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Will climate change benefit or hurt Russian grain production? A statistical evidence from a panel approach

Article

Abstract

Using recent advances in statistical crop yield modelling and a unique dataset consisting of yield time series for Russian regions over the period from 1955 to 2012, the study investigates the potential impact of climate change (CC) on the productivity of the three most important grains. Holding current grain growing areas fixed, the aggregate productivity of the three grains is predicted to decrease by 6.7% in 2046–2065 and increase by 2.6% in 2081–2100 compared to 1971–2000 under the most optimistic representative emission concentration pathway (RCP). Based on the projections for the three other RCPs, the aggregate productivity of the three studied crops is assessed to decrease by 18.0, 7.9 and 26.0% in the medium term and by 31.2, 25.9 and 55.4% by the end of the century. Our results indicate that CC might have a positive effect on winter wheat, spring wheat and spring barley productivity in a number of regions in the Northern and Siberian parts of Russia. However, due to the highly damaging CC impact on grain production in the most productive regions located in the South of the country, the overall impact tends to be negative. Therefore, a shift of agricultural production to the Northern regions of the country could reduce the negative impact of CC on grain production only to a limited extent. More vigorous adaptation measures are required to maintain current grain production volumes in Russia under CC.

Notes

Acknowledgements

The authors would like to thank Ariel Ortiz-Bobea and Pierre Merel for their valuable input and comments on an earlier version of the paper. They are also very much grateful to Yuri I. Kopenkin and Nikolai Svetlov for enabling access to statistics used in the study. The final version of the paper has benefited from insightful questions and helpful suggestions of Armen R. Kemanian and two anonymous reviewers.

Supplementary material

10584_2018_2221_MOESM1_ESM.docx (507 kb)
ESM 1 (DOCX 506 kb)

References

  1. Alcamo J, Dronin N, Endejan M, Golubev G, Kirilenko A (2007) A new assessment of climate change impacts on food production shortfalls and water availability in Russia. Glob Environ Chang 17(3–4):429–444.  https://doi.org/10.1016/j.gloenvcha.2006.12.006 CrossRefGoogle Scholar
  2. Burke M, Emerick K. (2013) Adaptation to climate change: evidence from US agriculture (no. available at SSRN 2144928)Google Scholar
  3. Chatzopoulos T, Lippert C (2015) Adaptation and climate change impacts: a structural Ricardian analysis of farm types in Germany, J Agr Econ, p. n/a–n/aGoogle Scholar
  4. Conley TG (1999) GMM estimation with cross sectional dependence. J Econometrics 92(1):1–45CrossRefGoogle Scholar
  5. Deschênes O, Greenstone M (2007) The economic impacts of climate change: evidence from agricultural output and random fluctuations in weather. Am Econ Rev 97(1):354–385CrossRefGoogle Scholar
  6. Fisher AC, Hanemann WM, Roberts MJ (2012) The economic impacts of climate change: evidence from agricultural output and random fluctuations in weather: comment. Am Econ Rev 102(7):3749–3760CrossRefGoogle Scholar
  7. Hsiang SM (2010) Temperatures and cyclones strongly associated with economic production in the Caribbean and Central America. PNAS 107(35):15367–15372CrossRefGoogle Scholar
  8. IPCC (2013) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment ReportGoogle Scholar
  9. IPCC (2014) Climate change 2014: impacts, adaptation and vulnerability—contributions of the Working Group II to the Fifth Assessment Report, doi:  https://doi.org/10.1016/j.renene.2009.11.012
  10. Jones P, Thornton P (2003) The potential impacts of climate change on maize production in Africa and Latin America in 2055. Global Environ Chang 13(1):51–59CrossRefGoogle Scholar
  11. Liefert WM, Liefert O (2015) Russia’s potential to increase grain production by expanding area. Eurasian Geogr Econ 25(5):505–523CrossRefGoogle Scholar
  12. Lobell DB, Burke MB (2010) On the use of statistical models to predict crop yield responses to cliamte change. Agric For Meteorol 150(11):1443–1452Google Scholar
  13. Lobell DB, Schlenker W, Costa-Roberts J (2011) Climate trends and global crop production since 1980. Science 333(6042):616–620CrossRefGoogle Scholar
  14. Mearns LO, Rosenzweig C, Goldberg R (1992) Effect of changes in interannual climatic variability on CERES-wheat yields: sensitivity and 2 × CO2 general circulation model studies. Agric For Meteorol 62:159–189CrossRefGoogle Scholar
  15. Mendelsohn BR, Nordhaus WD, Shaw D (1994) The impact of global warming on agriculture: a Ricardian analysis. Am Econ Rev 84(4):753–771Google Scholar
  16. Moore FC, Lobell DB (2014) Adaptation potential of European agriculture in response to climate change, Nature Clim Change Vol. 4 No. JulyGoogle Scholar
  17. Müller C, Robertson RD (2014) Projecting future crop productivity for global economic modeling. Agric Econ 45(1):37–50Google Scholar
  18. Ortiz-Bobea A, Just RE (2012) Modeling the structure of adaptation in climate change impact assessment. Am J Agric Econ 95(2):244–251CrossRefGoogle Scholar
  19. Prishchepov AV, Müller D, Dubinin M, Baumann M, Radeloff VC (2013) Determinants of agricultural land abandonment in post-Soviet European Russia. Land Use Policy 30(1):873–884Google Scholar
  20. Roberts MJ, Schlenker W, Eyer J (2012) Agronomic weather measures in econometric models of crop yield with implications for climate change. Am J Agric Econ 95(2):236–243CrossRefGoogle Scholar
  21. Rosstat (annual editions from 1992 to 2014) Statistical Yearbooks “Agriculture in Russia”, Russian Federation Federal Statistical Service (Rosstat), Moscow (Russia): 1992–2014Google Scholar
  22. Schierhorn F, Müller D, Prishchepov AV, Faramarzi M, Balmann A (2014) The potential of Russia to increase its wheat production through cropland expansion and intensification. Glob Food Sec 3:133–141CrossRefGoogle Scholar
  23. Schlenker W, Roberts MJ (2009) Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. PNAS 106(37):15594–15598CrossRefGoogle Scholar
  24. Semenov MA, Wolf J, Evans LG, Eckersten H, Iglesias A (1996) Comparison of wheat simulation models under climate change. II. Application of climate change scenarios. Clim Res 7(2):271–281CrossRefGoogle Scholar
  25. Sirotenko OD, Abashina HV, Pavlova VN (1997) Sensitivity of the Russian agriculture to changes in climate, CO2 and tropospheric ozone concentrations and soil fertility. Clim Change 36:217–232CrossRefGoogle Scholar
  26. Sirotenko OD, Pavlova VN (2012), “Methods of the estimation of climate change impact on agricultural productivity”, methods of the estimation of climate change impacts for physical and biological systems, Russian Federal Service for Hydrometeorology and Environmental Monitoring (Roshydromet), Moscow, pp 165–189Google Scholar
  27. Snyder RL (1985) Hand calculating degree days. Agric For Meteorol 35:353–358CrossRefGoogle Scholar
  28. Stöckle CO (2013) Temperature routines in CropSyst. In: Alderman PD, Quilligan E, Asseng S, Ewert F, Reynolds MP (eds) Proceedings of the Workshop Modeling Wheat Response to High Temperature. CIMMYT, El Batan, MexicoGoogle Scholar
  29. Tack J, Barkley A, Nalley LL (2015) Effect of warming temperatures on US wheat yields. PNAS 112(22):6931–6936CrossRefGoogle Scholar
  30. TsSU (annual editions from 1956 to 1991), Statistical Yearbooks “The economy of the Russian Soviet Federative Socialistic Republic”, Finance and Statistics, Central Statistical Directorate (TsSU): 1956–1991Google Scholar
  31. Trueblood MA, Arnade A (2001) Crop yield convergence: how Russia’s yield performance has compared to global yield leaders. Comp Econ Stud XLiII(2):59–81CrossRefGoogle Scholar
  32. Van Passel S, Masetti E, Mendelsohn R (2016) A Ricardian analysis of the impact of climate change on European agriculture, R. Environ Resource EconGoogle Scholar

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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Leibniz Institute of Agricultural Development in Transition Economies (IAMO)Halle (Saale)Germany
  2. 2.Agricultural and Resource Economics, Institute of Natural Resource SciencesThe ZHAW Zurich University of Applied SciencesWädenswilSwitzerland

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