Article Sidebar

Download
View Full Article XML
Statistic
Read Counter : 634 Download : 173

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Main Article Content

Abstract

The objective is to analyze the stochastic frontier function in comparing the performance of the Cobb-Douglas and Translog Frontier functions in the cultivation of cassava in the Bono Region of Ghana. The assumption is that given the same level of productive inputs of farmers at any given farming season, across heterogeneous farmlands, both functions are likely to produce the same results. The interview was used as the instrument for obtaining plot-specific data from 120 cassava farmers across six districts and data was analyzed using the quantitative technique. Direct predictors of output include plot size, labor, hoes, cutlasses, and cassava stems. Using a half-normal distributional assumption, the study evaluates variance parameters of the composed error terms. The results showed that the estimated functions produced comparable results in terms of magnitude and signs of input variables. While efficiency appeared to be much higher in Cobb-Douglas than in the Translog function, the variance parameter score for the CD function is significantly different from those of the Translog function, and the maximum output attainable for the given productive inputs was 40% and 15% respectively. This means that farmers can scale up their current crop yield by 60% and 85% respectively of their frontier functions using the same inputs and technology if the appropriate interventions are carried out. The limitation of the study is the non-inclusion of environmental factors such as rain as productive input and the study is limited to comparing frontier functions. The results underscored the importance of examining the current production behavior of farmers for reliability and policy inferences

Keywords

Stochastic frontier function cobb-douglas translog cross-sectional data cassava

Article Details

Creative Commons License

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

How to Cite
Awuma , W., Samuel, O., & Alex, A.-A. (2022). Cassava: Farmers Adoption and Livelihood in Bono Region Performance of Stochastic Frontier Functions. International Journal of Multidisciplinary Studies and Innovative Research, 10(2), 1531–1537. https://doi.org/10.53075/Ijmsirq/656357565465
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Aigner, D. J., Lovell, C. A. K., & Schmidt, P. (1977), “Formulation and Estimation of Stochastic Frontier
  2. Production Function Models” Journal of Econometrics, 6, 21–37.
  3. Ajibefun, I. A., & Aderinola, E. A. (2004). Determinants of technical efficiency and policy implication in
  4. traditional agricultural production: Empirical study of Nigerian food crop farmers. Final Report Presentation at Bi-annual Research Workshop of African Economic Research Consortium, Nairobi, Kenya.
  5. Ajibefun, I. A., & Daramola, A. G. (2003), “Efficiency of Micro Enterprises in the Nigerian Economy.
  6. AERC Research paper 134. African Economic Research Consortium, Nairobi, 7, 1-42.
  7. Ali, M., & Chaudhry, M. A. (1990), “Inter-regional Farm Efficiency in Pakistan’s Punjab: A Frontier
  8. Production Function Study,” Journal of Agricultural Economics, 41, 62–74.
  9. Battese, G. E., & Coelli, T. J. (1995), “A Model for Technical Inefficiency Effects in a Stochastic Frontier
  10. Production Function for Panel Data,” Empirical Economics, 20, 325–32
  11. Battese, G. E., & Coelli, T. J. (1992), “Frontier Production Functions, Technical Efficiency and Panel
  12. Data: With Application to Paddy Farmers in India,” Journal of Productivity Analysis, 3, 153–69
  13. Battese, G. E., Rao, D.S.P., and O’Donnell, C.J. (2004), “A Metafrontier Production Frontier for
  14. Estimation of Technical Efficiencies and Technology Gaps for Firms Operating under Different Technologies,” Journal of Productivity Analysis, 21, 91-103
  15. Christensen, L.R., Jorgenson, D.W., and Lau, L.J. (1971),” Conjugate Duality and Transcendental
  16. Logarithmic Function” Econometrica, 39, 255-6
  17. Coelli, T. J., Rao, D. S. P., & Battese, G. (1998), “An Introduction to Efficiency and Productivity
  18. Analysis,” Boston, MA: Kluwer Academic Press
  19. Coelli, T.J. (1995), “Estimators and Hypothesis Test for a Stochastic Frontier Functions: A Monte-Carlo
  20. Analysis,” Journal of Productivity Analysis, 6, 247-68
  21. Dawson, P. and Lingard, J. (1989), “Measuring Farm Efficiency over time on Philippine Rice Farms”,
  22. Journal of Agricultural Economics, 40, 168-77.
  23. GSS (2018), “Gross Domestic Product 2018,” Accra: Ghana Statistical Service.
  24. GSS (2021), “Gross Domestic Product 2021,” Accra: Ghana Statistical Service.
  25. Kalirajan, K. P. and Flinn, J, C. (1983), “Comparative Technical Inefficiency in Rice Production”,
  26. Philippines Economic Journal, 20, 31-34.
  27. Kumbhakar, S.C (1990), “Production Frontiers, Panel Data, and Time-Varying Technical Inefficiency,”
  28. Journal of Econometrics, 46, 201–11.
  29. Kumbhakar, S.C., Wang, H.J., and Horncastle, A. (2010), “Estimation of Technical Inefficiency in
  30. Production Frontier Models Using Cross-Sectional Data,” Indian Economic Review, 45, 7-77
  31. Lira, M., Shamsudin, M.N., Radam, A. and Mohamed Z. (2014), “Efficiency of Rice Farms and Its
  32. Determinant: Application of Stochastic Frontier Analysis,” Trends in Applied Science Research, 9, 360-371
  33. Meeusen, N., and van den Broeck, J. (1977), “Efficiency Estimation from Cobb Douglas Production
  34. Function with Composite Error,” International Economic Review, 18, 435–44.
  35. Wang, H.J. (2002), “Heteroscedasticity and Non-Monotonic Efficiency Effects of a Stochastic Frontier
  36. Model,” Journal of Productivity Analysis, 18, 241-53
  37. Atianashie Miracle, A., Armah, E. D. A., Mohammed, N., & Sackey-Sam, S. THE ANTITHETICAL EFFECT OF CYBERCRIME ON SMALL MEDIUM ENTERPRISE SMES: PUBLIC ASSESSMENT.
  38. Wang, H.J., Amsler, C., and Schmidt, P. (2011), “Goodness of Fit Tests in Stochastic Frontier Models,”
  39. Journal of Productivity Analysis, 35, 95-118