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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela; INIA Treinta y Tres. |
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Fecha : |
06/10/2014 |
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Actualizado : |
31/07/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Nacionales |
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Autor : |
JORGE, G.; PÉREZ BIDEGAIN, M.; TERRA, J.A.; SAWCHIK, J. |
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Afiliación : |
JOSÉ ALFREDO TERRA FERNÁNDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JORGE SAWCHIK PINTOS, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay. |
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Título : |
WEPP as a tool for enabling a more comprehensive analysis of the environmental impacts of soil erosion. |
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Fecha de publicación : |
2012 |
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Fuente / Imprenta : |
In: INTERNATIONAL SOIL TILLAGE RESEARCH ORGANIZATION. 19., SOCIEDAD URUGUAYA DE CIENCIA DEL SUELO, 4., 2012, Montevideo, UY. [Oral presentation]: paper no. 133. Montevideo, UY: ISTRO, 2012. |
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Idioma : |
Español
Inglés |
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Notas : |
También publicado en: Agrociencia Uruguay, v. 16, n. especial, p. 268-273, 2012. |
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Contenido : |
ABSTRACT.
Cropland area in Uruguay, mostly soybeans, increased 300% during the last decade due to expansion to new areas. Although no-tillage practices are generalized among farmers, soil erosion is still a major environmental and economic issue. A predictive tool as the Water Erosion Prediction Model Project (WEPP), based on soil processes, has never been used in Uruguay. The objective of this research was to evaluate the soil erosion impact of various managements of intensive agriculture on Mollisols of Uruguay, applying the WEPP erosion model. The model was fi rst adjusted and validated for annual erosion estimates of an Abruptic Argiudoll (Nash Sutcliffe (NS)= 0.81 and R2 = 0.89) and a
Vertic Argiudoll (NS= 0.86 and R2 = 0.90), and later applied to evaluate three Mollisols and one Vertisol with different soil managements. Treatments combined no tillage (NT) and reduced tillage (RT) with different crop rotations. Crop rotations were: continuous soybean (CS), soybean-wheat (SW), soybean-winter cover crop (S-Cover crop), cornsoybean-wheat-3/4 yr pasture (CSW-PPP/PPPP), and corn-soybean-wheat-soybean-wheat-3/4 yr pasture (CSWSWPPP/PPPP). Soil erosion under RT system or CS was always above 7Mg.ha-1 (T value). Pastures inclusion under NT showed values below 7 Mg.ha-1.WEPP simulated an average erosion rate below T for SW rotation with NT (100m; 3% slope) in three of the four soils studied. However, by varying the slope and the length of the hillslope, the range for which the average annual erosion remains below this level is limited (only 3% - 4%). Moreover, for those hillslopes
whose average annual erosion does not exceed the T value, there is still approximately a 25% probability that this may occur any given year. Our work highlights the potential of using WEPP in the development of criteria for assessing sustainability of soil management, alternative to T value of average annual erosion units, including risk analysis MenosABSTRACT.
Cropland area in Uruguay, mostly soybeans, increased 300% during the last decade due to expansion to new areas. Although no-tillage practices are generalized among farmers, soil erosion is still a major environmental and economic issue. A predictive tool as the Water Erosion Prediction Model Project (WEPP), based on soil processes, has never been used in Uruguay. The objective of this research was to evaluate the soil erosion impact of various managements of intensive agriculture on Mollisols of Uruguay, applying the WEPP erosion model. The model was fi rst adjusted and validated for annual erosion estimates of an Abruptic Argiudoll (Nash Sutcliffe (NS)= 0.81 and R2 = 0.89) and a
Vertic Argiudoll (NS= 0.86 and R2 = 0.90), and later applied to evaluate three Mollisols and one Vertisol with different soil managements. Treatments combined no tillage (NT) and reduced tillage (RT) with different crop rotations. Crop rotations were: continuous soybean (CS), soybean-wheat (SW), soybean-winter cover crop (S-Cover crop), cornsoybean-wheat-3/4 yr pasture (CSW-PPP/PPPP), and corn-soybean-wheat-soybean-wheat-3/4 yr pasture (CSWSWPPP/PPPP). Soil erosion under RT system or CS was always above 7Mg.ha-1 (T value). Pastures inclusion under NT showed values below 7 Mg.ha-1.WEPP simulated an average erosion rate below T for SW rotation with NT (100m; 3% slope) in three of the four soils studied. However, by varying the slope and the length of the hillslope, the range for which the a... Presentar Todo |
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Palabras claves : |
WATER EROSION PREDICTION PROJECT MODEL; WEPP MODEL. |
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Thesagro : |
EROSIÓN DEL SUELO; MODELOS; MODELOS DE PREDICCIÓN; URUGUAY; WATER EROSION PREDICTION PROJECT MODEL; WEPP MODEL. |
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Asunto categoría : |
P36 Erosión conservación y recuperación del suelo |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12187/1/Agrociencia-ISTRO-2012-2.-Gabriella-J..pdf
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Marc : |
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100 1 $aJORGE, G.
245 $aWEPP as a tool for enabling a more comprehensive analysis of the environmental impacts of soil erosion.
260 $c2012
500 $aTambién publicado en: Agrociencia Uruguay, v. 16, n. especial, p. 268-273, 2012.
520 $aABSTRACT. Cropland area in Uruguay, mostly soybeans, increased 300% during the last decade due to expansion to new areas. Although no-tillage practices are generalized among farmers, soil erosion is still a major environmental and economic issue. A predictive tool as the Water Erosion Prediction Model Project (WEPP), based on soil processes, has never been used in Uruguay. The objective of this research was to evaluate the soil erosion impact of various managements of intensive agriculture on Mollisols of Uruguay, applying the WEPP erosion model. The model was fi rst adjusted and validated for annual erosion estimates of an Abruptic Argiudoll (Nash Sutcliffe (NS)= 0.81 and R2 = 0.89) and a Vertic Argiudoll (NS= 0.86 and R2 = 0.90), and later applied to evaluate three Mollisols and one Vertisol with different soil managements. Treatments combined no tillage (NT) and reduced tillage (RT) with different crop rotations. Crop rotations were: continuous soybean (CS), soybean-wheat (SW), soybean-winter cover crop (S-Cover crop), cornsoybean-wheat-3/4 yr pasture (CSW-PPP/PPPP), and corn-soybean-wheat-soybean-wheat-3/4 yr pasture (CSWSWPPP/PPPP). Soil erosion under RT system or CS was always above 7Mg.ha-1 (T value). Pastures inclusion under NT showed values below 7 Mg.ha-1.WEPP simulated an average erosion rate below T for SW rotation with NT (100m; 3% slope) in three of the four soils studied. However, by varying the slope and the length of the hillslope, the range for which the average annual erosion remains below this level is limited (only 3% - 4%). Moreover, for those hillslopes whose average annual erosion does not exceed the T value, there is still approximately a 25% probability that this may occur any given year. Our work highlights the potential of using WEPP in the development of criteria for assessing sustainability of soil management, alternative to T value of average annual erosion units, including risk analysis
650 $aEROSIÓN DEL SUELO
650 $aMODELOS
650 $aMODELOS DE PREDICCIÓN
650 $aURUGUAY
650 $aWATER EROSION PREDICTION PROJECT MODEL
650 $aWEPP MODEL
653 $aWATER EROSION PREDICTION PROJECT MODEL
653 $aWEPP MODEL
700 1 $aPÉREZ BIDEGAIN, M.
700 1 $aTERRA, J.A.
700 1 $aSAWCHIK, J.
773 $tIn: INTERNATIONAL SOIL TILLAGE RESEARCH ORGANIZATION. 19., SOCIEDAD URUGUAYA DE CIENCIA DEL SUELO, 4., 2012, Montevideo, UY. [Oral presentation]: paper no. 133. Montevideo, UY: ISTRO, 2012.
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Registro original : |
INIA La Estanzuela (LE) |
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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 actual : |
23/10/2020 |
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Actualizado : |
09/04/2021 |
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Tipo de producción científica : |
Capítulo en Libro Técnico-Científico |
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Autor : |
HASTINGS, F.; FUENTES, I.; PÉREZ-BIDEGAIN, M.; NAVAS, R.; GORGOGLIONE, A. |
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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. |
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Título : |
Land-cover mapping of agricultural areas using machine learning in Google Earth engine. (Conference paper) |
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Fecha de publicación : |
2020 |
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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 |
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ISBN : |
e-ISBN: 978-3-030-58811-3 |
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DOI : |
10.1007/978-3-030-58811-3_52 |
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Idioma : |
Inglés |
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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 |
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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. |
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Palabras claves : |
Agricultural region; Google earth engine; Land-cover map; Supervised classification. |
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Asunto categoría : |
A50 Investigación agraria |
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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.
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