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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
21/02/2014 |
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Actualizado : |
28/12/2016 |
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Autor : |
MGAP (MINISTERIO DE GANADERIA AGRICULTURA Y PESCA), URUGUAY; DIEA (ESTADISTICAS AGROPECUARIAS), URUGUAY |
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Título : |
Encuesta agrícola: otoño-invierno 2006. |
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Fecha de publicación : |
2006 |
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Fuente / Imprenta : |
Montevideo (UY): DIEA, 2006. |
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Páginas : |
33 p. |
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Serie : |
(Serie encuestas; 237) |
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Idioma : |
Español |
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Palabras claves : |
CHACRAS. |
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Thesagro : |
CULTIVOS DE VERANO; URUGUAY. |
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Asunto categoría : |
F01 Cultivo |
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Marc : |
LEADER 00503nam a2200169 a 4500
001 1047291
005 2016-12-28
008 2006 bl uuuu u00u1 u #d
100 1 $aMGAP (MINISTERIO DE GANADERIA AGRICULTURA Y PESCA), URUGUAY
245 $aEncuesta agrícola$botoño-invierno 2006.
260 $aMontevideo (UY): DIEA$c2006
300 $a33 p.
490 $a(Serie encuestas; 237)
650 $aCULTIVOS DE VERANO
650 $aURUGUAY
653 $aCHACRAS
700 1 $aDIEA (ESTADISTICAS AGROPECUARIAS), URUGUAY
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INIA La Estanzuela (LE) |
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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
31/01/2020 |
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Actualizado : |
31/01/2020 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
GASO, D.; BERGER, A.; CIGANDA, V. |
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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. |
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Título : |
Predicting wheat grain yield and spatial variability at field scale using a simple regression or a crop model in conjunction with Landsat images. |
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Fecha de publicación : |
2019 |
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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 |
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ISSN : |
0168-1699 |
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DOI : |
10.1016/j.compag.2019.02.026 |
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Idioma : |
Inglés |
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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. |
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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 |
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Palabras claves : |
Crop growth model; Landsat; Leaf area index; Wheat; Yield. |
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Asunto categoría : |
F01 Cultivo |
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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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