IDEAS home Printed from https://ideas.repec.org/p/zbw/iamo11/1.html
   My bibliography  Save this paper

Determinants of agricultural land abandonment in post-soviet European Russia

Author

Listed:
  • Prishchepov, Alexander V.
  • Radeloff, Volker C.
  • Müller, Daniel
  • Dubinin, Maxim
  • Baumann, Matthias

Abstract

Socio-economic and institutional changes may accelerate land-use and land-cover change. Our goal was to explore the determinants of agricultural land abandonment within one agro-climatic and economic region of post-Soviet European Russia during the first decade of transition from a state-command to market-driven economy (between 1990 and 2000). We integrated maps of abandoned agricultural land derived from 30 m resolution Landsat TM/ETM+ images, environmental and socioeconomic variables and estimated logistic regressions. Results showed that post-Soviet agricultural land abandonment was significantly associated with lower average grain yields in the late 1980s, higher distance from the populated places, areas with low population densities, for isolated agricultural areas within the forest matrix and near the forest edges. Hierarchical partitioning showed that average grain yields in the late 1980s contributed the most in explaining the variability of agricultural land abandonment, followed by location characteristics of the land. While the spatial patterns correspond to the classic micro-economic theories of von Thünen and Ricardo, it was largely the macro-scale driving forces that fostered agricultural abandonment. In the light of continuum depopulation process in the studied region of European Russia, we expect continuing agricultural abandonment after the year 2000.

Suggested Citation

  • Prishchepov, Alexander V. & Radeloff, Volker C. & Müller, Daniel & Dubinin, Maxim & Baumann, Matthias, 2011. "Determinants of agricultural land abandonment in post-soviet European Russia," IAMO Forum 2011: Will the "BRICs Decade" Continue? – Prospects for Trade and Growth 1, Leibniz Institute of Agricultural Development in Central and Eastern Europe (IAMO).
  • Handle: RePEc:zbw:iamo11:1
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/50785/1/670731773.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Michael A Trueblood & Carlos Arnade, 2001. "Crop Yield Convergence: How Russia's Yield Performance Has Compared to Global Yield Leaders," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 43(2), pages 59-81, July.
    2. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    3. Holden, Ken & Klein, Philip A. & Lahiri, Kajal, 2001. "Introduction," International Journal of Forecasting, Elsevier, vol. 17(3), pages 329-332.
    4. Lerman, Zvi & Csaki, Csaba & Feder, Gershon, 2002. "Land policies and evolving farm structures in transition countries," Policy Research Working Paper Series 2794, The World Bank.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Das, Debojyoti & Bhatia, Vaneet & Kumar, Surya Bhushan & Basu, Sankarshan, 2022. "Do precious metals hedge crude oil volatility jumps?," International Review of Financial Analysis, Elsevier, vol. 83(C).
    2. P.A.V.B. Swamy & I-Lok Chang & Jatinder S. Mehta & William H. Greene & Stephen G. Hall & George S. Tavlas, 2016. "Removing Specification Errors from the Usual Formulation of Binary Choice Models," Econometrics, MDPI, vol. 4(2), pages 1-21, June.
    3. Carlo Altavilla & Raffaella Giacomini & Giuseppe Ragusa, 2017. "Anchoring the yield curve using survey expectations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(6), pages 1055-1068, September.
    4. Fernando Rios-Avila & Gustavo Canavire-Bacarreza, 2018. "Standard-error correction in two-stage optimization models: A quasi–maximum likelihood estimation approach," Stata Journal, StataCorp LP, vol. 18(1), pages 206-222, March.
    5. Sandy Fréret & Denis Maguain, 2017. "The effects of agglomeration on tax competition: evidence from a two-regime spatial panel model on French data," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 24(6), pages 1100-1140, December.
    6. Ai, Chunrong & Chen, Xiaohong, 2007. "Estimation of possibly misspecified semiparametric conditional moment restriction models with different conditioning variables," Journal of Econometrics, Elsevier, vol. 141(1), pages 5-43, November.
    7. Ayouz, Mourad K. & Remaud, Herve, 2003. "The Internationalization Determinants Of The Small Agro-Food Firms: Hypotheses And Statistical Tests," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 5(2), pages 1-27.
    8. Jan Fałkowski & Maciej Jakubowski & Paweł Strawiński, 2014. "Returns from income strategies in rural Poland," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 22(1), pages 139-178, January.
    9. Broze, Laurence & Gourieroux, Christian, 1998. "Pseudo-maximum likelihood method, adjusted pseudo-maximum likelihood method and covariance estimators," Journal of Econometrics, Elsevier, vol. 85(1), pages 75-98, July.
    10. Sridhar, Shrihari & Naik, Prasad A. & Kelkar, Ajay, 2017. "Metrics unreliability and marketing overspending," International Journal of Research in Marketing, Elsevier, vol. 34(4), pages 761-779.
    11. Yen, Steven T. & Chern, Wen S. & Lee, Hwang-Jaw, 1991. "Effects Of Income Sources On Household Food Expenditures," 1991 Annual Meeting, August 4-7, Manhattan, Kansas 271167, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    12. Ruoxuan Xiong & Allison Koenecke & Michael Powell & Zhu Shen & Joshua T. Vogelstein & Susan Athey, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Papers 2107.11732, arXiv.org, revised Apr 2023.
    13. Posch, Olaf, 2009. "Structural estimation of jump-diffusion processes in macroeconomics," Journal of Econometrics, Elsevier, vol. 153(2), pages 196-210, December.
    14. Koutmos, Dimitrios, 2012. "An intertemporal capital asset pricing model with heterogeneous expectations," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(5), pages 1176-1187.
    15. Gregory, Allan W. & McCurdy, Thomas H., 1986. "The unbiasedness hypothesis in the forward foreign exchange market: A specification analysis with application to France, Italy, Japan, the United Kingdom and West Germany," European Economic Review, Elsevier, vol. 30(2), pages 365-381, April.
    16. Lanot, Gauthier & Walker, Ian, 1998. "The union/non-union wage differential: An application of semi-parametric methods," Journal of Econometrics, Elsevier, vol. 84(2), pages 327-349, June.
    17. Magnus, Jan R., 2007. "The Asymptotic Variance Of The Pseudo Maximum Likelihood Estimator," Econometric Theory, Cambridge University Press, vol. 23(5), pages 1022-1032, October.
    18. Özlem Onaran & Engelbert Stockhammer, 2006. "The effect of FDI and foreign trade on wages in the Central and Eastern European Countries in the post-transition era: A sectoral analysis," Department of Economics Working Papers wuwp094, Vienna University of Economics and Business, Department of Economics.
    19. Pan, Wei & Louis, Thomas A., 1999. "Two semi-parametric empirical Bayes estimators," Computational Statistics & Data Analysis, Elsevier, vol. 30(2), pages 185-196, April.
    20. Frank X. Zhang, 2003. "What did the credit market expect of Argentina default? Evidence from default swap data," Finance and Economics Discussion Series 2003-25, Board of Governors of the Federal Reserve System (U.S.).

    More about this item

    Keywords

    agricultural land abandonment; institutional change; land use change; spatial analysis; logistic regression; remote sensing; Russia;
    All these keywords.

    JEL classification:

    • Q15 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:iamo11:1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/iamoode.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.