| |
|
|
|
Registro completo
|
|
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
|
|
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
Descargar
Esconder MarcPresentar Marc Completo |
|
Registro original : |
INIA La Estanzuela (LE) |
|
|
Biblioteca
|
Identificación
|
Origen
|
Tipo / Formato
|
Clasificación
|
Cutter
|
Registro
|
Volumen
|
Estado
|
Volver
|
|
|
| Registros recuperados : 5 | |
| 2. |  | CABRERA, A.; BERNÁ, L.; FRESIA, P.; SILVEIRA, C.S.; MACÍAS-RIOSECO, M.; PRITSCH,O.; RIET-CORREA, F.; GIANNITTI, F.; FRANCIA, M.E.; REBOLLO, C. Generación de nuevas herramientas para el control de Neospora caninum a partir de un enfoque epidemiológico y genómico. [Resumen] In: REDBIO; INIA (Instituto Nacional de Investigación Agropecuaria); REDBIO Argentina. X Encuentro Latinoamericano y del Caribe de Biotecnología Agropecuaria y XI Simposio Redbio Argentina. Libro de Resúmenes. Montevideo 12 - 15 Noviembre 2019. Montevideo (UY): INIA, 2019. p. 148. (INIA Serie Técnica; 253).| Tipo: Abstracts/Resúmenes |
| Biblioteca(s): INIA La Estanzuela. |
|    |
| 3. | | COLOMBATTI OLIVIERI, M. A.; FRESIA, P.; GRAÑA, M.; CUERDA, M.X.; NAGEL, A.; ALVARADO PINEDO, F.; ROMANO, M. I.; CAIMI, K.; BERNÁ, L.; SANTANGELO, M. P. Genomic comparison of two strains of Mycobacterium avium subsp. paratuberculosis with contrasting pathogenic phenotype. Letter. Tuberculosis, 2023, volume 138, article 102299. doi: https://doi.org/10.1016/j.tube.2022.102299 Article history: Received 30 September 2022; Received in revised form 28 November 2022; Accepted 19 December 2022; Available online 21 December 2022. -- Correspondence authors: Berná, L.; Unidad de Biología Molecular, Institut...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Las Brujas. |
|  |
| 4. | | CABRERA, A.; FRESIA, P.; BERNÁ, L.; SILVEIRA, C.S.; MACÍAS-RIOSECO, M.; AREVALO, A.P.; CRISPO, M.; PRITSCH, O.; RIET-CORREA, F.; GIANNITTI, F.; GIANNITTI, F.; FRANCIA, M.E.; ROBELLO, C. Isolation and molecular characterization of four novel Neospora caninum strains. Genetics, Evolution, and Phylogeny - Short Communication. Parasitology Research, 1 December 2019, Volume 118, Issue 12, Pages 3535-3542. Doi: 10.1007/s00436-019-06474-9 Article history: Received: 11 April 2019 / Accepted: 24 September 2019 / Published online: 7 November 2019.
Funding Sponsor: Agencia Nacional de Investigación e Innovación (ANII).
Funding Text: This project was funded by grant...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Las Brujas. |
| |
| 5. | | PRITSCH, C.; OBERTI, H.; VAIO, M.; BAZ, N.; MANGINO, M.; BERNÁ, L.; ROSSI, F.; RODRÍGUEZ DECUADRO, S.; QUEZADA, M.; GAIERO, P.; PESCE, M.; FILIPPI, C.; GARAYCOCHEA, S.; DINI, M.; GUTIÉRREZ GONZÁLEZ, J.; VARANI, A. Terra Incógnita: conociendo el genoma del guayabo del país. [Presentación oral]. Módulo 1. Recursos genéticos. Presentaciones Orales. In: Dini, M.; Speroni, G. (Eds.). Encuentro Nacional sobre Frutos Nativos, 11°. Universidad Tecnológica (UTEC), Durazno, Uruguay, 4 y 5 abril 2024, Libro de resúmenes. Canelones (UY): INIA, 2024. p.6. (Serie Actividades de Difusión; 804) Agradecimientos: A la Comisión Sectorial de Investigación Científica, Udelar por la financiación.| Tipo: Abstracts/Resúmenes |
| Biblioteca(s): INIA Las Brujas. |
| |
| Registros recuperados : 5 | |
|
| Expresión de búsqueda válido. Check! |
|
|
