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The impact of mechanized processing of cassava on farmers’ production efficiency in Uganda

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  • Adebayo Abass
  • Paul Amaza
  • Beatrice Bachwenkizi
  • Kelly Wanda
  • Ambrose Agona
  • Nicolaus Cromme

Abstract

The study investigates the impact of adopting mechanized processing of cassava on farmers’ production efficiency in Uganda. A stochastic production function, using translog functional form, was used to compare efficiency measures of farmers in mechanized cassava-processing villages compared with the farmers in nonmechanized cassava-processing villages in 2014. Given the specification of the translog production function, the mean technical efficiencies of the farmers were 0.69 and 0.52 in mechanized and nonmechanized villages, respectively. The significant determinants of technical inefficiency among the respondents are farming experience, education, membership of farmer association, access to markets, sale of cassava to processors and farmers who planted cassava as sole crop are all negative, which confirm to a priori expectations and significant at different levels. The policy implication of the study is that mechanization of cassava processing, particularly if done at the right scale, could create demand that can transform primary production for increased yields, higher incomes and production efficiency of smallholder farmers who constitute a significant proportion of Uganda’s agricultural sector.

Suggested Citation

  • Adebayo Abass & Paul Amaza & Beatrice Bachwenkizi & Kelly Wanda & Ambrose Agona & Nicolaus Cromme, 2017. "The impact of mechanized processing of cassava on farmers’ production efficiency in Uganda," Applied Economics Letters, Taylor & Francis Journals, vol. 24(2), pages 102-106, January.
    • Handle: RePEc:taf:apeclt:v:24:y:2017:i:2:p:102-106
    DOI: 10.1080/13504851.2016.1167817
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    References listed on IDEAS

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    4. Forsund, Finn R. & Lovell, C. A. Knox & Schmidt, Peter, 1980. "A survey of frontier production functions and of their relationship to efficiency measurement," Journal of Econometrics, Elsevier, vol. 13(1), pages 5-25, May.
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    Cited by:

    1. Francisca Ndinda Muteti & Irine Akite & Taddias Prince Mpofu & Basil Mugonola, 2024. "Determinants of technical efficiency among smallholder upland rice farmers in northern Uganda—a Cobb–Douglas stochastic frontier approach," SN Business & Economics, Springer, vol. 4(1), pages 1-20, January.
    2. Xiaoshi Zhou & Wanglin Ma & Gucheng Li & Huanguang Qiu, 2020. "Farm machinery use and maize yields in China: an analysis accounting for selection bias and heterogeneity," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 64(4), pages 1282-1307, October.
    3. Ma, Wanglin & Zhou, Xiaoshi & Boansi, David & Horlu, Godwin Seyram Agbemavor & Owusu, Victor, 2024. "Adoption and intensity of agricultural mechanization and their impact on non-farm employment of rural women," World Development, Elsevier, vol. 173(C).
    4. Lin, Bin & Wang, Xiaoxi & Jin, Songqing & Yang, Wanjiang & Li, Houjian, 2022. "Impacts of cooperative membership on rice productivity: Evidence from China," World Development, Elsevier, vol. 150(C).
    5. German, Laura A. & Bonanno, Anya M. & Foster, Laura Catherine & Cotula, Lorenzo, 2020. "“Inclusive business” in agriculture: Evidence from the evolution of agricultural value chains," World Development, Elsevier, vol. 134(C).

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