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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
18/08/2021 |
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Actualizado : |
02/09/2022 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
CARAM, N.; CASALÁS, F.; SOCA, P.; ANFUSO, V.; GARCÍA-FAVRE, J.; WALLAU, M.; ZANONIANI, R.; CADENAZZI, M.; BOGGIANO, P. |
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Afiliación : |
Departamento de Producción Animal y Pasturas, Facultad de Agronomía, Universidad de la Republica, Paysandú, Uruguay.; Departamento de Producción Animal y Pasturas, Facultad de Agronomía, Universidad de la Republica, Paysandú, Uruguay; Departamento de Producción Animal y Pasturas, Facultad de Agronomía, Universidad de la Republica, Paysandú, Uruguay; VALENTIN ANFUSO ETCHEVERRY, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Departamento de Producción Animal y Pasturas, Facultad de Agronomía, Universidad de la Republica, Paysandú, Uruguay; Agronomy Department, University of Florida, Gainesville.; Departamento de Producción Animal y Pasturas, Facultad de Agronomía, Universidad de la Republica, Paysandú, Uruguay.; Departamento de Biometría, Estadística y Computación, Facultad de Agronomía, Universidad de la República, Paysandú, Uruguay; Departamento de Producción Animal y Pasturas, Facultad de Agronomía, Universidad de la Republica, Paysandú, Uruguay. |
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Título : |
Configuration of daily grazing and searching of growing beef cattle in grassland: observational study. |
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Fecha de publicación : |
2021 |
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Fuente / Imprenta : |
Animal, 2021, volume 15, Issue 9, Article number 100336. Open Access. Doi: https://doi.org/10.1016/j.animal.2021.100336 |
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DOI : |
10.1016/j.animal.2021.100336 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 2 February 2021, Revised 29 June 2021, Accepted 2 July 2021. |
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Contenido : |
Abstract:
Many of the studies in Campos grasslands focus on management aspects such as the control of herbage allowance, and application of nutrients and/or overseeding with legumes. However, there is little literature on how the Campos grassland resource is utilised, especially regarding the grazing pattern and the relationship between pasture quantity and quality on daily grazing activities. The study of the ingestive behaviour in species-rich and heterogeneous native grasslands during daylight hours, and understanding how animals prioritise quality or quantity of intake in relation to pasture attributes, are important to comprehend the ingestive-digestive processes modulating the energy intake of animals and to achieve a better grazing management. Therefore, the objective was to describe and quantify the daily grazing behaviour of growing cattle grazing native pasture with different structures as a result of different management practices, and study the relationship of pasture attributes and intake through multivariate analysis. The study was carried out at the Faculty of Agronomy, Paysandú, Uruguay. Treatments were native grassland, overseeding with Trifolium pratense and Lotus tenuis + phosphorus, and native pasture + nitrogen-phosphorus. Grazing activities were discriminated into grazing, searching (defined when animals take 1?2 bites in one feeding station and then change to another feeding station and so on), ruminating and idling. The probability of time allocated to each activity was continuously measured during daylight hours (0700?1930) and was related to pasture structure and forage quality using regression tree models, while the bite rate was determined every 2 h. The diurnal pattern of growing cattle showed grazing and searching sessions, followed by ruminating and idling sessions. The length of sessions (as the probability of time allocated to each activity) varied throughout the day. The grazing probability was greater during afternoon than morning and midday (0.74 vs 0.45 vs 0.46, respectively), and it was associated with higher bite rate (34.2 bites/min). Regression tree models showed different grazing, searching and ruminating strategies according to pasture attributes. During the morning, animals modified grazing, searching, ruminating and idling strategies according to bite rate, crude protein in diet and herbage allowance. At midday, they only adjusted ruminating and idling, while during afternoon sessions, grazing activities were modified by pasture quantity attributes such as herbage mass and herbage allowance. By controlling the herbage allowance, herbage mass and pasture height, animals prioritise quality in the morning and quantity in the afternoon, integrating and modifying the grazing-searching and ruminating-idling pattern. MenosAbstract:
Many of the studies in Campos grasslands focus on management aspects such as the control of herbage allowance, and application of nutrients and/or overseeding with legumes. However, there is little literature on how the Campos grassland resource is utilised, especially regarding the grazing pattern and the relationship between pasture quantity and quality on daily grazing activities. The study of the ingestive behaviour in species-rich and heterogeneous native grasslands during daylight hours, and understanding how animals prioritise quality or quantity of intake in relation to pasture attributes, are important to comprehend the ingestive-digestive processes modulating the energy intake of animals and to achieve a better grazing management. Therefore, the objective was to describe and quantify the daily grazing behaviour of growing cattle grazing native pasture with different structures as a result of different management practices, and study the relationship of pasture attributes and intake through multivariate analysis. The study was carried out at the Faculty of Agronomy, Paysandú, Uruguay. Treatments were native grassland, overseeding with Trifolium pratense and Lotus tenuis + phosphorus, and native pasture + nitrogen-phosphorus. Grazing activities were discriminated into grazing, searching (defined when animals take 1?2 bites in one feeding station and then change to another feeding station and so on), ruminating and idling. The probability of time allocated t... Presentar Todo |
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Palabras claves : |
Cattle ingestive behaviour; Grazing management; Grazing pattern; Regression trees; Searching strategy. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16667/1/1-s2.0-S1751731121001798-main.pdf
https://www.sciencedirect.com/science/article/pii/S1751731121001798/pdfft?isDTMRedir=true&download=true
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Marc : |
LEADER 03831naa a2200301 a 4500
001 1062355
005 2022-09-02
008 2021 bl uuuu u00u1 u #d
024 7 $a10.1016/j.animal.2021.100336$2DOI
100 1 $aCARAM, N.
245 $aConfiguration of daily grazing and searching of growing beef cattle in grassland$bobservational study.$h[electronic resource]
260 $c2021
500 $aArticle history: Received 2 February 2021, Revised 29 June 2021, Accepted 2 July 2021.
520 $aAbstract: Many of the studies in Campos grasslands focus on management aspects such as the control of herbage allowance, and application of nutrients and/or overseeding with legumes. However, there is little literature on how the Campos grassland resource is utilised, especially regarding the grazing pattern and the relationship between pasture quantity and quality on daily grazing activities. The study of the ingestive behaviour in species-rich and heterogeneous native grasslands during daylight hours, and understanding how animals prioritise quality or quantity of intake in relation to pasture attributes, are important to comprehend the ingestive-digestive processes modulating the energy intake of animals and to achieve a better grazing management. Therefore, the objective was to describe and quantify the daily grazing behaviour of growing cattle grazing native pasture with different structures as a result of different management practices, and study the relationship of pasture attributes and intake through multivariate analysis. The study was carried out at the Faculty of Agronomy, Paysandú, Uruguay. Treatments were native grassland, overseeding with Trifolium pratense and Lotus tenuis + phosphorus, and native pasture + nitrogen-phosphorus. Grazing activities were discriminated into grazing, searching (defined when animals take 1?2 bites in one feeding station and then change to another feeding station and so on), ruminating and idling. The probability of time allocated to each activity was continuously measured during daylight hours (0700?1930) and was related to pasture structure and forage quality using regression tree models, while the bite rate was determined every 2 h. The diurnal pattern of growing cattle showed grazing and searching sessions, followed by ruminating and idling sessions. The length of sessions (as the probability of time allocated to each activity) varied throughout the day. The grazing probability was greater during afternoon than morning and midday (0.74 vs 0.45 vs 0.46, respectively), and it was associated with higher bite rate (34.2 bites/min). Regression tree models showed different grazing, searching and ruminating strategies according to pasture attributes. During the morning, animals modified grazing, searching, ruminating and idling strategies according to bite rate, crude protein in diet and herbage allowance. At midday, they only adjusted ruminating and idling, while during afternoon sessions, grazing activities were modified by pasture quantity attributes such as herbage mass and herbage allowance. By controlling the herbage allowance, herbage mass and pasture height, animals prioritise quality in the morning and quantity in the afternoon, integrating and modifying the grazing-searching and ruminating-idling pattern.
653 $aCattle ingestive behaviour
653 $aGrazing management
653 $aGrazing pattern
653 $aRegression trees
653 $aSearching strategy
700 1 $aCASALÁS, F.
700 1 $aSOCA, P.
700 1 $aANFUSO, V.
700 1 $aGARCÍA-FAVRE, J.
700 1 $aWALLAU, M.
700 1 $aZANONIANI, R.
700 1 $aCADENAZZI, M.
700 1 $aBOGGIANO, P.
773 $tAnimal, 2021, volume 15, Issue 9, Article number 100336. Open Access. Doi: https://doi.org/10.1016/j.animal.2021.100336
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Registro original : |
INIA La Estanzuela (LE) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
16/03/2022 |
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Actualizado : |
16/03/2022 |
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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 : |
HIRIGOYEN, A.; ACOSTA-MUÑOZ, C.; SALAMANCA, A.J.A.; VARO-MARTINEZ, M.Á.; RACHID, C.; FRANCO, J.; NAVARRO-CERRILLO, R. |
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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. |
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Título : |
A machine learning approach to model leaf area index in Eucalyptus plantations using high-resolution satellite imagery and airborne laser scanner data. |
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Fecha de publicación : |
2021 |
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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 |
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ISSN : |
1844-8135 |
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DOI : |
10.15287/afr.2021.2073 |
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Idioma : |
Inglés |
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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. |
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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 |
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Palabras claves : |
Intensive silviculture; LAI canopy; Machine learning algorithms. |
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Asunto categoría : |
K01 Ciencias forestales - Aspectos generales |
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URL : |
https://www.afrjournal.org/index.php/afr/article/viewFile/2073/1177
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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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