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The Nexus Between Cocoa Farmers’ Business Schools Participation and Impact to Support Livelihood Improvement Strategies in Ghana

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  • Richard Kwasi Bannor
  • Helena Oppong-Kyeremeh
  • Bismark Amfo
  • Lesley Hope
  • Samuel Kwabena Chaa Kyire

Abstract

The authors examined cocoa farmers’ participation in Farmer Business Schools (FBS) in Ghana, impacts of livelihood outcomes (human, physical, financial, and social capitals) on participation, and implications of participation on productivity and per capita expenditure. We used cross-sectional data from 150 cocoa farmers. Data was analyzed with descriptive statistics, heteroskedastic probit, and doubly robust augmented inverse probability weighted regression adjustment model (AIPW). More than half of cocoa farmers participated in FBS. Financial capital (income and loan accessibility) and social capital (farmer group membership) positively influenced participation in FBS. Nevertheless, human capital (ability to pay medical bills) and physical capital (number of sleeping rooms) negatively influenced participation in FBS. Knowledge and skills cocoa farmers obtained from FBS are new farming methods, creativity in solving farming problems, record keeping, and managerial skills. Participation in FBS had a positive impact and a heterogeneous effect on cocoa farmers’ productivity and per capita expenditure. Given that the impact of FBS was heterogeneous, the FBS curriculum and approach should be segmented for different types of farmers to the generic FBS approaches currently practiced.

Suggested Citation

  • Richard Kwasi Bannor & Helena Oppong-Kyeremeh & Bismark Amfo & Lesley Hope & Samuel Kwabena Chaa Kyire, 2022. "The Nexus Between Cocoa Farmers’ Business Schools Participation and Impact to Support Livelihood Improvement Strategies in Ghana," SAGE Open, , vol. 12(2), pages 21582440221, June.
    • Handle: RePEc:sae:sagope:v:12:y:2022:i:2:p:21582440221108170
    DOI: 10.1177/21582440221108170
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    1. Joanna Chilemba & Catherine Ragasa, 2020. "The Impact on Farmer Incomes of a Nationwide Scaling Up of the Farmer Business School Program: Lessons and Insights from Central Malawi," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 32(4), pages 906-938, September.
    2. Farrell, Max H., 2015. "Robust inference on average treatment effects with possibly more covariates than observations," Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
    3. Godtland, Erin M & Sadoulet, Elisabeth & De Janvry, Alain & Murgai, Rinku & Ortiz, Oscar, 2004. "The Impact of Farmer Field Schools on Knowledge and Productivity: A Study of Potato Farmers in the Peruvian Andes," Economic Development and Cultural Change, University of Chicago Press, vol. 53(1), pages 63-92, October.
    4. Wencong Lu & Kwabena Nyarko Addai & John N. Ng'ombe, 2021. "Impact of improved rice varieties on household food security in Northern Ghana: A doubly robust analysis," Journal of International Development, John Wiley & Sons, Ltd., vol. 33(2), pages 342-359, March.
    5. Enoch Kwame Tham-Agyekum & Ernest Laryea Okorley & Joseph Kwarteng & John-Eudes Andivi Bakang & Fred Nimoh, 2021. "Enhancing Market Orientation of Cocoa Farmers through Farmer Business Schools: The Ghana Cocobod Experience," Asian Journal of Agriculture and rural Development, Asian Economic and Social Society, vol. 11(1), pages 129-138, March.
    6. Kamdem, Cyrille Bergaly, 2016. "Impact of cocoa farmer field schools on cocoa yield: empirical evidence of cocoa farmers in Cameroon," 2016 Fifth International Conference, September 23-26, 2016, Addis Ababa, Ethiopia 246388, African Association of Agricultural Economists (AAAE).
    7. Harvey, A C, 1976. "Estimating Regression Models with Multiplicative Heteroscedasticity," Econometrica, Econometric Society, vol. 44(3), pages 461-465, May.
    8. Amfo, Bismark & Ali, Ernest Baba, 2020. "Climate change coping and adaptation strategies: How do cocoa farmers in Ghana diversify farm income?," Forest Policy and Economics, Elsevier, vol. 119(C).
    9. Greene,William H. & Hensher,David A., 2010. "Modeling Ordered Choices," Cambridge Books, Cambridge University Press, number 9780521194204, September.
    10. Francisco Cribari-Neto & Wilton Silva, 2011. "A new heteroskedasticity-consistent covariance matrix estimator for the linear regression model," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(2), pages 129-146, June.
    11. Enoch Kwame Tham-Agyekum & Ernest Laryea Okorley & Joseph Kwarteng & John-Eudes Andivi Bakang & Fred Nimoh, 2021. "Enhancing Market Orientation of Cocoa Farmers through Farmer Business Schools: The Ghana Cocobod Experience," Asian Journal of Agriculture and Rural Development, Asian Economic and Social Society, vol. 11(1), pages 129-138.
    12. Tukundane, Cuthbert & Minnaert, Alexander & Zeelen, Jacques & Kanyandago, Peter, 2015. "Building vocational skills for marginalised youth in Uganda: A SWOT analysis of four training programmes," International Journal of Educational Development, Elsevier, vol. 40(C), pages 134-144.
    13. Bannor, Richard Kwasi & Ros-Tonen, Mirjam A.F. & Mensah, Princess Ophelia & Derkyi, Mercy & Nassah, Valerie Fumey, 2021. "Entrepreneurial behaviour among non-timber forest product-growing farmers in Ghana: An analysis in support of a reforestation policy," Forest Policy and Economics, Elsevier, vol. 122(C).
    14. Glynn, Adam N. & Quinn, Kevin M., 2010. "An Introduction to the Augmented Inverse Propensity Weighted Estimator," Political Analysis, Cambridge University Press, vol. 18(1), pages 36-56, January.
    15. Afriyie-Kraft, Lydia & Zabel, Astrid & Damnyag, Lawrence, 2020. "Adaptation strategies of Ghanaian cocoa farmers under a changing climate," Forest Policy and Economics, Elsevier, vol. 113(C).
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