"V\u00E1\u0148ov\u00E1, Marie" . "Model predikce obsahu deoxynivalenolu v zrnu p\u0161enice na z\u00E1klad\u011B meteorologick\u00FDch \u00FAdaj\u016F a p\u0159edplodiny" . "22" . "CZ - \u010Cesk\u00E1 republika" . "1"^^ . . . . "Model predikce obsahu deoxynivalenolu v zrnu p\u0161enice na z\u00E1klad\u011B meteorologick\u00FDch \u00FAdaj\u016F a p\u0159edplodiny"@cs . . . "2"^^ . "29755" . "Obiln\u00E1\u0159sk\u00E9 listy" . "2" . "[E01719C38AC0]" . . . . "Deoxynivalenol (DON) je nej\u010Dast\u011Bj\u0161\u00EDm fuz\u00E1riov\u00FDm toxinem ve vzorc\u00EDch p\u0161enice v \u010CR, a proto predikce jeho v\u00FDskytu m\u016F\u017Ee b\u00FDt vhodn\u00FDm n\u00E1strojem prevence jeho vstupu do potravn\u00EDho \u0159et\u011Bzce. \u00DAdaje o obsahu DON v p\u0161eni\u010Dn\u00E9m zrnu, meteorologick\u00FDch podm\u00EDnk\u00E1ch b\u011Bhem vegeta\u010Dn\u00ED doby a metod\u00E1ch zpracov\u00E1n\u00ED p\u016Fdy ze dvou poln\u00EDch pokus\u016F byly vyu\u017Eity k vytvo\u0159en\u00ED modelu na b\u00E1zi neuronov\u00E9 s\u00EDt\u011B pro predikci obsahu DON. Nejlep\u0161\u00ED neuronov\u00E1 s\u00ED\u0165 je zalo\u017Eena na p\u011Bti vstupn\u00EDch prom\u011Bnn\u00FDch: p\u0159edplodina, pr\u016Fm\u011Brn\u00E1 teplota v dubnu, suma sr\u00E1\u017Eek v dubnu, pr\u016Fm\u011Brn\u00E1 teplota 5 dn\u016F p\u0159ed kveten\u00EDm, suma sr\u00E1\u017Eek 5 dn\u016F p\u0159ed kveten\u00EDm. Nejd\u016Fle\u017Eit\u011Bj\u0161\u00EDmi vstupn\u00EDmi parametry jsou p\u0159edplodina a suma sr\u00E1\u017Eek 5 dn\u016F p\u0159ed kveten\u00EDm. Meteorologick\u00E9 podm\u00EDnky v dubnu, kter\u00E9 jsou d\u016Fle\u017Eit\u00E9 pro tvorbu inokula na rostlinn\u00FDch zbytc\u00EDch, jsou pro model tak\u00E9 d\u016Fle\u017Eit\u00E9. Meteorologick\u00E9 podm\u00EDnky v kv\u011Btnu a 5 dn\u016F po kveten\u00ED nejsou pro obsah DON v zrnu p\u0159\u00EDli\u0161 v\u00FDznamn\u00E9. Bylo zji\u0161t\u011Bno, \u017Ee zpracov\u00E1n\u00ED p\u016Fdy m\u00E1 na funkci modelu tak\u00E9 mal\u00FD vliv. Korelace mezi pozorovan\u00FDmi a predikovan\u00FDmi daty s vyu\u017Eit\u00EDm modelu neuronov\u00E9 s\u00EDt\u011B byla R2 = 0,87." . . "I, P(QI111B044), P(QJ1210008)" . "Forecasts model for deoxynivalenol content in wheat grain based on metheorological data and previous crop"@en . "Deoxynivalenol (DON) je nej\u010Dast\u011Bj\u0161\u00EDm fuz\u00E1riov\u00FDm toxinem ve vzorc\u00EDch p\u0161enice v \u010CR, a proto predikce jeho v\u00FDskytu m\u016F\u017Ee b\u00FDt vhodn\u00FDm n\u00E1strojem prevence jeho vstupu do potravn\u00EDho \u0159et\u011Bzce. \u00DAdaje o obsahu DON v p\u0161eni\u010Dn\u00E9m zrnu, meteorologick\u00FDch podm\u00EDnk\u00E1ch b\u011Bhem vegeta\u010Dn\u00ED doby a metod\u00E1ch zpracov\u00E1n\u00ED p\u016Fdy ze dvou poln\u00EDch pokus\u016F byly vyu\u017Eity k vytvo\u0159en\u00ED modelu na b\u00E1zi neuronov\u00E9 s\u00EDt\u011B pro predikci obsahu DON. Nejlep\u0161\u00ED neuronov\u00E1 s\u00ED\u0165 je zalo\u017Eena na p\u011Bti vstupn\u00EDch prom\u011Bnn\u00FDch: p\u0159edplodina, pr\u016Fm\u011Brn\u00E1 teplota v dubnu, suma sr\u00E1\u017Eek v dubnu, pr\u016Fm\u011Brn\u00E1 teplota 5 dn\u016F p\u0159ed kveten\u00EDm, suma sr\u00E1\u017Eek 5 dn\u016F p\u0159ed kveten\u00EDm. Nejd\u016Fle\u017Eit\u011Bj\u0161\u00EDmi vstupn\u00EDmi parametry jsou p\u0159edplodina a suma sr\u00E1\u017Eek 5 dn\u016F p\u0159ed kveten\u00EDm. Meteorologick\u00E9 podm\u00EDnky v dubnu, kter\u00E9 jsou d\u016Fle\u017Eit\u00E9 pro tvorbu inokula na rostlinn\u00FDch zbytc\u00EDch, jsou pro model tak\u00E9 d\u016Fle\u017Eit\u00E9. Meteorologick\u00E9 podm\u00EDnky v kv\u011Btnu a 5 dn\u016F po kveten\u00ED nejsou pro obsah DON v zrnu p\u0159\u00EDli\u0161 v\u00FDznamn\u00E9. Bylo zji\u0161t\u011Bno, \u017Ee zpracov\u00E1n\u00ED p\u016Fdy m\u00E1 na funkci modelu tak\u00E9 mal\u00FD vliv. Korelace mezi pozorovan\u00FDmi a predikovan\u00FDmi daty s vyu\u017Eit\u00EDm modelu neuronov\u00E9 s\u00EDt\u011B byla R2 = 0,87."@cs . "http://www.vukrom.cz/obilnarske-listy/obsah/2-2014/52-54" . . . . . "RIV/25328859:_____/14:#0000792!RIV15-MZE-25328859" . "Model predikce obsahu deoxynivalenolu v zrnu p\u0161enice na z\u00E1klad\u011B meteorologick\u00FDch \u00FAdaj\u016F a p\u0159edplodiny"@cs . "3"^^ . "Klem, Karel" . "Deoxynivalenol (DON) is the most common mycotoxin in samples of wheat in the Czech Republic, and therefore the prediction of its presence may be an appropriate tool of preventing its entry into the food chain. Data on the content of DON in wheat grain, weather conditions during the growing season and methods of tillage of two field trials were used to create the model based on neural network to predict DON content. The best neural network is based on five input variables: the previous crop, the average temperature in April, the amount of precipitation in April, average temperature 5 days before flowering, the amount of precipitation 5 days before flowering. The most important input parameters are preceding crop and amount of precipitation 5 days before flowering. Meteorological conditions in April, which are important for the formation of the inoculum, are also important for the model. Weather conditions in May and 5 days after flowering are not for DON content in grain so important. It was found that the tillage has on function of model a small influence. The correlation between observed and predicted data using neural network model was R2 = 0,87."@en . "forecast model; deoxynivalenol content; meteorological data; previous crops"@en . . . . . "1212-138X" . "RIV/25328859:_____/14:#0000792" . "Forecasts model for deoxynivalenol content in wheat grain based on metheorological data and previous crop"@en . "Model predikce obsahu deoxynivalenolu v zrnu p\u0161enice na z\u00E1klad\u011B meteorologick\u00FDch \u00FAdaj\u016F a p\u0159edplodiny" . .