Smallholders are small-scale farmers who manage areas varying from less than 1 ha to 10 ha, and represent an important portion of the farming population, especially in developing countries. However, they are usually abandoned in a market dominated by big players or lack government support. Their poor conditions not only affect the well-being of these families, but also—as can be observed from the example of Turkey—the isolated position of these people causes the emergence of more intermediaries than necessary. This, in turn, results in hyperinflation, where neither the customer nor the producer is financially satisfied. By the same token, product waste due to poor logistical conditions increases significantly. Based on these observations, a digital market model together with an AI- and optimization-integrated decision support framework is proposed in this chapter. Analytical (AI algorithm-based predictive and mathematical modelling-based prescriptive) components of the digital tool are explained rigorously. Additionally, a case study is provided to elaborate on the potential contributions of the tool.

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Empowering Smallholder Farmers with Digital Supply Chain Management Tools

  • Ozgu Turgut

摘要

Smallholders are small-scale farmers who manage areas varying from less than 1 ha to 10 ha, and represent an important portion of the farming population, especially in developing countries. However, they are usually abandoned in a market dominated by big players or lack government support. Their poor conditions not only affect the well-being of these families, but also—as can be observed from the example of Turkey—the isolated position of these people causes the emergence of more intermediaries than necessary. This, in turn, results in hyperinflation, where neither the customer nor the producer is financially satisfied. By the same token, product waste due to poor logistical conditions increases significantly. Based on these observations, a digital market model together with an AI- and optimization-integrated decision support framework is proposed in this chapter. Analytical (AI algorithm-based predictive and mathematical modelling-based prescriptive) components of the digital tool are explained rigorously. Additionally, a case study is provided to elaborate on the potential contributions of the tool.