Geospatial analysis of Maize yield vulnerability to climate change in Nigeria
- 14 Downloads
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.
KeywordsMaize yield Yield vulnerability Adaptive capacity Climate change Yield sensitivity
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.
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.
- 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
- BNRCC. (2011). National adaptation strategy and plan of action on climate change for Nigeria. Retrieved from Ibadan.Google Scholar
- 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
- Central Intelligence Agency. (2015). Nigeria: The World Factbook. Retrieved from https://www.cia.gov/library/publications/the-world-factbook/geos/ni.html.
- ESRI. (2017). ArcGIS desktop (version 10.6). Redlands, CA: Environmental Systems Research Institute.Google Scholar
- 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
- 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
- Hagerstrand, T. (1968). Innovation diffusion as a spatial process. Innovation diffusion as a spatial process. Google Scholar
- ILO. (2018a). Employment by sector—ILO modelled estimates, November 2017. Retrieved from: http://www.ilo.org/ilostat/faces/oracle/webcenter/portalapp/pagehierarchy/Page3.jspx?MBI_ID=33&_adf.ctrl-state=15hlydqmj3_62&_afrLoop=395783025779298&_afrWindowMode=0&_afrWindowId=15hlydqmj3_59#!
- ILO. (2018b). World employment and social outlook—trend 2018. Retrieved from: http://www.ilo.org/wesodata/?chart = Z2VuZGVyPVsiVG90YWwiXSZ1bml0PSJOdW1iZXIiJnNlY3Rvcj1bIkluZHVzdHJ5IiwiU2VydmljZXMiLCJBZ3JpY3VsdHVyZSJdJnllYXJGcm9tPTE5OTEmaW5jb21lPVtdJmluZGljYXRvcj1bImVtcGxveW1lbnREaXN0cmlidXRpb24iXSZzdGF0dXM9WyJUb3RhbCJdJnJlZ2lvbj1bXSZjb3VudHJ5PVsiTmlnZXJpYSJdJnllYXJUbz0yMDE5JnZpZXdGb3JtYXQ9IlRhYmxlIiZhZ2U9WyJBZ2UxNXBsdXMiXSZsYW5ndWFnZT0iZW4iGoogle Scholar
- 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
- Jenks, G. F. (1967). The data model concept in statistical mapping. International Yearbook of Cartography, 7(1), 186–190.Google Scholar
- 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
- NAERLS. (2017). Agricultural performance survey of 2017 Wet Season in Nigeria: National Report. Retrieved from Zaria: https://naerls.gov.ng/publications/#
- NAERLS. (2018). Agricultural performance survey of 2018 wet season in Nigeria: National Report. Retrieved from Zaria: https://naerls.gov.ng/publications/#
- 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
- 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
- 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.
- The World Bank Group. (2015). Nigeria. Retrieved from http://www.worldbank.org/en/country/nigeria/overview
- 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
- 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