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Biblioteca (s) : |
INIA La Estanzuela. |
Fecha : |
03/08/2021 |
Actualizado : |
02/09/2022 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
GASO, D.; DE WIT, A.; BERGER, A.; KOOISTRA, L. |
Afiliación : |
DEBORAH VIVIANA GASO MELGAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ALLARD DE WIT, Wageningen Environmental Research, Wageningen 6708 PB, The Netherlands.; 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, Netherlands. |
Título : |
Predicting within-field soybean yield variability by coupling Sentinel-2 leaf area index with a crop growth model. |
Fecha de publicación : |
2021 |
Fuente / Imprenta : |
Agricultural and Forest Meteorology, 2021, Volumes 308-309, article 108553. OPEN ACCESS. Doi: https://doi.org/10.1016/j.agrformet.2021.108553 |
DOI : |
10.1016/j.agrformet.2021.108553 |
Idioma : |
Inglés |
Notas : |
Article history: Received 15 February 2021, Revised 3 June 2021, Accepted 9 July 2021, Available online 22 July 2021. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Contenido : |
ABSTRACT: Accurate within-field yield estimation is an essential step to conduct yield gap analysis and steer crop management towards more efficient use of resources. This study aims to develop and validate a process-based soybean model and to predict within-field yield variability by coupling leaf area index (LAI) retrieval from Sentinel-2 into the crop model. First, a soybean model is presented, which was successfully validated with field observations of total aboveground biomass, LAI and yield from seven contrasting field campaigns with strongly varying conditions. Within-field yield predictions were achieved by combining the model and the observations of LAI through an assimilation strategy. Four model parameters were chosen to optimize against the LAI curve: soil
depth, field capacity, initial LAI and nitrogen translocated from leaves to seed. Six fields were used to evaluate the methodology (21175 pixels). The accuracy assessment was conducted on a pixel-by-pixel basis using high density of information from the yield monitor. The overall accuracy quantified by the relative root mean square error (rRMSE) ranged from 28 to 51% (overall rRMSE 35.8%) across the studied fields. The Lee statistics index ranged from 0.61 to 0.71, confirming a high level of similarity between observed and simulated yield maps. Therefore, the methodology was capable of representing the observed spatial patterns of yield. Furthermore, the high consistency of the optimized WHC reflects the value of the assimilation data strategy to spatialize this relevant characteristic. Some challenges were identified for further study to reduce the sources of uncertainty and improve accuracy: i) the inability of the model to reallocate biomass by simulating plant response to source limitation, ii) the generalization of empirical algorithms to retrieve LAI, and iii) the exploration of an updating method as an assimilation strategy to overcome discrepancy between simulated and retrieved LAI. MenosABSTRACT: Accurate within-field yield estimation is an essential step to conduct yield gap analysis and steer crop management towards more efficient use of resources. This study aims to develop and validate a process-based soybean model and to predict within-field yield variability by coupling leaf area index (LAI) retrieval from Sentinel-2 into the crop model. First, a soybean model is presented, which was successfully validated with field observations of total aboveground biomass, LAI and yield from seven contrasting field campaigns with strongly varying conditions. Within-field yield predictions were achieved by combining the model and the observations of LAI through an assimilation strategy. Four model parameters were chosen to optimize against the LAI curve: soil
depth, field capacity, initial LAI and nitrogen translocated from leaves to seed. Six fields were used to evaluate the methodology (21175 pixels). The accuracy assessment was conducted on a pixel-by-pixel basis using high density of information from the yield monitor. The overall accuracy quantified by the relative root mean square error (rRMSE) ranged from 28 to 51% (overall rRMSE 35.8%) across the studied fields. The Lee statistics index ranged from 0.61 to 0.71, confirming a high level of similarity between observed and simulated yield maps. Therefore, the methodology was capable of representing the observed spatial patterns of yield. Furthermore, the high consistency of the optimized WHC reflects the valu... Presentar Todo |
Palabras claves : |
Crop growth model; Data assimilation; Sentinel-2; Soybean; Yield prediction. |
Thesagro : |
Soja. |
Asunto categoría : |
F01 Cultivo |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16670/1/1-s2.0-S0168192321002379-main.pdf
https://www.sciencedirect.com/science/article/pii/S0168192321002379/pdfft?md5=c4c224be450c53eba73eada140b04cc2&pid=1-s2.0-S0168192321002379-main.pdf
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Marc : |
LEADER 03060naa a2200253 a 4500 001 1062331 005 2022-09-02 008 2021 bl uuuu u00u1 u #d 024 7 $a10.1016/j.agrformet.2021.108553$2DOI 100 1 $aGASO, D. 245 $aPredicting within-field soybean yield variability by coupling Sentinel-2 leaf area index with a crop growth model.$h[electronic resource] 260 $c2021 500 $aArticle history: Received 15 February 2021, Revised 3 June 2021, Accepted 9 July 2021, Available online 22 July 2021. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). 520 $aABSTRACT: Accurate within-field yield estimation is an essential step to conduct yield gap analysis and steer crop management towards more efficient use of resources. This study aims to develop and validate a process-based soybean model and to predict within-field yield variability by coupling leaf area index (LAI) retrieval from Sentinel-2 into the crop model. First, a soybean model is presented, which was successfully validated with field observations of total aboveground biomass, LAI and yield from seven contrasting field campaigns with strongly varying conditions. Within-field yield predictions were achieved by combining the model and the observations of LAI through an assimilation strategy. Four model parameters were chosen to optimize against the LAI curve: soil depth, field capacity, initial LAI and nitrogen translocated from leaves to seed. Six fields were used to evaluate the methodology (21175 pixels). The accuracy assessment was conducted on a pixel-by-pixel basis using high density of information from the yield monitor. The overall accuracy quantified by the relative root mean square error (rRMSE) ranged from 28 to 51% (overall rRMSE 35.8%) across the studied fields. The Lee statistics index ranged from 0.61 to 0.71, confirming a high level of similarity between observed and simulated yield maps. Therefore, the methodology was capable of representing the observed spatial patterns of yield. Furthermore, the high consistency of the optimized WHC reflects the value of the assimilation data strategy to spatialize this relevant characteristic. Some challenges were identified for further study to reduce the sources of uncertainty and improve accuracy: i) the inability of the model to reallocate biomass by simulating plant response to source limitation, ii) the generalization of empirical algorithms to retrieve LAI, and iii) the exploration of an updating method as an assimilation strategy to overcome discrepancy between simulated and retrieved LAI. 650 $aSoja 653 $aCrop growth model 653 $aData assimilation 653 $aSentinel-2 653 $aSoybean 653 $aYield prediction 700 1 $aDE WIT, A. 700 1 $aBERGER, A. 700 1 $aKOOISTRA, L. 773 $tAgricultural and Forest Meteorology, 2021, Volumes 308-309, article 108553. OPEN ACCESS. Doi: https://doi.org/10.1016/j.agrformet.2021.108553
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INIA La Estanzuela (LE) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
01/03/2021 |
Actualizado : |
01/03/2021 |
Tipo de producción científica : |
Documentos |
Autor : |
MONDINO, P.; SEVERINO, V.; FOURMENT, M.; LEONI, C.; MUJICA, V.; FASIOLO, C.; ZOPPOLO, R.; RABELLINO, F.; CESCATO, I.; ROLANDO, E.; BUSCHIAZZO, M.; CARREGA, E.; OSORIO, F. |
Afiliación : |
PEDRO MONDINO, Universidad de la República (UdelaR)/ Facultad de Agronomía; VIVIAN SEVERINO, Universidad de la República (UdelaR)/ Facultad de Agronomía; MERCEDES FOURMENT, Universidad de la República (UdelaR)/ Facultad de Agronomía; CAROLINA LEONI VELAZCO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIA VALENTINA MUJICA TELIZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANDREA CAROLINA FASIOLO FERREIRO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ROBERTO JOSE ZOPPOLO GOLDSCHMIDT, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO RABELLINO, AFRUPI (Asociación de Fruticultores Producción Integrada); IVAN CESCATO, AFRUPI (Asociación de Fruticultores Producción Integrada); ERICK ROLANDO, AFRUPI (Asociación de Fruticultores Producción Integrada); MARCELO BUSCHIAZZO, MGAP/ DIGEGRA (Dirección General de la Granja, Ex-JUNAGRA); ELISABETH CARREGA, MGAP/ DIGEGRA (Dirección General de la Granja, Ex-JUNAGRA); FABIANA OSORIO, MGAP/ DIGEGRA (Dirección General de la Granja, Ex-JUNAGRA). |
Título : |
Directivas generales para la producción integrada frutícola de Uruguay. Actualización 2019. |
Fecha de publicación : |
2019 |
Fuente / Imprenta : |
Montevideo (UY): DIGEGRA-Facultad de Agronomía-INIA, 2019. |
Páginas : |
8 p. |
Idioma : |
Español |
Notas : |
Actualización realizada sobre la norma original y sus respectivas actualizaciones. |
Contenido : |
La producción horti-frutícola integrada (PI) tiene como objetivo general lograr un aumento de la competitividad de la producción uruguaya, contando con productos de mejor calidad para el consumidor, mediante una producción sustentable y respetuosa del ambiente.
Directrices indicadas: Cuidado de la imagen y el entorno del predio - Manejo del suelo, Restos de cultivo - Manejo integrado de plagas y enfermedades - Monitoreo - Manejo de los plaguicidas - Almacenamiento de los Plaguicidas, Eliminación de recipientes, Equipo de protección para el Aplicador, Fraccionamiento de Producto y Preparación del caldo - Técnica de aplicación - Tiempo de carencia o tiempo de espera - Límites Máximos de Residuos - Control de heladas - Salud, Seguridad y Bienestar Laboral. |
Palabras claves : |
NORMAS DE PRODUCCIÓN INTEGRADA. |
Thesagro : |
FRUTALES; FRUTICULTURA; NORMAS; PRODUCCIÓN FRUTICOLA. |
Asunto categoría : |
F01 Cultivo |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/15172/1/Directivas-Generales-Produccion-Integrada-Fruticola-Actualizacion-2019.pdf
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Marc : |
LEADER 01773nam a2200337 a 4500 001 1061780 005 2021-03-01 008 2019 bl uuuu u0uu1 u #d 100 1 $aMONDINO, P. 245 $aDirectivas generales para la producción integrada frutícola de Uruguay. Actualización 2019.$h[electronic resource] 260 $aMontevideo (UY): DIGEGRA-Facultad de Agronomía-INIA$c2019 300 $a8 p. 500 $aActualización realizada sobre la norma original y sus respectivas actualizaciones. 520 $aLa producción horti-frutícola integrada (PI) tiene como objetivo general lograr un aumento de la competitividad de la producción uruguaya, contando con productos de mejor calidad para el consumidor, mediante una producción sustentable y respetuosa del ambiente. Directrices indicadas: Cuidado de la imagen y el entorno del predio - Manejo del suelo, Restos de cultivo - Manejo integrado de plagas y enfermedades - Monitoreo - Manejo de los plaguicidas - Almacenamiento de los Plaguicidas, Eliminación de recipientes, Equipo de protección para el Aplicador, Fraccionamiento de Producto y Preparación del caldo - Técnica de aplicación - Tiempo de carencia o tiempo de espera - Límites Máximos de Residuos - Control de heladas - Salud, Seguridad y Bienestar Laboral. 650 $aFRUTALES 650 $aFRUTICULTURA 650 $aNORMAS 650 $aPRODUCCIÓN FRUTICOLA 653 $aNORMAS DE PRODUCCIÓN INTEGRADA 700 1 $aSEVERINO, V. 700 1 $aFOURMENT, M. 700 1 $aLEONI, C. 700 1 $aMUJICA, V. 700 1 $aFASIOLO, C. 700 1 $aZOPPOLO, R. 700 1 $aRABELLINO, F. 700 1 $aCESCATO, I. 700 1 $aROLANDO, E. 700 1 $aBUSCHIAZZO, M. 700 1 $aCARREGA, E. 700 1 $aOSORIO, F.
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