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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
31/01/2020 |
Actualizado : |
31/01/2020 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
GASO, D.; BERGER, A.; CIGANDA, V. |
Afiliación : |
DEBORAH VIVIANA GASO MELGAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANDRES GUSTAVO BERGER RICCA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; VERONICA SOLANGE CIGANDA BRASCA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Predicting wheat grain yield and spatial variability at field scale using a simple regression or a crop model in conjunction with Landsat images. |
Fecha de publicación : |
2019 |
Fuente / Imprenta : |
Computers and Electronics in Agriculture, April 2019, Volume 159, Pages 75-83. Doi: https://doi.org/10.1016/j.compag.2019.02.026 |
ISSN : |
0168-1699 |
DOI : |
10.1016/j.compag.2019.02.026 |
Idioma : |
Inglés |
Notas : |
Article history: Received 8 February 2018 / Revised 22 February 2019 / Accepted 25 February 2019 / Available online 4 March 2019..
This work was supported by ANII fellowship program and INIA fundings. The authors thank farmers who provided field data. |
Contenido : |
ABSTRACT.
Early prediction of crop yields has been a challenge frequently resolved through the combination of remote sensing data and crop models. The aim of this study was to evaluate two different methods based on remote sensing data for predicting winter wheat (Triticum aestivum L.) yield at field scale. We compared the accuracy of: (i) a simple regression method between different vegetation indices at anthesis and grain yield, and (ii) a crop model method based on optimization of two parameters (specific leaf nitrogen and initial aboveground-biomass) using time series of vegetation indices. Vegetation indices were derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images acquired for two growing seasons (2013, 2014) across 22 fields in south western Uruguay with an average size of 128 ha. At all sites, leaf area index (LAI) was measured during a field campaign, and grain yield was measured with yield monitors on harvesters. The simple regression method (SRM) achieved higher accuracy than the model-based method (CMM) for the estimation of yield at field scale (RMSE = 966 kg ha −1 and RMSE = 1532 kg ha −1 , respectively). When deviations between observed and estimated yields were evaluated at pixel (30 × 30 m) level, the model-based method was better at detecting existing spatial variability in grain yield and at identifying areas of different yield potential. Even though both methods have limited utility to estimate yield at field scale with very high accuracy due to large RMSE, the methodologies are suitable to predict harvest volumes at large agricultural areas or at country level, and to construct synthetic yield maps reflecting within field variability. Higher temporal resolution of images would improve accuracy in estimating yield and spatial variability at field scale. © 2019 Elsevier B.V. MenosABSTRACT.
Early prediction of crop yields has been a challenge frequently resolved through the combination of remote sensing data and crop models. The aim of this study was to evaluate two different methods based on remote sensing data for predicting winter wheat (Triticum aestivum L.) yield at field scale. We compared the accuracy of: (i) a simple regression method between different vegetation indices at anthesis and grain yield, and (ii) a crop model method based on optimization of two parameters (specific leaf nitrogen and initial aboveground-biomass) using time series of vegetation indices. Vegetation indices were derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images acquired for two growing seasons (2013, 2014) across 22 fields in south western Uruguay with an average size of 128 ha. At all sites, leaf area index (LAI) was measured during a field campaign, and grain yield was measured with yield monitors on harvesters. The simple regression method (SRM) achieved higher accuracy than the model-based method (CMM) for the estimation of yield at field scale (RMSE = 966 kg ha −1 and RMSE = 1532 kg ha −1 , respectively). When deviations between observed and estimated yields were evaluated at pixel (30 × 30 m) level, the model-based method was better at detecting existing spatial variability in grain yield and at identifying areas of different yield potential. Even though both methods have limited utility to ... Presentar Todo |
Palabras claves : |
Crop growth model; Landsat; Leaf area index; Wheat; Yield. |
Asunto categoría : |
F01 Cultivo |
Marc : |
LEADER 02944naa a2200241 a 4500 001 1060735 005 2020-01-31 008 2019 bl uuuu u00u1 u #d 022 $a0168-1699 024 7 $a10.1016/j.compag.2019.02.026$2DOI 100 1 $aGASO, D. 245 $aPredicting wheat grain yield and spatial variability at field scale using a simple regression or a crop model in conjunction with Landsat images.$h[electronic resource] 260 $c2019 500 $aArticle history: Received 8 February 2018 / Revised 22 February 2019 / Accepted 25 February 2019 / Available online 4 March 2019.. This work was supported by ANII fellowship program and INIA fundings. The authors thank farmers who provided field data. 520 $aABSTRACT. Early prediction of crop yields has been a challenge frequently resolved through the combination of remote sensing data and crop models. The aim of this study was to evaluate two different methods based on remote sensing data for predicting winter wheat (Triticum aestivum L.) yield at field scale. We compared the accuracy of: (i) a simple regression method between different vegetation indices at anthesis and grain yield, and (ii) a crop model method based on optimization of two parameters (specific leaf nitrogen and initial aboveground-biomass) using time series of vegetation indices. Vegetation indices were derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images acquired for two growing seasons (2013, 2014) across 22 fields in south western Uruguay with an average size of 128 ha. At all sites, leaf area index (LAI) was measured during a field campaign, and grain yield was measured with yield monitors on harvesters. The simple regression method (SRM) achieved higher accuracy than the model-based method (CMM) for the estimation of yield at field scale (RMSE = 966 kg ha −1 and RMSE = 1532 kg ha −1 , respectively). When deviations between observed and estimated yields were evaluated at pixel (30 × 30 m) level, the model-based method was better at detecting existing spatial variability in grain yield and at identifying areas of different yield potential. Even though both methods have limited utility to estimate yield at field scale with very high accuracy due to large RMSE, the methodologies are suitable to predict harvest volumes at large agricultural areas or at country level, and to construct synthetic yield maps reflecting within field variability. Higher temporal resolution of images would improve accuracy in estimating yield and spatial variability at field scale. © 2019 Elsevier B.V. 653 $aCrop growth model 653 $aLandsat 653 $aLeaf area index 653 $aWheat 653 $aYield 700 1 $aBERGER, A. 700 1 $aCIGANDA, V. 773 $tComputers and Electronics in Agriculture, April 2019, Volume 159, Pages 75-83. Doi: https://doi.org/10.1016/j.compag.2019.02.026
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Registro original : |
INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas; INIA Tacuarembó. |
Fecha actual : |
21/02/2014 |
Actualizado : |
21/10/2019 |
Tipo de producción científica : |
Actividades de Difusión |
Autor : |
ROVIRA, P.J.; VELAZCO, J.I. |
Afiliación : |
PABLO JUAN ROVIRA SANZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSÉ IGNACIO VELAZCO DE LOS REYES, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Evaluación de un sistema de autoconsumo restringido con distinto contenido de sal en la ración en la suplementación de terneros sobre campo natural |
Fecha de publicación : |
2009 |
Fuente / Imprenta : |
In: INIA TREINTA Y TRES. Jornada de divulgación de Producción Animal - Pasturas Treinta y Tres (Uruguay): INIA, 2009 |
Páginas : |
p. 69-77 |
Serie : |
(INIA Serie Actividades de Difusión ; 591) |
Idioma : |
Español |
Notas : |
Programa Nacional Pasturas y Forrajes: Ing. Agr., PhD. Walter Ayala, Director de Programa, Ing. Agr., MPhil. Raúl Bermúdez, Ing. Agr., MSc. Virginia Pravia, Lic., MSc. Felipe Lezama, Téc. en Sistemas Intensivos de Prod.Animal Ethel Barrios; Programa Nacional Producción de Carne y Lana: Ing. Agr., PhD. Graciela Quintans, Ing. Agr., MSc. Pablo Rovira, Ing. Agr. José I. Velazco; Unidad de Comunicación y Transferencia de Tecnología: Ing. Agr., MSc. Horacio Saravia |
Thesagro : |
BOVINOS; RACIONES; SAL; SALES MINERALES; SUPLEMENTACION; SUPLEMENTOS; TERNERO; TERNEROS; URUGUAY. |
Asunto categoría : |
-- A50 Investigación agraria |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/567/1/14445281009113646.pdf
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Marc : |
LEADER 01310naa a2200265 a 4500 001 1009057 005 2019-10-21 008 2009 bl uuuu u00u1 u #d 100 1 $aROVIRA, P.J. 245 $aEvaluación de un sistema de autoconsumo restringido con distinto contenido de sal en la ración en la suplementación de terneros sobre campo natural 260 $c2009 300 $ap. 69-77 490 $a(INIA Serie Actividades de Difusión ; 591) 500 $aPrograma Nacional Pasturas y Forrajes: Ing. Agr., PhD. Walter Ayala, Director de Programa, Ing. Agr., MPhil. Raúl Bermúdez, Ing. Agr., MSc. Virginia Pravia, Lic., MSc. Felipe Lezama, Téc. en Sistemas Intensivos de Prod.Animal Ethel Barrios; Programa Nacional Producción de Carne y Lana: Ing. Agr., PhD. Graciela Quintans, Ing. Agr., MSc. Pablo Rovira, Ing. Agr. José I. Velazco; Unidad de Comunicación y Transferencia de Tecnología: Ing. Agr., MSc. Horacio Saravia 650 $aBOVINOS 650 $aRACIONES 650 $aSAL 650 $aSALES MINERALES 650 $aSUPLEMENTACION 650 $aSUPLEMENTOS 650 $aTERNERO 650 $aTERNEROS 650 $aURUGUAY 700 1 $aVELAZCO, J.I. 773 $tIn: INIA TREINTA Y TRES. Jornada de divulgación de Producción Animal - Pasturas Treinta y Tres (Uruguay): INIA, 2009
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