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LASAM Model: An Important Tool in the Decision Support System for Policymakers and Farmers

Author

Listed:
  • Irina Pilvere

    (Economic and Social Development Faculty, Latvia University of Life Sciences and Technologies, 18 Svetes Street, LV-3001 Jelgava, Latvia)

  • Aleksejs Nipers

    (Economic and Social Development Faculty, Latvia University of Life Sciences and Technologies, 18 Svetes Street, LV-3001 Jelgava, Latvia)

  • Agnese Krievina

    (Department of Bioeconomics, Institute of Agricultural Resources and Economics, 14 Struktoru Street, LV-1039 Riga, Latvia)

  • Ilze Upite

    (Economic and Social Development Faculty, Latvia University of Life Sciences and Technologies, 18 Svetes Street, LV-3001 Jelgava, Latvia)

  • Daniels Kotovs

    (Information Technology Faculty, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia)

Abstract

Today’s global food system (including production, transportation, processing, packing, storage, retail sale, consumption, losses and waste) provides income to more than a billion people all over the world and makes up a significant part of many countries’ economies. The 21st century’s food systems that bring food from “farm to fork” face various challenges, including a shortage of agricultural land and water, competition with the energy industry, changes in consumption preferences, a rising global population, negative effects of climate change, etc. Therefore, many countries are working on creating various models to function as an important decision support system tool for policymakers, farmers and other stakeholders. Various agricultural sector models see particularly extensive use in the European Union (EU), determining the impact of the Common Agricultural Policy (CAP) and helping to create future development scenarios. This is why a special model adapted to the national conditions, called LASAM (Latvian Agricultural Sector Analysis Model), was created in Latvia, making it possible to use historical data on the development of agricultural sectors, medium-term price projections for agricultural products in the EU, changes in support policy, as well as the necessity for the resources used to project the long-term (up to 2050) development of agriculture. The LASAM model covers the crop sector, the animal sector and the overall socioeconomic development, as well as the growth of organic farming and greenhouse gas (GHG) emissions. This paper discusses the main objectives achieved in developing a decision support tool and presenting the research results: LASAM was used to prepare projections of the possible development of Latvia’s principal sectors of agriculture until 2050, considering the necessity to reduce GHG emissions, made available through the LASAM web application. Given that the projection data obtained by LASAM are public, they can be used (1) for national policy making in rural business development, which affects the development of the economy as a whole; and (2) internationally, to compare the projections made in Latvia with those obtained through various agricultural sector models and projected development trends.

Suggested Citation

  • Irina Pilvere & Aleksejs Nipers & Agnese Krievina & Ilze Upite & Daniels Kotovs, 2022. "LASAM Model: An Important Tool in the Decision Support System for Policymakers and Farmers," Agriculture, MDPI, vol. 12(5), pages 1-26, May.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:5:p:705-:d:817457
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    1. Aftab Wajid & Khalid Hussain & Ayesha Ilyas & Muhammad Habib-ur-Rahman & Qamar Shakil & Gerrit Hoogenboom, 2021. "Crop Models: Important Tools in Decision Support System to Manage Wheat Production under Vulnerable Environments," Agriculture, MDPI, vol. 11(11), pages 1-22, November.
    2. World Bank, 2012. "Carbon Sequestration in Agricultural Soils," World Bank Publications - Reports 11868, The World Bank Group.
    3. Chen, Kefei & O'Leary, Rebecca A. & Evans, Fiona H., 2019. "A simple and parsimonious generalised additive model for predicting wheat yield in a decision support tool," Agricultural Systems, Elsevier, vol. 173(C), pages 140-150.
    4. Tingting Liu & Randall J. F. Bruins & Matthew T. Heberling, 2018. "Factors Influencing Farmers’ Adoption of Best Management Practices: A Review and Synthesis," Sustainability, MDPI, vol. 10(2), pages 1-26, February.
    5. Fritz, Steffen & See, Linda & Bayas, Juan Carlos Laso & Waldner, François & Jacques, Damien & Becker-Reshef, Inbal & Whitcraft, Alyssa & Baruth, Bettina & Bonifacio, Rogerio & Crutchfield, Jim & Rembo, 2019. "A comparison of global agricultural monitoring systems and current gaps," Agricultural Systems, Elsevier, vol. 168(C), pages 258-272.
    6. I. García-Garizábal & J. Causapé & R. Abrahao & D. Merchan, 2014. "Impact of Climate Change on Mediterranean Irrigation Demand: Historical Dynamics of Climate and Future Projections," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1449-1462, March.
    7. Taghikhah, Firouzeh & Voinov, Alexey & Shukla, Nagesh & Filatova, Tatiana & Anufriev, Mikhail, 2021. "Integrated modeling of extended agro-food supply chains: A systems approach," European Journal of Operational Research, Elsevier, vol. 288(3), pages 852-868.
    8. Matthew Gorton & Sophia Davidova & Martin Banse & Alistair Bailey, 2006. "The International Competitiveness of Hungarian Agriculture: Past Performance and Future Projections," Post-Communist Economies, Taylor & Francis Journals, vol. 18(1), pages 69-84.
    9. Topping, Christopher J. & Dalby, Lars & Valdez, Jose W., 2019. "Landscape-scale simulations as a tool in multi-criteria decision making to support agri-environment schemes," Agricultural Systems, Elsevier, vol. 176(C).
    10. Akpoti, Komlavi & Kabo-bah, Amos T. & Zwart, Sander J., 2019. "Agricultural land suitability analysis: State-of-the-art and outlooks for integration of climate change analysis," Agricultural Systems, Elsevier, vol. 173(C), pages 172-208.
    11. Gerald C. Nelson & Dominique Mensbrugghe & Helal Ahammad & Elodie Blanc & Katherine Calvin & Tomoko Hasegawa & Petr Havlik & Edwina Heyhoe & Page Kyle & Hermann Lotze-Campen & Martin Lampe & Daniel Ma, 2014. "Agriculture and climate change in global scenarios: why don't the models agree," Agricultural Economics, International Association of Agricultural Economists, vol. 45(1), pages 85-101, January.
    12. Linwood A. Hoffman & Xiaoli L. Etienne & Scott H. Irwin & Evelyn V. Colino & Jose I. Toasa, 2015. "Forecast performance of WASDE price projections for U.S. corn," Agricultural Economics, International Association of Agricultural Economists, vol. 46(S1), pages 157-171, November.
    13. Yu, Wusheng & Hertel, Thomas W. & Preckel, Paul V. & Eales, James S., 2004. "Projecting world food demand using alternative demand systems," Economic Modelling, Elsevier, vol. 21(1), pages 99-129, January.
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