| |
|
|
 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
|
Registro completo
|
|
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
Descargar
Esconder MarcPresentar Marc Completo |
|
Registro original : |
INIA Las Brujas (LB) |
|
|
Biblioteca
|
Identificación
|
Origen
|
Tipo / Formato
|
Clasificación
|
Cutter
|
Registro
|
Volumen
|
Estado
|
Volver
|
|
|
| Registros recuperados : 46 | |
| 1. |  | MONTOSSI, F.; GUTIERREZ, D.; PRAVIA, M.I.; PORCILE, M.; PORCILE, V.; JAURENA, M.; AYALA, W. Caracterización del componente pasturas y forrajes en predios del GIPROCAR II: disponibilidad, crecimiento, composición botánica y valor nutritivo. In: MONTOSSI, F. (Ed.). Invernada de precisión: Pasturas, Calidad de Carne, Genética, Gestión Empresarial e Impacto Ambiental (GIPROCAR II) Montevideo (UY): INIA, 2013. p. 69-107 (Serie Técnica; 211)| Tipo: Capítulo en Libro Técnico-Científico |
| Biblioteca(s): INIA Treinta y Tres. |
|    |
| 2. | | TISCORNIA, G.; PORCILE, V.; BIDEGAIN, M.; DE LOS SANTOS, B.; DE BRUM RODRÍGUEZ, F.; VAN LIER, E.; OLIVERA, J.; CASARETTO, A.; MARCHELLI, J.; FIERRO, S.; SARAVIA, C.; DE BARBIERI, I. Comportamiento histórico del Índice de enfriamiento (Chill index) para ovinos durante la estación fria. Producción Animal. Revista INIA Uruguay, 2020, no. 61, p. 23-27. (Revista INIA; 61).| Tipo: Artículos en Revistas Agropecuarias |
| Biblioteca(s): INIA Las Brujas; INIA Tacuarembó. |
| |
| 3. | | GÓMEZ MILLER, R.; PORCILE, V.; BECOÑA, G.; WEDDERBURN, L. Conclusiones. ln: GÓMEZ MILLER, R.; PORCILE, V. (Ed.). Mejora de la sostenibilidad de la ganadería familiar en Uruguay. Montevideo (Uruguay): INIA, 2018. p. 93-96 (INIA Serie Técnica; 240)| Tipo: Capítulo en Libro Técnico-Científico |
| Biblioteca(s): INIA Tacuarembó. |
| |
| 5. | | PORCILE, V.; REYNO, R.; MARANGES, F.; NOLLA, F.; BECOÑA, G.; DE BRUM RODRÍGUEZ, F.; LLOVET, P.; GUTIERREZ, F.; BARAIBAR, N.; LATTANZI, F.; SOTELO, D.; ROSSI, C. ¿Conocemos los materiales forrajeros generados por la investigación uruguaya en los últimos años? Informe especial. Revista INIA Uruguay, Setiembre 2021, no.66, p. 68-77. (Revista INIA; 66).| Tipo: Artículos en Revistas Agropecuarias |
| Biblioteca(s): INIA Treinta y Tres. |
| |
| 7. | | PLATERO, P.; CHALKING, D.; FERREIRA, G. F. DE; PORCILE, V. CRILUMERINO$. Proyecto Fpta 350. Montevideo (UY): INIA, 2023. 45 p. (Serie FPTA-INIA; 99). Proyecto FPTA 350: "Implementación de alternativas tecnológicas que incrementen la competitividad de los sistemas de producción ovino-laneros de la región de basalto".
Período de Ejecución: Mayo 2017 - Mayo 2021. Institución ejecutora:...| Biblioteca(s): INIA Las Brujas. |
| |
| 11. | | MONTOSSI, F.; SAN JULIÁN, R.; CORREA, D.; GONZALES, F.; PORCILE, V. Efecto de la carga animal, sistema de pastoreo y suplementación sobre la performance de corderos corriedale sobre una pastura de Triticale secale y Lolium multiflorum en la región de areniscas de Uruguay. ln: Bemhaja, M.; Pittaluga, O., eds. 30 años de investigación en suelos de areniscas, INIA Tacuarembó. Montevideo (Uruguay): INIA, 2006. p. 151-165 (INIA Serie Técnica ; 159)| Tipo: Capítulo en Libro Técnico-Científico |
| Biblioteca(s): INIA Las Brujas; INIA Tacuarembó. |
| |
| 12. | | MONTOSSI, F.; SAN JULIÁN, R.; CORREA, D.; GONZÁLES, F.; PORCILE, V. Efecto de la carga animal, sistema de pastoreo y suplementación sobre la performance de una pastura de Triticale secale y Lolium multiflorum pastoreada por corderos corriedale en la región de areniscas de Uruguay. ln: Bemhaja, M.; Pittaluga, O., eds. 30 años de investigación en suelos de areniscas, INIA Tacuarembó. Montevideo (Uruguay): INIA, 2006. p. 139-150 (INIA Serie Técnica ; 159)| Tipo: Capítulo en Libro Técnico-Científico |
| Biblioteca(s): INIA Las Brujas; INIA Tacuarembó. |
| |
| 13. | | MONTOSSI, F.; SAN JULIÁN, R.; CORREA, D.; GONZÁLEZ, F.; PORCILE, V. Efecto de la carga animal, sistema de pastoreo y suplementación sobre la performance de una pastura de Triticale secale y Lolium multiflorum pastoreada por corderos Corriedale en Uruguay. [Resumen]. In: CONGRESO MUNDIAL CORRIEDALE, 12, 2003, SET 1-10: MONTEVIDEO, URUGUAY. Montevideo (Uruguay): INIA; SUL; Sociedad Criadores de Corriedale, 2003. p. 130| Tipo: Abstracts/Resúmenes |
| Biblioteca(s): INIA Tacuarembó. |
| |
| 14. | | MONTOSSI, F.; DE BARBIERI, I.; CIAPPESONI, G.; GANZÁBAL, A.; BANCHERO, G.; SOARES DE LIMA, J.M.; BRITO, G.; LUZARDO, S.; SAN JULIÁN, R.; SILVEIRA, C.; VÁZQUEZ, A.; RAMOS, Z.; PORCILE, V. En tiempos de agricultura y forestación: ¿No existe espacios competitivos para la producción ovina moderna?. In: JORNADAS URUGUAYAS DE BUIATRÍA, 41, 2013, Paysandú, Uruguay. Posters. Paysandú, UY: Centro Médico Veterinario de Paysandú, SUB, SMVU. 2013. p. 103-119.| Tipo: Trabajos en Congresos/Conferencias |
| Biblioteca(s): INIA La Estanzuela. |
| |
| 15. | | PRAVIA, M.I.; MONTOSSI, F.; GUTIERREZ, C.; AYALA, W.; ANDREGNETTE, B.; INVERNIZZI, G.; PORCILE, V. Estimación de la disponibilidad de pasturas y forrajes en predios de GIPROCAR II. Ajustes del "Rising plate meter" para las condiciones de Uruguay In: MONTOSSI, F. (Ed.). Invernada de precisión: Pasturas, Calidad de Carne, Genética, Gestión Empresarial e Impacto Ambiental (GIPROCAR II) Montevideo (UY): INIA, 2013. p. 31-67 (Serie Técnica; 211)| Tipo: Capítulo en Libro Técnico-Científico |
| Biblioteca(s): INIA Tacuarembó; INIA Treinta y Tres. |
| |
| 17. | | PORCILE, V.; LAPETINA, J.; MENESES, L.; TERRA, A. Evaluación del daño causado por Isoca en áreas de campo natural y mejoramientos: una experiencia en predios comerciales de la zona de Sarandí del Yí. Revista INIA Uruguay, 2020, no. 60, p. 45-48. (Revista INIA; 60) Equipo técnico involucrado. Por INIA: Dr. Fernando Lattanzi, Asist. Inv. Pablo Calistro, Lic. Biol. MSc Ximena Cibils, Ings. Agrs. Lucía Meneses, Javier Do Canto, Daniel Formoso y Virginia Porcile. Por PLAN AGROPECUARIO: Ing. Agr....| Tipo: Artículos en Revistas Agropecuarias |
| Biblioteca(s): INIA La Estanzuela; INIA Las Brujas. |
| |
| Registros recuperados : 46 | |
|
| Expresión de búsqueda válido. Check! |
|
|
