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 | Acceso al texto completo restringido a Biblioteca INIA La Estanzuela. Por información adicional contacte bib_le@inia.org.uy. |
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
23/09/2019 |
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Actualizado : |
21/09/2020 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
GRAHMANN, K.; DITTERT, K.; VERHULST, N.; GOVAERTS, B.; BUERKERT, A. |
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Afiliación : |
KATHRIN GRAHMANN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay./Organic Plant Production and Agroecosystems Research in the Tropics and Subtropics, University of Kassel, Witzenhausen, Germany.; KLAUS DITTERT, Institute of Applied Plant Nutrition, University of Göttingen, Göttingen, Germany.; NELE VERHULST, International Maize and Wheat Improvement Center (CIMMYT), Mexico City, DF, Mexico.; BRAM GOVAERTS, International Maize and Wheat Improvement Center (CIMMYT), Mexico City, DF, Mexico.; ANDREAS BUERKERT, Organic Plant Production and Agroecosystems Research in the Tropics and Subtropics, University of Kassel, Witzenhausen, Germany. |
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Título : |
15N Fertilizer recovery in different tillage-straw systems on a Vertisol in north-west Mexico. |
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Fecha de publicación : |
2019 |
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Fuente / Imprenta : |
Soil Use and Management, 1 September 2019, Volume 35, Issue 3, Pages 482-491. Doi: https://doi.org/10.1111/sum.12495 |
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DOI : |
10.1111/sum.12495 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: 10 June 2018 | Revised: 12 January 2019 | Accepted: 21 January 2019. |
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Contenido : |
AbstractTillage and residue retention affect nitrogen (N) dynamics and nutrient losses and therefore nitrogen use efficiency (NUE) and crop fertilizer use, however, there is little information about residual fertilizer effects on the subsequent crop. Micro- plots with 15N- labelled urea were established in 2014/2015 on a long- term experi-ment on a Vertisol in north- west Mexico. N fertilizer recovery (NFR) and the effects of residual fertilizer N for summer maize (Zea mays L.) and the subsequent wheat (Triticum durum L.) crop were studied in three tillage?straw management practices (CTB: conventionally tilled beds; PB- straw: permanent raised beds with residue retention; PB- burn: permanent raised beds with residue burning). Fertilizer 15N recovery rates for maize grain across all treatments were low with an average of 11%, but after wheat harvest total recovered 15N (15N in maize and wheat straw and grain, residual soil 15N) was over 50% for the PB- burn treatment. NFR was lowest in CTB after two cropping cycles (32%). Unaccounted N from applied fer-tilizer for the maize crop averaged 120 kg 15N ha?1 after wheat harvest. However, more than 20% of labelled 15N was found in the 0?90 cm soil profile in both PB treatments after wheat harvest, which highlights the need for long- term studies and continuous monitoring of the soil nutrient status to avoid over- application of min-eral N fertilizer. |
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Palabras claves : |
15N LABELLED UREA; FERTILIZANTES NITROGENADOS; NITROGEN BALANCE; NITROGEN FERTILIZER RECOVERY; PERMANENT BEDS; WHEAT-MAIZE CROPPING SYSTEM. |
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Thesagro : |
MAIZ; TRIGO. |
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Asunto categoría : |
P35 Fertilidad del suelo |
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Marc : |
LEADER 02462naa a2200289 a 4500
001 1060201
005 2020-09-21
008 2019 bl uuuu u00u1 u #d
024 7 $a10.1111/sum.12495$2DOI
100 1 $aGRAHMANN, K.
245 $a15N Fertilizer recovery in different tillage-straw systems on a Vertisol in north-west Mexico.$h[electronic resource]
260 $c2019
500 $aArticle history: Received: 10 June 2018 | Revised: 12 January 2019 | Accepted: 21 January 2019.
520 $aAbstractTillage and residue retention affect nitrogen (N) dynamics and nutrient losses and therefore nitrogen use efficiency (NUE) and crop fertilizer use, however, there is little information about residual fertilizer effects on the subsequent crop. Micro- plots with 15N- labelled urea were established in 2014/2015 on a long- term experi-ment on a Vertisol in north- west Mexico. N fertilizer recovery (NFR) and the effects of residual fertilizer N for summer maize (Zea mays L.) and the subsequent wheat (Triticum durum L.) crop were studied in three tillage?straw management practices (CTB: conventionally tilled beds; PB- straw: permanent raised beds with residue retention; PB- burn: permanent raised beds with residue burning). Fertilizer 15N recovery rates for maize grain across all treatments were low with an average of 11%, but after wheat harvest total recovered 15N (15N in maize and wheat straw and grain, residual soil 15N) was over 50% for the PB- burn treatment. NFR was lowest in CTB after two cropping cycles (32%). Unaccounted N from applied fer-tilizer for the maize crop averaged 120 kg 15N ha?1 after wheat harvest. However, more than 20% of labelled 15N was found in the 0?90 cm soil profile in both PB treatments after wheat harvest, which highlights the need for long- term studies and continuous monitoring of the soil nutrient status to avoid over- application of min-eral N fertilizer.
650 $aMAIZ
650 $aTRIGO
653 $a15N LABELLED UREA
653 $aFERTILIZANTES NITROGENADOS
653 $aNITROGEN BALANCE
653 $aNITROGEN FERTILIZER RECOVERY
653 $aPERMANENT BEDS
653 $aWHEAT-MAIZE CROPPING SYSTEM
700 1 $aDITTERT, K.
700 1 $aVERHULST, N.
700 1 $aGOVAERTS, B.
700 1 $aBUERKERT, A.
773 $tSoil Use and Management, 1 September 2019, Volume 35, Issue 3, Pages 482-491. Doi: https://doi.org/10.1111/sum.12495
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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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