Vigilancia científica sobre procesos de gestión de información agroclimática

Vigilancia científica sobre procesos de gestión de información agroclimática

Los avances recientes en tecnologías como el Sistema de Observación de la Tierra, el Acceso Abierto, la Inteligencia Artificial, el Aprendizaje Automático, las Tecnologías de la Información y la Comunicación, las Plataformas de Computación en Nube y la Ciencia Ciudadana han ampliado enormemente el p...

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Autores principales: Cárdenas Solano, Leidy Johanna, Contreras Pedraza, Carlos Alberto
Formato: Estudio de vigilancia
Lenguaje:Español
Publicado: Corporación colombiana de investigación agropecuaria - AGROSAVIA 2024
Materias:
Meteorología y climatología - P40
Agro climatología
Ciencia
Tecnología
Innovación
Transversal
http://aims.fao.org/aos/agrovoc/c_17010
http://aims.fao.org/aos/agrovoc/c_37989
http://aims.fao.org/aos/agrovoc/c_7644
http://aims.fao.org/aos/agrovoc/c_27560
Acceso en línea:http://hdl.handle.net/20.500.12324/39914
id RepoAGROSAVIA39914
record_format dspace
institution Corporación Colombiana de Investigación Agropecuaria
collection Repositorio AGROSAVIA
language Español
topic Meteorología y climatología - P40
Agro climatología
Ciencia
Tecnología
Innovación
Transversal
http://aims.fao.org/aos/agrovoc/c_17010
http://aims.fao.org/aos/agrovoc/c_37989
http://aims.fao.org/aos/agrovoc/c_7644
http://aims.fao.org/aos/agrovoc/c_27560
spellingShingle Meteorología y climatología - P40
Agro climatología
Ciencia
Tecnología
Innovación
Transversal
http://aims.fao.org/aos/agrovoc/c_17010
http://aims.fao.org/aos/agrovoc/c_37989
http://aims.fao.org/aos/agrovoc/c_7644
http://aims.fao.org/aos/agrovoc/c_27560
Cárdenas Solano, Leidy Johanna
Contreras Pedraza, Carlos Alberto
Vigilancia científica sobre procesos de gestión de información agroclimática
description Los avances recientes en tecnologías como el Sistema de Observación de la Tierra, el Acceso Abierto, la Inteligencia Artificial, el Aprendizaje Automático, las Tecnologías de la Información y la Comunicación, las Plataformas de Computación en Nube y la Ciencia Ciudadana han ampliado enormemente el potencial del análisis de Big Data. Estas tecnologías ofrecen herramientas más inteligentes, interoperables y útiles para la toma de decisiones en agricultura, generando información valiosa. Se están realizando esfuerzos para recopilar datos geoetiquetados e información sobre la producción agrícola a diferentes escalas, aprovechando técnicas de aprendizaje automático y datos satelitales. Además, se está integrando la digitalización con varios aspectos del agroecosistema, como la fitogenética, la diversificación de cultivos, el uso eficiente de insumos, las prácticas agronómicas, la gestión de recursos relacionados con el suelo, la tierra y las cuencas hidrográficas, la estabilidad económica, la integración de la economía y el medio ambiente, y la gestión adecuada de los servicios ecosistémicos. Se espera que, mediante intervenciones tecnológicas y análisis de macrodatos, los agricultores puedan proteger sus cultivos de las fluctuaciones meteorológicas y las amenazas naturales, y se puedan sugerir cultivos adecuados durante períodos de barbecho o regeneración del suelo. Esto aumentaría la productividad y garantizaría una valoración sostenible del agroecosistema. En resumen, las investigaciones sobre el uso de información agroclimática se centran en comprender los desafíos y oportunidades relacionados con el cambio climático en la agricultura, así como en implementar prácticas agrícolas sostenibles y adaptativas. Según las investigaciones publicadas en Scopus, se ha identificado que los principales países que abordan esta temática desde un punto de vista científico son India, China, Estados Unidos, Italia, Francia, Alemania, España, Australia, Irán y Brasil. A continuación, se muestra una visión general de algunas acciones destacadas que están llevando a cabo en cada país: Cabe destacar que estos países están realizando avances significativos en la gestión de información agroclimática, pero las iniciativas y proyectos específicos pueden variar en cada caso y estar en diferentes etapas de implementación.
format Estudio de vigilancia
author Cárdenas Solano, Leidy Johanna
Contreras Pedraza, Carlos Alberto
author_facet Cárdenas Solano, Leidy Johanna
Contreras Pedraza, Carlos Alberto
author_sort Cárdenas Solano, Leidy Johanna
title Vigilancia científica sobre procesos de gestión de información agroclimática
title_short Vigilancia científica sobre procesos de gestión de información agroclimática
title_full Vigilancia científica sobre procesos de gestión de información agroclimática
title_fullStr Vigilancia científica sobre procesos de gestión de información agroclimática
title_full_unstemmed Vigilancia científica sobre procesos de gestión de información agroclimática
title_sort vigilancia científica sobre procesos de gestión de información agroclimática
publisher Corporación colombiana de investigación agropecuaria - AGROSAVIA
publishDate 2024
url http://hdl.handle.net/20.500.12324/39914
work_keys_str_mv AT cardenassolanoleidyjohanna vigilanciacientificasobreprocesosdegestiondeinformacionagroclimatica
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spelling RepoAGROSAVIA399142024-08-23T03:00:32Z Vigilancia científica sobre procesos de gestión de información agroclimática Cárdenas Solano, Leidy Johanna Contreras Pedraza, Carlos Alberto Meteorología y climatología - P40 Agro climatología Ciencia Tecnología Innovación Transversal http://aims.fao.org/aos/agrovoc/c_17010 http://aims.fao.org/aos/agrovoc/c_37989 http://aims.fao.org/aos/agrovoc/c_7644 http://aims.fao.org/aos/agrovoc/c_27560 Los avances recientes en tecnologías como el Sistema de Observación de la Tierra, el Acceso Abierto, la Inteligencia Artificial, el Aprendizaje Automático, las Tecnologías de la Información y la Comunicación, las Plataformas de Computación en Nube y la Ciencia Ciudadana han ampliado enormemente el potencial del análisis de Big Data. Estas tecnologías ofrecen herramientas más inteligentes, interoperables y útiles para la toma de decisiones en agricultura, generando información valiosa. Se están realizando esfuerzos para recopilar datos geoetiquetados e información sobre la producción agrícola a diferentes escalas, aprovechando técnicas de aprendizaje automático y datos satelitales. Además, se está integrando la digitalización con varios aspectos del agroecosistema, como la fitogenética, la diversificación de cultivos, el uso eficiente de insumos, las prácticas agronómicas, la gestión de recursos relacionados con el suelo, la tierra y las cuencas hidrográficas, la estabilidad económica, la integración de la economía y el medio ambiente, y la gestión adecuada de los servicios ecosistémicos. Se espera que, mediante intervenciones tecnológicas y análisis de macrodatos, los agricultores puedan proteger sus cultivos de las fluctuaciones meteorológicas y las amenazas naturales, y se puedan sugerir cultivos adecuados durante períodos de barbecho o regeneración del suelo. Esto aumentaría la productividad y garantizaría una valoración sostenible del agroecosistema. En resumen, las investigaciones sobre el uso de información agroclimática se centran en comprender los desafíos y oportunidades relacionados con el cambio climático en la agricultura, así como en implementar prácticas agrícolas sostenibles y adaptativas. Según las investigaciones publicadas en Scopus, se ha identificado que los principales países que abordan esta temática desde un punto de vista científico son India, China, Estados Unidos, Italia, Francia, Alemania, España, Australia, Irán y Brasil. A continuación, se muestra una visión general de algunas acciones destacadas que están llevando a cabo en cada país: Cabe destacar que estos países están realizando avances significativos en la gestión de información agroclimática, pero las iniciativas y proyectos específicos pueden variar en cada caso y estar en diferentes etapas de implementación. 2024-08-22T20:30:05Z 2024-08-22T20:30:05Z 2023-06 2023 Estudio de vigilancia http://hdl.handle.net/20.500.12324/39914 spa Serie Documentos de Trabajo Adnan, R. M., Liang, Z., Kuriqi, A., Kisi, O., Malik, A., Li, B., & Mortazavizadeh, F. (2021). Air temperature prediction using different machine learning models. Indonesian Journal of Electrical Engineering and Computer Science, 22(1), 534–541. https://doi.org/10.11591/IJEECS.V22.I1.PP534-541 Alhajj Ali, S., Vivaldi, G. A., Garofalo, S. Pietro, Costanza, L., & Camposeo, S. (2023). 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