Assessing field experts yield maize estimations with satellite information derived from Sentinel-2
Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In thi...
| Autores principales: | , , , , , |
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| Formato: | Conferencia |
| Lenguaje: | Inglés |
| Publicado: |
Universidad Técnica Federico Santa María, Chile
2024
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12123/19389 |
| _version_ | 1855486300058025984 |
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| author | Carcedo, Diego Pons, Diego Hernan Alonso, Cesar Fiant, Silvina Scavuzzo, Carlos Marcelo Marinelli, María Victoria |
| author_browse | Alonso, Cesar Carcedo, Diego Fiant, Silvina Marinelli, María Victoria Pons, Diego Hernan Scavuzzo, Carlos Marcelo |
| author_facet | Carcedo, Diego Pons, Diego Hernan Alonso, Cesar Fiant, Silvina Scavuzzo, Carlos Marcelo Marinelli, María Victoria |
| author_sort | Carcedo, Diego |
| collection | INTA Digital |
| description | Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a multiple regression site-specific crop yield model based on the Normalized Difference Vegetation Index (NDVI) extracted from Sentinel – 2 data at 10 meters resolution calibrated with yield corn estimations reported by local experts at a field level. |
| format | Conferencia |
| id | INTA19389 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Universidad Técnica Federico Santa María, Chile |
| publisherStr | Universidad Técnica Federico Santa María, Chile |
| record_format | dspace |
| spelling | INTA193892024-09-13T13:27:00Z Assessing field experts yield maize estimations with satellite information derived from Sentinel-2 Carcedo, Diego Pons, Diego Hernan Alonso, Cesar Fiant, Silvina Scavuzzo, Carlos Marcelo Marinelli, María Victoria Maiz Zea Mays Rendimiento de Cultivos Economía Agrícola Maize Crop Yield Agricultural Economics Agricultural Sector Sector Agrario Sector Agrícola Sentinel-2 Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a multiple regression site-specific crop yield model based on the Normalized Difference Vegetation Index (NDVI) extracted from Sentinel – 2 data at 10 meters resolution calibrated with yield corn estimations reported by local experts at a field level. EEA Manfredi Fil: Carcedo, Diego. Universidad Nacional de Córdoba. Instituto de Estudios Espaciales Avanzados Mario Gulich (IG). Comisión Nacional de Actividades Espaciales (CONAE); Argentina Fil: Pons, Diego Hernan. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentina Fil: Pons, Diego Hernan. Universidad Nacional de Córdoba. Instituto de Estudios Espaciales Avanzados Mario Gulich (IG). Comisión Nacional de Actividades Espaciales (CONAE); Argentina Fil: Alonso, Cesar. Bolsa de Cereales de Córdoba; Argentina Fil: Fiant, Silvina. Bolsa de Cereales de Córdoba; Argentina Fil: Scavuzzo, Carlos Marcelo. Universidad Nacional de Córdoba. Instituto de Estudios Espaciales Avanzados Mario Gulich (IG). Comisión Nacional de Actividades Espaciales (CONAE); Argentina Fil: Marinelli, María Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Córdoba; Argentina Fil: Marinelli, María Victoria. Comisión Nacional de Actividades Espaciales (CONAE). Instituto de Altos Estudios Espaciales "Mario Gulich"; Argentina 2024-09-13T13:19:21Z 2024-09-13T13:19:21Z 2019-09-25 info:ar-repo/semantics/documento de conferencia info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/19389 978-956-356-095-4 (Online) eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf Universidad Técnica Federico Santa María, Chile Proceedings of BigDSSAgro 2019. III International Conference on Agro BigData and Decision Support Systems in Agriculture. 25-27 September 2019, Valparaíso, Chile. p. 63-68 |
| spellingShingle | Maiz Zea Mays Rendimiento de Cultivos Economía Agrícola Maize Crop Yield Agricultural Economics Agricultural Sector Sector Agrario Sector Agrícola Sentinel-2 Carcedo, Diego Pons, Diego Hernan Alonso, Cesar Fiant, Silvina Scavuzzo, Carlos Marcelo Marinelli, María Victoria Assessing field experts yield maize estimations with satellite information derived from Sentinel-2 |
| title | Assessing field experts yield maize estimations with satellite information derived from Sentinel-2 |
| title_full | Assessing field experts yield maize estimations with satellite information derived from Sentinel-2 |
| title_fullStr | Assessing field experts yield maize estimations with satellite information derived from Sentinel-2 |
| title_full_unstemmed | Assessing field experts yield maize estimations with satellite information derived from Sentinel-2 |
| title_short | Assessing field experts yield maize estimations with satellite information derived from Sentinel-2 |
| title_sort | assessing field experts yield maize estimations with satellite information derived from sentinel 2 |
| topic | Maiz Zea Mays Rendimiento de Cultivos Economía Agrícola Maize Crop Yield Agricultural Economics Agricultural Sector Sector Agrario Sector Agrícola Sentinel-2 |
| url | http://hdl.handle.net/20.500.12123/19389 |
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