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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 : |
08/02/2024 |
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Actualizado : |
08/02/2024 |
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Tipo de producción científica : |
Abstracts/Resúmenes |
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Autor : |
GELÓS, M.; NEIGHBUR, N.; KOK, P.; BADANO, L.; HASTINGS, F.; NERVI, E.; ALONSO, J.; NAVAS, R.; VERVOORT, W.; BAETHGEN, W. |
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Afiliación : |
MERCEDES GELÓS, Dirección Nacional de Calidad y Evaluación Ambiental, Ministerio de Ambiente; Grupo de Modelación Integrada de Cuencas Uruguay; NATALIA NEIGHBUR, Grupo de Modelación Integrada de Cuencas Uruguay.; PABLO KOK, Dirección Nacional de Calidad y Evaluación Ambiental, Ministerio de Ambiente; Grupo de Modelación Integrada de Cuencas Uruguay.; LUCIANA BADANO, Grupo de Modelación Integrada de Cuencas Uruguay.; FLORENCIA HASTINGS, Dirección Nacional de Calidad y Evaluación Ambiental, Ministerio de Ambiente; Grupo de Modelación Integrada de Cuencas Uruguay; ELIANA NERVI, Grupo de Modelación Integrada de Cuencas Uruguay; INIA FPTA 358; JIMENA ALONSO, Grupo de Modelación Integrada de Cuencas Uruguay; Instituto de Mecánica de los Fluidos e Ingeniería Ambiental, Facultad de Ingeniería, Universidad de la República.; RAFAEL NAVAS, Grupo de Modelación Integrada de Cuencas Uruguay;; WILLEM VERVOORT, Grupo de Modelación Integrada de Cuencas Uruguay; School of Life and Environmental Sciences, The University of Sydney, Australia.; WALTER E. BAETHGEN, Grupo de Modelación Integrada de Cuencas Uruguay; International Research Institute for Climate and Society, Earth Institute, Columbia University, Estados Unidos. |
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Título : |
On the prediction of phosphorus fluxes in the santa lucía basin under different land use and management practices using swat model. [abstract] |
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Complemento del título : |
Theme 5 - Impact of phosphorus on environmental quality and on biodiversity. Oral presentation. |
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Fecha de publicación : |
2022 |
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Fuente / Imprenta : |
In: Michelini, D.; Garaycochea, S. (Eds.). 7th Phosphorus in Soils and Plants Symposium (PSP7). "Towards a sustainable phosphorus utilization in agroecosystems." Book of abstracts. PSP7, 3-7 October 2022, Montevideo, Uruguay. p.81. |
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Idioma : |
Inglés |
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Notas : |
Published By: The organizing committee of the 7th Symposium on Phosphorus in Soils and Plants (PSP7)- National Agricultural Research Institute and School of Agronomy, Universidad de la República, Uruguay. |
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Contenido : |
Several actions have been carried out in the Santa Lucia River basin (1.343.300 ha) in order to reduce the impact of anthropogenic pressure and improve water quality. Environmental models may be used as supportive tools when assessing the effect of local actions at a basin scale. |
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Palabras claves : |
Phosphorus fluxes; SWAT model. |
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Asunto categoría : |
A50 Investigación agraria |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/17494/1/Gelos-PSP7-2022-p81.pdf
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Marc : |
LEADER 01427naa a2200265 a 4500
001 1064461
005 2024-02-08
008 2022 bl uuuu u00u1 u #d
100 1 $aGELÓS, M.
245 $aOn the prediction of phosphorus fluxes in the santa lucía basin under different land use and management practices using swat model. [abstract]$h[electronic resource]
260 $c2022
500 $aPublished By: The organizing committee of the 7th Symposium on Phosphorus in Soils and Plants (PSP7)- National Agricultural Research Institute and School of Agronomy, Universidad de la República, Uruguay.
520 $aSeveral actions have been carried out in the Santa Lucia River basin (1.343.300 ha) in order to reduce the impact of anthropogenic pressure and improve water quality. Environmental models may be used as supportive tools when assessing the effect of local actions at a basin scale.
653 $aPhosphorus fluxes
653 $aSWAT model
700 1 $aNEIGHBUR, N.
700 1 $aKOK, P.
700 1 $aBADANO, L.
700 1 $aHASTINGS, F.
700 1 $aNERVI, E.
700 1 $aALONSO, J.
700 1 $aNAVAS, R.
700 1 $aVERVOORT, W.
700 1 $aBAETHGEN, W.
773 $tIn: Michelini, D.; Garaycochea, S. (Eds.). 7th Phosphorus in Soils and Plants Symposium (PSP7). "Towards a sustainable phosphorus utilization in agroecosystems." Book of abstracts. PSP7, 3-7 October 2022, Montevideo, Uruguay. p.81.
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