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Forecasting the Productivity of the Agrophytocenoses of the Miscanthus Giganteus for the Fertilization Based on the Wastewater Sedimentation Using Artificial Neural Networks
 
Więcej
Ukryj
1
National University of Life and Environmental Sciences of Ukraine, Heroiv Oborony 15, Kyiv, Ukraine
2
Ivano-Frankivsk National Technical University of Oil and Gas, Vulytsya Karpatsʹka 15, Ivano-Frankivsk, Ukraine
3
Vadym Hetman Kyiv National University of Economics, Peremohy Ave 54/1, Kyiv, Ukraine
AUTOR DO KORESPONDENCJI
Halyna Myhaylovna Hrytsuliak   

Ivano-Frankivsk National Technical University of Oil and Gas, Heroiv Oborony 15, Kyiv, Ukraine
Data publikacji: 01-05-2021
 
Ecol. Eng. Environ. Technol. 2021; 3:11–19
 
SŁOWA KLUCZOWE
DZIEDZINY
STRESZCZENIE
The observations of plant development were carried out for three years. The most desirable period for harvesting the miscanthus is December. During this period, the humidity of the stems decreases to 17%. For this reason, the samples for laboratory tests were taken in December. According to the obtained research data, the sewage sludge we used is characterized by the following indicators: humidity - 76%, ash content - 5%, nitrogen - 0.66%, P2O5 - 2.51%, K2O - 2.16%. The artificial neural networks are widely used in various fields of knowledge, namely to predict the productivity of the agrophytocenoses of the energy crops. This research technology - artificial neural networks - is a mathematical model that allows you to find relationships between variables and predicted results of the studied variables, depending on the initial conditions. In this study, a mathematical model was successfully implemented, which allowed to predict the yield of the miscanthus at given levels, with the introduction of the mineral and organic (sewage sludge) fertilizers. According to the received researches, the application of a sewage sludge in norm of 20 - 40 t/ha promotes productivity of the power cultures (the miscanthus) within 24,5–27,1 t/ha, thus increases productivity on 2,3 - 5,1 t/ha, compared with control.