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Big Data and Smallholder Farmers: Big Data Applications in the Agri-Food Supply Chain in Developing Countries

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  • Protopop, Iuliia
  • Shanoyan, Aleksan

Abstract

The potential of big data (BD) applications in agriculture is attracting a growing interest from food and agribusiness industry players, researchers, and policy makers. Possible gains in agricultural productivity and supply chain efficiency from BD-based solutions can help address the challenge of doubling the food supply by 2050. Most of the research in this area evolves around commercial agricultural production in developed countries with relatively limited attention to BD-based solutions focused on smallholder farms in developing countries. This paper provides an overview of the existing and emerging technologies that can potentially enhance the big data application in the agribusiness value chain in developing countries, and presents a discussion of four successful cases of big data applications targeting smallholder producers. This paper also highlights drivers and barriers for smallholder-oriented applications in the agri-food supply chain in developing countries and discusses related implications for policy makers, private industry, and NGOs.

Suggested Citation

  • Protopop, Iuliia & Shanoyan, Aleksan, 2016. "Big Data and Smallholder Farmers: Big Data Applications in the Agri-Food Supply Chain in Developing Countries," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 19(A), pages 1-18, June.
    • Handle: RePEc:ags:ifaamr:240705
    DOI: 10.22004/ag.econ.240705
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    References listed on IDEAS

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    Cited by:

    1. El-Haddadeh, Ramzi & Osmani, Mohamad & Hindi, Nitham & Fadlalla, Adam, 2021. "Value creation for realising the sustainable development goals: Fostering organisational adoption of big data analytics," Journal of Business Research, Elsevier, vol. 131(C), pages 402-410.
    2. Yao, Becatien H. & Shanoyan, Aleksan & Schwab, Benjamin & Amanor-Boadu, Vincent, 2022. "Mobile money, transaction costs, and market participation: evidence from Côte d’Ivoire and Tanzania," Food Policy, Elsevier, vol. 112(C).
    3. Ioannis Margaritis & Michael Madas & Maro Vlachopoulou, 2022. "Big Data Applications in Food Supply Chain Management: A Conceptual Framework," Sustainability, MDPI, vol. 14(7), pages 1-21, March.
    4. Chunling Li & Ben Niu, 2020. "Design of smart agriculture based on big data and Internet of things," International Journal of Distributed Sensor Networks, , vol. 16(5), pages 15501477209, May.
    5. Jui-Hsiung Chuang & Jiun-Hao Wang & Yu-Chang Liou, 2020. "Farmers’ Knowledge, Attitude, and Adoption of Smart Agriculture Technology in Taiwan," IJERPH, MDPI, vol. 17(19), pages 1-8, October.
    6. Jérémie Forney & Angga Dwiartama, 2023. "The project, the everyday, and reflexivity in sociotechnical agri-food assemblages: proposing a conceptual model of digitalisation," Agriculture and Human Values, Springer;The Agriculture, Food, & Human Values Society (AFHVS), vol. 40(2), pages 441-454, June.
    7. Gina Rico Mendez & Giusy Pappalardo & Bryan Farrell, 2021. "Practicing Fair and Sustainable Local Food Systems: Elements of Food Citizenship in the Simeto River Valley," Agriculture, MDPI, vol. 11(1), pages 1-22, January.
    8. Arroyo Esteban, Sonia & Urquía Grande, Elena & Martínez de Silva, Alberto & Pérez Estébanez, Raquel, 2022. "Big Data, Accounting and International Development: Trends and challenges," Cuadernos de Gestión, Universidad del País Vasco - Instituto de Economía Aplicada a la Empresa (IEAE).
    9. Yao, Becatien & Shanoyan, Aleksan & Schwab, Ben & Amanor-Boadu, Vincent, 2023. "The role of mobile money in household resilience: Evidence from Kenya," World Development, Elsevier, vol. 165(C).

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