{"id":12582,"date":"2025-11-09T18:06:42","date_gmt":"2025-11-09T17:06:42","guid":{"rendered":"https:\/\/geopard.tech\/?p=12582"},"modified":"2026-01-16T18:52:26","modified_gmt":"2026-01-16T17:52:26","slug":"geopard-y-la-universidad-de-cornell-impulsan-la-colaboracion-de-nmsp-en-la-agricultura-basada-en-datos","status":"publish","type":"post","link":"https:\/\/geopard.tech\/es\/blog\/geopard-and-cornell-university-cast-nmsp-collaboration-on-data-driven-agriculture\/","title":{"rendered":"Colaboraci\u00f3n entre GeoPard, Cornell University CAST y NMSP en agricultura basada en datos."},"content":{"rendered":"<p>El Centro de Pruebas y Demostraci\u00f3n de Sistemas Agr\u00edcolas de Cornell (CAST) para la Granja del Futuro y el Programa de Gesti\u00f3n de Nutrientes de Cornell (NMSP) se han asociado con la plataforma de agricultura de precisi\u00f3n GeoPard Agriculture para convertir los datos brutos de rendimiento en recomendaciones agr\u00edcolas pr\u00e1cticas.<\/p>\n<p>CAST, un conjunto de tres granjas reales en el estado de Nueva York, sirve como banco de pruebas para las tecnolog\u00edas de la &quot;Granja del Futuro&quot;, impulsando soluciones basadas en datos para la producci\u00f3n agr\u00edcola y l\u00e1ctea. El equipo de NMSP realiza investigaciones, facilita la transferencia de tecnolog\u00eda y conocimientos, y ayuda en la implementaci\u00f3n en las granjas de estrategias beneficiosas para la gesti\u00f3n de nutrientes en los cultivos, con el fin de aumentar la sostenibilidad agr\u00edcola en todo el estado de Nueva York.<\/p>\n<p>GeoPard est\u00e1 colaborando con CAST y NMSP para impulsar el procesamiento de datos y convertirlos en herramientas agron\u00f3micas digitales mejoradas: una combinaci\u00f3n de ciencia, tecnolog\u00eda y ensayos dirigidos por agricultores que ejemplifica el futuro de la agricultura de precisi\u00f3n.<\/p>\n<h2>CAST y NMSP de Cornell<\/h2>\n<p><img data-recalc-dims=\"1\" fetchpriority=\"high\" decoding=\"async\" data-attachment-id=\"12594\" data-permalink=\"https:\/\/geopard.tech\/es\/blog\/geopard-and-cornell-university-cast-nmsp-collaboration-on-data-driven-agriculture\/cornells-cast-testbed-and-the-nmsp\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/11\/Cornells-CAST-Testbed-and-the-NMSP.png?fit=1920%2C1080&amp;ssl=1\" data-orig-size=\"1920,1080\" data-comments-opened=\"1\" data-image-title=\"Cornell\u2019s CAST Testbed and the NMSP\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/11\/Cornells-CAST-Testbed-and-the-NMSP.png?fit=1024%2C576&amp;ssl=1\" class=\"alignnone size-full wp-image-12594\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/11\/Cornells-CAST-Testbed-and-the-NMSP.png?resize=810%2C456&#038;ssl=1\" alt=\"El banco de pruebas CAST de Cornell y el NMSP\" width=\"810\" height=\"456\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/11\/Cornells-CAST-Testbed-and-the-NMSP.png?w=1920&amp;ssl=1 1920w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/11\/Cornells-CAST-Testbed-and-the-NMSP.png?resize=300%2C169&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/11\/Cornells-CAST-Testbed-and-the-NMSP.png?resize=1024%2C576&amp;ssl=1 1024w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/11\/Cornells-CAST-Testbed-and-the-NMSP.png?resize=768%2C432&amp;ssl=1 768w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/11\/Cornells-CAST-Testbed-and-the-NMSP.png?resize=1536%2C864&amp;ssl=1 1536w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/11\/Cornells-CAST-Testbed-and-the-NMSP.png?w=1620&amp;ssl=1 1620w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/><\/p>\n<p>CAST forma parte de la iniciativa \u201cGranja del Futuro\u201d del USDA NIFA y opera bajo los auspicios del Instituto Cornell para la Agricultura Digital. CAST re\u00fane tres granjas comerciales (cultivos y productos l\u00e1cteos) en un ecosistema unificado de I+D. Su misi\u00f3n es probar y demostrar tecnolog\u00edas integradas de agricultura de precisi\u00f3n (desde rob\u00f3tica hasta an\u00e1lisis de datos) en condiciones agr\u00edcolas reales.<\/p>\n<p>Paralelamente, el Programa de Gesti\u00f3n de Nutrientes (NMSP, por sus siglas en ingl\u00e9s), dirigido por la Dra. Quirine Ketterings, se centra en la investigaci\u00f3n y extensi\u00f3n aplicada sobre suelos y cultivos. El equipo del NMSP eval\u00faa el uso de nutrientes y la variabilidad del rendimiento en las explotaciones agr\u00edcolas; por ejemplo, fue pionero en la creaci\u00f3n de &quot;zonas de estabilidad de rendimiento&quot;, combinando datos de rendimiento de tres o m\u00e1s a\u00f1os para delimitar \u00e1reas de alto y bajo rendimiento de forma consistente, en contraste con zonas de rendimiento variable.<\/p>\n<p>Estos mapas de zonas de estabilidad (con zonas Q1-Q4 para una consistencia de rendimiento alto\/bajo) y nuevos dise\u00f1os de ensayos como el Enfoque de Evaluaci\u00f3n Espacial de Franja \u00danica (SSEA, por sus siglas en ingl\u00e9s) ayudan a los agricultores a probar, mediante investigaciones en la propia explotaci\u00f3n, si los cambios en la gesti\u00f3n resultan rentables, dada la variabilidad conocida dentro del campo.<\/p>\n<h2>La plataforma agr\u00edcola GeoPard<\/h2>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"10804\" data-permalink=\"https:\/\/geopard.tech\/es\/home\/geopard-background-precision-ag-software-do-more-with-your-data\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/10\/GeoPard-Background-Precision-Ag-software-Do-more-with-your-data.png?fit=3116%2C1754&amp;ssl=1\" data-orig-size=\"3116,1754\" data-comments-opened=\"1\" data-image-title=\"GeoPard &amp;#8211; Background &amp;#8211; Precision Ag software &amp;#8211; Do more with your data\" data-image-description=\"&lt;p&gt;GeoPard-Background-Precision-Ag-software-Do-more-with-your-data&lt;\/p&gt;\" data-image-caption=\"&lt;p&gt;GeoPard-Background-Precision-Ag-software-Do-more-with-your-data&lt;\/p&gt;\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/10\/GeoPard-Background-Precision-Ag-software-Do-more-with-your-data.png?fit=1024%2C576&amp;ssl=1\" class=\"size-full wp-image-10804\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/10\/GeoPard-Background-Precision-Ag-software-Do-more-with-your-data.png?resize=810%2C456&#038;ssl=1\" alt=\"GeoPard-Background-Precision-Ag-software-Haga-m\u00e1s-con-sus-datos\" width=\"810\" height=\"456\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/10\/GeoPard-Background-Precision-Ag-software-Do-more-with-your-data.png?w=3116&amp;ssl=1 3116w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/10\/GeoPard-Background-Precision-Ag-software-Do-more-with-your-data.png?resize=300%2C169&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/10\/GeoPard-Background-Precision-Ag-software-Do-more-with-your-data.png?resize=1024%2C576&amp;ssl=1 1024w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/10\/GeoPard-Background-Precision-Ag-software-Do-more-with-your-data.png?resize=768%2C432&amp;ssl=1 768w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/10\/GeoPard-Background-Precision-Ag-software-Do-more-with-your-data.png?resize=1536%2C865&amp;ssl=1 1536w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/10\/GeoPard-Background-Precision-Ag-software-Do-more-with-your-data.png?resize=2048%2C1153&amp;ssl=1 2048w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/10\/GeoPard-Background-Precision-Ag-software-Do-more-with-your-data.png?w=1620&amp;ssl=1 1620w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2024\/10\/GeoPard-Background-Precision-Ag-software-Do-more-with-your-data.png?w=2430&amp;ssl=1 2430w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/><\/p>\n<p>&nbsp;<\/p>\n<p>GeoPard Agriculture ofrece un motor de an\u00e1lisis basado en la nube que procesa cualquier dato geoespacial agr\u00edcola. Seg\u00fan GeoPard, la plataforma es una potente herramienta de an\u00e1lisis en la nube, imparcial y eficaz para la agricultura de precisi\u00f3n, capaz de gestionar datos multicapa y multianuales. En la pr\u00e1ctica, GeoPard procesa archivos de monitorizaci\u00f3n de rendimiento, mapas de suelos, im\u00e1genes satelitales y otros datos para generar mapas y prescripciones listos para usar. Entre sus caracter\u00edsticas principales se incluyen la generaci\u00f3n de zonas de gesti\u00f3n multianuales y mapas de prescripci\u00f3n de tasa variable (VRA), an\u00e1lisis detallados de datos de rendimiento y herramientas de monitorizaci\u00f3n de cultivos.<\/p>\n<p>Por ejemplo, GeoPard puede limpiar y calibrar datos brutos de rendimiento de cosechadoras e incluso crear mapas de rendimiento sint\u00e9ticos (estimando los rendimientos a partir de datos satelitales\/de drones) para cubrir las lagunas donde faltan datos hist\u00f3ricos. La plataforma tambi\u00e9n admite an\u00e1lisis de datos topogr\u00e1ficos y de suelo, visualizaci\u00f3n de campo en 3D, comparaci\u00f3n de datos aplicados y m\u00e1s.<\/p>\n<ul>\n<li><strong>Zona de gesti\u00f3n y mapeo de VRA:<\/strong> GeoPard utiliza capas de rendimiento\/suelo para delimitar zonas de campo y crear mapas de prescripci\u00f3n para una aplicaci\u00f3n precisa de insumos.<\/li>\n<li><strong>Procesamiento de datos de rendimiento:<\/strong> El sistema implementa la limpieza y calibraci\u00f3n automatizadas de los registros de rendimiento de las cosechadoras, y puede generar mapas de rendimiento sint\u00e9ticos a partir de datos multianuales y de teledetecci\u00f3n.<\/li>\n<li><strong>Seguimiento de cultivos y campos:<\/strong> Las herramientas basadas en sat\u00e9lites y el an\u00e1lisis de \u00edndices m\u00faltiples ayudan a detectar tendencias espaciales en el rendimiento de los cultivos y factores limitantes a\u00f1o tras a\u00f1o.<\/li>\n<\/ul>\n<h2>Investigaci\u00f3n colaborativa y ensayos de campo<\/h2>\n<p>En esta colaboraci\u00f3n, CAST y NMSP est\u00e1n explorando la plataforma GeoPard Agriculture para procesar datos brutos de ensilado de ma\u00edz y monitores de rendimiento de grano, transform\u00e1ndolos en mapas de rendimiento depurados, zonas de estabilidad de rendimiento y para evaluar la precisi\u00f3n de la generaci\u00f3n de datos de rendimiento sint\u00e9ticos.<\/p>\n<p>Quirine Ketterings y su equipo en NMSP est\u00e1n utilizando datos de los campos de CAST para evaluar y mejorar la precisi\u00f3n de los algoritmos de GeoPard para mapear el rendimiento, as\u00ed como para determinar el rendimiento sint\u00e9tico tanto para evaluaciones de campo completo como para experimentos. Junto con GeoPard, Ketterings y su equipo est\u00e1n evaluando la calidad de los datos y los protocolos para generar mapas de zonas de estabilidad de rendimiento para campos con datos de varios a\u00f1os.<\/p>\n<div id=\"attachment_12591\" style=\"width: 810px\" class=\"wp-caption aligncenter\"><img data-recalc-dims=\"1\" decoding=\"async\" aria-describedby=\"caption-attachment-12591\" data-attachment-id=\"12591\" data-permalink=\"https:\/\/geopard.tech\/es\/blog\/geopard-and-cornell-university-cast-nmsp-collaboration-on-data-driven-agriculture\/geopard-and-cornell-university-cast-nmsp-collaboration-on-data-driven-agriculture-2\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/11\/GeoPard-and-Cornell-University-CAST-NMSP-Collaboration-on-Data-Driven-Agriculture.jpg?fit=800%2C450&amp;ssl=1\" data-orig-size=\"800,450\" data-comments-opened=\"1\" data-image-title=\"GeoPard and Cornell University CAST, NMSP Collaboration on Data-Driven Agriculture\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/11\/GeoPard-and-Cornell-University-CAST-NMSP-Collaboration-on-Data-Driven-Agriculture.jpg?fit=800%2C450&amp;ssl=1\" class=\"wp-image-12591 size-full\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/11\/GeoPard-and-Cornell-University-CAST-NMSP-Collaboration-on-Data-Driven-Agriculture.jpg?resize=800%2C450&#038;ssl=1\" alt=\"Colaboraci\u00f3n entre GeoPard, Cornell University CAST y NMSP en agricultura basada en datos.\" width=\"800\" height=\"450\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/11\/GeoPard-and-Cornell-University-CAST-NMSP-Collaboration-on-Data-Driven-Agriculture.jpg?w=800&amp;ssl=1 800w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/11\/GeoPard-and-Cornell-University-CAST-NMSP-Collaboration-on-Data-Driven-Agriculture.jpg?resize=300%2C169&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/11\/GeoPard-and-Cornell-University-CAST-NMSP-Collaboration-on-Data-Driven-Agriculture.jpg?resize=768%2C432&amp;ssl=1 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/><p id=\"caption-attachment-12591\" class=\"wp-caption-text\">Fotograf\u00eda de Madeline Hanscom\/NMSP<\/p><\/div>\n<p>Su objetivo es poder analizar tanto los ensayos de investigaci\u00f3n convencionales en fincas como los ensayos de reciente desarrollo del Enfoque de Evaluaci\u00f3n Espacial en Franja \u00danica (SSEA, por sus siglas en ingl\u00e9s), que utilizan el conocimiento de la variabilidad del rendimiento dentro del campo. Los aspectos clave de la colaboraci\u00f3n incluyen:<\/p>\n<ol>\n<li><strong>Integraci\u00f3n de datos:<\/strong> Los datos de cosecha y precisi\u00f3n de CAST se procesan en GeoPard, transformando los archivos sin procesar en mapas de rendimiento georreferenciados y capas de desempe\u00f1o.<\/li>\n<li><strong>Validaci\u00f3n del algoritmo:<\/strong> Los algoritmos de GeoPard para el mapeo de rendimientos y el c\u00e1lculo de rendimientos sint\u00e9ticos se prueban con los rigurosos datos de ensayos en fincas de CAST\/NMSP, lo que permite perfeccionar la tecnolog\u00eda bas\u00e1ndose en mediciones reales.<\/li>\n<li><strong>An\u00e1lisis espacial:<\/strong> El equipo genera mapas de zonas de estabilidad de rendimiento a partir de datos de varios a\u00f1os y eval\u00faa la eficacia con la que los modelos de GeoPard capturan los patrones reales del terreno.<\/li>\n<li><strong>Dise\u00f1o innovador de ensayos cl\u00ednicos:<\/strong> Los nuevos m\u00e9todos, como el SSEA, utilizan patrones de rendimiento dentro del campo para comparar tratamientos de manera eficiente, con el apoyo del an\u00e1lisis espacial de GeoPard.<\/li>\n<\/ol>\n<p>Gracias a la colaboraci\u00f3n entre Cornell y GeoPard, los resultados de la plataforma se basan en la realidad de las explotaciones agr\u00edcolas. Si bien GeoPard utiliza modelos avanzados (incluida la estimaci\u00f3n de rendimiento mediante sat\u00e9lite), la validaci\u00f3n en las granjas de Cornell, ricas en datos, otorga mayor fiabilidad a sus recomendaciones. Esta integraci\u00f3n de ciencia, tecnolog\u00eda y pr\u00e1cticas agr\u00edcolas reales crea un ciclo de retroalimentaci\u00f3n: tanto los agricultores como los investigadores se benefician de herramientas de agricultura de precisi\u00f3n m\u00e1s fiables.<\/p>\n<h2>Impacto pr\u00e1ctico: Hacia una agricultura de precisi\u00f3n sostenible<\/h2>\n<p>El objetivo final es convertir los resultados de las parcelas de investigaci\u00f3n en gu\u00edas pr\u00e1cticas para los agricultores. El monitoreo y la cartograf\u00eda del rendimiento les brindan visibilidad sobre la variabilidad dentro de cada parcela, lo que permite implementar acciones espec\u00edficas en lugar de tratamientos generalizados. De hecho, los estudios demuestran que la cartograf\u00eda sistem\u00e1tica del rendimiento ayuda a los agricultores a identificar qu\u00e9 \u00e1reas de sus parcelas tienen un buen desempe\u00f1o y cu\u00e1les necesitan mejorar, lo que les permite planificar acciones m\u00e1s precisas.<\/p>\n<p>Por ejemplo, un agricultor podr\u00eda usar los mapas de GeoPard para aplicar fertilizante solo donde los cultivos tengan un rendimiento inferior al promedio del campo, o adoptar pr\u00e1cticas de conservaci\u00f3n en zonas con rendimientos consistentemente bajos. Al proporcionar datos depurados, mapas de an\u00e1lisis de estabilidad de rendimiento y puntos de referencia sint\u00e9ticos, la colaboraci\u00f3n entre Cornell y GeoPard fomenta la toma de decisiones rentables y respetuosas con el medio ambiente.<\/p>","protected":false},"excerpt":{"rendered":"<p>El Centro de Pruebas y Demostraci\u00f3n de Sistemas Agr\u00edcolas de Cornell (CAST) para la Granja del Futuro y el Programa de Lanzamiento de Gesti\u00f3n de Nutrientes de Cornell (NMSP) se han unido\u2026<\/p>","protected":false},"author":210157960,"featured_media":12593,"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_hide_featured_image":false,"_jetpack_featured_image_duplicate_dismissed":0,"_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":false,"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":[1587],"tags":[],"class_list":["post-12582","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-partnership"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - 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