Production Structure, Yield Dynamics, and Technical Efficiency of Ginger Farming Systems in Sierra Leone
DOI:
https://doi.org/10.14738/ejas.1403.11887Keywords:
Ginger production, technical efficiency, smallholder farmers, yield dynamics, agriculture systemsAbstract
Ginger production plays an increasingly important role in rural livelihoods in Sierra Leone, yet empirical evidence on its structural characteristics, productivity dynamics, and efficiency performance remains limited. This study examines the production structure, yield determinants, spatial productivity differences, and technical efficiency of ginger farming systems in two major producing districts Kenema and Kailahun. Data were collected from 160 smallholder farmers using a structured questionnaire and analyzed using descriptive statistics, chi-square tests, t-tests, multiple regression, and efficiency analysis. Results indicate a predominantly smallholder system characterized by low educational attainment and small farm sizes averaging one acre, with balanced gender participation. Yield analysis reveals significant spatial variation, with Njaluahun Chiefdom outperforming Nongowa Chiefdom (t = -5.21, p < 0.001). Regression results show that education, household labor, organic input use, and access to extension services significantly influence productivity. Efficiency analysis further indicates that only 11.2% of farmers operate at full efficiency, while the majority remain below optimal performance, reflecting substantial productivity gaps. Overall, the findings demonstrate that ginger production is constrained more by inefficiencies in resource use and weak knowledge systems than by resource scarcity. Strengthening extension services and promoting efficient input use are therefore critical for unlocking productivity gains and improving smallholder livelihoods.
References
Food and Agriculture Organization of the United Nations. (2022). FAOSTAT statistical database. FAO. https://www.fao.org/faostat
Food and Agriculture Organization of the United Nations. (2019). Smallholders and family farmers. FAO. https://www.fao.org
World Bank. (2020). Agriculture and food systems in Sub-Saharan Africa: Transforming the sector for inclusive growth. World Bank Group. https://www.worldbank.org
Battese, G. E., & Coelli, T. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function. Empirical Economics, 20(2), 325–332. https://doi.org/10.1007/BF01205442
Bravo-Ureta, B. E., Solís, D., Moreira López, V. H., Maripani, J. F., Thiam, A., & Rivas, T. (2007). Technical efficiency in farming: A meta-regression analysis. Journal of Productivity Analysis, 27(1), 57–72. https://doi.org/10.1007/s11123-006-0025-3
Weir, S., & Knight, J. (2000). Education externalities and agricultural productivity: Evidence from rural Ethiopia. Journal of Development Studies, 36(2), 1–24. https://doi.org/10.1080/00220380008422628
Anderson, J. R., & Feder, G. (2007). Agricultural extension. In Handbook of Agricultural Economics (Vol. 3, pp. 2343–2378). Elsevier. https://doi.org/10.1016/S1574-0072(06)03044-1
Lal, R. (2015). Restoring soil quality to mitigate soil degradation. Sustainability, 7(5), 5875–5895. https://doi.org/10.3390/su7055875 Scholar Publishing
Doss, C. R. (2001). Designing agricultural technology for African women farmers: Lessons from 25 years of experience. World Development, 29(12), 2075–2092. https://doi.org/10.1016/S0305-750X(01)00088-2
Tittonell, P., & Giller, K. E. (2013). When yield gaps are poverty traps: The paradigm of ecological intensification in African smallholder agriculture. Field Crops Research, 143, 76–90. https://doi.org/10.1016/j.fcr.2012.10.007
Nweke, F. I., Spencer, D. S. C., & Lynam, J. K. (2002). The cassava transformation in Africa. International Food Policy Research Institute.
Schultz, T. W. (1964). Transforming traditional agriculture. Yale University Press.
Otsuka, K., & Place, F. (2014). Rural poverty and income dynamics in Asia and Africa. Routledge.
Barrett, C. B., Bellemare, M. F., & Hou, J. Y. (2010). Reconsidering conventional explanations of the inverse productivity–size relationship. World Development, 38(1), 88–97. https://doi.org/10.1016/j.worlddev.2009.06.002
Kumbhakar, S. C., & Lovell, C. A. K. (2000). Stochastic frontier analysis. Cambridge University Press.
Awotide, B. A., Karimov, A. A., & Diagne, A. (2023). Agricultural technology adoption and productivity efficiency in Sub-Saharan Africa: Evidence from smallholder farmers. World Development, 162, 106123. https://doi.org/10.1016/j.worlddev.2022.106123
Mignouna, D. B., Saidou, A., & Manyong, V. M. (2022). Agricultural intensification and productivity in Sub-Saharan Africa: The role of improved inputs. Food Policy, 112, 102365. https://doi.org/10.1016/j.foodpol.2022.102365
Aye, G. C., & Mungatana, E. D. (2022). Farm-level efficiency and productivity in African agriculture: A stochastic frontier analysis. Agricultural Economics, 53(4), 621–636. https://doi.org/10.1111/agec.12701
Ogundari, K., & Awokuse, T. (2022). Productivity and efficiency in African agriculture: A meta-analysis of stochastic frontier studies. World Development Perspectives, 26, 100414. https://doi.org/10.1016/j.wdp.2022.100414
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 European Journal of Applied Sciences

This work is licensed under a Creative Commons Attribution 4.0 International License.