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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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Biblioteca (s) : |
INIA Tacuarembó. |
Fecha actual : |
25/06/2018 |
Actualizado : |
20/02/2019 |
Tipo de producción científica : |
Documentos |
Autor : |
MONTOSSI, F.; DE BARBIERI, I.; BURGEL, A.; LUZARDO, S.; SOARES DE LIMA, J.M.; FRUGONI, J.C.; MARTINEZ, H.; SILVEIRA, C.; PLATERO, P.; LEVRATTO, J.; BOTTERO, D.; MOREIRA, L.; RODRÍGUEZ, H.; LIENDO, F.; ROVIRA, F.; BENTANCURT, M.; CUADRO, P. |
Afiliación : |
FABIO MARCELO MONTOSSI PORCHILE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUIS IGNACIO DE BARBIERI ETCHEBERRY, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; AGUSTÍN BURGEL; SANTIAGO FELIPE LUZARDO VILLAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JUAN MANUEL SOARES DE LIMA LAPETINA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JULIO CESAR FRUGONI SILVEIRA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; HOMERO MARTINEZ FORMOSO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CAROLINA INES SILVEIRA ROJAS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PABLO NICOLAS PLATERO CLAVIER, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JUAN CARLOS LEVRATTO CORTES, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SERGIO DANIEL BOTTERO REGGI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUIS EDUARDO MOREIRA SANTANA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FRANCO ALBERTO LIENDO RODRIGUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO ROVIRA GALARRAGA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MAURO ANDRES BENTANCURT PONTTI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PABLO ANDRES CUADRO BRAS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Presentación diferencial del producto final. |
Fecha de publicación : |
2006 |
Fuente / Imprenta : |
ln: INIA TACUAREMBÓ. ESTACIÓN EXPERIMENTAL GLENCOE. Día de campo. Producción animal, pasturas. Estación Experimental Glencoe, Paysandú, 14 noviembre, 2006. Tacuarembó (Uruguay): INIA, 2006. |
Páginas : |
p. 9-10 |
Serie : |
(INIA Serie Actividades de Difusión ; 473) |
Idioma : |
Español |
Contenido : |
Objetivo: Evaluar el impacto productivo (en términos de calidad y cantidad) y económico de la separación del vellón, de acuerdo con la región del animal, al momento de la esquila, para su posterior enfardado según esos criterios, utilizando animales finos y superfinos de la raza Merino Australiano. Objetivos específicos: • Caracterizar el diámetro de la fibra y largo de mecha en distintas regiones del cuerpo de animales que producen en promedio fibras finas y superfinas. • Conocer la importancia relativa de cada región del cuerpo en la producción de lana del animal. • Adaptar esta alternativa para su implementación en máquinas de esquila a nivel de predios comerciales.
• Generar fardos con pesos entre 80 - 120 kilos. |
Palabras claves : |
ANIMAL PRODUCTION; SEPARACIÓN DEL VELLÓN; WOOL. |
Thesagro : |
LANAS. |
Asunto categoría : |
L01 Ganadería |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/10480/1/SAD-473p9-10.pdf
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Marc : |
LEADER 01879naa a2200385 a 4500 001 1058732 005 2019-02-20 008 2006 bl uuuu u00u1 u #d 100 1 $aMONTOSSI, F. 245 $aPresentación diferencial del producto final. 260 $c2006 300 $ap. 9-10 490 $a(INIA Serie Actividades de Difusión ; 473) 520 $aObjetivo: Evaluar el impacto productivo (en términos de calidad y cantidad) y económico de la separación del vellón, de acuerdo con la región del animal, al momento de la esquila, para su posterior enfardado según esos criterios, utilizando animales finos y superfinos de la raza Merino Australiano. Objetivos específicos: • Caracterizar el diámetro de la fibra y largo de mecha en distintas regiones del cuerpo de animales que producen en promedio fibras finas y superfinas. • Conocer la importancia relativa de cada región del cuerpo en la producción de lana del animal. • Adaptar esta alternativa para su implementación en máquinas de esquila a nivel de predios comerciales. • Generar fardos con pesos entre 80 - 120 kilos. 650 $aLANAS 653 $aANIMAL PRODUCTION 653 $aSEPARACIÓN DEL VELLÓN 653 $aWOOL 700 1 $aDE BARBIERI, I. 700 1 $aBURGEL, A. 700 1 $aLUZARDO, S. 700 1 $aSOARES DE LIMA, J.M. 700 1 $aFRUGONI, J.C. 700 1 $aMARTINEZ, H. 700 1 $aSILVEIRA, C. 700 1 $aPLATERO, P. 700 1 $aLEVRATTO, J. 700 1 $aBOTTERO, D. 700 1 $aMOREIRA, L. 700 1 $aRODRÍGUEZ, H. 700 1 $aLIENDO, F. 700 1 $aROVIRA, F. 700 1 $aBENTANCURT, M. 700 1 $aCUADRO, P. 773 $tln: INIA TACUAREMBÓ. ESTACIÓN EXPERIMENTAL GLENCOE. Día de campo. Producción animal, pasturas. Estación Experimental Glencoe, Paysandú, 14 noviembre, 2006. Tacuarembó (Uruguay): INIA, 2006.
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