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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
04/01/2023 |
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Actualizado : |
08/02/2023 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
GASO, D.; DE WIT, A.; DE BRUIN, S.; PUNTEL, L.A.; BERGER, A.; KOOISTRA, L. |
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Afiliación : |
DEBORAH VIVIANA GASO MELGAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Wageningen 6708 PB, the Netherlands; ALLARD DE WIT, Wageningen Environmental Research, Wageningen 6708 PB, the Netherlands; SYTZE DE BRUIN, Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Wageningen 6708 PB, the Netherlands; LAILA A. PUNTEL, Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Keim Hall, 1825 N 38th Street, Lincoln 68583-0915, NE, USA; ANDRES GUSTAVO BERGER RICCA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LAMMERT KOOISTRA, Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Wageningen 6708 PB, the Netherlands. |
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Título : |
Efficiency of assimilating leaf area index into a soybean model to assess within-field yield variability. |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
European Journal of Agronomy, February 2023, Volume 143, 126718. OPEN ACCESS. doi: https://doi.org/10.1016/j.eja.2022.126718 |
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ISSN : |
1873-7331 (online) |
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DOI : |
10.1016/j.eja.2022.126718 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 7 March 2022, Revised 17 October 2022, Accepted 5 December 2022, Available online 22 December 2022, Version of Record 22 December 2022. -- Corresponding author: Deborah Gaso, E-mail addresses: deborah.gasomelgar@wur.nl, dgaso@inia.org.uy (D.V. Gaso). Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Wageningen 6708 PB, the Netherlands. -- LICENSE: This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). -- |
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Contenido : |
ABSTRACT.- Methods for accurately estimating within-field yield are essential to improve site-specific crop management and resource use efficiencies, which would be a major step toward sustainable intensification of agricultural systems. We set out to assess the accuracy of within-field soybean yields predicted by two data assimilation methods and to assess these methods? assimilation efficiency (AE). Yields were estimated by assimilating remotely sensed leaf area index (LAI) data from Sentinel-2 into a soybean crop growth model on a pixel basis. The LAI data was integrated into the model by Ensemble Kalman Filtering (EnKF) or by recalibrating with the Subplex algorithm (recalibration-based). An open-loop setting which only integrates information on the soil layers was used as a baseline scenario for quantifying the AE. We assessed both data assimilation techniques on eight fields (3067 pixels) in the Corn Belt region (Nebraska, Kansas and Kentucky) in the United States. The data set encompassed substantial variation in crop growth conditions: three growing seasons (2018, 2019 and 2020), rainfed and irrigated fields, and early and late planting dates. Ground truth yield acquired from combine monitors was used to validate the yield estimations. Agreement between predicted and observed yield at pixel level was two times higher for both data assimilation methods compared to the open-loop. The root mean square error (RMSE) was 476 kg.ha-1 (RRMSE of 10 %) in the recalibration-based method and 573 kg.ha-1 (RRMSE of 12 %) in the EnKFbased method. For both data assimilation methods, assimilating the LAI improved predictions for 68 % of the pixels. For a further 12 % of pixels, there was no accuracy improvement. For the remaining 20 %, AE was positive for one of the two assimilation methods. The high proportion of pixels with positive AE indicates the potential for overcoming the limitations in applying crop models at high spatial resolution by integrating a crop growth indicator. Assimilating an in-season indicator of crop growth (LAI) into a soybean model made it possible to adjust the simulation pathway, thereby greatly improving the accuracy of the yield estimations at the pixel level. This study elucidates the practical applications of data assimilation strategies for fine-scale within-field crop yield mapping. © 2022 The Author(s). Published by Elsevier B.V. MenosABSTRACT.- Methods for accurately estimating within-field yield are essential to improve site-specific crop management and resource use efficiencies, which would be a major step toward sustainable intensification of agricultural systems. We set out to assess the accuracy of within-field soybean yields predicted by two data assimilation methods and to assess these methods? assimilation efficiency (AE). Yields were estimated by assimilating remotely sensed leaf area index (LAI) data from Sentinel-2 into a soybean crop growth model on a pixel basis. The LAI data was integrated into the model by Ensemble Kalman Filtering (EnKF) or by recalibrating with the Subplex algorithm (recalibration-based). An open-loop setting which only integrates information on the soil layers was used as a baseline scenario for quantifying the AE. We assessed both data assimilation techniques on eight fields (3067 pixels) in the Corn Belt region (Nebraska, Kansas and Kentucky) in the United States. The data set encompassed substantial variation in crop growth conditions: three growing seasons (2018, 2019 and 2020), rainfed and irrigated fields, and early and late planting dates. Ground truth yield acquired from combine monitors was used to validate the yield estimations. Agreement between predicted and observed yield at pixel level was two times higher for both data assimilation methods compared to the open-loop. The root mean square error (RMSE) was 476 kg.ha-1 (RRMSE of 10 %) in the recalibration-bas... Presentar Todo |
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Palabras claves : |
Assimilation efficiency; Crop models; Sentinel-2; Soybean; Yield prediction. |
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Asunto categoría : |
F01 Cultivo |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16956/1/10.1016-j.eja.2022.126718.pdf
https://www.sciencedirect.com/sdfe/reader/pii/S1161030122002660/pdf
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Marc : |
LEADER 03785naa a2200277 a 4500
001 1063938
005 2023-02-08
008 2023 bl uuuu u00u1 u #d
022 $a1873-7331 (online)
024 7 $a10.1016/j.eja.2022.126718$2DOI
100 1 $aGASO, D.
245 $aEfficiency of assimilating leaf area index into a soybean model to assess within-field yield variability.$h[electronic resource]
260 $c2023
500 $aArticle history: Received 7 March 2022, Revised 17 October 2022, Accepted 5 December 2022, Available online 22 December 2022, Version of Record 22 December 2022. -- Corresponding author: Deborah Gaso, E-mail addresses: deborah.gasomelgar@wur.nl, dgaso@inia.org.uy (D.V. Gaso). Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Wageningen 6708 PB, the Netherlands. -- LICENSE: This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). --
520 $aABSTRACT.- Methods for accurately estimating within-field yield are essential to improve site-specific crop management and resource use efficiencies, which would be a major step toward sustainable intensification of agricultural systems. We set out to assess the accuracy of within-field soybean yields predicted by two data assimilation methods and to assess these methods? assimilation efficiency (AE). Yields were estimated by assimilating remotely sensed leaf area index (LAI) data from Sentinel-2 into a soybean crop growth model on a pixel basis. The LAI data was integrated into the model by Ensemble Kalman Filtering (EnKF) or by recalibrating with the Subplex algorithm (recalibration-based). An open-loop setting which only integrates information on the soil layers was used as a baseline scenario for quantifying the AE. We assessed both data assimilation techniques on eight fields (3067 pixels) in the Corn Belt region (Nebraska, Kansas and Kentucky) in the United States. The data set encompassed substantial variation in crop growth conditions: three growing seasons (2018, 2019 and 2020), rainfed and irrigated fields, and early and late planting dates. Ground truth yield acquired from combine monitors was used to validate the yield estimations. Agreement between predicted and observed yield at pixel level was two times higher for both data assimilation methods compared to the open-loop. The root mean square error (RMSE) was 476 kg.ha-1 (RRMSE of 10 %) in the recalibration-based method and 573 kg.ha-1 (RRMSE of 12 %) in the EnKFbased method. For both data assimilation methods, assimilating the LAI improved predictions for 68 % of the pixels. For a further 12 % of pixels, there was no accuracy improvement. For the remaining 20 %, AE was positive for one of the two assimilation methods. The high proportion of pixels with positive AE indicates the potential for overcoming the limitations in applying crop models at high spatial resolution by integrating a crop growth indicator. Assimilating an in-season indicator of crop growth (LAI) into a soybean model made it possible to adjust the simulation pathway, thereby greatly improving the accuracy of the yield estimations at the pixel level. This study elucidates the practical applications of data assimilation strategies for fine-scale within-field crop yield mapping. © 2022 The Author(s). Published by Elsevier B.V.
653 $aAssimilation efficiency
653 $aCrop models
653 $aSentinel-2
653 $aSoybean
653 $aYield prediction
700 1 $aDE WIT, A.
700 1 $aDE BRUIN, S.
700 1 $aPUNTEL, L.A.
700 1 $aBERGER, A.
700 1 $aKOOISTRA, L.
773 $tEuropean Journal of Agronomy, February 2023, Volume 143, 126718. OPEN ACCESS. doi: https://doi.org/10.1016/j.eja.2022.126718
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INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
16/04/2024 |
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Actualizado : |
16/04/2024 |
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Tipo de producción científica : |
Abstracts/Resúmenes |
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Autor : |
GONZÁLEZ, A.C.; RUBIO, L.; MENA, E.; DELGADO-CERRONE, L.; ARRUABARRENA, A.; GONZÁLEZ-ARCOS, M.; PONCE DE LEÓN, I. |
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Afiliación : |
ANA CLARA GONZÁLEZ, Departamento de Biología Molecular, Instituto de Investigaciones Biológicas Clemente Estable, Montevideo, Uruguay; LETICIA PAOLA RUBIO CATTANI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; EILYN MENA, Departamento de Biología Molecular, Instituto de Investigaciones Biológicas Clemente Estable, Montevideo, Uruguay; LEONARDO DELGADO-CERRONE, Departamento de Biología Molecular, Instituto de Investigaciones Biológicas Clemente Estable, Montevideo, Uruguay; ANA ARRUABARRENA PASCOVICH, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MATIAS GONZÁLEZ-ARCOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; INÉS PONCE DE LEÓN, Departamento de Biología Molecular, Instituto de Investigaciones Biológicas Clemente Estable, Montevideo, Uruguay. |
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Título : |
P40. Identificación y caracterización de especies de Stemphylium causantes de la mancha gris de la hoja del tomate en Uruguay. [Poster]. |
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Complemento del título : |
Posters. |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
In: Sociedad Uruguaya de Fitopatología (SUFIT). Jornada Uruguaya de Fitopatología, 7., Jornada Uruguaya de Protección Vegetal, 5., 10 noviembre 2023, Montevideo, Uruguay. Libro de resúmenes. 30 años SUFIT, 1993-2023. Montevideo (UY): Sociedad Uruguay de Fitopatología (SUFIT), 2023. p. 66. |
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Idioma : |
Español |
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Notas : |
Financiamiento: ANII beca de doctorado por proyecto FCE_1_2021_1_166555. -- Autor correspondencia: e-mail: anaclgonzalez92@gmail.com |
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Contenido : |
El tomate (Solanum lycopersicum L.) es un cultivo relevante a nivel mundial y es el segundo hortícola más importante en Uruguay. Hongos del género Stemphylium, causan la mancha gris de la hoja del tomate, una enfermedad destructiva responsable de grandes pérdidas de producción en variedades susceptibles. A partir de esto, como objetivo inicial planteamos identificar las especies de Stemphylium asociadas con la mancha gris de la hoja del tomate en Uruguay y evaluar la diversidad genética y agresividad de los aislados. |
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Palabras claves : |
Mancha gris de la hoja del tomate; SISTEMA VEGETAL INTENSIVO - INIA. |
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Thesagro : |
ENFERMEDADES FUNGOSAS; STEMPHYLIUM; TOMATE. |
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Asunto categoría : |
H20 Enfermedades de las plantas |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/17600/1/SUFIT-2023-P40.pdf
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Marc : |
LEADER 01723nam a2200253 a 4500
001 1064584
005 2024-04-16
008 2023 bl uuuu u01u1 u #d
100 1 $aGONZÁLEZ, A.C.
245 $aP40. Identificación y caracterización de especies de Stemphylium causantes de la mancha gris de la hoja del tomate en Uruguay. [Poster].$h[electronic resource]
260 $aIn: Sociedad Uruguaya de Fitopatología (SUFIT). Jornada Uruguaya de Fitopatología, 7., Jornada Uruguaya de Protección Vegetal, 5., 10 noviembre 2023, Montevideo, Uruguay. Libro de resúmenes. 30 años SUFIT, 1993-2023. Montevideo (UY): Sociedad Uruguay de Fitopatología (SUFIT), 2023. p. 66.$c2023
500 $aFinanciamiento: ANII beca de doctorado por proyecto FCE_1_2021_1_166555. -- Autor correspondencia: e-mail: anaclgonzalez92@gmail.com
520 $aEl tomate (Solanum lycopersicum L.) es un cultivo relevante a nivel mundial y es el segundo hortícola más importante en Uruguay. Hongos del género Stemphylium, causan la mancha gris de la hoja del tomate, una enfermedad destructiva responsable de grandes pérdidas de producción en variedades susceptibles. A partir de esto, como objetivo inicial planteamos identificar las especies de Stemphylium asociadas con la mancha gris de la hoja del tomate en Uruguay y evaluar la diversidad genética y agresividad de los aislados.
650 $aENFERMEDADES FUNGOSAS
650 $aSTEMPHYLIUM
650 $aTOMATE
653 $aMancha gris de la hoja del tomate
653 $aSISTEMA VEGETAL INTENSIVO - INIA
700 1 $aRUBIO, L.
700 1 $aMENA, E.
700 1 $aDELGADO-CERRONE, L.
700 1 $aARRUABARRENA, A.
700 1 $aGONZÁLEZ-ARCOS, M.
700 1 $aPONCE DE LEÓN, I.
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