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| 22. |  | GONZÁLEZ, A.; HERNÁNDEZ, J.; DEL PINO, A.; HIRIGOYEN, A. Nutrient use efficiency in commercial eucalypt plantations in different soils under temperate climate. Southern Forests: a Journal of Forest Science, 2022. [Article in Press]. doi: https://doi.org/10.2989/20702620.2022.2066488 Article history: Published online 31 May 2022.
Corresponding author: González, A.; University of the Republic, College of Agronomy, Soil and Water Department, Montevideo, Uruguay; email:alejandrogonzalezuruguay@gmail.com| Biblioteca(s): INIA Las Brujas. |
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| 25. | | RESQUÍN, F.; RACHID, C.; HIRIGOYEN, A.; DOLDÁN, J.; LOPRETTI, M.; BONIFACINO, S.; BUXEDAS, L.; VÁZQUEZ, S.; SAPOLINSKI, A.; GONZÁLEZ, M.; CARRASCO-LETELIER, L.; CAPDEVIELLE, F. Producción de biomasa y etanol a partir de Eucalyptus en plantaciones de alta densidad. Revista INIA Uruguay, 2015, n. 41, p. 35-38 (Revista INIA; 41)| Biblioteca(s): INIA La Estanzuela; INIA Tacuarembó. |
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| 26. | | BALMELLI, G.; SIMETO, S.; TORRES, D.; HIRIGOYEN, A.; CASTILLO, A.; ALTIER, N.; PÉREZ, G.; DIEZ, J. Productivity losses caused by Teratosphaeria nubilosa on Eucalyptus globulus and Eucalyptus maidenii in Uruguay. [Resumen]. In: Anniversary Congress, 125th, 19-22 September, Freiburg, Germany, 2017. p. 187| Biblioteca(s): INIA Tacuarembó. |
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| 27. | | COELHO-DUARTE, A.P.; DANILUK-MOSQUERA, G.; GRAVINA, V.; HIRIGOYEN, A.; VALLEJOS-BARRA, O.; PONCE-DONOSO, M. Proposal of two visual tree risk assessment methods for urban parks in Montevideo, Uruguay. [Propuesta de dos métodos de evaluación visual del riesgo de árboles para parques urbanos en Montevideo, Uruguay.] Bosque, 2021, Volume 42, Issue 2, Pages 259-268. Gold Open Access. doi: http://dx.doi.org/10.4067/S0717-92002021000200259 Article history: Received 4 November 2020; Accepted 21 June 21 2021.
Corresponding author: Ponce-Donoso, M.; Universidad de Talca, Escuela de Ingeniería Forestal, Avda. Lircay s/n, Talca, Chile; email:mponce@utalca.cl| Biblioteca(s): INIA Las Brujas. |
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| 28. | | NÚÑEZ, L.; HIRIGOYEN, A.; DURANTE, M.; ARROYO, J.; CAZZULI, F.; BREMM, C.; JAURENA, M. Qué factores controlan la proteína del forraje del campo natural?. Pasturas. Revista INIA Uruguay, Setiembre 2022, no.70, p.43-46. (Revista INIA; 70).| Biblioteca(s): INIA Las Brujas. |
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| 31. | | HIRIGOYEN, A.; VARELA, B.C.; CELLINI, J.M.; ACHINELLI, F.G. Selección de modelos hipsométricos locales y generales para Eucalyptus globulus en macizos del sudeste de la provincia de Buenos Aires, Argentina. [Selection of local and general hypsometric models for Eucalyptus globulus in stands of the southeast of Buenos Aires province, Argentina]. Sección: Trabajos científicos. Revista de la Facultad de Agronomía, La Plata, 2021, Volume 120, nro. 2, pages 077-077. OPEN ACCESS. doi: https://doi.org/10.24215/16699513e077 Article history: Recepción: 13/07/2020 Aprobación: 12/04/2021. -- Autor de correspondencia: fachinel@agro.unlp.edu.ar --| Biblioteca(s): INIA Las Brujas. |
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| 38. | | RIZZO-MARTÍN, I.; HIRIGOYEN, A.; ARTHUS-BACOVICH, R.; VARO-MARTÍNEZ, M.A.; NAVARRO-CERRILLO, R. Site index estimation using airborne laser scanner data in Eucalyptus dunnii Maide stands in Uruguay. Forests, 2023, Volume 14, Issue 5, article 933. https://doi.org/10.3390/f14050933 -- OPEN ACCESS. Article history: Received 16 March 2023; Revised 23 April 2023; Accepted 27 April 2023; Published 1 May 2023. -- Correspondence: Rizzo-Martín, I.; Department of Forest Production and Wood Technology, Faculty of Agronomy, University of the...| Biblioteca(s): INIA Las Brujas. |
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| 39. | | HIRIGOYEN, A.; VARO-MARTINEZ, M.A.; RACHID, C.; FRANCO, J.; NAVARRO-CERRILLO, R.M. Stand characterization of eucalyptus spp. Plantations in uruguay using airborne lidar scanner technology. Remote Sensing, 1 December 2020, Volume 12, Issue 23, Article number 3947, Pages 1-19. Open Access. Doi: https://doi.org/10.3390/rs12233947 Article history: Received: 16 October 2020 / Revised: 5 November 2020 / Accepted: 21 November 2020 / Published: 2 December 2020. Acknowledgments: The authors thank the Instituto Nacional de Investigaciones Agropecuarias (INIA-Uruguay) for...| Biblioteca(s): INIA Tacuarembó. |
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| 40. | | CAZZULI, F.; SÁNCHEZ, J.; HIRIGOYEN, A.; ROVIRA, P.J.; BERETTA, V.; SIMEONE, A.; JAURENA, M.; DURANTE, M.; SAVIAN, J.V.; POPPI, D.; MONTOSSI, F.; LAGOMARSINO, X.; LUZARDO, S.; BRITO, G.; VELAZCO, J.I.; BREMM, C.; LATTANZI, F. Supplement feed efficiency of growing beef cattle grazing native Campos grasslands during winter: a collated analysis. Translational Animal Science. 2023, Volume 7, Issue 1, txad028. https://doi.org/10.1093/tas/txad028 -- OPEN ACCESS Article history: Received 03 October 2022; Accepted 09 March 2023; Published 10 March 2023; Corrected and typeset 01 April 2023. -- Corresponding author: fcazzuli@inia.org.uy -- Issue Section: Forage Based Livestock Systems. -- License:...| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 46 | |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
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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Circulación / Nivel : |
Internacional - -- |
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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
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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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