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Prognostic Models of Panicum virgatum L. Using Artificial Neural Networks

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article shows the possibility of using modern methods of artificial intelligence to calculate the yield of biomass of crops according to the given set input data (fertilizer doses, agrochemical parameters of the soil, productivity). The study reflects the results of testing a model of a computer program of an artificial neural network, which allowed forecasting the yield of Panicum virgatum L. (Switchgrass) depending on the joint application of fertilizers mineral and precipitate. On the basis of the calculations, the obtained model of productivity of vegetative mass of switchgrass shows a high level of forecasting efficiency (up to 97%). According to the results of experimental studies, the use of sewage sludge at a doses of 20–40 t/ha provides a dry biomass yield of Panicum virgatum L. (Switchgrass) in the range of 13.1–20.3 t/ha, which is 3.4–7.2 t/ha more than in the option without fertilizer.
Rocznik
Strony
62--71
Opis fizyczny
Bibliogr. 43 poz., rys., tab.
Twórcy
  • National University of Life and Environmental Sciences of Ukraine, Heroiv Oborony Str.15, 03041, Kyiv, Ukraine
  • Ivano-Frankivsk National Technical University of Oil and Gas, Karpatska Street, 15, 76000, Ivano-Frankivsk, Ukraine
  • National University of Life and Environmental Sciences of Ukraine, Heroiv Oborony Str.15, 03041, Kyiv, Ukraine
  • National University of Life and Environmental Sciences of Ukraine, Heroiv Oborony Str.15, 03041, Kyiv, Ukraine
  • National University of Life and Environmental Sciences of Ukraine, Heroiv Oborony Str.15, 03041, Kyiv, Ukraine
  • National University of Life and Environmental Sciences of Ukraine, Heroiv Oborony Str.15, 03041, Kyiv, Ukraine
  • Ivano-Frankivsk National Medical University, Halytska Str. 2, 76018, Ivano-Frankivsk, Ukraine
Bibliografia
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  • 3. David K., Ragauskas A.J. 2010. Switchgrass as an energy crop for biofuel production: a review of its ligno-cellulosic chemical properties. Energy Env Sci, 3, 1182–1190. https://doi.org/10.1039/B926617H
  • 4. Dumitrache A., Natzke J., Rodriguez M.J., Yee K.L., Thompson O.A., Poovaiah C.R., et al. 2017. Transgenic switchgrass (Panicum virgatum L.) targeted for reduced recalcitrance to bioconversion: a two-year comparative analysis of field-grown lines modified for target gene or genetic element expression. Plant Biotechnol, J., 15, 688–697. https://doi.org/10.1111/pbi.12666
  • 5. Lehtokangas M. 2000. Determining the number of centroids for CMLP network Neural networks. Elsevier https://doi.org/10.1016/S0893–6080(00)00021–6
  • 6. Eli-Chukwu N.C. 2019. Applications of artificial intelligence in agriculture: A review. Engineering, Technology & Applied Science Research, 9(4), 4377–4383. https://doi.org/10.48084/etasr.2756
  • 7. Elbersen H.W., Christian D.G., El-Bassem N., Bacher W., Sauerbeck G., Alexopoulou E., Sharma N., Piscioneri I., de Visser P., van den Berg D. 2001. Switchgrass variety choice in Europe. Aspects Appl. Biol., 65, 21–28.
  • 8. Elbersen W., Poppens R., Bakker R. 2013. Switchgrass (Panicum virgatum L.). A perennial biomass grass for efficient production of feedstock for the biobased economy. A report for the Netherlands Programmes Sustainable Biomass of NL Agency.
  • 9. Iulian B. 2010. Ciocoiu Hybrid Feedforward Neural Networks for Solving Classification Problems DOI: 10.1023/A:1019755726221
  • 10. Lesschen J.-P., Kulyk M., Galytska M. 2013 Switchgrass (Panicum virgatum L.). а perennial biomass grass for efficient production of feedstock for the biobased economy, 4–28.
  • 11. Jefferson P.G., McCaughey M.P. 2012. Switchgrass (Panicum virgatum L.) cultivar adaptation, biomass production, and cellulose concentration as affected by latitude of origin. ISRN Agronomy; 2012763046 http://dx.doi.org/10.5402/2012/763046
  • 12. Jha K., Doshi A., Patel P., Shah M. 2019. A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture, 2, 1–12. DOI: 10.1016/j.aiia.2019.05.004
  • 13. Karbivska U., Kurgak V., Gamayunova V., Butenko A., Malynka L., Kovalenko I., Onychko V., Masyk I., Chyrva A., Zakharchenko E., Tkachenko O., Pshychenko O. 2020. Productivity and Quality of Diverse Ripe Pasture Grass Fodder Depends on the Method of Soil Cultivation. Acta Agrobotanica 73(3). https://doi.org/10.5586/aa.7334
  • 14. Karlik B., Olgac A. V. 2011. Performance analysis of various activation functions in generalized MLP architectures of neural networks. International Journal of Artificial Intelligence and Expert Systems, 1(4), 111–122.
  • 15. Kharchenko O., Zakharchenko E., Kovalenko I., Prasol V., Pshychenko O., Mishchenko Y. 2019. On problem of establishing the intensity level of crop variety and its yield value subject to the environmental conditions and constraints. AgroLife scientific journal, 8(1), 113–119.
  • 16. Lesschen J.P., Elbersen W., Poppens R., Galytskaya M., Kulyk M., Lerminiaux L. 2012. The financial and greenhouse gas cost of avoiding ILUC in biomass sourcing – A comparison between switchgrass produced with and without ILUC in Ukraine. In. Eur. Biomass Conf., Milan, Italy 2012.
  • 17. Liu W., Mazarei M., Ye R., Peng Y., Shao Y., Baxter H.L., et al. 2018. Switchgrass (Panicum virgatum L.) promoters for green tissue-specific expression of the MYB4 transcription factor for reduced-recalcitrance transgenic switchgrass. Biotechnol. Biofuels, 11, 122. https://doi.org/10.1186/s13068–018–1119–7
  • 18. Lopushniak V., Hrytsuliak H. 2021. The Models of the Heavy Metal Accumulation of the Multiple Grain Energy Cultures for Wasterwater Deposition on Oil-Polluted Degraded Soils. Ecological Engineering & Environmental Technology. 22(4), 1–13.
  • 19. Mann D.G.J., LaFayette P.R., Abercrombie L.L., King Z.R., Mazarei M., Halter M.C., et al. 2012. Gateway-compatible vectors for high-throughput gene functional analysis in switchgrass (Panicum virgatum L.) and other monocot species. Plant Biotechnol. J. 10, 226–236. https://doi.org/10.1111/j.1467–7652.2011.00658.
  • 20. McLaughlin S.B., Kszos L.A. 2005. Development of switchgrass Panicum virgatum as a bioenergy feedstock in the United States. Biomass and Bioenergy, 28(6), 515–535. http://dx.doi.org/10.1016/j.biombioe.2004.05.006
  • 21. Mininni G., Blanch A.R., Lucena F., Berselli S. 2014. EU policy on sewage sludge utilization and perspectives on new approaches of sludge management. Environmental Science and Pollution Research, 22(10), 7361–7374. https://doi.org/10.1007/s11356–014–3132–0
  • 22. Mohammed Y.A., Raun W., Kakani G., et al. 2015. Nutrient sources and harvesting frequenting on quality biomass production of switchgrass (Panicum virgatum L.) for biofuel. Biomass Bioenergy, 81, 242. http://dx.doi.org/10.1016/j.biombioe.2015.06.027
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  • 24. Oliveira J., West C., Afif E., Palencia P. 2017. Comparison of miscanthus and switchgrass cultivars for biomass yield, soil nutrients, and nutrient removal in northwest Spain, 109–122. http://dx.doi.org/10.2134/agronj2016.07.0440
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  • 26. Parrish D.J., Fike J.H., Bransby D.I., Samson R. 2008. Establishing and Managing Switchgrass as an Energy Crop. Forage Grazinglands. http://dx.doi.org/10.1094/FG-2008–0220–01-RV
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  • 30. Tigunova O.O., Shulga S.M. 2015.New strain-producers of biobutanol. III. Methods of increased butanol accumulation from biomass of switchgrass Panicum virgatum L. Biotechnol Acta; 8(4):92. http://dx.doi.org/10.15407/biotech8.04.092
  • 31. Kalynychenko A.V., Kulyk M.I. 2018. Economic efficiency of growing millet (switchgrass) in the conditions of the Forest-Steppe of Ukraine. Economics of agro-industrial complex. 11, 19. https://doi.org/10.32317/2221–1055.201811019
  • 32. Lopushniak V.I., Hrytsuliak H.M., Kotsiubynsky A.O. 2021 Forecasting the productivity of the agrophytocenoses of the miscanthus giganteus for the fertilization based on the wastewater sedimentation using artificial neural networks. Ecological Engineering & Environmental Technology, 22(3), 11–19.
  • 33. Lopushniak V., Hrytsulyak H. 2021. The Models of the Heavy Metal Accumulation of the Multiple Grain Energy Cultures for Wastewater Deposition on Oil-Polluted Degraded Soils. Ecological Engineering & Environmental Technology, 22(4), 1–13. https://doi.org/10.12912/27197050/137873
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  • 38. Tatarchuk T., Mironyuk I., Kotsyubynsky V., Shyichuk A., Myslin M., Boychuk V. 2020. Structure, morphology and adsorption properties of titania shell immobilized onto cobalt ferrite nanoparticle core. Journal of Molecular Liquids, 297.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6e413fb2-6fdc-4ff3-b1ff-c72bc2b0aef0
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