Skip to main content

Advertisement

SpringerLink
Log in

Impacts of improved maize varieties in Nigeria: ex-post assessment of productivity and welfare outcomes

  • Original Paper
  • Published:
Food Security Aims and scope Submit manuscript

Abstract

Investment in agricultural research and development is an important intervention for improving crop productivity and household welfare in most developing countries where agriculture is the main source of livelihoods. This paper uses nationally representative plot- and household-level data from the major maize producing regions of Nigeria to assess the impacts of adoption of improved maize varieties on maize yield and household welfare outcomes. The paper employed an endogenous switching regression approach to control for both observed and unobserved sources of heterogeneity between adopters and non-adopters. Adoption of improved maize varieties increased maize grain yield by 574 kg/ha and per-capita total expenditure by US$ 77 (US$ 0.21/day). We found that the incidence of poverty among adopters would have been higher by 6% without adoption of the improved varieties. These findings underscore that investments and policy measures to increase and sustain the adoption of improved maize cultivars are critical for improving the productivity of maize in Nigeria and reducing poverty.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. In this paper productivity is defined as maize output per unit of land. Hence productivity and maize yield are used interchangeably. Adoption here refers to the use of improved maize varieties, not areas under improved maize varieties

  2. This value is based on the World Bank’s US$1.25 per day per capita.

  3. Note that improved maize varieties refer to OPVs, hybrids and drought tolerant varieties, while unimproved varieties are considered ‘landraces’

  4. Note that these results are associations or correlations, and not necessarily causal effects.

  5. Adoption in these states is low as the farmers there have less access to improved seed. On average they are located about 25 km away from the nearest seed dealer. This is high compared to the average distance, which is about 18 km.

  6. The counterfactual analysis shows the level of outcome (e.g., the maize yield) had the farmer that uses improved maize varieties chosen not to adopt improved maize varieties. In Table 3, the average yield of farmers who adopted improved maize varieties was 2337 kg/ha. If these farmers were non-adopters, their average maize yield would have been 1763 kg/ha (this is the counterfactual outcome). The difference between the two values is interpreted as the effect of adoption on maize yield.

  7. First stage estimates for net-returns are available from the authors upon request. Net-returns are calculated as maize income (revenue) minus production cost incurred for producing maize per ha.

  8. We also estimated effects by measuring adoption based on maize area under improved maize varieties (the intensification rate). For each farmer, intensification rate is calculated by estimating area under improved maize varieties. Since maize area was self-reported, we opted to use binary adoption rate in our main analysis. Using two-stage least square, we found that maize intensification decision affects productivity positively, the effect size being about 33%, which was significant at the 1% significance level.

  9. Incidence of poverty and poverty headcount ratio are used interchangeably

  10. Note that both PSM and IPWRA controls only for observed heterogeneity between adopters and non-adopters.

  11. This argument is based on the literature on “learning by doing”. Farmers learn through self-experimentation. The more they experiment, the better would be their knowledge about expected yield. Since new varieties require advanced and new knowledge, expectations about yield would be more precise with local varieties, where farmers have conducted sufficient self-experimentation.

References

  • Abate, T., Shiferaw, B., Menkir, A., Wegary, D., Kebede, Y., Tesfaye, K., & Keno, T. (2015). Factors that transformed maize productivity in Ethiopia. Food Security, 7(5), 965–981.

    Article  Google Scholar 

  • Abate, T., Fisher, M., Abdoulaye, T., Kassie, G. T., Lunduka, R., Marenya, P., & Asnake, W. (2017). Characteristics of maize cultivars in Africa: how modern are they and how many do smallholder farmers grow? Agriculture and Food Security, 6, 30.

    Article  Google Scholar 

  • Abdoulaye, T., & Sanders, J. H. (2005). Stages and determinants of fertilizer use in semiarid African agriculture: the Niger experience. Agricultural Economics, 32, 167−179.

    Article  Google Scholar 

  • Alene, A. D. (2010). Productivity growth and the effects of R&D in African agriculture. Agricultural Economics, 41, 223–238.

    Article  Google Scholar 

  • Alene, A. D., & Manyong, V. M. (2007). The effects of education on agricultural productivity under traditional and improved technology in northern Nigeria: an endogenous switching regression analysis. Empirical Economics, 32(1), 141–159.

    Article  Google Scholar 

  • Alene, A. D., Manyong, V. M., Tollens, E., & Abele, S. (2007). Targeting agricultural research based on potential impacts on poverty reduction: strategic program priorities by agro-ecological zone in Nigeria. Food Policy, 32(3), 394–412.

    Article  Google Scholar 

  • Amare, M., Asfaw, S., & Shiferaw, B. (2012). Welfare impacts of maize-pigeonpea intensification in Tanzania. Agricultural Economics, 43(1), 1–17.

    Article  Google Scholar 

  • Asfaw, S., Kassie, M., Simtowe, F., & Leslie, L. (2012). Poverty reduction effects of agricultural technology adoption: a micro-evidence from rural Tanzania. Journal of Development Studies, 48(9), 1288–1305.

    Article  Google Scholar 

  • Bezu, S., Kassie, G., Shiferaw, B., & Gilber, J. R. (2014). Impact of improved maize adoption on welfare of farm households in Malawi: a panel data analysis. World Development, 59, 120–131.

    Article  Google Scholar 

  • Bold, T., Kaizzi, K. C., Svensson, J., & Yanagizawa-Drott, D. (2017). Lemon technologies and adoption: measurement, theory and evidence from agricultural markets in Uganda. Quarterly Journal of Economics, 132(3), 1055–1100.

    Article  Google Scholar 

  • Dercon, S., & Christiaensen, L. (2011). Consumption risk, technology adoption and poverty traps: evidence from Ethiopia. Journal of Development Economics, 96(2), 159–173.

    Article  Google Scholar 

  • Dorosh, P., & Thurlow, J. (2018). Beyond agriculture versus non-agriculture: decomposing sectoral growth–poverty linkages in five African countries. World Development. https://doi.org/10.1016/j.worlddev.2016.08.014.

  • FAOSTAT. (2016). Statistical database. Retrieved on 19 August 2016 from: http://faostat3.fao.org/browse/Q/*/E.

  • Foster, J., Greer, J., & Thorbecke, E. (1984). A class of decomposable poverty measures. Econometrica, 52(3), 761–765.

    Article  Google Scholar 

  • Ilukor, J. Kilic, T., Stevenson, J., Gourlay, S., Kosmowski, F., Kilian, A., Serumaga, J., & Asea, G. (2017). Blowing in the wind: the quest for accurate crop variety identification in field research, with an application to maize in Uganda. Paper presented at the CGIAR SPIA Conference on the Impact of Agricultural research: Rigorous Evidence for Policy, Nairobi, July 2017.

  • Karanja, D. D., Renkow, M., & Crawford, E. W. (2003). Welfare effects of maize technologies in marginal and high potential regions of Kenya. Agricultural Economics, 29, 331–341.

    Article  Google Scholar 

  • Kassie, M., Shiferaw, B., & Muricho, G. (2011). Agricultural technology, crop income, and poverty alleviation in Uganda. World Development, 39(10), 1784–1795.

    Article  Google Scholar 

  • Kostandini, G., La Rovere, R., & Abdoulaye, T. (2013). Potential impacts of increasing average yields and reducing maize yield variability in Africa. Food Policy, 43, 213–226.

    Article  Google Scholar 

  • Lokshin, M., & Sajaia, Z. (2004). Maximum likelihood estimation of endogenous switching regression models. Stata Journal, 4, 282–289.

    Google Scholar 

  • Maddalla, G. S. (1983). Limited dependent and qualitative variables in econometrics. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Magrini, E., & Vigani, M. (2016). Technology adoption and the multiple dimensions of food security: the case of maize in Tanzania. Food Security, 8(4), 707–726.

    Article  Google Scholar 

  • Mason, N. M., & Smale, M. (2013). Impacts of subsidized hybrid seed on indicators of economic well-being among smallholder maize growers in Zambia. Agricultural Economics, 44, 659–670.

    Article  Google Scholar 

  • NACGRAB (National Centre for Genetic Resources and Biotechnology). (2016). Varieties released catalogue. Retrieved on 15 August 2016 from http://www.nacgrab.gov.ng/images/Varieties_Released_Catalogue.pdf.

  • NBSN (National Bureau of Statistics Nigeria). (2010). Nigeria poverty profile 2010. Retrieved on 15 August 2016 from: http://www.nigerianstat.gov.ng/pdfuploads/Nigeria%20Poverty%20Profile%202010.pdf.

  • Rosenzweig, M. R. (2010). Microeconomic approaches to development: schooling, learning, and growth. Journal of Economic Perspectives, 24(3), 81–96.

    Article  Google Scholar 

  • Shiferaw, B., Kebede, T. A., & You, Z. (2008). Technology adoption under seed access constraints and the economic impacts of improved pigeonpea varieties in Tanzania. Agricultural Economics, 39, 1–15.

    Article  Google Scholar 

  • Shiferaw, B., Kassie, M., Jaleta, M., & Yirga, C. (2014). Adoption of improved wheat varieties and impacts on household food security in Ethiopia. Food Policy, 44, 272–284.

    Article  Google Scholar 

  • Suri, T. (2011). Selection and comparative advantage in technology adoption. Econometrica, 79(1), 159–209.

    Article  Google Scholar 

  • Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. USA: The MIT Press.

    Google Scholar 

  • Wossen, T., Berger, T., & Di Falco, S. (2015). Social capital, risk preference and adoption of improved farm land management practices in Ethiopia. Agricultural Economics, 46(1), 81–97.

    Article  Google Scholar 

  • Wossen, T., Abdoulaye, T., Alene, A., Feleke, S., Gilbert, J. R., Manyong, V., & Awotide, B. A. (2017a). Productivity and welfare effects of Nigeria's e-voucher-based input subsidy program. World Development, 97, 251–265.

    Article  PubMed  PubMed Central  Google Scholar 

  • Wossen, T., Abdoulaye, T., Alene, A., Feleke, S., Menkir, A., & Manyong, V. (2017b). Measuring the impacts of adaptation strategies to drought stress: the case of drought tolerant maize varieties. Journal of Environmental Management, 203, 106–113.

  • Wossen, T., Abdoulaye, T., Alene, A., Nguimkeu, P., Feleke, S., Haile, M., Rabbi, I., & Manyong, V. (2018). Measuring the productivity impacts of technology adoption in the presence of misclassification. Forthcoming, American Journal of Agricultural Economics.

  • Zeng, D., Alwang, J., Norton, G. W., Shiferaw, B., Jaleta, M., & Yirga, C. (2015). Ex post impacts of improved maize varieties on poverty in rural Ethiopia. Agricultural Economics, 46(4), 515–526.

    Article  Google Scholar 

Download references

Acknowledgment

This work was partly funded through the CGIAR Research Program on Maize (Maize-CRP) and a CIMMYT and IITA project, Drought Tolerant Maize for Africa (DTMA).

Author information

Authors and Affiliations

  1. International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria

    Tahirou Abdoulaye

  2. IITA, Abuja, Nigeria

    Tesfamicheal Wossen

  3. World Vegetable Center, Bamako, Mali

    Bola Awotide

Authors
  1. Tahirou Abdoulaye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tesfamicheal Wossen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bola Awotide
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tesfamicheal Wossen.

Ethics declarations

Conflict of interest

The authors declared that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abdoulaye, T., Wossen, T. & Awotide, B. Impacts of improved maize varieties in Nigeria: ex-post assessment of productivity and welfare outcomes. Food Sec. 10, 369–379 (2018). https://doi.org/10.1007/s12571-018-0772-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12571-018-0772-9

Keywords

Search

Navigation