Skip to main content

Crop yield gap and yield convergence in African countries

Abstract

Raising crop yield per unit land area remains a key to eliminating food shortages given limited land resources. However, Africa still falls far behind other continents in crop yield and there is major variation across regions within Africa. This paper analyzed regional yield gaps and yield convergence in Africa for four major crops: maize, millets, sorghum, and cassava. The club convergence test was employed to test whether yields are converging in Africa. Our results found no evidence of population convergence as a whole for any of the crops. However, we found that crop yields are converging into several clubs or groups of countries, implying successful technology diffusion and use within, but not between, specific regions. Furthermore, we predicted the attainable output for the four crops based on club convergence using the highest actual yield in each club as the attainable yield for all countries in the same club, and found that the gap between actual output and attainable output is narrowing gradually. Nevertheless, actual output could still increase by 70% if all countries reached the yield frontier, which we defined as the highest actual yield in each club. We suggest that policies aiming to end hunger in Africa should focus on eliminating barriers to technology diffusion and use of agricultural support mechanisms between countries, particularly those in the same club.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Notes

  1. If there is no convergence for the whole sample, it can be that countries converge into several different steady state levels, which are named as convergence clubs (Phillips and Sul 2007).

  2. The ranking is based only on the last fraction rather than whole period, that is because the yields in the last fraction are more similar to the long-run steady-state level. Therefore, regions with higher yields in the last fraction are more likely to be in the core group. Using the last third or half to rank the yield of all countries can significantly reduce the number of steps needed for identifying convergent clubs according to the simulation by Phillips and Sul (2007). Using different fractions for ranking will get the same results, but starting with the last fraction can shorten the time used for the convergence test.

  3. Output gap is defined as “The gap between attainable and actual output”.

References

  • Adjognon, S. G., Liverpool-Tasie, L. S. O., & Reardon, T. A. (2017). Agricultural input credit in Sub-Saharan Africa: telling myth from facts. Food Policy, 67, 93–105.

    PubMed  PubMed Central  Google Scholar 

  • Alexandratos, N., & Bruinsma, J. (2012). World agriculture towards 2030/2050: the 2012 revision. Rome: FAO.

    Google Scholar 

  • Apergis, N., Christou, C., & Miller, S. (2012). Convergence patterns in financial development: evidence from club convergence. Empirical Economics, 43(3), 1011–1040.

    Google Scholar 

  • Balmford, A., Green, R. E., & Scharlemann, J. P. W. (2005). Sparing land for nature: exploring the potential impact of changes in agricultural yield on the area needed for crop production. Global Change Biology, 11, 1594–1605.

    Google Scholar 

  • Barro, R. J., & Sala-i-Martin, X. (1992). Convergence. Journal of Political Economy, 100(2), 223–251.

    Google Scholar 

  • Becker, R., Enders, W., & Lee, J. (2006). A stationarity test in the presence of an unknown number of smooth breaks. Journal of Time Series Analysis, 27, 381–409.

    Google Scholar 

  • Benin, S., Nin Pratt, A., Wood, S. & Guo, Z. (2011). Trends and spatial patterns in agricultural productivity in Africa: 1961–2010. ReSAKSS Annual Trends and Outlook Report 2011. International Food Policy Research Institute (IFPRI).

  • Bernard, A. B., & Jones, C. I. (1996a). Productivity across industries and countries: Time series theory and evidence. Review of Economics and Statistics, 78, 135–146.

    Google Scholar 

  • Bernard, A. B., & Jones, C. I. (1996b). Productivity and convergence across US states and industries. Empirical Economics, 21, 113–135.

    Google Scholar 

  • Breitung, J., & Pesaran, H. (2008). Unit roots and cointegration in panels. In L. Matyas & P. Sevestre (Eds.), The Econometrics of Panel Data: Fundamentals and Recent Developments in Theory and Practice (pp. 279–322). Berlin: Springer.

    Google Scholar 

  • Brown, L. R. (2001). Eradicating hunger: a growing challenge. In In State of the World 2001. New York: World Watch Institute, Norton.

    Google Scholar 

  • Cistulli, V., Heikkila, M., & Vos, R. (2016). Global dimensions of malnutrition: territorial perspectives on food security and nutrition policies. In OECD (Ed.), OECD Regional Outlook 2016. Productive regions for inclusive societies (pp. 281–294). OECD Publishing: Paris.

    Google Scholar 

  • Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74, 574–599.

    Google Scholar 

  • Evans, P. (1998). Using panel data to evaluate growth theories. International Economic Review, 29(2), 249–265.

    Google Scholar 

  • Evans, L. T., & Fischer, R. A. (1999). Yield potential: Its definition, measurement, and significance. Crop Science, 39, 1544–1551.

    Google Scholar 

  • FAO. (2016). The State of Food and Agriculture 2016. Climate change, agriculture and food security. Rome: FAO.

    Google Scholar 

  • FAO. (2017). The future of food and agriculture-Trends and challenges. Rome: FAO.

    Google Scholar 

  • FAO, WFP, & IFAD. (2012). The State of Food Insecurity in the World 2012- Economic growth is necessary but not sufficient to accelerate reduction of hunger and malnutrition. Rome: FAO.

    Google Scholar 

  • FAO, IFAD, UNICEF, WFP, & WHO. (2017). The State of Food Security and Nutrition in the World 2017. Building resilience for peace and food security. Rome: FAO.

    Google Scholar 

  • Fermont, A. M., van Asten, P. J. A., Tittonell, P., van Wijk, M. T., & Giller, K. E. (2009). Closing the cassava yield gap: An analysis from smallholder farms in East Africa. Field Crops Research, 112(1), 24–36.

    Google Scholar 

  • Filmer, D., & Fox, L. (2014). Youth employment in sub-Saharan Africa. Washington, DC: World Bank.

    Google Scholar 

  • Fischer, R.A., Byerlee, D., & Edmeades, G.O. (2009). Can technology deliver on the yield challenge to 2050?. Paper prepared for the Expert Meeting on How to feed the World in 2050. Food and Agriculture Organization of the United Nations. Available at: http://www.fao.org/tempref/docrep/fao/012/ak977e/ak977e00.pdf. Access 26 June 2009

  • Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., et al. (2010). Food security: The challenge of feeding 9 billion people. Science, 327, 812–818.

    CAS  PubMed  Google Scholar 

  • Greene, W. H. (2002). Econometric analysis (5th ed.). Prentice Hall: Upper Saddle River, New Jersey.

    Google Scholar 

  • Holden, S. T., & Ghebru, H. (2016). Land tenure reforms, tenure security and food security in poor agrarian economies: causal linkages and research gaps. Global Food Security, 10, 21–28.

    Google Scholar 

  • Islam, N. (1995). Growth empirics: A panel data approach. Quarterly Journal of Economics, 110(4), 1127–1170.

    Google Scholar 

  • Lau, C. K. M. (2010). New evidence about regional income divergence in China. China Economic Review, 21(2), 293–309.

    Google Scholar 

  • Lichtenberg, F. R. (1994). Testing the convergence hypothesis. The Review of Economics and Statistics, 76(3), 576–579.

    Google Scholar 

  • Licker, R., Johnston, M., Foley, J., Barford, C., Kucharik, C. J., et al. (2010). Mind the gap: how do climate and agricultural management explain the ‘yield gap’ of croplands around the world? Global Ecology and Biogeography, 19(6), 769–782.

    Google Scholar 

  • Lynd, L. R., & Woods, J. (2011). A new hope for Africa. Nature, 474, 20–22.

    Google Scholar 

  • Mankiw, N. G., Romer, D., & Weil, D. N. (1992). A contribution to the empirics of economic growth. Quarterly Journal of Economics, 107, 407–437.

    Google Scholar 

  • Neumann, K., Verburg, P. H., Stehfest, E., & Müller, C. (2010). The yield gap of global grain production: Spatial analysis. Agricultural Systems, 103(5), 316–326.

    Google Scholar 

  • Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708.

    Google Scholar 

  • Nin-Pratt, A., Johnson, M., Magalhaes, E., You, L., Diao, X., et al. (2011). Yield Gaps and Potential Agricultural Growth in West and Central Africa. Washington D.C.: International Food Policy Research Institute.

    Google Scholar 

  • Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265–312.

    Google Scholar 

  • Phillips, P., & Sul, D. (2007). Transition modeling and econometric convergence tests. Econometrica, 75(6), 1771–1855.

    Google Scholar 

  • Quah, D. H. (1993). Galton’s fallacy and the convergence hypothesis. Scandinavian Journal of Economics, 95, 427–443.

    Google Scholar 

  • Sala-i-Martin, X. (1996). Regional cohesion: Evidence and theories of regional growth and convergence. European Review of Economic Review, 40, 1325–1352.

    Google Scholar 

  • Schwan, S., & Yu, X. (2018). Social protection as a strategy to address climate-induced migration. International Journal of Climate Change Strategies and Management, 10(1), 43–64.

    Google Scholar 

  • Tian, X., & Yu, X. (2015). Using semiparametric models to study nutrition improvement and dietary change with different indices: The case of China. Food Policy, 53, 67–81.

    Google Scholar 

  • Tian, X., Zhang, X., Zhou, Y., & Yu, X. (2016). Regional income inequality in China revisited: A perspective from club convergence. Economic Modelling, 56, 50–58.

    Google Scholar 

  • van Ittersum, M. K., Cassman, K. G., Grassini, P., Wolf, J., Tittonell, P., & Hochman, Z. (2013). Yield gap analysis with local to global relevance a review. Field Crops Research, 143, 4–17.

    Google Scholar 

  • von Braun, J. (2007). The world food situation. IFPRI Food Policy Report.

  • Yu, X., & Shimokawa, S. (2016). Nutrition impacts of rising food prices in African countries: A review. Food Security, 8(5), 985–997.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiaohua Yu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Tian, X., Yu, X. Crop yield gap and yield convergence in African countries. Food Sec. 11, 1305–1319 (2019). https://doi.org/10.1007/s12571-019-00972-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12571-019-00972-5

Keywords

  • Club convergence
  • Land productivity
  • Yield gap
  • Attainable output
  • Africa