Assessment and Prediction of Maize Production Considering Climate Change by Extreme Learning Machine in Czechia

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12410%2F21%3A43903625" target="_blank" >RIV/60076658:12410/21:43903625 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60460709:41110/21:89224 RIV/60460709:41330/21:89224

Výsledek na webu

<a href="https://doi.org/10.3390/agronomy11112344" target="_blank" >https://doi.org/10.3390/agronomy11112344</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/agronomy11112344" target="_blank" >10.3390/agronomy11112344</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Assessment and Prediction of Maize Production Considering Climate Change by Extreme Learning Machine in Czechia

Popis výsledku v původním jazyce

Machine learning algorithms have been applied in the agriculture field to forecast crop productivity. Previous studies mainly focused on the whole crop growth period while different time windows on yield prediction were still unknown. The entire growth period was separated into each month to assess their corresponding predictive ability by taking maize production (silage and grain) in Czechia. We present a thorough assessment of county-level maize yield prediction in Czechia using a machine learning algorithm (extreme learning machine (ELM)) and an extensive set of weather data and maize yields from 2002 to 2018.

Název v anglickém jazyce

Assessment and Prediction of Maize Production Considering Climate Change by Extreme Learning Machine in Czechia

Popis výsledku anglicky

Machine learning algorithms have been applied in the agriculture field to forecast crop productivity. Previous studies mainly focused on the whole crop growth period while different time windows on yield prediction were still unknown. The entire growth period was separated into each month to assess their corresponding predictive ability by taking maize production (silage and grain) in Czechia. We present a thorough assessment of county-level maize yield prediction in Czechia using a machine learning algorithm (extreme learning machine (ELM)) and an extensive set of weather data and maize yields from 2002 to 2018.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40102 - Forestry

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Agronomy

ISSN

2073-4395

e-ISSN

—

Svazek periodika

11

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

14

Strana od-do

1-14

Kód UT WoS článku

000733903100001

EID výsledku v databázi Scopus

2-s2.0-85122746101