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Warianty tytułu
Zastosowanie metody Recursive Partitioning w ocenie wpływu warunków klimatycznych na rolnicze emisje gazów cieplarnianych innych niż CO2
Języki publikacji
Abstrakty
Agricultural practices contribute to emissions of the greenhouse gases (GHGs) such a methan (CH4) and nitrous oxide (N2O). Their estimated share from agricultural sources is assessed at around 50% of CH4 and 60% of N2O emissions. The efforts made by the agricultural sector aim to limit and reduce emissions. Due to their significant share, all the complementary knowledge information concerning their reduction are highly precious. The paper proposes the use of neural modeling techniques and the summary of results by modeling based on a decision tree (Recursive Partitioning) to estimate the levels of methane and nitrous oxide emissions from agriculture under varying weather conditions in Poland. The obtained results support the hypothesis that neural model describing the effect of meteorological conditions on the CH4and N2O emissions is an appropriate tool for the assessment of the projected emission level.
Praktyki rolnicze przyczyniają się do emisji gazów cieplarnianych (GGC), takich jak metan (CH4) i podtlenku azotu (N2O). Ich szacunkowy udział ze źródeł rolniczych oceniany jest na około 50% emisji CH4 i 60% emisji N2O. Wysiłki podejmowane przez sektor rolny mają na celu ograniczenie i redukcję ich emisji. Ze względu na ich znaczący udział, wszelkie informacje dopełniające wiedzę na temat możliwości ich redukcji są niezwykle cenne. W pracy zaproponowano wykorzystanie technik neuronowego modelowania oraz posumowania wyników z wykorzystaniem modelowania w oparciu o drzewo decyzyjne (Recursive Partitioning) do estymacji poziomu metanu i podtlenku azotu emitowanych z rolnictwa przy zmiennych warunkach meteorologicznych w Polsce. Uzyskane wyniki badań potwierdzają hipotezę, że model neuronowy, opisujący wpływ warunków meteorologicznych na emisję CH4 i N2O, jest właściwym instrumentem dla dokonania oceny prognozowania poziomu tej emisji.
Rocznik
Tom
Strony
96--101
Opis fizyczny
Bibliogr. 31 poz., rys., tab.
Twórcy
autor
- Opole University of Technology, Department of Economics and Regional Research, Faculty of Economy and Management Waryńskiego 4, 45-047 Opole
Bibliografia
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- [6] Food and Agricultural Organization, 2003: World Agriculture: Towards 2015/2030. An FAO Perspective, FAO, Rome 2003.
- [7] Furrer R., Sain S.R., Nychka D., Meehl G.A:. Multivariate Bayesian Analysis in atmosphere – ocean general circulation models. Environmental and Ecological Statistics, 14, 3: 249-266, 2007.
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- [9] Heping Z.: Recursive Partitioning and Tree-based Methods. Springer Handbooks of Computational Statistics, 853-882, 2012.
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- [13] Mosier A., Kroeze C.: Potential impact on the global atmospheric N2O budget of the increased nitrogen input required to meet future global food demands. Chemosphere-Global Change Science, 2: 465-473, 2000.
- [14] Nazir N., Mir A.H., A.A. Khan A.A: Bayesian analysis of mixed effect models and its applications in agriculture, Electronic Journal of Applied Statistical Analysis, 4, 2: 164 – 171, 2011.
- [15] Neal R.: Bayesian Learning for Neural Networks, Springer – Verlag GnbH, 1996. A. Kolasa-Więcek „Journal of Research and Applications in Agricultural 101 Engineering” 2013, Vol. 58(1)
- [16] Neal R.: Flexible Bayesian Models on Neural Networks, Gaussian Processes and Mixtures v. 2004-11-10, University of Toronto, 2004.
- [17] Nelson L.M., Bloch D.A., Longstreth W.T. Jr., Shi H.: Recursive partitioning for the identification of disease risk subgroups: a case-control study of subarachnoid hemorrhage, Journal of Clinical Epidemiology, 51, 3: 199-209, 1998.
- [18] Pasyniuk P.: Reducing greenhouse gas emissions by replacing diesel by vegetable oils. Problems of Agricultural Engineering, 4: 79-89, 2010.
- [19] Sadoddin A., Shahabi M.: Agricultural drought management using a Bayesian decision model for rainfed wheat farmlands in east of Golestan Province, Iran, Workshop on metrics and methodologies of estimation of extreme climate event, UNESCO, Paris, 2010.
- [20] Smith P., Martino D., Cai Z., Gwary D., Janzen H., Kumar P., McCarl B., Ogle S., O’Mara F., Rice C., Scholes B., Sirotenko O.: Agriculture. In Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Eds. B. Metz, O.R. Davidson, P.R. Bosch, R. Dave, L.A. Meyer, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2007.
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- [28] United States Environmental Protection Agency (USEPA): Global Anthropogenic Non-CO2 Greenhouse Gas Emissions: 1990- 2020. EPA, 2006. Washington, http://www.epa.gov/nonco2/econinv/downloads/GlobalAnthroEmissionsReport.pdf [Accessed: 3.02.2013].
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Typ dokumentu
Bibliografia
Identyfikator YADDA
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