{"id":1195,"date":"2020-04-30T10:55:00","date_gmt":"2020-04-30T08:55:00","guid":{"rendered":"https:\/\/geopard.tech\/?p=1195"},"modified":"2024-12-01T23:12:12","modified_gmt":"2024-12-01T22:12:12","slug":"analisis-multicapa","status":"publish","type":"post","link":"https:\/\/geopard.tech\/es\/blog\/multi-layer-analytics\/","title":{"rendered":"An\u00e1lisis de datos multicapa (integrado) en la agricultura de precisi\u00f3n"},"content":{"rendered":"

La agricultura de precisi\u00f3n es capaz de generar\u00a0enormes cantidades de datos<\/span>\u00a0en forma de datos de rendimiento, im\u00e1genes satelitales y fertilidad del suelo, entre otros.<\/p>\r\n

La falta de herramientas de software de precisi\u00f3n en la nube f\u00e1ciles de usar que ayuden a los productores agr\u00edcolas a convertir las capas de datos de campo en conocimiento \u00fatil y recomendaciones pr\u00e1cticas limita la aplicaci\u00f3n de las tecnolog\u00edas de agricultura de precisi\u00f3n.<\/p>\r\n

En la agricultura de precisi\u00f3n, las zonas de manejo son \u00e1reas dentro de un campo que tienen un potencial de rendimiento similar en funci\u00f3n del tipo de suelo, la posici\u00f3n de la pendiente, la qu\u00edmica del suelo, el microclima y\/u otros factores que influyen en la producci\u00f3n de cultivos.<\/p>\r\n

En\u00a0conocimiento del productor sobre un campo<\/span>\u00a0Es una parte muy importante del proceso. Las zonas de manejo se conciben como un mecanismo para optimizar los insumos agr\u00edcolas y el potencial de rendimiento.<\/p>\r\n\t\t

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\"Mapa<\/figure><\/div>
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<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\r\n

\r\n\r\n<\/p>\r\n

Mapas generados con una sola capa de datos y con varias capas de datos.<\/em><\/p>\r\n

El gran desaf\u00edo es construir zonas de manejo que reflejen perfectamente la variabilidad del campo. Una combinaci\u00f3n de diferentes capas como im\u00e1genes satelitales, fertilidad del suelo, derivados topogr\u00e1ficos y datos de monitoreo de rendimiento es el siguiente paso l\u00f3gico para generar zonas de gesti\u00f3n m\u00e1s receptivas<\/span>.<\/p>\r\n\r\n\r\n\r\n

An\u00e1lisis multicapa (tambi\u00e9n conocido como an\u00e1lisis integrado<\/span>) se est\u00e1 integrando al motor de an\u00e1lisis geoespacial GeoPard.<\/p>\r\n\r\n\r\n\r\n

Las combinaciones cl\u00e1sicas de par\u00e1metros de an\u00e1lisis integrados incluyen uno o m\u00e1s datos de rendimiento, mapa NDVI, elevaci\u00f3n y caracter\u00edsticas fisicoqu\u00edmicas del sensor de suelo.\u00a0<\/p>\r\n

GeoPard admite estos par\u00e1metros y, adem\u00e1s, permite la inclusi\u00f3n de otras capas de datos de campo, ya sean las que ya est\u00e1n disponibles en el sistema o las que el usuario carga directamente (muestreo de suelos, conjuntos de datos de rendimiento, etc.).<\/p>\r\n

Como resultado, usted es libre de operar con el\u00a0conjunto completo de par\u00e1metros<\/span>\u00a0Realizando an\u00e1lisis integrados:<\/p>\r\n\r\n\r\n\r\n

An\u00e1lisis de datos de rendimiento multicapa<\/strong><\/p>\r\n

Datos de teledetecci\u00f3n:<\/p>\r\n

    \r\n
  • Mapa de productividad potencial (anual y plurianual)<\/li>\r\n
  • Mapa de estabilidad\/variaci\u00f3n<\/li>\r\n
  • \u00cdndices de vegetaci\u00f3n NDVI, EVI2, WDRVI, LAI, SAVI, OSAVI, GCI, GNDVI<\/li>\r\n<\/ul>\r\n

    Topograf\u00eda:<\/p>\r\n

      \r\n
    • Elevaci\u00f3n digital<\/li>\r\n
    • Pendiente<\/li>\r\n
    • Curvatura<\/li>\r\n
    • \u00cdndice de humedad<\/li>\r\n
    • Sombras de colinas<\/li>\r\n<\/ul>\r\n

      Datos del suelo:<\/p>\r\n

        \r\n
      • pH<\/li>\r\n
      • CIC (capacidad de intercambio cati\u00f3nico)<\/li>\r\n
      • MOS (materia org\u00e1nica del suelo)<\/li>\r\n
      • K (potasio)<\/li>\r\n
      • Escaso espesor de la capa superficial del suelo, menor capacidad de retenci\u00f3n de agua disponible (suelo propenso a la sequ\u00eda).<\/li>\r\n
      • CE (conductividad el\u00e9ctrica)<\/li>\r\n
      • y otros atributos qu\u00edmicos disponibles en el conjunto de datos cargado<\/li>\r\n<\/ul>\r\n\r\n\r\n\r\n

        Es importante destacar que los factores personalizados se configuran sobre cada capa de datos para asignar el valor deseado.\u00a0peso de la capa<\/span>.Le invitamos cordialmente a compartir sus casos de uso de an\u00e1lisis integrados y a crear mapas de zonas de gesti\u00f3n basados en su conocimiento del sector, seleccionando las fuentes de datos y sus ponderaciones en GeoPard.<\/p>\r\n\r\n\r\n\r\n

        Las im\u00e1genes de este blog contienen un campo de muestra con capas de datos (como un mapa de productividad que abarca 18 a\u00f1os, un modelo digital de elevaci\u00f3n, pendiente, sombreado de relieve, datos de rendimiento de 2019) y varias combinaciones de mapas de an\u00e1lisis de integraci\u00f3n.\u00a0<\/p>\r\n

        Puedes seguir los pasos de la evoluci\u00f3n de las zonas de gesti\u00f3n mientras ampl\u00edas el an\u00e1lisis de integraci\u00f3n con una capa de datos adicional.<\/p>\r\n

        \r\n

        Preguntas frecuentes<\/h2>\r\n
        \r\n

        1. \u00bfQu\u00e9 son las capas de datos?<\/strong><\/p>\r\n

        Las capas de datos se refieren a los componentes o elementos individuales de los datos que se organizan y apilan para crear una representaci\u00f3n completa de un \u00e1rea o tema espec\u00edfico.<\/p>\r\n

        Cada capa representa un aspecto espec\u00edfico de los datos, como caracter\u00edsticas geogr\u00e1ficas, uso del suelo, densidad de poblaci\u00f3n o factores ambientales. Estas capas se pueden combinar y analizar en conjunto para obtener informaci\u00f3n valiosa, visualizar patrones y tomar decisiones fundamentadas.<\/p>\r\n

        Las capas de datos se utilizan habitualmente en los sistemas de informaci\u00f3n geogr\u00e1fica (SIG) y en el an\u00e1lisis espacial para comprender y representar mejor los datos complejos de forma visual e interpretable.<\/p>\r\n

        2. \u00bfQu\u00e9 es el an\u00e1lisis integrado?<\/strong><\/p>\r\n

        El an\u00e1lisis integrado se refiere al proceso de combinar y analizar datos de m\u00faltiples fuentes o disciplinas para obtener una comprensi\u00f3n m\u00e1s completa y hol\u00edstica de un problema o fen\u00f3meno en particular.<\/p>\r\n

        Consiste en combinar conjuntos de datos, aplicar t\u00e9cnicas estad\u00edsticas y explorar relaciones entre diferentes variables o dominios.<\/p>\r\n

        El an\u00e1lisis integrado permite una visi\u00f3n m\u00e1s matizada e interconectada de los sistemas complejos, lo que facilita la identificaci\u00f3n de patrones, tendencias y relaciones causales que pueden no ser evidentes al analizar los datos de forma aislada.<\/p>\r\n

        Este enfoque permite a los investigadores y a quienes toman las decisiones adoptar decisiones m\u00e1s informadas y eficaces basadas en una gama m\u00e1s amplia de informaci\u00f3n.<\/p>","protected":false},"excerpt":{"rendered":"

        La agricultura de precisi\u00f3n es capaz de generar grandes cantidades de datos en forma de datos de rendimiento, im\u00e1genes satelitales y fertilidad del suelo, entre otros. La falta de una nube f\u00e1cil de usar\u2026<\/p>","protected":false},"author":210157960,"featured_media":1331,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_eb_attr":"","content-type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_wpcom_ai_launchpad_first_post":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"{title}\n\n{excerpt}\n\n{url}","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"_wpas_customize_per_network":false,"jetpack_post_was_ever_published":false},"categories":[1657,1366,1368],"tags":[1598],"class_list":["post-1195","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-precision-farming","category-topography","category-yield","tag-precision-agriculture"],"acf":[],"yoast_head":"\nIntegrated analysis for agribusiness<\/title>\n<meta name=\"description\" content=\"Multi layer yield data analytics (also known as integrated analysis) is becoming a part of the GeoPard geospatial analytics engine.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/geopard.tech\/es\/blog\/analisis-multicapa\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Integrated analysis for agribusiness\" \/>\n<meta property=\"og:description\" content=\"Multi layer yield data analytics (also known as integrated analysis) is becoming a part of the GeoPard geospatial analytics engine.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/geopard.tech\/es\/blog\/analisis-multicapa\/\" \/>\n<meta property=\"og:site_name\" content=\"GeoPard - 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