Small-scale Farming, Agricultural Productivity and Poverty Reduction in Nigeria: The Enabling Role of Agricultural Technology Adoption

Main Article Content

Ojo Olusayo
Ogunniyi Adebayo
Salman K. Kayode
Kehinde Olagunju
Iwinlade Ayodeji
Adeyemi A. Ogundipe

Abstract

Existing literature affirms the importance of agricultural technology adoption on productivity, income and livelihood outcomes. Evidences subsist on the adoption of improved cassava varieties (ICVs) in Nigeria but little is known about its impact among the farmers. We used data from a survey conducted by International Institute of Tropical Agriculture (IITA) to explore this research gap.  Propensity Score Matching and Heckman’s two-stage model were the analytical tools.  Given an estimated poverty line of (₦21717.53); 52.0% of the farmers were poor.  We found that 75.6% of the respondents are adopters of ICVs. Primary occupation of household head and total non-production asset of farmers were key determinants for adoption.  Adoption of improved cassava variety has positive effect on farmers’ productivity and poverty reduction. The Average Treatment Effect on the Treated (ATT) for productivity increased by 70 percent among ICVs farmers. Income was also higher among the adopters than the non- adopters by ₦43463.77. In the same vein, the income of the adopters increased by 17%. Furthermore, adopters of ICVs have the probability of reducing poverty headcount by 20%. The empirical results suggest that improved agricultural innovation adoption can play a key role in strengthening and impacting agricultural productivity of smallholder farmers for increased income generation and food security.

Keywords:
Adoption, productivity, Nigeria

Article Details

How to Cite
Olusayo, O., Adebayo, O., Kayode, S. K., Olagunju, K., Ayodeji, I., & Ogundipe, A. A. (2019). Small-scale Farming, Agricultural Productivity and Poverty Reduction in Nigeria: The Enabling Role of Agricultural Technology Adoption. Journal of Agriculture and Ecology Research International, 19(1), 1-15. https://doi.org/10.9734/jaeri/2019/v19i130074
Section
Original Research Article

References

National Bureau of Statistics (NBS). Nigeria, Abuja National Population Census (NPC). National Bureau of Statistics Official Gazatte (FGP 71/52007/2,500 (OL24) Abuja; 2006.
Available:http://www.nigerianstat.gov.ng

Omonona BT. Poverty and its correlates among rural farming households in Kogi State, Nigeria. Unpublished Ph.D. Thesis, University of Ibadan, Ibadan. Nigeria; 2001.

Oyekale AS, Adepoju AO, Balogun AM. Determinants of poverty among riverine rural households in Ogun State, Nigeria. Studies of Tribes and Tribals. 2012;10(2): 99–105.

Igbalajobi O, Fatuase AI, Ajibefun I. Determinants of poverty incidence among rural farmers in Ondo State, Nigeria. American Journal of Rural Development. 2013;1(5):131–137.

Foster JJ. Greer, Thorbecke E. A class of decomposable poverty measures Econometrica. 1984;52:761–766.

Cochran W, Rubin D. Controlling bias in observational studies. Sankhyā: The Indian Journal of Statistics, Series A (1961-2002). 1973;35:417–446.

Bassi L. Estimating the effects of training programs with nonrandom selection. The Review of Economics and Statistics. 1984;66:36–43.

Rosenbaum PR, Rubin DB. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. American Statistician. 1985;39(1):33–38.

Friedlander D, Greenberg DH, Robins PK. Evaluating government training programs for the economically disadvantaged. Journal of Economic Literature. 1997; XXXV:1809–1855.

Heckman J, Navarro-Lozano S. Using matching, instrumental variables and control functions to estimate economic choice models. Review of Economics and Statistics. 2004;86(1):30–57.

Becerril J, Abdulai A. The impact of improved maize varieties on poverty in Mexico: A propensity score marching approach. World Development. 2009; 38(7):1024–1035.

Mendola M. Agricultural technology adoption and poverty reduction: A propensity-score matching analysis for rural Bangladesh. Food Policy. 2007;32(3): 372–93.