SpringerLink
Log in

Impact of Climate Change on Agriculture: Empirical Evidence from South Asian Countries

  • Chapter
  • First Online:
Persistent and Emerging Challenges to Development

Part of the book series: India Studies in Business and Economics ((ISBE))

Abstract

Human-induced climate change is occurring at a fast rate, and agriculture, for its greater dependence on nature, is the most vulnerable sector to climate change. The rate of global warming has increased, and among the greenhouse gases, carbon dioxide is mainly responsible for this. It is projected that climate change may adversely affect global food security in this century. South Asia accommodated nearly half (48%) of the World's multidimensional poverty in 2017, and any adverse impact on agriculture will hurt South Asian countries very badly. Among eight South Asian countries, Bangladesh, India, Nepal, Pakistan, and Sri Lanka together have nearly 99% share of total GDP and 97.8% of the total population of South Asia in 2018. This study attempts to find out the evidence of the impact of climate change on agriculture in South Asia and five selected countries mentioned above based on data collected from the central database of the World Bank for the period of 1960 to 2016. We begin by assuming that CO2 can affect the agricultural value-added and examine whether there is any equilibrium long-run relationship among value added by agriculture, CO2 emissions, land under cultivation of cereal crops, and rainfall using the ARDL bounds test and error correction model. We do not find any evidence of the adverse impact of climate change on agriculture in South Asia and five selected countries.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 117.69
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 160.49
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
EUR 160.49
Price includes VAT (Germany)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.

    Article  Google Scholar 

  • Ali, S., Ying, L., Shah, T., Tariq, A., Ali Chandio, A., & Ali, I. (2019). Analysis of the Nexus of CO2 emissions, economic growth, land under cereal crops and agriculture value-added in Pakistan: Using an ARDL approach. Energies, 12.

    Google Scholar 

  • Amponsah, L., Hoggar, G. K., & Asuamah, S. Y. (2015). Climate change and agriculture: Modeling the impact of carbon dioxide emission on cereal yield in Ghana. Agriculture and Food Sciences Research, 2(2), 32–38.

    Google Scholar 

  • Asumadu-Sarkodie, S., & Owusu, P. A. (2016). Carbon dioxide emissions, GDP, energy use, and population growth: A multivariate and causality analysis for Ghana, 1971–2013. Environmental Science and Pollution Research, 23, 13508–13520.

    Article  Google Scholar 

  • Ayinde, O. E., Muchie, M., & Olatunji, G. B. (2011). Effect of climate change on agricultural productivity in Nigeria: A co-integration model approach. Journal of Human Ecology, 35, 189–194.

    Google Scholar 

  • Breusch, T. S. (1978). Testing for autocorrelation in dynamic linear models. Australian Economic Papers., 17, 334–355.

    Article  Google Scholar 

  • Chaves, D. R., Izaurralde, R. C., Thomson, A. M., & Gao, X. (2009). Long term climate change impacts on agricultural productivity in Eastern China. Agriculture, and Forest Meteorology, 149, 1118–1128.

    Article  Google Scholar 

  • Chogyel, N., & Kumar, L. (2018). Climate change and potential impacts on agriculture in Bhutan: A discussion of potential issues. Agriculture and Food Security. https://doi.org/10.1186/s40066-018-0229-6

    Article  Google Scholar 

  • Das, L, Prasad, H., Meher, J. K., Akhter, J., & Mahdi, S. S. (2019). Are GCMs and crop model capable to provide useful information for decision making? In S. S. Madhi (Eds.), Climate change and agriculture in India: Impact and adaptation. Springer, Switzerland.

    Google Scholar 

  • Dickey, D. & W. Fuller(1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root,Journal of the American Statistical Association,74:427–431.

    Google Scholar 

  • Dumrul, Y., & Kilicaslan, Z. (2017). Economic impacts of climate change on agriculture: Empirical evidence from ARDL approach for Turkey. Pressacademia, 6, 336–347.

    Article  Google Scholar 

  • Easterling, W., & APPS, M. (2005). Assessing the consequences of climate change for food and forest resources: A view from the IPCC. In J. Salinger, M. V. K. Sivakumar, & R. P. Motha (Eds.), Increasing climate variability and change, reducing the vulnerability of agriculture and forestry. Springer, The Netherlands.

    Google Scholar 

  • Giles, D. (2013). ARDL Models—Part II-Bounds Test, Econometrics Beat: Dave Giles Blog. https://davegiles.blogspot.com

  • Godfrey, L. G. (1978). Testing against general autoregressive and moving average error models when the regressors include lagged dependent variables. Econometrica, 46, 1293–1301.

    Article  Google Scholar 

  • Gokmenoglu, K. K., & Taspinar, N. (2018). Testing the AgrICULTURE-INDUCED EKC HYPOTHesis: The case of Pakistan. Environmental Science and Pollution Research, 25, 22829–22841.

    Article  Google Scholar 

  • Granger, C. J. (1969). Investigating causal relationships by econometrics models and cross spectral methods. Econometrica, 37, 425–435.

    Google Scholar 

  • Hille K. (2016). Carbon Dioxide Fertilisation Greening Earth, Study Find, NASA.

    Google Scholar 

  • IPCC. (2001). Climate change 2001: The scientific basis—contribution of working group I to the third assessment report of the intergovernmental panel on climate change (IPCC). Cambridge University Press.

    Google Scholar 

  • IPCC. (2013). Summary for policymakers. In T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. **a, V. Bex, & P. M. Midgley (Eds.), Climate change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press.

    Google Scholar 

  • Jarraud, M. (2005). Foreword. In: J. Salinger, M.V.K. Sivakumar, & R. P. Motha (Eds.), Increasing climate variability and change, reducing the vulnerability of agriculture and forestry. Springer, The Netherlands.

    Google Scholar 

  • Krischbaum, M. U. F. (2011). Does enhanced photosynthesis enhance growth? Lessons learned from CO2 enhancement studies. Plant Physiology, 155(1), 117–124.

    Article  Google Scholar 

  • Kumar, K. K., Kumar, K. R., Ashrit, R. G., Deshpande, N. R., & Hansen, J. W. (2004). Climate impacts on Indian agriculture. International Journal of Climatology, 24(11), 1375–1393.

    Article  Google Scholar 

  • Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54, 159–178.

    Google Scholar 

  • Liu, X., Zhang, S., & Bae, J. (2017). The Impact of renewable energy and agriculture on carbon dioxide emissions: Investigating the environmental Kuznets curve in four selected ASEAN Countries. Journal of Cleaner Production, 164, 1239–1247.

    Article  Google Scholar 

  • Lobell, D. B., & Gourdji, S. M. (2012). The influence of climate change on global crop productivity. Plant Physiology, 160, 1686–1697.

    Article  Google Scholar 

  • Mendelsohn, R. (2009). The impact of climate change on agriculture in Develo** Countries. Journal of Natural Resources Policy Research, 1(1), 5–19.

    Article  Google Scholar 

  • Mendelsohn, R. (2014). The impact of climate change on agriculture in Asia. Journal of Integrative Agriculture, 13(4), 660–665.

    Article  Google Scholar 

  • Mohta, R. P., & Wolfgang, B. (2005). Impacts of present and future climate change and climate variability on agriculture in the temperate regions: North America. In J. Salinger, M.V.K. Sivakumar, & R. P. Motha (Eds.), Increasing climate variability and change, reducing the vulnerability of agriculture and forestry (pp. 116–136). Springer, The Netherlands.

    Google Scholar 

  • Newey, W. K., & West, K. (1994). Automatic lag selection in covariance matrix estimation. Review of Economic Studies, 61(4), 631–654.

    Google Scholar 

  • NOAA. (2009) National Oceanic and Atmospheric Administration. cpo.noaa.gov/warmingworld/images/TenSignsofaWarmingWorld.jpg

    Google Scholar 

  • NOOA. (2017) National Oceanic and Atmospheric Administration. Global Climate Report - Annual Report 2017. https://www.nooa.gov/sotc/global/201713

  • Olesen, J. E., & M. Bindi (2002). Consequences of climate change for European agricultural productivity, land use, and policy. European Journal of Agronomy,16, 239–262.

    Google Scholar 

  • Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16, 289–326.

    Article  Google Scholar 

  • Praveen, B., & Sharma, P. (2019). Climate change and its impact on Indian agriculture: An econometric analysis. Journal of Public Affairs. https://doi.org/10.1002/pa.1972

  • Phillips, P. C. B.,& Perron, P. (1988). Testing for unit roots in time series regression. Biometrika, 75, 335–346.

    Google Scholar 

  • Salinger, M. J. (2005). Increasing climate variability and change: Reducing the vulnerability. In J. Salinger, M.V.K. Sivakumar, & R. P. Motha (Guest Editorial) (Eds.), Increasing climate variability and change, reducing the vulnerability of agriculture and forestry. Springer, The Netherlands.

    Google Scholar 

  • Schwert, W.(1989). Test for unit roots: A Monte Carlo investigation. Journal of Business and Economic Statistics, 7, 147–159.

    Google Scholar 

  • Shakoor, U., Saboor, A., Ali, I., & Mohsinegion, A. Q. (2011). Impact of climate change on agriculture: empirical evidence from arid region. Pakistan Journal of Agricultural Science, 48(4), 327–333.

    Google Scholar 

  • Sivakumar, M. V. K., Das, H. P., & Brunini, O. (2005). Impacts of present and future climate variability and change on agriculture and forestry in the arid and semi-arid tropics. In J. Salinger, M.V.K. Sivakumar, & R. P. Motha (Eds.), Increasing climate variability and change, reducing the vulnerability of agriculture and forestry. Springer, The Netherlands.

    Google Scholar 

  • Tol, R. S. J., Downing, T. E., Kuik, O. J., & Smith, J. B. (2004). Distributional aspects of climate change impacts. Global Environmental Change, 14, 259–272.

    Article  Google Scholar 

  • UNFPA. https:\\asiapacific.unfpa.org

    Google Scholar 

  • World Bank. (2020a). The World Bank, Central Database. https://data.worldbank.org.

  • World Bank. (2020b). Climate change knowledge portal. https://climateknowledgeportal.worldbank.org/

  • Zhang, L., Pang, J., Chen, X., & Lu, Z. (2019). Carbon emissions, energy consumption and economic growth: Evidence from the agricultural sector of China’s main grain-producing areas. Science of Total Environment, 665, 1017–1025.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Economics, Jogesh Chandra Chaudhuri College, Kolkata, India

    Bipradas Rit

Authors
  1. Bipradas Rit
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Economics, Presidency University, Kolkata, West Bengal, India

    Supravat Bagli

  2. Department of Economics, Presidency University, Kolkata, West Bengal, India

    Gagari Chakrabarti

  3. Department of Economics, Presidency University, Kolkata, West Bengal, India

    Prithviraj Guha

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Rit, B. (2022). Impact of Climate Change on Agriculture: Empirical Evidence from South Asian Countries. In: Bagli, S., Chakrabarti, G., Guha, P. (eds) Persistent and Emerging Challenges to Development. India Studies in Business and Economics. Springer, Singapore. https://doi.org/10.1007/978-981-16-4181-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-4181-7_5

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-4180-0

  • Online ISBN: 978-981-16-4181-7

  • eBook Packages: Economics and FinanceEconomics and Finance (R0)

Publish with us

Policies and ethics

Search

Navigation