{"id":322,"date":"2021-02-04T18:39:00","date_gmt":"2021-02-04T17:39:00","guid":{"rendered":"https:\/\/geopard.tech\/?p=322"},"modified":"2026-09-13T10:54:19","modified_gmt":"2026-09-13T08:54:19","slug":"7b27v4yf41-datos-de-rendimiento-y-analisis-en-geopard","status":"publish","type":"post","link":"https:\/\/geopard.tech\/es\/blog\/7b27v4yf41-yield-data-and-analytics-in-geopard\/","title":{"rendered":"Datos y anal\u00edticas de rendimiento en GeoPard"},"content":{"rendered":"<p class=\"wp-block-paragraph\">En este art\u00edculo:<\/p>\r\n\r\n\r\n\r\n<ul class=\"wp-block-list\">\r\n<li>Uso de an\u00e1lisis de rendimiento en la agricultura de precisi\u00f3n<\/li>\r\n<li>An\u00e1lisis exhaustivo de datos de rendimiento en GeoPard Agriculture\u00a0<\/li>\r\n<li>Visualizaci\u00f3n de cada atributo en los archivos Yield<\/li>\r\n<li>Correcci\u00f3n de datos brutos de rendimiento\u00a0<\/li>\r\n<li>5 ejemplos pr\u00e1cticos del uso de mapas de rendimiento<\/li>\r\n<\/ul>\r\n\r\n\r\n\r\n<figure class=\"wp-block-image size-large is-resized\"><img data-recalc-dims=\"1\" fetchpriority=\"high\" decoding=\"async\" data-attachment-id=\"324\" data-permalink=\"https:\/\/geopard.tech\/es\/blog\/7b27v4yf41-yield-data-and-analytics-in-geopard\/2021-02-03__181226\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-03__181226.png?fit=1680%2C964&amp;ssl=1\" data-orig-size=\"1680,964\" data-comments-opened=\"1\" data-image-title=\"Raw and cleaned yield data in GeoPard\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-03__181226.png?fit=1024%2C588&amp;ssl=1\" class=\"alignnone wp-image-324\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-03__181226.png?resize=810%2C465&#038;ssl=1\" alt=\"Datos de rendimiento brutos y depurados en GeoPard\" width=\"810\" height=\"465\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-03__181226.png?resize=1024%2C588&amp;ssl=1 1024w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-03__181226.png?resize=300%2C172&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-03__181226.png?resize=768%2C441&amp;ssl=1 768w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-03__181226.png?resize=1536%2C881&amp;ssl=1 1536w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-03__181226.png?resize=1200%2C689&amp;ssl=1 1200w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-03__181226.png?w=1680&amp;ssl=1 1680w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-03__181226.png?w=1620&amp;ssl=1 1620w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/>\r\n<figcaption><em>Datos de rendimiento brutos y depurados en GeoPard<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n<p class=\"wp-block-paragraph\"><strong>Datos de rendimiento\u00a0<\/strong>Le permite tomar decisiones mejor fundamentadas y mejorar la eficiencia del crecimiento.<br \/>Las zonas de manejo de campo construidas a partir de datos de rendimiento de varios a\u00f1os son adecuadas para una evaluaci\u00f3n inicial del rendimiento potencial y la variabilidad de los nutrientes del suelo, lo que permite tomar decisiones futuras sobre el manejo de los cultivos.<\/p>\r\n<p>El an\u00e1lisis de los datos de rendimiento puede convertirse en un mapa de aplicaci\u00f3n de dosis variable y utilizarse, por ejemplo, para la aplicaci\u00f3n de fertilizantes. Su calibraci\u00f3n es otro tema que debe tenerse en cuenta; lo abordaremos en una entrada de blog aparte.<\/p>\r\n\r\n\r\n\r\n<p class=\"wp-block-paragraph\"><strong>El an\u00e1lisis avanzado en GeoPard\u00a0<\/strong>Se trata de que puedes realizar an\u00e1lisis multicapa combinando varias capas de datos en un solo mapa y buscando relaciones entre las capas de datos.\u00a0<\/p>\r\n<p>Se pueden generar zonas de productividad combinadas a partir de \u00edndices de vegetaci\u00f3n obtenidos mediante im\u00e1genes satelitales, topograf\u00eda, datos de maquinaria como rendimiento, conductividad el\u00e9ctrica, humedad del suelo, entre otros, as\u00ed como resultados de an\u00e1lisis agroqu\u00edmicos.<\/p>\r\n\r\n\r\n\r\n<p class=\"wp-block-paragraph\"><strong>Visualizaci\u00f3n de archivos de rendimiento\u00a0<\/strong>Tras descargar el archivo, se realiza autom\u00e1ticamente el procesamiento y la limpieza. A continuaci\u00f3n se muestran dos versiones de los mapas: la imagen original con los datos del monitor del equipo tal cual y la visualizaci\u00f3n de GeoPard.<\/p>\r\n<p>Los datos brutos se han convertido en una imagen de superficie continua con gradiente, para facilitar la comprensi\u00f3n de la heterogeneidad del terreno y para la creaci\u00f3n de zonas de gesti\u00f3n.<\/p>\r\n<p>Cada uno de los atributos del archivo de rendimiento est\u00e1 disponible para su visualizaci\u00f3n, como la humedad, la masa de rendimiento, el volumen de rendimiento en seco y en h\u00famedo, la fuerza descendente, el consumo de combustible, la velocidad de la m\u00e1quina, etc.<\/p>\r\n\r\n\r\n\r\n<p class=\"wp-block-paragraph\"><strong>Correcci\u00f3n de datos brutos\u00a0<\/strong>Esto significa que si un punto en el campo no es natural, se suavizar\u00e1 (por ejemplo, al trabajar en un \u00e1rea que no abarca todo el ancho del cabezal de la cosechadora). Al crear datos de rendimiento basados en zonas, puede corregir zonas y pol\u00edgonos individuales.\u00a0<\/p>\r\n\r\n\r\n\r\n<p class=\"wp-block-paragraph\"><strong>Veamos algunos ejemplos pr\u00e1cticos del uso de mapas de rendimiento y otras capas de datos de GeoPard.<\/strong><\/p>\r\n\r\n\r\n\r\n<p class=\"wp-block-paragraph\"><strong>1. Zonas de gesti\u00f3n basadas en datos de rendimiento.<\/strong>\u00a0Las zonas de gesti\u00f3n pueden definirse a partir de los datos de rendimiento de un solo a\u00f1o o de varios. Es importante tener en cuenta que no se pueden sumar directamente los rendimientos de diferentes a\u00f1os, ya que esto generar\u00eda un sesgo a favor de uno de ellos.<\/p>\r\n<p>Para reducir este efecto, GeoPard aplica varios algoritmos para que el peso de cada a\u00f1o sea uniforme. Puedes establecer la importancia de un a\u00f1o espec\u00edfico mediante la herramienta Peso al crear un mapa multicapa.<\/p>\r\n<p>Dichas zonas de gesti\u00f3n de campo pueden utilizarse para elaborar mapas de aplicaci\u00f3n\/prescripci\u00f3n\/Rx (VRA), calculando el rendimiento potencial en cada zona.<\/p>\r\n\r\n\r\n\r\n<figure class=\"wp-block-image size-large\"><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"326\" data-permalink=\"https:\/\/geopard.tech\/es\/blog\/7b27v4yf41-yield-data-and-analytics-in-geopard\/geopard_vr_seeding_m\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_VR_seeding_m.png?fit=1680%2C874&amp;ssl=1\" data-orig-size=\"1680,874\" data-comments-opened=\"1\" data-image-title=\"Multi-year and multi-layer yield potential map\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_VR_seeding_m.png?fit=1024%2C533&amp;ssl=1\" class=\"alignnone wp-image-326\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_VR_seeding_m.png?resize=810%2C422&#038;ssl=1\" alt=\"Mapa de potencial de rendimiento multianual y multicapa\" width=\"810\" height=\"422\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_VR_seeding_m.png?resize=1024%2C533&amp;ssl=1 1024w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_VR_seeding_m.png?resize=300%2C156&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_VR_seeding_m.png?resize=768%2C400&amp;ssl=1 768w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_VR_seeding_m.png?resize=1536%2C799&amp;ssl=1 1536w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_VR_seeding_m.png?resize=1200%2C624&amp;ssl=1 1200w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_VR_seeding_m.png?w=1680&amp;ssl=1 1680w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_VR_seeding_m.png?w=1620&amp;ssl=1 1620w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/>\r\n<figcaption><em>Mapa de potencial de rendimiento multianual y multicapa<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n<p class=\"wp-block-paragraph\"><strong>2. Zonas multicapa con datos de rendimiento y otras fuentes de datos (topograf\u00eda, suelo, sensor, sat\u00e9lite).\u00a0<\/strong>Se puede agregar a <a href=\"\/es\/blog\/analisis-multicapa\/\">an\u00e1lisis multicapa<\/a>\u00a0y establecer el peso que tendr\u00e1 en las zonas finales.<\/p>\r\n<p>En este ejemplo, se a\u00f1aden tres capas de datos al mapa: Rendimiento, Im\u00e1genes satelitales y Topograf\u00eda. Puede combinar cualquier capa de datos que considere relevante para el an\u00e1lisis. El mapa multicapa puede utilizarse para un an\u00e1lisis m\u00e1s profundo del rendimiento y para la creaci\u00f3n de mapas VRA.\u00a0<\/p>\r\n\r\n\r\n\r\n<figure class=\"wp-block-image size-large\"><img data-recalc-dims=\"1\" decoding=\"async\" width=\"810\" height=\"351\" data-attachment-id=\"327\" data-permalink=\"https:\/\/geopard.tech\/es\/blog\/7b27v4yf41-yield-data-and-analytics-in-geopard\/2021-02-02__184019\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-02__184019.png?fit=1680%2C729&amp;ssl=1\" data-orig-size=\"1680,729\" data-comments-opened=\"1\" data-image-title=\"Yield, Topography and Satellite imagery\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-02__184019.png?fit=1024%2C444&amp;ssl=1\" class=\"wp-image-327\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-02__184019.png?resize=810%2C351&#038;ssl=1\" alt=\"Rendimiento, topograf\u00eda e im\u00e1genes satelitales\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-02__184019.png?resize=1024%2C444&amp;ssl=1 1024w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-02__184019.png?resize=300%2C130&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-02__184019.png?resize=768%2C333&amp;ssl=1 768w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-02__184019.png?resize=1536%2C667&amp;ssl=1 1536w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-02__184019.png?resize=1200%2C521&amp;ssl=1 1200w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-02__184019.png?w=1680&amp;ssl=1 1680w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-02__184019.png?w=1620&amp;ssl=1 1620w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/>\r\n<figcaption><em>Zonas multicapa: rendimiento, topograf\u00eda e im\u00e1genes satelitales<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n<p class=\"wp-block-paragraph\"><strong>3. C\u00e1lculo del rendimiento a nivel de zona y de campo.\u00a0<\/strong>Para analizar diferentes tratamientos, variedades de semillas y pr\u00e1cticas agron\u00f3micas, probablemente querr\u00e1 comparar el rendimiento promedio y total en cada zona, franja o entre campos.<\/p>\r\n<p>GeoPard calcula esto autom\u00e1ticamente para que te resulte m\u00e1s f\u00e1cil comparar el rendimiento en cifras absolutas.\u00a0<\/p>\r\n\r\n\r\n\r\n<figure class=\"wp-block-image size-full\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" data-attachment-id=\"329\" data-permalink=\"https:\/\/geopard.tech\/es\/blog\/7b27v4yf41-yield-data-and-analytics-in-geopard\/geopard_yield_estima\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_Yield_estima.png?fit=626%2C938&amp;ssl=1\" data-orig-size=\"626,938\" data-comments-opened=\"1\" data-image-title=\"GeoPard calculates yield in abs numbers based on Yield files. Total and average for field and each zone\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_Yield_estima.png?fit=626%2C938&amp;ssl=1\" class=\"alignnone wp-image-329\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_Yield_estima.png?resize=626%2C938&#038;ssl=1\" alt=\"GeoPard calcula el rendimiento en n\u00fameros absolutos bas\u00e1ndose en los archivos de rendimiento. Total y promedio para el campo y cada zona.\" width=\"626\" height=\"938\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_Yield_estima.png?w=626&amp;ssl=1 626w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_Yield_estima.png?resize=200%2C300&amp;ssl=1 200w\" sizes=\"(max-width: 626px) 100vw, 626px\" \/>\r\n<figcaption><em>GeoPard calcula el rendimiento en n\u00fameros absolutos bas\u00e1ndose en los archivos de rendimiento. Total y promedio para el campo y cada zona.<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n<p class=\"wp-block-paragraph\"><strong>4. Zonas de dependencia basadas en<\/strong> Datos de rendimiento. Las zonas basadas en datos de rendimiento se pueden superponer a otras zonas de datos, y se pueden buscar dependencias entre las capas de datos. Este ejemplo muestra la superposici\u00f3n de alto rendimiento y prote\u00edna promedio (1) y bajo rendimiento y prote\u00edna alta (2) de diferentes variedades de trigo en un campo.<\/p>\r\n<p>Otros ejemplos incluyen la relaci\u00f3n entre la influencia de la topograf\u00eda en el rendimiento, la intersecci\u00f3n entre los bajos rendimientos y la falta de macro y micronutrientes en el suelo, la humedad del suelo y las capas de conductividad el\u00e9ctrica (CE).<\/p>\r\n\r\n\r\n\r\n<figure class=\"wp-block-image size-full\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" data-attachment-id=\"330\" data-permalink=\"https:\/\/geopard.tech\/es\/blog\/7b27v4yf41-yield-data-and-analytics-in-geopard\/2021-02-02__184254\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-02__184254.png?fit=895%2C1003&amp;ssl=1\" data-orig-size=\"895,1003\" data-comments-opened=\"1\" data-image-title=\"Intersections of different yield and protein levels\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-02__184254.png?fit=895%2C1003&amp;ssl=1\" class=\"alignnone wp-image-330\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-02__184254.png?resize=810%2C908&#038;ssl=1\" alt=\"Intersecciones de diferentes niveles de rendimiento y prote\u00edna. \" width=\"810\" height=\"908\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-02__184254.png?w=895&amp;ssl=1 895w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-02__184254.png?resize=268%2C300&amp;ssl=1 268w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/2021-02-02__184254.png?resize=768%2C861&amp;ssl=1 768w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/>\r\n<figcaption><em>Intersecciones de diferentes niveles de rendimiento y prote\u00edna.<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n<p class=\"wp-block-paragraph\"><strong>5. Mapas de aplicaci\u00f3n de tasa variable (VRA).\u00a0<\/strong>Bas\u00e1ndose en datos de rendimiento, puede crear mapas de prescripci\u00f3n para diferentes operaciones: fertilizaci\u00f3n, siembra, fumigaci\u00f3n, riego y planificaci\u00f3n del muestreo de suelo. Puede editar el n\u00famero y la forma de las zonas.<\/p>\r\n<p>Tambi\u00e9n se puede elaborar un mapa de prescripci\u00f3n para una aplicaci\u00f3n de tasa variable combinando datos de rendimiento con otras fuentes de datos (suelo, CE, sat\u00e9lite, topograf\u00eda).\u00a0<\/p>\r\n\r\n\r\n\r\n<figure class=\"wp-block-image size-large is-resized\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" data-attachment-id=\"332\" data-permalink=\"https:\/\/geopard.tech\/es\/blog\/7b27v4yf41-yield-data-and-analytics-in-geopard\/geopard_seeding_rate\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_seeding_rate.png?fit=1680%2C696&amp;ssl=1\" data-orig-size=\"1680,696\" data-comments-opened=\"1\" data-image-title=\"Variable rate Seeding rates per zone\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_seeding_rate.png?fit=1024%2C424&amp;ssl=1\" class=\"wp-image-332\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_seeding_rate.png?resize=810%2C336&#038;ssl=1\" alt=\"Tasas de siembra de tasa variable por zona\" width=\"810\" height=\"336\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_seeding_rate.png?resize=1024%2C424&amp;ssl=1 1024w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_seeding_rate.png?resize=300%2C124&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_seeding_rate.png?resize=768%2C318&amp;ssl=1 768w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_seeding_rate.png?resize=1536%2C636&amp;ssl=1 1536w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_seeding_rate.png?resize=1200%2C497&amp;ssl=1 1200w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_seeding_rate.png?w=1680&amp;ssl=1 1680w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2021\/09\/GeoPard_seeding_rate.png?w=1620&amp;ssl=1 1620w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/>\r\n<figcaption><em>Tasas de siembra de tasa variable por zona<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n<p class=\"wp-block-paragraph\">Aunque no tengas datos de rendimiento, puedes usar GeoPard.\u00a0<a href=\"https:\/\/geopard.tech\/es\/blog\/r8yc32d9jc-zonas-plurianuales\/\" target=\"_blank\" rel=\"noreferrer noopener\">zonas plurianuales (hasta 33 a\u00f1os)<\/a>\u00a0basado en im\u00e1genes satelitales o\u00a0<a href=\"https:\/\/geopard.tech\/es\/blog\/comparacion-de-capas-de-datos-para-tomar-decisiones-informadas-sobre-cultivos\/\">combinarlo con otras capas de datos como la topograf\u00eda<\/a>\u00a0para iniciar tu andadura en la agricultura de precisi\u00f3n. Estos an\u00e1lisis suelen correlacionarse con los datos de an\u00e1lisis de rendimiento, pero esa es otra historia.<\/p>\r\n<hr \/>\r\n<h2>Preguntas frecuentes<\/h2>\r\n<hr \/>\r\n<p><strong>1. \u00bfC\u00f3mo se realiza un an\u00e1lisis de rendimiento?<\/strong><\/p>\r\n<p>El an\u00e1lisis de rendimiento es un proceso que se utiliza para evaluar la productividad y el desempe\u00f1o de un cultivo o sistema agr\u00edcola. A continuaci\u00f3n, se detallan los pasos para realizar un an\u00e1lisis de rendimiento b\u00e1sico:<\/p>\r\n<ul>\r\n<li><strong>Medir el rendimiento total cosechado<\/strong>: Recoger todos los productos cosechados en una zona espec\u00edfica y pesarlos.<\/li>\r\n<li><strong>Determinar el \u00e1rea<\/strong>: Medir o calcular la superficie total de terreno de la que se obtuvo el rendimiento.<\/li>\r\n<li><strong>Calcula el rendimiento por unidad de \u00e1rea:<\/strong> Para obtener el rendimiento por unidad de superficie (por ejemplo, rendimiento por hect\u00e1rea), divida la producci\u00f3n total cosechada entre la superficie cultivada.<\/li>\r\n<li><strong>Comparar y analizar:<\/strong> Compare el rendimiento obtenido con los datos de a\u00f1os anteriores o con los promedios regionales para evaluar el desempe\u00f1o e identificar cualquier variaci\u00f3n o tendencia.<\/li>\r\n<\/ul>\r\n<p>El an\u00e1lisis de rendimiento ayuda a los agricultores a tomar decisiones informadas, controlar la productividad de los cultivos e identificar \u00e1reas de mejora en sus pr\u00e1cticas agr\u00edcolas.<\/p>\r\n<p><strong>2. \u00bfQu\u00e9 son los datos de rendimiento?<\/strong><\/p>\r\n<p>Los datos de rendimiento se refieren a la informaci\u00f3n recopilada y registrada sobre la cantidad de cosecha o producto agr\u00edcola obtenido de una superficie de tierra espec\u00edfica. Incluyen mediciones o estimaciones de la cantidad de rendimiento cosechado, generalmente expresadas en t\u00e9rminos de peso o volumen.<\/p>\r\n<p>Proporciona informaci\u00f3n valiosa sobre la productividad y el rendimiento de los cultivos, lo que ayuda a los agricultores a tomar decisiones informadas sobre sus pr\u00e1cticas agr\u00edcolas, evaluar la eficacia de diferentes t\u00e9cnicas o insumos y monitorear las tendencias o variaciones en los rendimientos de los cultivos a lo largo del tiempo.<\/p>\r\n<p><strong>3. \u00bfQu\u00e9 es el potencial de rendimiento?<\/strong><\/p>\r\n<p>El potencial de rendimiento se refiere al rendimiento m\u00e1ximo o nivel de producci\u00f3n que puede alcanzar un cultivo en condiciones de crecimiento ideales. Representa el l\u00edmite superior de lo que una variedad de cultivo o especie vegetal espec\u00edfica puede producir en t\u00e9rminos de cantidad y calidad.<\/p>\r\n<p>El potencial de rendimiento est\u00e1 influenciado por diversos factores como la gen\u00e9tica, las condiciones ambientales, la disponibilidad de nutrientes y las pr\u00e1cticas de manejo. Sirve como punto de referencia para que los agricultores y agr\u00f3nomos eval\u00faen el desempe\u00f1o y la productividad de diferentes variedades de cultivos e identifiquen \u00e1reas donde se pueden realizar mejoras para optimizar los niveles de rendimiento.<\/p>","protected":false},"excerpt":{"rendered":"<p>En este art\u00edculo: Uso de an\u00e1lisis de rendimiento en agricultura de precisi\u00f3n An\u00e1lisis en profundidad de datos de rendimiento en GeoPard Agriculture Visualizaci\u00f3n de cada atributo en archivos de rendimiento Correcci\u00f3n de\u2026<\/p>","protected":false},"author":210157960,"featured_media":1472,"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":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":[1587,1368],"tags":[1617],"class_list":["post-322","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-partnership","category-yield","tag-geopard"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Yield analytics for Ag business<\/title>\n<meta name=\"description\" content=\"In this article, you can find content to help you properly use yield data in precision farming. 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