Impacts of improved maize varieties in Nigeria: ex-post assessment of productivity and welfare outcomes
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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.
KeywordsAdoption Improved maize varieties Nigeria Productivity Poverty
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).
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Conflict of interest
The authors declared that they have no conflict of interest.
- 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.
- 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.Google Scholar
- Lokshin, M., & Sajaia, Z. (2004). Maximum likelihood estimation of endogenous switching regression models. Stata Journal, 4, 282–289.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.
- Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. USA: The MIT Press.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.Google Scholar
- 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. Google Scholar