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    • DEVELOPMENT OF A DYNAMIC MODELLING TOOL FOR AGRICULTURAL PRODUCTION PROJECTIONS IN RELATION TO GHG MITIGATION MEASURES

    Peteris Rivza, Ivars Mozga, Laima Berzina


    ##manager.scheduler.building##: Atbrivosanas aleja 115, k-4 (Faculty of Engineering)
    ##manager.scheduler.room##: Room 111
    Last modified: 05.06.2017

    Abstract

    The present research study outlines a methodology for assessing agricultural production forecasts in Latvia with regard to the outcome of GHG emissions. A dynamic model was developed, which allows assessment of effects of various decisions and measures on agricultural production. The model consists of several mutually connected blocks: 1) modelling of agricultural indicators with relation to macroeconomic indicators; 2) calculation of GHG emissions according to Intergovernmental Panel on Climate Change (IPCC) guidelines; 3) scenarios for analysing the impact on emissions by various mitigation measures, and 4) results for summarising the modelling outcome. The developed model may be used as a decision support tool for impact assessment of various measures to reduce emissions and for seeking solutions to GHG emission mitigation by agricultural policy decisions. The model was developed using the Powersim Studio software.

    Keywords


    GHG emissions; dynamic model; agriculture

    References


    [1]     Central Statistical Bureau of the Republic of Latvia. Agricultural Output Indices (at constant prices). CSB database. [online] [20.02.2016] Available at: http://data.csb.gov.lv/pxweb/lv/lauks/lauks__ikgad__01Lauks_visp/LI0010.px/?rxid=cdcb978c-22b0-416a-aacc-aa650d3e2ce0

    [2]     Rivza P., Berzina L., Mozga I., Lauva D. Long-term Forecasting of Agricultural Indicators and GHG Emissions in Latvia. Proceedings of the 25th NJF Congress, Riga, 2015, pp. 281-288.

    [3]     Informatīvais ziņojums par darba tirgus vidēja un ilgtermiņa prognozēm. EM. (Informative Report on Labour Market Medium and Long-term Forecasts. Ministry of Economics), 2012, 88 p. (in Latvian) [online] [01.03.2016] Available at: https://www.em.gov.lv/files/tautsaimniecibas_attistiba/EMZino_150814.pdf .

    [4]     Ozolina V., Pocs R. Macroeconomic Modelling and Elaboration of the Macro-Econometric Model for the Latvian Economy. Scientific Monograph. Riga, RTU Press, 2013, 191 p.

    [5]     Fischer G. World Food and Agriculture to 2030/50: How do Climate Change and Bioenergy Alter the Long-term Outlook for Food, Agriculture and Resource Availability? Proceedings of an Expert Meeting on How to Feed the World in 2050. Food and Agriculture Organization of the United Nations Economic and Social Development Department, 2016. [online] [05.03.2016] Available at: http://www.fao.org/3/a-ak542e/ak542e07.pdflauk

    [6]     Fekedulegn, D., Mac Siurtain, M.P., & Colbert, J.J. (1999). Parameter Estimation of Nonlinear Growth Models in Forestry. Silva Fennica 33 (4): 32712.

    [7]     2006 IPCC Guidelines for National Greenhouse Gas Inventories, Volume 4, Agriculture, Forestry and Other Land Use, 2006. [online] [02.03.2016] Available at: http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html






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