Analysis of Economic Efficiency and its Determinants in Millet Based Production Systems in the Derived Savanna Zone of Nigeria

Millet is grown in the large savanna region of Nigeria mostly in a system of intercropping with other crops. The study seeks to analyse the efficiencies of millet-based production pattern and its determining factors in the derived savanna zone of Nigeria. Data were collected from primary sources using a structured questionnaire administered to the selected 196 millet-based farmers. Input oriented Data Envelope Analysis (DEA) and Tobit regression model were used to achieve the aims of the research. The mean Technical Efficiency (TE) of the millet and sorghum (MS), millet-sorghum-groundnut and cowpea (MSGC), millet-sorghum and groundnut (MSG), millet-sorghum and cowpea (MSC) and sole millet (SM) were 40, 21, 38, 32 and 48 % respectively. This suggests that in the short run, there are gaps of 60, 79, 62, 68 and 52 % to increase the efficiency levels respectively. This may be through enhanced use of accessible production inputs. The mean Allocative Efficiency (AE) for the millet-based farmers was 0.56, 0.55, 0.67, 0.56 and 0.91 for MS, MSGC, MSG, MSC and SM respectively. The results revealed that estimates of factors that influence millet-based farmers’ systems have different degrees of statistical significance and where the level of significance is the same, the magnitude and direction were not the same. The numbers of millet-based farms operating under constant, increasing, and decreasing returns to scale were also estimated. The result of sensitivity analysis for an optimum plan for millet-based inputs used showed that land, seed, labour, fertilizer and agrochemicals are not limiting resources to obtain optimal farm plan. These results indicate the units needed to be decreased from various millet farms respectively for optimal production. More youths should be encouraged by the government and private organizations by providing them with necessary incentives to engage in farming to minimize inefficiency associated with older aged farmers.

Analysis of Economic Efficiency and its Determinants in Millet Based Production Systems in the Derived Savanna Zone of Nigeria

Millet is grown in the large savanna region of Nigeria mostly in a system of intercropping with other crops. The study seeks to analyse the efficiencies of millet-based production pattern and its determining factors in the derived savanna zone of Nigeria. Data were collected from primary sources using a structured questionnaire administered to the selected 196 millet-based farmers. Input oriented Data Envelope Analysis (DEA) and Tobit regression model were used to achieve the aims of the research. The mean Technical Efficiency (TE) of the millet and sorghum (MS), millet-sorghum-groundnut and cowpea (MSGC), millet-sorghum and groundnut (MSG), millet-sorghum and cowpea (MSC) and sole millet (SM) were 40, 21, 38, 32 and 48 % respectively. This suggests that in the short run, there are gaps of 60, 79, 62, 68 and 52 % to increase the efficiency levels respectively. This may be through enhanced use of accessible production inputs. The mean Allocative Efficiency (AE) for the millet-based farmers was 0.56, 0.55, 0.67, 0.56 and 0.91 for MS, MSGC, MSG, MSC and SM respectively. The results revealed that estimates of factors that influence millet-based farmers’ systems have different degrees of statistical significance and where the level of significance is the same, the magnitude and direction were not the same. The numbers of millet-based farms operating under constant, increasing, and decreasing returns to scale were also estimated. The result of sensitivity analysis for an optimum plan for millet-based inputs used showed that land, seed, labour, fertilizer and agrochemicals are not limiting resources to obtain optimal farm plan. These results indicate the units needed to be decreased from various millet farms respectively for optimal production. More youths should be encouraged by the government and private organizations by providing them with necessary incentives to engage in farming to minimize inefficiency associated with older aged farmers.

___

  • Abdulrahman, S. & Yusuf, H. O. (2018). Sensitivity analysis and efficiency of cocoyam farmers in Kaduna State: An application of data envelopment approach. Journal of Agriculture and Environment, 14(2), 41 – 54.
  • Abubakar, A. D. (2014). Economic analysis of millet-based cropping systems in Bindawa and Charanchi local government area, Katsina State, Nigeria. M.Sc. dissertation. Faculty of Agriculture, Department of Agricultural Economics, Ahmadu Bello University, Zaria, Nigeria. 89pp.
  • Adebayo, E. F., Mohammed, A. N. & Mshelia, S. I. (2008). Economic analysis of millet production in Famawa local government area of Bauchi State, Nigeria. Nigerian Journal of Rural Sociology, 8(1), 66 – 75
  • Akpan, S. B., Okon, U. E., Jeiyol, E. N., Nkeme, K. K. & John, D. E. (2013). Economic efficiency of cassava based farmers in southern wetland region of Cross River State, Nigeria: A translog model approach. International Journal of Humanities and Social Science, 3(12), 173 – 181. Amos, T. T. (2007). An analysis of productivity and technical efficiency of small-holder cocoa farmers in Nigeria. Journal of Social Science, 15(2), 127 – 133.
  • Asogwa, B. C., Umeh. J. C. & Penda, S. T. (2011). Analysis of economic efficiency of Nigerian small scale farmers: A parametric frontier approach. Journal of Economics, 2(2), 89 – 98.
  • Banker, R. D., Charnes, A. & Cooper, W. W. (1984). Some models of estimating technical and scale efficiencies in data envelopment analysis. Management Science, 30(9), 1078 – 1092.
  • Bashir, A. B. & Yakaka, B. M. (2013). Marketing margin and transaction cost in pearl millet market supply in Borno State, Nigeria. Green Journal of Business and Management studies, 3(5), 201 – 206.
  • Charnes, A., Cooper, W. & Rhodes, E. (1978). Measuring the efficiency of decision-making units, European Journal of Operational Research, 2(1), 249 – 444.
  • Clark, C. & Haswell, M. R. (1970). The economics of subsistence agriculture. Palgrave Macmillan publishers, 4th edition. 284pp.
  • Coker, A. A. A., Ibrahim, F. D. & Ibeziako, U. N. (2018). Effect of household demographics on the technical efficiency of cowpea farmers: Evidence from stochastic frontier analysis in Nigeria. Rjoas, 1(73), 179 – 186.
  • Cooper, W. W., Seiford, L. M. & Tone, K. (2006). Introduction to DEA and its uses with DEA-solver software and references. New York: Springer.
  • Etonihu, K. I., Rahman, S. A. & Usman, S. (2013). Determinants of access to agricultural credit among crop farmers in a farming community of Nasarawa State, Nigeria. Journal of Development and Agricultural Economics, 5(5), 192 – 196.
  • Fakayode, S. B., Babatunde, R. O. & Ajao, R. (2008). Productivity analysis of cassava-based production systems in the Guinea savannah: Case study of Kwara State, Nigeria. American-Eurasian Journal of Scientific Research, 3(1), 33 – 39.
  • FAOSTAT, (2018). Food and agriculture organization statistical data base [http://faostat.fao.org/] site visited on 01/04/2018.
  • Greene, W. (2000). Econometric analysis textbook, 4th Edition. Published by New York University Press. Idi, A. S., Damisa, M. A., Edekhegregor, O. I. & Oladimeji, Y. U. (2019). Determinants of Household Food Security among Maize Farmers’ Utilizing Micro-Credit in Kaduna State, Nigeria. Dutse Journal of Agriculture and Food Science, 6(1): 59-68.
  • Iheke, O. R. & Onyendi, C. O. (2017). Economic efficiency and food security status of rural farm households in Abia State of Nigeria. American Journal of Food Science and Nutrition, 4(5), 52 - 58. Iheanacho, A. C. (2000). Economics of millet production under different cropping systems in Borno State, Nigeria. A PhD thesis Ahmadu Bello University, Zaria, Nigeria 134pp.
  • Katsina State Agricultural and Rural Development Authority, (KTARDA), 2019 Bulletin.
  • Mukhtar, U., Mohamed, Z., Shamsuddin, M. N., Sharifuddin, J. & Bala, M. (2018). Econometric analysis of technical efficiency of pearl millet farmers in Kano State, Nigeria. Sarhad Journal of Agriculture, 4(1), 965 – 978.
  • National Bureau of Statistic NBS, (2019). Facts and figures about Nigeria. National Bureau of Statistics, Abuja, Nigeria.
  • National Population Commission NPC, (2006). Provisional 2006 Nigeria census figures.
  • Obasi, P. C., Henri-Ukoha, A., Ukewuihe, I. S. & Chidiebere-Mark, M. N. (2013). Factors affecting agricultural productivity among arable crop farmers in Imo State, Nigeria. American Journal of Experimental Agriculture, 3(2), 443 – 454.
  • Odoh, N. E. & Nwibo, S. U. (2017). Socio-economic determinants of rural non-farm households’ income diversification in Southeast Nigeria. International Research Journal of Finance and Economics, 164(1), 1450-2887.
  • Okech, S. O., Ngigi, M. W. & Kimurto, P. K. (2015). Analysis of performance and efficiency of pearl millet (Pennisetum glaucum) market value chain: A case of Mbeere district, Kenya. Research paper, 1 – 24.
  • Okoye, B. C., Okoye, A. C., Dimelu, M. U., Agbaeze, C. C., Okoroafor, O. N. & Amaefula, A. B. (2009). Adoption scale analysis improved cocoyam production processing and storage technologies in Enugu-North agricultural zone of Enugu State, Nigeria. American International Journal of Contemporary Research, 4(6), 619 - 630.
  • Oladimeji, Y. U. & Abdulsalam, Z. (2017). Efficiency of Watermelon (Citrullus lanatus Thunb.) Production Technologies in North Central Nigeria. FUOYE Journal of Engineering and Technology, 2(2): 29-32.
  • Oluwatusin, F. & Shittu, G. (2014). Effect of socio-economic characteristics on the farm productivity performance of yam farmers in Nigeria. Research on Humanities and Social Sciences, 4(6), 31 – 37.
  • Rahman, S. A. (2013). Farm production efficiency: The scale of success in agriculture. 4th inaugural lecture. Nasarawa State University, Keffi- Nigeria.
  • Yakubu, A., Oladimeji, Y. U. & Hassan, A. A. (2019). Technical Efficiency of Maize Farmers in Kano State of Nigeria Using: A Data Envelopment Analysis Approach. Ethiopian Journal of Environmental Studies & Management, 12(2): 136 - 147.