{"id":5639,"date":"2022-10-10T11:47:41","date_gmt":"2022-10-10T09:47:41","guid":{"rendered":"https:\/\/geopard.tech\/?p=5639"},"modified":"2023-05-06T12:36:23","modified_gmt":"2023-05-06T10:36:23","slug":"los-mapas-de-potencial-de-campo-generan-datos","status":"publish","type":"post","link":"https:\/\/geopard.tech\/es\/blog\/field-potential-maps-yield-data\/","title":{"rendered":"Mapas de potencial de campo GeoPard vs. datos de rendimiento"},"content":{"rendered":"<p class=\"wp-block-paragraph\">Los mapas de potencial de campo de GeoPard a menudo parecen... <strong>exactamente como rendimiento<\/strong> datos.<\/p>\r\n\r\n\r\n\r\n<p class=\"wp-block-paragraph\">Los creamos usando <a href=\"https:\/\/geopard.tech\/es\/blog\/analisis-multicapa\/\">an\u00e1lisis multicapa<\/a> de informaci\u00f3n hist\u00f3rica, topograf\u00eda y an\u00e1lisis del suelo desnudo.<\/p>\r\n\r\n\r\n\r\n<p class=\"wp-block-paragraph\">El proceso de tal <strong>Los mapas de rendimiento sint\u00e9ticos son autom\u00e1ticos.<\/strong> (y patentado) y cualquier campo del mundo tarda aproximadamente 1 minuto en generarlo.<\/p>\r\n\r\n\r\n\r\n<p class=\"wp-block-paragraph\">&nbsp;<\/p>\r\n\r\n\r\n\r\n<figure class=\"wp-block-image size-large\"><img data-recalc-dims=\"1\" fetchpriority=\"high\" decoding=\"async\" data-attachment-id=\"5640\" data-permalink=\"https:\/\/geopard.tech\/es\/blog\/field-potential-maps-yield-data\/image-4-2\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2022\/10\/image-4.png?fit=2048%2C1087&amp;ssl=1\" data-orig-size=\"2048,1087\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"GeoPard Field Potential maps\u00a0vs Yield data\" data-image-description=\"\" data-image-caption=\"&lt;p&gt;GeoPard Field Potential is your start into Precision Agriculture&lt;\/p&gt;\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2022\/10\/image-4.png?fit=1024%2C544&amp;ssl=1\" class=\"alignnone wp-image-5640\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2022\/10\/image-4.png?resize=810%2C430&#038;ssl=1\" alt=\"Mapas de potencial de campo GeoPard vs. datos de rendimiento\" width=\"810\" height=\"430\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2022\/10\/image-4.png?resize=1024%2C544&amp;ssl=1 1024w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2022\/10\/image-4.png?resize=300%2C159&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2022\/10\/image-4.png?resize=768%2C408&amp;ssl=1 768w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2022\/10\/image-4.png?resize=1536%2C815&amp;ssl=1 1536w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2022\/10\/image-4.png?resize=1200%2C637&amp;ssl=1 1200w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2022\/10\/image-4.png?w=2048&amp;ssl=1 2048w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2022\/10\/image-4.png?w=1620&amp;ssl=1 1620w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/><\/figure>\r\n\r\n\r\n\r\n<p class=\"wp-block-paragraph\">Puede utilizarse como base para:<\/p>\r\n<ul>\r\n<li><strong>Zonal o h\u00edbrido <a href=\"https:\/\/geopard.tech\/es\/muestreo-de-suelo\/\">Muestreo de suelo<\/a><\/strong> (reducci\u00f3n de costes en muestras \u00b150%)<\/li>\r\n<li>Planificaci\u00f3n de <strong><a href=\"https:\/\/geopard.tech\/es\/agricultura-de-carbono\/\">Muestreo de carbono<\/a><\/strong> (y tras la verificaci\u00f3n)<\/li>\r\n<li><strong><a href=\"https:\/\/geopard.tech\/es\/blog\/lo-que-se-necesita-para-practicar-la-fertilizacion-de-tasa-variable\/\">Fertilizaci\u00f3n de tasa variable<\/a><\/strong> (PAQUETE)<\/li>\r\n<li><strong><a href=\"https:\/\/geopard.tech\/es\/blog\/como-funciona-la-siembra-de-tasa-variable\/\">Plantaci\u00f3n en realidad virtual<\/a><\/strong> aplicaciones (mejor usar con datos de topograf\u00eda, suelo o rendimiento) con <a href=\"https:\/\/geopard.tech\/es\/blog\/analisis-multicapa\/\">Mapas VRA multicapa<\/a><\/li>\r\n<\/ul>\r\n<div class=\"wp-block-group\">\r\n<div class=\"wp-block-group\">\r\n<ul>\r\n\r\n\r\n\r\n\r\n\r\n<\/ul>\r\n<!-- \/wp:post-content --><\/div>\r\n<!-- \/wp:group --><\/div>\r\n<!-- \/wp:group -->\r\n\r\n<!-- wp:paragraph -->\r\n<h2>\u00bfQu\u00e9 son los mapas de potencial de campo?<\/h2>\r\n<p>Los mapas de potencial de campo, tambi\u00e9n conocidos como mapas de potencial de rendimiento o mapas de potencial de productividad, son representaciones visuales de la variabilidad espacial del rendimiento o la productividad potencial de un cultivo dentro de un campo. Estos mapas se crean analizando diversos factores que influyen en el crecimiento del cultivo, como las propiedades del suelo, la topograf\u00eda y los datos hist\u00f3ricos de rendimiento.<\/p>\r\n<p>Estos mapas pueden utilizarse en la agricultura de precisi\u00f3n para orientar las decisiones de gesti\u00f3n, como la aplicaci\u00f3n de fertilizantes a dosis variables, el riego y otros insumos, as\u00ed como para identificar \u00e1reas que requieren atenci\u00f3n o pr\u00e1cticas de gesti\u00f3n espec\u00edficas.<\/p>\r\n<!-- \/wp:paragraph -->\r\n\r\n<!-- wp:paragraph -->\r\n<p>Algunos factores clave que se suelen tener en cuenta al crear mapas de potencial de campo incluyen:<\/p>\r\n<ol>\r\n<li><strong>Propiedades del suelo:<\/strong> Las caracter\u00edsticas del suelo, como la textura, la estructura, el contenido de materia org\u00e1nica y la disponibilidad de nutrientes, influyen significativamente en el potencial de rendimiento de los cultivos. Al mapear las propiedades del suelo en un campo, los agricultores pueden identificar \u00e1reas con alto o bajo potencial de productividad.<\/li>\r\n<li><strong>Topograf\u00eda<\/strong>Factores como la altitud, la pendiente y la orientaci\u00f3n pueden influir en el crecimiento de los cultivos y su potencial de rendimiento. Por ejemplo, las zonas bajas pueden ser propensas al encharcamiento o tener un mayor riesgo de heladas, mientras que las pendientes pronunciadas pueden ser m\u00e1s susceptibles a la erosi\u00f3n. La elaboraci\u00f3n de mapas de estas caracter\u00edsticas topogr\u00e1ficas puede ayudar a los agricultores a comprender c\u00f3mo afectan al potencial de productividad y a ajustar sus pr\u00e1cticas de manejo en consecuencia.<\/li>\r\n<li><strong>Datos hist\u00f3ricos de rendimiento:<\/strong> Al analizar los datos hist\u00f3ricos de rendimiento de a\u00f1os o temporadas anteriores, los agricultores pueden identificar tendencias y patrones de productividad en sus campos. Esta informaci\u00f3n se puede utilizar para crear mapas que resalten las \u00e1reas con un potencial de rendimiento consistentemente alto o bajo.<\/li>\r\n<li><strong>Datos de teledetecci\u00f3n:<\/strong> Las im\u00e1genes satelitales, la fotograf\u00eda a\u00e9rea y otros datos de teledetecci\u00f3n pueden utilizarse para evaluar la salud, el vigor y la etapa de crecimiento de los cultivos. Esta informaci\u00f3n permite crear mapas que reflejan la variabilidad espacial del potencial de productividad de los cultivos.<\/li>\r\n<li><strong>Datos clim\u00e1ticos:<\/strong> Variables clim\u00e1ticas como la temperatura, las precipitaciones y la radiaci\u00f3n solar tambi\u00e9n pueden influir en el crecimiento y el rendimiento de los cultivos. Al incorporar datos clim\u00e1ticos en estos mapas, los agricultores pueden comprender mejor c\u00f3mo los factores ambientales afectan la productividad de sus campos.<\/li>\r\n<\/ol>\r\n<p>Son herramientas valiosas en la agricultura de precisi\u00f3n, ya que ayudan a los agricultores a visualizar la variabilidad espacial del potencial de productividad dentro de sus campos. Al utilizar estos mapas para guiar las decisiones de gesti\u00f3n, los agricultores pueden optimizar el uso de los recursos, mejorar el rendimiento general de los cultivos y reducir el impacto ambiental de sus actividades agr\u00edcolas.<\/p>\r\n<h2>Diferencia entre mapas de potencial de campo y datos de rendimiento<\/h2>\r\n<p>Los mapas de potencial de campo y los datos de rendimiento se utilizan en la agricultura de precisi\u00f3n para ayudar a los agricultores a comprender la variabilidad espacial de sus campos y tomar decisiones de gesti\u00f3n mejor fundamentadas. Sin embargo, existen algunas diferencias clave entre ambos:<\/p>\r\n<p><strong>Fuentes de datos:<\/strong><\/p>\r\n<p>Estos mapas se crean integrando datos de diversas fuentes, como propiedades del suelo, topograf\u00eda, datos hist\u00f3ricos de rendimiento, datos de teledetecci\u00f3n y datos clim\u00e1ticos. Sin embargo, estos datos se recopilan mediante monitores de rendimiento instalados en la maquinaria de cosecha, que registran el rendimiento del cultivo a medida que se recolecta.<\/p>\r\n<p><strong>Aspecto temporal:<\/strong><\/p>\r\n<p>Estos mapas representan una estimaci\u00f3n de la productividad potencial de un campo, que generalmente es est\u00e1tica o cambia lentamente con el tiempo, salvo cambios significativos en las propiedades del suelo u otros factores influyentes. Sin embargo, los datos de rendimiento son espec\u00edficos de una temporada de cultivo determinada o de varias temporadas y pueden variar significativamente de un a\u00f1o a otro en funci\u00f3n de factores como las condiciones clim\u00e1ticas, la presi\u00f3n de las plagas y las pr\u00e1cticas de manejo.<\/p>\r\n<p>En resumen, los mapas de potencial de campo y los datos de rendimiento son herramientas complementarias en la agricultura de precisi\u00f3n. Estos mapas proporcionan una estimaci\u00f3n de la productividad potencial de un campo, lo que ayuda a los agricultores a identificar \u00e1reas que pueden requerir pr\u00e1cticas de manejo diferentes. Los datos de rendimiento, por otro lado, documentan la producci\u00f3n real del cultivo y pueden utilizarse para evaluar la eficacia de las pr\u00e1cticas de manejo y fundamentar la toma de decisiones futuras.<\/p>\r\n<!-- \/wp:paragraph -->","protected":false},"excerpt":{"rendered":"<p>Los mapas de potencial de campo de GeoPard a menudo se parecen mucho a los datos de rendimiento. Los creamos utilizando an\u00e1lisis multicapa de informaci\u00f3n hist\u00f3rica, topograf\u00eda y an\u00e1lisis de suelo desnudo\u2026.<\/p>","protected":false},"author":210157960,"featured_media":5640,"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":"","_crdt_document":"","content-type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","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":[1586,1587,1372,1377,1376,1378,1366,1367,1368,1369],"tags":[1617],"class_list":["post-5639","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-product-features","category-partnership","category-blog","category-crop-monitoring","category-management-zones","category-remote-sensing","category-topography","category-use-cases","category-yield","category-vra","tag-geopard"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - 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