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

Result code in 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>
Alternative codes found
RIV/60460709:41110/21:89224 RIV/60460709:41330/21:89224
Result on the web
<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>

Result language
angličtina
Original language name
Assessment and Prediction of Maize Production Considering Climate Change by Extreme Learning Machine in Czechia
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
40102 - Forestry

Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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