| |
|
|
 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
|
Registro completo
|
|
Biblioteca (s) : |
INIA Las Brujas. |
|
Fecha : |
23/10/2020 |
|
Actualizado : |
09/04/2021 |
|
Tipo de producción científica : |
Capítulo en Libro Técnico-Científico |
|
Autor : |
HASTINGS, F.; FUENTES, I.; PÉREZ-BIDEGAIN, M.; NAVAS, R.; GORGOGLIONE, A. |
|
Afiliación : |
FLORENCIA HASTINGS, School of Agronomy Universidad de la República, Montevideo, Uruguay; Directorate of Natural Resources, Ministry of Agriculture, Livestock and Fisheries, Montevideo, Uruguay; IGNACIO FUENTES, School of Life and Environmental Sciences, University of Sydney, Sydney, Australia; MARIO PÉREZ-BIDEGAIN, School of Agronomy, Universidad de la República, Montevideo, Uruguay; RAFAEL NAVAS NÚÑEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ÁNGELA GORGOGLIONE, School of Engineering, Universidad de la República, Montevideo, Uruguay. |
|
Título : |
Land-cover mapping of agricultural areas using machine learning in Google Earth engine. (Conference paper) |
|
Fecha de publicación : |
2020 |
|
Fuente / Imprenta : |
In: Gervasi O. et al. (eds) Computational Science and Its Applications - ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science, vol 12252. International Conference on Computational Science and Its Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_52 |
|
ISBN : |
e-ISBN: 978-3-030-58811-3 |
|
DOI : |
10.1007/978-3-030-58811-3_52 |
|
Idioma : |
Inglés |
|
Notas : |
Article history: First Online 29 September 2020. Volume Editors: Gervasi O.,Murgante B.,Misra S. .,Garau C.,Blecic I.,Taniar D.,Apduhan B.O.,Rocha A.M.A.C.,Tarantino E.,Torre C.M.,Karaca Y. Publisher: Springer Science and Business Media Deutschland GmbH.
20th International Conference on Computational Science and Its Applications, ICCSA 2020; Cagliari; Italy; 1 July 2020 through 4 July 2020; Code 249529.
Corresponding author: Hastings, F.; School of Agronomy, Universidad de la República, Av. Gral. Eugenio Garzón 780, Montevideo, Uruguay; email:fhastings@mgap.gub.uy |
|
Contenido : |
Land-cover mapping is critically needed in land-use planning and policy making. Compared to other techniques, Google Earth Engine (GEE) offers a free cloud of satellite information and high computation capabilities. In this context, this article examines machine learning with GEE for land-cover mapping. For this purpose, a five-phase procedure is applied: (1) imagery selection and pre-processing, (2) selection of the classes and training samples, (3) classification process, (4) post-classification, and (5) validation. The study region is located in the San Salvador basin (Uruguay), which is under agricultural intensification. As a result, the 1990 land-cover map of the San Salvador basin is produced. The new map shows good agreements with past agriculture census and reveals the transformation of grassland to cropland in the period 1990?2018. © 2020, Springer Nature Switzerland AG. |
|
Palabras claves : |
Agricultural region; Google earth engine; Land-cover map; Supervised classification. |
|
Asunto categoría : |
A50 Investigación agraria |
|
Marc : |
LEADER 02413nam a2200229 a 4500
001 1061424
005 2021-04-09
008 2020 bl uuuu u0uu1 u #d
024 7 $a10.1007/978-3-030-58811-3_52$2DOI
100 1 $aHASTINGS, F.
245 $aLand-cover mapping of agricultural areas using machine learning in Google Earth engine. (Conference paper)$h[electronic resource]
260 $aIn: Gervasi O. et al. (eds) Computational Science and Its Applications - ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science, vol 12252. International Conference on Computational Science and Its Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_52$c1007
500 $aArticle history: First Online 29 September 2020. Volume Editors: Gervasi O.,Murgante B.,Misra S. .,Garau C.,Blecic I.,Taniar D.,Apduhan B.O.,Rocha A.M.A.C.,Tarantino E.,Torre C.M.,Karaca Y. Publisher: Springer Science and Business Media Deutschland GmbH. 20th International Conference on Computational Science and Its Applications, ICCSA 2020; Cagliari; Italy; 1 July 2020 through 4 July 2020; Code 249529. Corresponding author: Hastings, F.; School of Agronomy, Universidad de la República, Av. Gral. Eugenio Garzón 780, Montevideo, Uruguay; email:fhastings@mgap.gub.uy
520 $aLand-cover mapping is critically needed in land-use planning and policy making. Compared to other techniques, Google Earth Engine (GEE) offers a free cloud of satellite information and high computation capabilities. In this context, this article examines machine learning with GEE for land-cover mapping. For this purpose, a five-phase procedure is applied: (1) imagery selection and pre-processing, (2) selection of the classes and training samples, (3) classification process, (4) post-classification, and (5) validation. The study region is located in the San Salvador basin (Uruguay), which is under agricultural intensification. As a result, the 1990 land-cover map of the San Salvador basin is produced. The new map shows good agreements with past agriculture census and reveals the transformation of grassland to cropland in the period 1990?2018. © 2020, Springer Nature Switzerland AG.
653 $aAgricultural region
653 $aGoogle earth engine
653 $aLand-cover map
653 $aSupervised classification
700 1 $aFUENTES, I.
700 1 $aPÉREZ-BIDEGAIN, M.
700 1 $aNAVAS, R.
700 1 $aGORGOGLIONE, A.
Descargar
Esconder MarcPresentar Marc Completo |
|
Registro original : |
INIA Las Brujas (LB) |
|
|
Biblioteca
|
Identificación
|
Origen
|
Tipo / Formato
|
Clasificación
|
Cutter
|
Registro
|
Volumen
|
Estado
|
Volver
|
|
|
|
Registro completo
|
|
Biblioteca (s) : |
INIA Las Brujas. |
|
Fecha actual : |
04/10/2014 |
|
Actualizado : |
09/10/2019 |
|
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
|
Circulación / Nivel : |
B - 1 |
|
Autor : |
BLUMETTO, O.; SANZ, S.C.; BARBER, F.E.; GARCÍA, A.V. |
|
Afiliación : |
OSCAR RICARDO BLUMETTO VELAZCO, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay. |
|
Título : |
Comparison of extensive and intensive pig production systems in Uruguay in terms of ethologic, physiologic and meat quality parameters. |
|
Fecha de publicación : |
2013 |
|
Fuente / Imprenta : |
Revista Brasileira de Zootecnia, 2013, v.42, no.7, p.521-529. |
|
ISSN : |
1806-9290 |
|
DOI : |
10.1590/S1516-35982013000700009 |
|
Idioma : |
Inglés |
|
Notas : |
Article history: Received July 17, 2012 / Accepted January 16, 2013. |
|
Contenido : |
ABSTRACT.
The objective of this work is to characterize two contrasting systems of fattening pigs in Uruguay. A total of 96 pigs (average 41.7 kg) were divided into eight groups of 12 animals, representing two production systems: (IN) pigs confined in pens of 12 m2 or (OUT) kept in plots with field shelters and access to pasture. Behavioral observations were performed by scan sampling at 5-minute intervals, three times a day during weeks 6, 8, 10 and 12 of the experiment. Aggressions were also observed at the end of the experimental period. Blood samples were taken for cortisol analysis and other physiological parameters, during growth period and slaughter and meat quality characteristics were assessed after slaughter. Differences were found in carcass characteristics, wherein IN presented a higher dorsal fat. These animals presented an overall lower activity and spent less time resting, with a stable pattern throughout the day. In OUT, pigs usually rested at midday hours, more active in the morning and afternoon. The number of total reciprocal aggressions in the observation period was 4.2±3.7 for IN and 2.3±2.2 for OUT. Cortisol levels and biochemical profile did not show evidence of important problems in the animals. Welfare is not compromised in any of the systems, although higher levels of cortisol and aggressions could be indicating some stress problems in the confinement system. Meat characteristics in OUT were considered better than in IN from a nutritional point of view. MenosABSTRACT.
The objective of this work is to characterize two contrasting systems of fattening pigs in Uruguay. A total of 96 pigs (average 41.7 kg) were divided into eight groups of 12 animals, representing two production systems: (IN) pigs confined in pens of 12 m2 or (OUT) kept in plots with field shelters and access to pasture. Behavioral observations were performed by scan sampling at 5-minute intervals, three times a day during weeks 6, 8, 10 and 12 of the experiment. Aggressions were also observed at the end of the experimental period. Blood samples were taken for cortisol analysis and other physiological parameters, during growth period and slaughter and meat quality characteristics were assessed after slaughter. Differences were found in carcass characteristics, wherein IN presented a higher dorsal fat. These animals presented an overall lower activity and spent less time resting, with a stable pattern throughout the day. In OUT, pigs usually rested at midday hours, more active in the morning and afternoon. The number of total reciprocal aggressions in the observation period was 4.2±3.7 for IN and 2.3±2.2 for OUT. Cortisol levels and biochemical profile did not show evidence of important problems in the animals. Welfare is not compromised in any of the systems, although higher levels of cortisol and aggressions could be indicating some stress problems in the confinement system. Meat characteristics in OUT were considered better than in IN from a nutritional point of ... Presentar Todo |
|
Thesagro : |
CERDOS; COMPORTAMIENTO ANIMAL; INVESTIGACIÓN EN CERDOS; URUGUAY. |
|
Asunto categoría : |
L01 Ganadería |
|
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/3436/1/Blumetto-O.-2013.-Rev.Bras.Zootec.-v.427-p.521-529.pdf
http://www.scielo.br/pdf/rbz/v42n7/a09v42n7.pdf
|
|
Marc : |
LEADER 02324naa a2200241 a 4500
001 1050902
005 2019-10-09
008 2013 bl uuuu u00u1 u #d
022 $a1806-9290
024 7 $a10.1590/S1516-35982013000700009$2DOI
100 1 $aBLUMETTO, O.
245 $aComparison of extensive and intensive pig production systems in Uruguay in terms of ethologic, physiologic and meat quality parameters.$h[electronic resource]
260 $c2013
500 $aArticle history: Received July 17, 2012 / Accepted January 16, 2013.
520 $aABSTRACT. The objective of this work is to characterize two contrasting systems of fattening pigs in Uruguay. A total of 96 pigs (average 41.7 kg) were divided into eight groups of 12 animals, representing two production systems: (IN) pigs confined in pens of 12 m2 or (OUT) kept in plots with field shelters and access to pasture. Behavioral observations were performed by scan sampling at 5-minute intervals, three times a day during weeks 6, 8, 10 and 12 of the experiment. Aggressions were also observed at the end of the experimental period. Blood samples were taken for cortisol analysis and other physiological parameters, during growth period and slaughter and meat quality characteristics were assessed after slaughter. Differences were found in carcass characteristics, wherein IN presented a higher dorsal fat. These animals presented an overall lower activity and spent less time resting, with a stable pattern throughout the day. In OUT, pigs usually rested at midday hours, more active in the morning and afternoon. The number of total reciprocal aggressions in the observation period was 4.2±3.7 for IN and 2.3±2.2 for OUT. Cortisol levels and biochemical profile did not show evidence of important problems in the animals. Welfare is not compromised in any of the systems, although higher levels of cortisol and aggressions could be indicating some stress problems in the confinement system. Meat characteristics in OUT were considered better than in IN from a nutritional point of view.
650 $aCERDOS
650 $aCOMPORTAMIENTO ANIMAL
650 $aINVESTIGACIÓN EN CERDOS
650 $aURUGUAY
700 1 $aSANZ, S.C.
700 1 $aBARBER, F.E.
700 1 $aGARCÍA, A.V.
773 $tRevista Brasileira de Zootecnia, 2013$gv.42, no.7, p.521-529.
Descargar
Esconder MarcPresentar Marc Completo |
|
Registro original : |
INIA Las Brujas (LB) |
|
|
Biblioteca
|
Identificación
|
Origen
|
Tipo / Formato
|
Clasificación
|
Cutter
|
Registro
|
Volumen
|
Estado
|
Volver
|
| Expresión de búsqueda válido. Check! |
|
|
