Advertisement

GeoJournal

pp 1–12 | Cite as

Geospatial analysis of Maize yield vulnerability to climate change in Nigeria

  • Olanrewaju LawalEmail author
  • M. Olufemi Adesope
Article
  • 9 Downloads

Abstract

The fifth assessment report (AR5) predicted that land temperatures would rise faster over Africa than other global averages while changes in rainfall are uncertain across Sub-Saharan Africa. These portend water availability challenges with direct impacts on agricultural production. Existing studies on yield vulnerability in Nigeria are mostly at a national scale, which is not adequate for local decision making. This study provides a spatially explicit model of Maize yield vulnerabilities across the growing areas (GA). Thereby, turning available data into actionable information to support development actions. Yield vulnerability index was constructed as a relationship among exposure, yield sensitivity and adaptive capacity. Exposure was computed as the ratio between long and short-term climatic factors. Yield sensitivities were expressed as the ratio between expected and actual yield. Adaptive capacity was captured using a combination of socio-economic proxies. The result shows that Maize yields were vulnerable to climate variability across most of the GAs. Exposure values indicate a very high level of climate variability with the northern region more exposed. Yield sensitivity ranges between ranges 0.47 and 0.95, and highest along the northern extremes, moderate sensitivities were observed across large tracts of the north-west, northeast, south-east and south–south geopolitical regions. Adaptive capacity is highly variable ranging between 0.27 and 1. Yield vulnerability ranges between 0.46 and 1.51. The general assumption of a north–south divide for yield vulnerability was invalidated. Vulnerability is more disparate beyond latitudinal differences. The model presented, creates a framework to support targeted response, and opportunity for building resilience to climate change impact for crop yield.

Keywords

Maize yield Yield vulnerability Adaptive capacity Climate change Yield sensitivity 

Notes

Funding

The authors did not receive any funding from any organisation/institution for this study (study was not funded by any grant).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies involving animals performed by any of the authors.

Human and animal rights

This article does not contain any studies involving human participants performed by any of the authors.

References

  1. Ajetomobi, J. O. (2016). Sensitivity of crop yield to extreme weather in Nigeria. Paper presented at the Proceedings of the African Association of Agricultural Economists (AAAE) Fifth International Conference, Addis Ababa, Ethiopia.Google Scholar
  2. Akpodiogaga-a, P., & Odjugo, O. (2010). General overview of climate change impacts in Nigeria. Journal of Human Ecology, 29(1), 47–55.  https://doi.org/10.1080/09709274.2010.11906248.CrossRefGoogle Scholar
  3. 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(3), 189–194.  https://doi.org/10.1080/09709274.2011.11906406.CrossRefGoogle Scholar
  4. BNRCC. (2011). National adaptation strategy and plan of action on climate change for Nigeria. Retrieved from Ibadan.Google Scholar
  5. Bosco, C., Alegana, V., Bird, T., Pezzulo, C., Bengtsson, L., Sorichetta, A., et al. (2017). Exploring the high-resolution mapping of gender-disaggregated development indicators. Journal of the Royal Society, Interface, 14(129), 20160825.CrossRefGoogle Scholar
  6. CBN. (2018). New GDP at 2010 constant basic prices (Naira Million). Time series. Retrieved from: http://nigeria.opendataforafrica.org/afnwpjg/new-gdp-at-2010-constant-basic-prices-naira-million-1960-2017?descriptor=1000550
  7. Central Intelligence Agency. (2015). Nigeria: The World Factbook. Retrieved from https://www.cia.gov/library/publications/the-world-factbook/geos/ni.html.
  8. Epule, T., Ford, J., Lwasa, S., & Lepage, L. (2017). Vulnerability of maize yields to droughts in Uganda. Water, 9(3), 181.CrossRefGoogle Scholar
  9. Eriksen, S. H., & Kelly, P. M. (2007). Developing credible vulnerability indicators for climate adaptation policy assessment. Mitigation and Adaptation Strategies for Global Change, 12(4), 495–524.  https://doi.org/10.1007/s11027-006-3460-6.CrossRefGoogle Scholar
  10. ESRI. (2017). ArcGIS desktop (version 10.6). Redlands, CA: Environmental Systems Research Institute.Google Scholar
  11. FAO. (2018). Global information and early warning system: Country brief on Nigeria. Country Analysis. Retrieved from http://www.fao.org/giews/countrybrief/country.jsp?lang=en&code=NGA
  12. Ford, J. D., Keskitalo, E. C. H., Smith, T., Pearce, T., Berrang-Ford, L., Duerden, F., et al. (2010). Case study and analogue methodologies in climate change vulnerability research. Wiley Interdisciplinary Reviews: Climate Change, 1(3), 374–392.  https://doi.org/10.1002/wcc.48.CrossRefGoogle Scholar
  13. Girardin, P., Bockstaller, C., & Werf, H. V. D. (1999). Indicators: Tools to evaluate the environmental impacts of farming systems. Journal of Sustainable Agriculture, 13(4), 5–21.  https://doi.org/10.1300/J064v13n04_03.CrossRefGoogle Scholar
  14. Hagerstrand, T. (1968). Innovation diffusion as a spatial process. Innovation diffusion as a spatial process. Google Scholar
  15. ILO. (2018b). World employment and social outlooktrend 2018. Retrieved from: http://www.ilo.org/wesodata/?chart = Z2VuZGVyPVsiVG90YWwiXSZ1bml0PSJOdW1iZXIiJnNlY3Rvcj1bIkluZHVzdHJ5IiwiU2VydmljZXMiLCJBZ3JpY3VsdHVyZSJdJnllYXJGcm9tPTE5OTEmaW5jb21lPVtdJmluZGljYXRvcj1bImVtcGxveW1lbnREaXN0cmlidXRpb24iXSZzdGF0dXM9WyJUb3RhbCJdJnJlZ2lvbj1bXSZjb3VudHJ5PVsiTmlnZXJpYSJdJnllYXJUbz0yMDE5JnZpZXdGb3JtYXQ9IlRhYmxlIiZhZ2U9WyJBZ2UxNXBsdXMiXSZsYW5ndWFnZT0iZW4iGoogle Scholar
  16. Intergovernmental Panel on Climate Change. (2007). Climate change 2007-impacts, adaptation and vulnerability: Working group II contribution to the fourth assessment report of the IPCC. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  17. IPCC. (2014). Summary for policymakers. In C. B. Field, V. R. Barros, D. J. Dokken, K. J. Mach, M. D. Mastrandrea, T. E. Bilir, M. Chatterjee, K. L. Ebi, Y. O. Estrada, R. C. Genova, B. Girma, E. S. Kissel, A. N. Levy, S. MacCracken, P. R. Mastrandrea, & L. L. White (Eds.), Climate change 2014: Impacts, adaptation, and vulnerability. Part A: Global and sectoral aspects. Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change (pp. 1–32). Cambridge, UK: Cambridge University Press.Google Scholar
  18. Jenks, G. F. (1967). The data model concept in statistical mapping. International Yearbook of Cartography, 7(1), 186–190.Google Scholar
  19. Jones, P. G., & Thornton, P. K. (2003). The potential impacts of climate change on maize production in Africa and Latin America in 2055. Global Environmental Change, 13(1), 51–59.  https://doi.org/10.1016/S0959-3780(02)00090-0.CrossRefGoogle Scholar
  20. Lawal, O. (2017). Mapping economic potential using spatial structure of age dependency and socio-economic factors. African Journal of Applied and Theoretical Economics, Special Edition (November), pp. 32–49.Google Scholar
  21. Lawal, O., & Arokoyu, S. B. (2015). Modelling social vulnerability in sub-Saharan West Africa using a geographical information system. Jàmbá: Journal of Disaster Risk Studies, 7(1), 11.  https://doi.org/10.4102/jamba.v7i1.155.CrossRefGoogle Scholar
  22. Linard, C., Gilbert, M., Snow, R. W., Noor, A. M., & Tatem, A. J. (2012). Population distribution, settlement patterns and accessibility across Africa in 2010. PLoS ONE, 7(2), e31743.  https://doi.org/10.1371/journal.pone.0031743.CrossRefGoogle Scholar
  23. Lobell, D. B., & Field, C. B. (2007). Global scale climate–crop yield relationships and the impacts of recent warming. Environmental Research Letters, 2(1), 014002.CrossRefGoogle Scholar
  24. McQueen, D., & Noack, H. (1988). Health promotion indicators: current status, issues and problems. Health Promotion International, 3(1), 117–125.  https://doi.org/10.1093/heapro/3.1.117.CrossRefGoogle Scholar
  25. Müller, C., Cramer, W., Hare, W. L., & Lotze-Campen, H. (2011). Climate change risks for African agriculture. Proceedings of the National Academy of Sciences of the United States of America, 108(11), 4313–4315.  https://doi.org/10.1073/pnas.1015078108.CrossRefGoogle Scholar
  26. NAERLS. (2017). Agricultural performance survey of 2017 Wet Season in Nigeria: National Report. Retrieved from Zaria: https://naerls.gov.ng/publications/#
  27. NAERLS. (2018). Agricultural performance survey of 2018 wet season in Nigeria: National Report. Retrieved from Zaria: https://naerls.gov.ng/publications/#
  28. Niang, I., Ruppel, O. C., Abdrabo, M. A., Essel, A., Lennard, C., Padgham, J., et al. (2014). Africa. In V. R. Barros, C. B. Field, D. J. Dokken, M. D. Mastrandrea, K. J. Mach, T. E. Bilir, M. Chatterjee, K. L. Ebi, Y. O. Estrada, R. C. Genova, B. Girma, E. S. Kissel, A. N. Levy, S. MacCracken, P. R. Mastrandrea, & L. L. White (Eds.), Climate change 2014: Impacts, adaptation, and vulnerability. Part B: Regional aspects. contribution of working group II to the fifth assessment report of the intergovernmental panel of climate change (pp. 1199–1265). Cambridge, UK: Cambridge University Press.Google Scholar
  29. Sherman, M., Ford, J., Llanos-Cuentas, A., Valdivia, M. J., & IHACC Research Group. (2016). Food system vulnerability amidst the extreme 2010–2011 flooding in the Peruvian Amazon: A case study from the Ucayali region. Food Security, 8(3), 551–570.  https://doi.org/10.1007/s12571-016-0583-9.CrossRefGoogle Scholar
  30. Shi, W., & Tao, F. (2014). Vulnerability of African maize yield to climate change and variability during 1961–2010. Food Security, 6(4), 471–481.  https://doi.org/10.1007/s12571-014-0370-4.CrossRefGoogle Scholar
  31. Tatem, A., Weiss, D., & Pezzulo, C. (2013). Pilot high resolution poverty maps (Publication No.  https://doi.org/10.5258/soton/wp00200). from University of Southampton and University of Oxford.
  32. The World Bank Group. (2015). Nigeria. Retrieved from http://www.worldbank.org/en/country/nigeria/overview
  33. The World Bank Group. (2016a). Annual population growth rate. Retrieved December 23, 2016, from The World Bank Group http://data.worldbank.org/indicator/SP.POP.GROW?contextual=aggregate&locations=NG
  34. The World Bank Group. (2016b). World Bank Development indicators: Nigeria. Retrieved December 23, 2016, from The World Bank Group http://data.worldbank.org/country/nigeria?view=chart
  35. Zhao, H., Kim, S., Suh, T., & Du, J. (2007). Social institutional explanations of global Internet diffusion: A cross-country analysis. Journal of Global Information Management (JGIM), 15(2), 28–55.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Geography and Environmental Management, Faculty of Social SciencesUniversity of Port HarcourtPort HarcourtNigeria
  2. 2.Department of Agriculture Economics and Extension, Faculty of AgricultureUniversity of Port HarcourtPort HarcourtNigeria

Personalised recommendations