/09/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
WICKS, J.; BELINE, M.; GOMEZ, J.F.M.; LUZARDO, S.; SILVA, S.L.; GERRARD, D. |
Afiliación : |
JORDAN WICKS, Department of Animal and Poultry Sciences, Virginia Tech, Blacksburg, USA.; MARIANE BELINE; JUAN FERNANDO MORALES GOMEZ, Faculdade de Zootecnia e Engenharia de Alimentos, University of São Paulo, São Paulo, Brazil.; SANTIAGO FELIPE LUZARDO VILLAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SAULO LUZ SILVA, Faculdade de Zootecnia e Engenharia de Alimentos, University of São Paulo, São Paulo, Brazil.; DAVID GERRARD, Department of Animal and Poultry Sciences, Virginia Tech, Blacksburg, USA. |
Título : |
Muscle Energy Metabolism, Growth, and Meat Quality in Beef Cattle. |
Fecha de publicación : |
2019 |
Fuente / Imprenta : |
Agriculture, v. 9, no. 9, 2019. |
DOI : |
10.3390/agriculture9090195 |
Idioma : |
Inglés |
Notas : |
OPEN ACCESS. Article history: Received: 16 July 2019 // Accepted: 2 September 2019 // Published: 7 September 2019. |
Contenido : |
Abstract: World meat production must increase substantially to support current projections in population growth over the next 30 years. However, maximizing product quality remains a focus for many in the meat industry, as incremental increases in product quality often signal potential increases
in segment profitability. Moreover, increases in meat quality also address concerns raised by an ever-growing auent society demanding greater eating satisfaction. Production strategies and valued endpoints dier worldwide, though this makes the global marketing of meat challenging. Moreover,
this variation in production schemes makes it dicult for the scientific community to understand precisely those mechanisms controlling beef quality. For example, some cattle are produced in low input, extensive, forage-based systems. In contrast, some producers raise cattle in more intensive
operations where feeding programs are strategically designed to maximal growth rates and achieve significant fat deposition. Yet, others produce cattle that perform between these two extremes. Fresh meat quality, somewhat like the variation observed in production strategies, is perceived dierently
across the globe. Even so, meat quality is largely predicated on those characteristics visible at the retail counter, namely color and perceived texture and firmness. Once purchased, however, the eating experience is a function of flavor and tenderness. In this review, we attempt to identify a few areas
where animal growth may impact postmortem energy metabolism and thereby alter meat quality. Understanding how animals grow and how this aects meat quality development is incumbent to all vested in the meat industry. MenosAbstract: World meat production must increase substantially to support current projections in population growth over the next 30 years. However, maximizing product quality remains a focus for many in the meat industry, as incremental increases in product quality often signal potential increases
in segment profitability. Moreover, increases in meat quality also address concerns raised by an ever-growing auent society demanding greater eating satisfaction. Production strategies and valued endpoints dier worldwide, though this makes the global marketing of meat challenging. Moreover,
this variation in production schemes makes it dicult for the scientific community to understand precisely those mechanisms controlling beef quality. For example, some cattle are produced in low input, extensive, forage-based systems. In contrast, some producers raise cattle in more intensive
operations where feeding programs are strategically designed to maximal growth rates and achieve significant fat deposition. Yet, others produce cattle that perform between these two extremes. Fresh meat quality, somewhat like the variation observed in production strategies, is perceived dierently
across the globe. Even so, meat quality is largely predicated on those characteristics visible at the retail counter, namely color and perceived texture and firmness. Once purchased, however, the eating experience is a function of flavor and tenderness. In this review, we attempt to identify a few areas
where animal g... Presentar Todo |
Palabras claves : |
ANIMALS; BEEF; COLOR; GROWTH; MEAT QUALITY; MUSCLE; TENDERNESS. |
Asunto categoría : |
L01 Ganadería |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/13328/1/Luzardo-2019.pdf
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Marc : |
LEADER 02529naa a2200289 a 4500 001 1016091 005 2019-09-24 008 2019 bl uuuu u00u1 u #d 024 7 $a10.3390/agriculture9090195$2DOI 100 1 $aWICKS, J. 245 $aMuscle Energy Metabolism, Growth, and Meat Quality in Beef Cattle.$h[electronic resource] 260 $c2019 500 $aOPEN ACCESS. Article history: Received: 16 July 2019 // Accepted: 2 September 2019 // Published: 7 September 2019. 520 $aAbstract: World meat production must increase substantially to support current projections in population growth over the next 30 years. However, maximizing product quality remains a focus for many in the meat industry, as incremental increases in product quality often signal potential increases in segment profitability. Moreover, increases in meat quality also address concerns raised by an ever-growing auent society demanding greater eating satisfaction. Production strategies and valued endpoints dier worldwide, though this makes the global marketing of meat challenging. Moreover, this variation in production schemes makes it dicult for the scientific community to understand precisely those mechanisms controlling beef quality. For example, some cattle are produced in low input, extensive, forage-based systems. In contrast, some producers raise cattle in more intensive operations where feeding programs are strategically designed to maximal growth rates and achieve significant fat deposition. Yet, others produce cattle that perform between these two extremes. Fresh meat quality, somewhat like the variation observed in production strategies, is perceived dierently across the globe. Even so, meat quality is largely predicated on those characteristics visible at the retail counter, namely color and perceived texture and firmness. Once purchased, however, the eating experience is a function of flavor and tenderness. In this review, we attempt to identify a few areas where animal growth may impact postmortem energy metabolism and thereby alter meat quality. Understanding how animals grow and how this aects meat quality development is incumbent to all vested in the meat industry. 653 $aANIMALS 653 $aBEEF 653 $aCOLOR 653 $aGROWTH 653 $aMEAT QUALITY 653 $aMUSCLE 653 $aTENDERNESS 700 1 $aBELINE, M. 700 1 $aGOMEZ, J.F.M. 700 1 $aLUZARDO, S. 700 1 $aSILVA, S.L. 700 1 $aGERRARD, D. 773 $tAgriculture$gv. 9, no. 9, 2019.
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INIA Tacuarembó (TBO) |
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
16/03/2022 |
Actualizado : |
16/03/2022 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
HIRIGOYEN, A.; ACOSTA-MUÑOZ, C.; SALAMANCA, A.J.A.; VARO-MARTINEZ, M.Á.; RACHID, C.; FRANCO, J.; NAVARRO-CERRILLO, R. |
Afiliación : |
ANDRES EDUARDO HIRIGOYEN DOMINGUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CRISTINA ACOSTA-MUÑOZ, Department of Forestry Engineering, Laboratory of Silviculture, Dendrochronology and Climate Change, DendrodatLab-ERSAF, University of Cordoba, Córdoba, Spain; ANTONIO JESÚS ARIZA SALAMANCA, Department of Forestry Engineering, Laboratory of Silviculture, Dendrochronology and Climate Change, DendrodatLab-ERSAF, University of Cordoba, Córdoba, Spain; MARIA ÁNGELES VARO-MARTINEZ, Department of Forestry Engineering, Laboratory of Silviculture, Dendrochronology and Climate Change, DendrodatLab-ERSAF, University of Cordoba, Córdoba, Spain; ANA CECILIA RACHID CASNATI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JORGE FRANCO, Faculty of Agronomy, University of the Republic, Paysandú, Uruguay; RAFAEL NAVARRO-CERRILLO, Department of Forestry Engineering, Laboratory of Silviculture, Dendrochronology and Climate Change, DendrodatLab-ERSAF, University of Cordoba, Córdoba, Spain. |
Título : |
A machine learning approach to model leaf area index in Eucalyptus plantations using high-resolution satellite imagery and airborne laser scanner data. |
Fecha de publicación : |
2021 |
Fuente / Imprenta : |
Annals of Forest Research, 2021, Volume 64, Issue 2, Pages 165-183. OPEN ACCESS. doi: https://doi.org/10.15287/afr.2021.2073 |
ISSN : |
1844-8135 |
DOI : |
10.15287/afr.2021.2073 |
Idioma : |
Inglés |
Notas : |
Article history: Received October 27, 2020; Revised December 14, 2021; Accepted December 21, 2021.
Corresponding author: Hirigoyen, A.; National Institute of Agricultural Research (INIA), Tacuarembó, Uruguay; email:ahirigoyen@inia.org.uy -- The authors thank the Instituto Nacional de Investigaciones Agropecuarias (INIA-Uruguay) for supporting our research work and for help during the fieldwork. We are particularly grateful to Roberto Scoz, Demian Gomez, Leonidas Carrasco and Alicia Peduzzi for their assistance during this research. RMNC acknowledge the institutional support of the Ministerio de Ciencia, Innovaci?n y Universidades (Spain), through the ESPECTRAMED (CGL2017-86161-R). show significant changes. |
Contenido : |
ABSTRACT. - As a forest structural parameter, leaf area index (LAI) is crucial for efficient intensive plantation management. Leaf area is responsible for the energy absorption needed for photosynthetic production and transpiration, both affecting growth. Currently, LAI can be estimated either by remote-sensing methods or ground-based methods. However, unlike ground-based methods, remote estimation provides a cost-effective and ecologically significant advance. The aim of our study was to evaluate whether machine learning algorithms can be used to quantify LAI, using either optical remote sensing or LiDAR metrics in Eucalyptus dunnii and Eucalyptus grandis stands. First, empirical relationships between LAI and remote-sensing data using LiDAR metrics and multispectral high-resolution satellite metrics, were assessed. Selected variables for LAI estimation were: forest canopy cover, laser penetration index, canopy relief ratio (from among the LiDAR data), the green normalized difference vegetation index, and normalized difference vegetation index (from among spectral vegetation indices). We compared the accuracy of three machine learning algorithms: artificial neural networks (ANN), random forest (RF) and support vector regression (SVR). The coefficient of determination ranged from 0.60, for ANN, to 0.84, for SVR. The SVR regression methods showed the best performance in terms of overall model accuracy and RMSE (0.60). The results show that the remote sensing data applied through machine learning algorithms provide an effective method to estimate LAI in eucalypt plantations. The methodology proposed is directly applicable for operational forest planning at the landscape level. © 2021, Editura Silvica. All rights reserved. MenosABSTRACT. - As a forest structural parameter, leaf area index (LAI) is crucial for efficient intensive plantation management. Leaf area is responsible for the energy absorption needed for photosynthetic production and transpiration, both affecting growth. Currently, LAI can be estimated either by remote-sensing methods or ground-based methods. However, unlike ground-based methods, remote estimation provides a cost-effective and ecologically significant advance. The aim of our study was to evaluate whether machine learning algorithms can be used to quantify LAI, using either optical remote sensing or LiDAR metrics in Eucalyptus dunnii and Eucalyptus grandis stands. First, empirical relationships between LAI and remote-sensing data using LiDAR metrics and multispectral high-resolution satellite metrics, were assessed. Selected variables for LAI estimation were: forest canopy cover, laser penetration index, canopy relief ratio (from among the LiDAR data), the green normalized difference vegetation index, and normalized difference vegetation index (from among spectral vegetation indices). We compared the accuracy of three machine learning algorithms: artificial neural networks (ANN), random forest (RF) and support vector regression (SVR). The coefficient of determination ranged from 0.60, for ANN, to 0.84, for SVR. The SVR regression methods showed the best performance in terms of overall model accuracy and RMSE (0.60). The results show that the remote sensing data applied throu... Presentar Todo |
Palabras claves : |
Intensive silviculture; LAI canopy; Machine learning algorithms. |
Asunto categoría : |
K01 Ciencias forestales - Aspectos generales |
URL : |
https://www.afrjournal.org/index.php/afr/article/viewFile/2073/1177
|
Marc : |
LEADER 03380naa a2200265 a 4500 001 1062842 005 2022-03-16 008 2021 bl uuuu u00u1 u #d 022 $a1844-8135 024 7 $a10.15287/afr.2021.2073$2DOI 100 1 $aHIRIGOYEN, A. 245 $aA machine learning approach to model leaf area index in Eucalyptus plantations using high-resolution satellite imagery and airborne laser scanner data.$h[electronic resource] 260 $c2021 500 $aArticle history: Received October 27, 2020; Revised December 14, 2021; Accepted December 21, 2021. Corresponding author: Hirigoyen, A.; National Institute of Agricultural Research (INIA), Tacuarembó, Uruguay; email:ahirigoyen@inia.org.uy -- The authors thank the Instituto Nacional de Investigaciones Agropecuarias (INIA-Uruguay) for supporting our research work and for help during the fieldwork. We are particularly grateful to Roberto Scoz, Demian Gomez, Leonidas Carrasco and Alicia Peduzzi for their assistance during this research. RMNC acknowledge the institutional support of the Ministerio de Ciencia, Innovaci?n y Universidades (Spain), through the ESPECTRAMED (CGL2017-86161-R). show significant changes. 520 $aABSTRACT. - As a forest structural parameter, leaf area index (LAI) is crucial for efficient intensive plantation management. Leaf area is responsible for the energy absorption needed for photosynthetic production and transpiration, both affecting growth. Currently, LAI can be estimated either by remote-sensing methods or ground-based methods. However, unlike ground-based methods, remote estimation provides a cost-effective and ecologically significant advance. The aim of our study was to evaluate whether machine learning algorithms can be used to quantify LAI, using either optical remote sensing or LiDAR metrics in Eucalyptus dunnii and Eucalyptus grandis stands. First, empirical relationships between LAI and remote-sensing data using LiDAR metrics and multispectral high-resolution satellite metrics, were assessed. Selected variables for LAI estimation were: forest canopy cover, laser penetration index, canopy relief ratio (from among the LiDAR data), the green normalized difference vegetation index, and normalized difference vegetation index (from among spectral vegetation indices). We compared the accuracy of three machine learning algorithms: artificial neural networks (ANN), random forest (RF) and support vector regression (SVR). The coefficient of determination ranged from 0.60, for ANN, to 0.84, for SVR. The SVR regression methods showed the best performance in terms of overall model accuracy and RMSE (0.60). The results show that the remote sensing data applied through machine learning algorithms provide an effective method to estimate LAI in eucalypt plantations. The methodology proposed is directly applicable for operational forest planning at the landscape level. © 2021, Editura Silvica. All rights reserved. 653 $aIntensive silviculture 653 $aLAI canopy 653 $aMachine learning algorithms 700 1 $aACOSTA-MUÑOZ, C. 700 1 $aSALAMANCA, A.J.A. 700 1 $aVARO-MARTINEZ, M.Á. 700 1 $aRACHID, C. 700 1 $aFRANCO, J. 700 1 $aNAVARRO-CERRILLO, R. 773 $tAnnals of Forest Research, 2021, Volume 64, Issue 2, Pages 165-183. OPEN ACCESS. doi: https://doi.org/10.15287/afr.2021.2073
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