{"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":"spolupraca-geopard-a-cornell-university-v-ramci-programu-nmsp-na-polnohospodarstve-zalozenom-na-datach","status":"publish","type":"post","link":"https:\/\/geopard.tech\/sk\/blog\/geopard-and-cornell-university-cast-nmsp-collaboration-on-data-driven-agriculture\/","title":{"rendered":"Spolupr\u00e1ca GeoPard a Cornell University CAST, NMSP v oblasti po\u013enohospod\u00e1rstva zalo\u017een\u00e9ho na d\u00e1tach"},"content":{"rendered":"<p>Testovacie a demon\u0161tra\u010dn\u00e9 prostredie po\u013enohospod\u00e1rskych syst\u00e9mov Cornell (CAST) pre Farmu bud\u00facnosti a program Cornell Nutrient Management Spear Program (NMSP) sa spojili s platformou pre presn\u00e9 po\u013enohospod\u00e1rstvo GeoPard Agriculture, aby premenili surov\u00e9 \u00fadaje o v\u00fdnosoch na praktick\u00e9 po\u013enohospod\u00e1rske odpor\u00fa\u010dania.<\/p>\n<p>CAST \u2013 zoskupenie troch re\u00e1lnych fariem v \u0161t\u00e1te New York \u2013 sl\u00fa\u017ei ako testovacie prostredie pre technol\u00f3gie \u201cFarma bud\u00facnosti\u201d a rozv\u00edja d\u00e1tovo orientovan\u00e9 rie\u0161enia pre produkciu plod\u00edn a mlie\u010dnych v\u00fdrobkov. T\u00edm NMSP vykon\u00e1va v\u00fdskum, u\u013eah\u010duje prenos technol\u00f3gi\u00ed a znalost\u00ed a pom\u00e1ha pri implement\u00e1cii prospe\u0161n\u00fdch strat\u00e9gi\u00ed pre mana\u017ement \u017eiv\u00edn v po\u013en\u00fdch plodin\u00e1ch priamo na farm\u00e1ch s cie\u013eom zv\u00fd\u0161i\u0165 udr\u017eate\u013enos\u0165 fariem v celom \u0161t\u00e1te New York.<\/p>\n<p>Spolo\u010dnos\u0165 GeoPard spolupracuje s CAST a NMSP na zdokonalen\u00ed spracovania \u00fadajov do vylep\u0161en\u00fdch n\u00e1strojov digit\u00e1lnej agron\u00f3mie \u2013 kombin\u00e1cie vedy, technol\u00f3gie a pokusov veden\u00fdch farm\u00e1rmi, ktor\u00e1 je pr\u00edkladom bud\u00facnosti presn\u00e9ho po\u013enohospod\u00e1rstva.<\/p>\n<h2>Cornellov CAST a NMSP<\/h2>\n<p><img data-recalc-dims=\"1\" fetchpriority=\"high\" decoding=\"async\" data-attachment-id=\"12594\" data-permalink=\"https:\/\/geopard.tech\/sk\/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=\"Cornellovo testovacie prostredie CAST a 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 je s\u00fa\u010das\u0165ou iniciat\u00edvy USDA NIFA \u201cFarma bud\u00facnosti\u201d a funguje pod z\u00e1\u0161titou Cornell Institute for Digital Agriculture. CAST sp\u00e1ja tri komer\u010dne p\u00f4sobiace farmy (pestovate\u013esk\u00e9 a mlie\u010dne v\u00fdrobky) v r\u00e1mci jednotn\u00e9ho ekosyst\u00e9mu v\u00fdskumu a v\u00fdvoja. Jeho poslan\u00edm je testova\u0165 a demon\u0161trova\u0165 integrovan\u00e9 technol\u00f3gie presn\u00e9ho po\u013enohospod\u00e1rstva (od robotiky a\u017e po anal\u00fdzu \u00fadajov) v re\u00e1lnych po\u013enohospod\u00e1rskych podmienkach.<\/p>\n<p>S\u00fabe\u017ene sa program Nutrient Management Spear Program (NMSP), ktor\u00fd vedie Dr. Quirine Ketterings, zameriava na aplikovan\u00fd v\u00fdskum a roz\u0161\u00edrenie v oblasti p\u00f4dy a plod\u00edn. T\u00edm NMSP hodnot\u00ed vyu\u017e\u00edvanie \u017eiv\u00edn a variabilitu v\u00fdnosov na farm\u00e1ch; napr\u00edklad bol priekopn\u00edkom v oblasti \u201cz\u00f3n stability v\u00fdnosov\u201d, ktor\u00e9 kombinuj\u00fa \u00fadaje o v\u00fdnosoch za tri alebo viac rokov s cie\u013eom zmapova\u0165 oblasti s konzistentne vysok\u00fdmi a n\u00edzkymi v\u00fdnosmi oproti miestam s variabiln\u00fdmi v\u00fdnosmi.<\/p>\n<p>Tieto mapy z\u00f3n stability (so z\u00f3nami Q1 \u2013 Q4 pre konzistentnos\u0165 vysok\u00fdch\/n\u00edzkych v\u00fdnosov) a nov\u00e9 n\u00e1vrhy pokusov, ako napr\u00edklad pr\u00edstup priestorov\u00e9ho hodnotenia jedn\u00e9ho p\u00e1su (SSEA), pom\u00e1haj\u00fa po\u013enohospod\u00e1rom prostredn\u00edctvom v\u00fdskumu na farme overi\u0165, \u010di sa zmeny v hospod\u00e1ren\u00ed vypl\u00e1caj\u00fa vzh\u013eadom na zn\u00e1mu variabilitu v r\u00e1mci po\u013ea.<\/p>\n<h2>Po\u013enohospod\u00e1rska platforma GeoPard<\/h2>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"10804\" data-permalink=\"https:\/\/geopard.tech\/sk\/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-Urobte-viac-so-svojimi-d\u00e1tami\" 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 poskytuje cloudov\u00fd analytick\u00fd n\u00e1stroj, ktor\u00fd spracov\u00e1va ak\u00e9ko\u013evek geopriestorov\u00e9 \u00fadaje o farm\u00e1ch. Ako GeoPard uv\u00e1dza, platforma je \u201cnestrann\u00fdm cloudov\u00fdm analytick\u00fdm n\u00e1strojom pre presn\u00e9 po\u013enohospod\u00e1rstvo\u201d, schopn\u00fdm spracova\u0165 viacvrstvov\u00e9, viacro\u010dn\u00e9 \u00fadaje. V praxi GeoPard prij\u00edma s\u00fabory monitorovania v\u00fdnosov, p\u00f4dne mapy, satelitn\u00e9 sn\u00edmky a \u010fal\u0161ie \u00fadaje na vytvorenie m\u00e1p a predpisov pripraven\u00fdch na pou\u017eitie. Medzi k\u013e\u00fa\u010dov\u00e9 funkcie patr\u00ed generovanie viacro\u010dn\u00fdch z\u00f3n hospod\u00e1renia a m\u00e1p s variabiln\u00fdmi predpismi (VRA), podrobn\u00e1 anal\u00fdza \u00fadajov o v\u00fdnosoch a n\u00e1stroje na monitorovanie plod\u00edn.<\/p>\n<p>Napr\u00edklad GeoPard dok\u00e1\u017ee \u010disti\u0165 a kalibrova\u0165 nespracovan\u00e9 \u00fadaje o v\u00fdnosoch kombajnov a dokonca vytv\u00e1ra\u0165 syntetick\u00e9 mapy v\u00fdnosov (odhaduj\u00face v\u00fdnosy zo satelitn\u00fdch\/dronov\u00fdch vstupov) na vyplnenie medzier tam, kde ch\u00fdbaj\u00fa historick\u00e9 \u00fadaje. Platforma tie\u017e podporuje anal\u00fdzu topografick\u00fdch a p\u00f4dnych \u00fadajov, 3D vizualiz\u00e1ciu pol\u00ed, porovn\u00e1vanie \u00fadajov o aplik\u00e1cii a \u010fal\u0161ie.<\/p>\n<ul>\n<li><strong>Mapovanie z\u00f3ny riadenia a VRA:<\/strong> GeoPard pou\u017e\u00edva vrstvy v\u00fdnosov\/p\u00f4dy na vymedzenie z\u00f3n po\u013ea a vytv\u00e1ranie predp\u00edsan\u00fdch m\u00e1p pre presn\u00fa aplik\u00e1ciu vstupn\u00fdch \u00fadajov.<\/li>\n<li><strong>Spracovanie \u00fadajov o v\u00fdnosoch:<\/strong> Syst\u00e9m implementuje automatizovan\u00e9 \u010distenie a kalibr\u00e1ciu z\u00e1znamov o v\u00fdnosoch zbera\u010dov a dok\u00e1\u017ee generova\u0165 syntetick\u00e9 mapy v\u00fdnosov z viacro\u010dn\u00fdch \u00fadajov a \u00fadajov dia\u013ekov\u00e9ho prieskumu Zeme.<\/li>\n<li><strong>Monitorovanie plod\u00edn a pol\u00ed:<\/strong> Satelitn\u00e9 n\u00e1stroje a multiindexov\u00e1 anal\u00fdza pom\u00e1haj\u00fa odha\u013eova\u0165 priestorov\u00e9 trendy vo v\u00fdkonnosti plod\u00edn a obmedzuj\u00face faktory z roka na rok.<\/li>\n<\/ul>\n<h2>Spolo\u010dn\u00fd v\u00fdskum a ter\u00e9nne pokusy<\/h2>\n<p>V r\u00e1mci tejto spolupr\u00e1ce CAST a NMSP sk\u00famaj\u00fa platformu GeoPard Agriculture na spracovanie \u00fadajov zo surovej kukuri\u010dnej sil\u00e1\u017ee a monitorovania \u00farody zrna do vy\u010disten\u00fdch m\u00e1p \u00farody, z\u00f3n stability \u00farody a na vyhodnotenie presnosti generovania syntetick\u00fdch \u00fadajov o \u00farode.<\/p>\n<p>Quirine Ketterings a jej t\u00edm v NMSP pou\u017e\u00edvaj\u00fa \u00fadaje z pol\u00ed CAST na vyhodnotenie a zlep\u0161enie presnosti algoritmov GeoPard na mapovanie v\u00fdnosov, ako aj na ur\u010denie syntetick\u00e9ho v\u00fdnosu pre hodnotenie cel\u00fdch pol\u00ed aj pre experimenty. Spolu s GeoPard Ketterings a jej t\u00edm hodnotia kvalitu \u00fadajov a protokoly na generovanie m\u00e1p z\u00f3n stability v\u00fdnosov pre polia s viacro\u010dn\u00fdmi \u00fadajmi.<\/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\/sk\/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=\"Spolupr\u00e1ca GeoPard a Cornell University CAST, NMSP v oblasti po\u013enohospod\u00e1rstva zalo\u017een\u00e9ho na d\u00e1tach\" 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\">Fotografia od Madeline Hanscomovej\/NMSP<\/p><\/div>\n<p>Ich cie\u013eom je by\u0165 schopn\u00fd analyzova\u0165 konven\u010dn\u00e9 v\u00fdskumn\u00e9 pokusy na farm\u00e1ch, ako aj novovyvinut\u00e9 pokusy s vyu\u017eit\u00edm pr\u00edstupu priestorov\u00e9ho hodnotenia jednotliv\u00fdch pr\u00fa\u017ekov (SSEA), ktor\u00e9 vyu\u017e\u00edvaj\u00fa znalosti o variabilite v\u00fdnosov v r\u00e1mci po\u013ea. Medzi k\u013e\u00fa\u010dov\u00e9 aspekty spolupr\u00e1ce patria:<\/p>\n<ol>\n<li><strong>Integr\u00e1cia \u00fadajov:<\/strong> \u00dadaje o \u00farode a presnosti z CAST sa spracov\u00e1vaj\u00fa v GeoParde, \u010d\u00edm sa surov\u00e9 s\u00fabory premie\u0148aj\u00fa na georeferencovan\u00e9 mapy v\u00fdnosov a v\u00fdkonnostn\u00e9 vrstvy.<\/li>\n<li><strong>Valid\u00e1cia algoritmu:<\/strong> Algoritmy mapovania v\u00fdnosov a syntetick\u00fdch v\u00fdnosov spolo\u010dnosti GeoPard s\u00fa testovan\u00e9 na z\u00e1klade pr\u00edsnych \u00fadajov z pokusov CAST\/NMSP z farmy, \u010d\u00edm sa technol\u00f3gia zdokona\u013euje na z\u00e1klade skuto\u010dn\u00fdch meran\u00ed.<\/li>\n<li><strong>Priestorov\u00e1 anal\u00fdza:<\/strong> T\u00edm generuje mapy z\u00f3n stability v\u00fdnosu z viacer\u00fdch rokov \u00fadajov a vyhodnocuje, ako dobre modely GeoPard zachyt\u00e1vaj\u00fa skuto\u010dn\u00e9 vzorce na poliach.<\/li>\n<li><strong>Inovat\u00edvny dizajn sk\u00fa\u0161obnej verzie:<\/strong> Nov\u00e9 met\u00f3dy, ako napr\u00edklad SSEA, vyu\u017e\u00edvaj\u00fa vzorce v\u00fdnosov v r\u00e1mci po\u013ea na efekt\u00edvne porovn\u00e1vanie o\u0161etren\u00ed, podporen\u00e9 priestorovou anal\u00fdzou GeoPard.<\/li>\n<\/ol>\n<p>Spolupr\u00e1cou Cornell a GeoPard zabezpe\u010duj\u00fa, aby v\u00fdstupy platformy boli zalo\u017een\u00e9 na realite po\u013enohospod\u00e1rskych podnikov. Hoci GeoPard vyu\u017e\u00edva pokro\u010dil\u00e9 modelovanie (vr\u00e1tane odhadu v\u00fdnosov zo satelitov), valid\u00e1cia na t\u00fdchto farm\u00e1ch Cornell bohat\u00fdch na d\u00e1ta zvy\u0161uje d\u00f4veryhodnos\u0165 jeho odpor\u00fa\u010dan\u00ed. T\u00e1to integr\u00e1cia vedy, techniky a skuto\u010dnej praxe v ter\u00e9ne vytv\u00e1ra sp\u00e4tn\u00fa v\u00e4zbu: pestovatelia aj v\u00fdskumn\u00edci profituj\u00fa zo spo\u013eahlivej\u0161\u00edch n\u00e1strojov prec\u00edzneho po\u013enohospod\u00e1rstva.<\/p>\n<h2>Praktick\u00fd dopad: Smerom k udr\u017eate\u013en\u00e9mu presn\u00e9mu po\u013enohospod\u00e1rstvu<\/h2>\n<p>Kone\u010dn\u00fdm cie\u013eom je premeni\u0165 v\u00fdskumn\u00e9 pozemky na praktick\u00e9 usmernenia pre po\u013enohospod\u00e1rov. Monitorovanie a mapovanie v\u00fdnosov poskytuje po\u013enohospod\u00e1rom preh\u013ead o variabilite v r\u00e1mci po\u013ea, \u010do umo\u017e\u0148uje cielen\u00e9 opatrenia namiesto plo\u0161n\u00fdch \u00faprav. \u0160t\u00fadie skuto\u010dne ukazuj\u00fa, \u017ee konzistentn\u00e9 mapovanie v\u00fdnosov pom\u00e1ha po\u013enohospod\u00e1rom \u201cvidie\u0165, ktor\u00e9 oblasti pol\u00ed dosahuj\u00fa dobr\u00e9 v\u00fdsledky a ktor\u00e9 potrebuj\u00fa zlep\u0161enie, \u010do im pom\u00e1ha pl\u00e1nova\u0165 presnej\u0161ie opatrenia\u201d.<\/p>\n<p>Napr\u00edklad farm\u00e1r m\u00f4\u017ee pou\u017ei\u0165 mapy GeoPard na aplik\u00e1ciu hnoj\u00edv iba tam, kde plodiny dosahuj\u00fa slab\u0161ie v\u00fdsledky v porovnan\u00ed s priemerom na poli, alebo prija\u0165 ochrann\u00e9 postupy v z\u00f3nach s trvalo n\u00edzkymi v\u00fdnosmi. 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