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Cycle 6 (2017 Deadline)

Evaluation study of the use of digital technologies for agriculture and food security in Mali

PI: Amadou Sidibé,, Rural Polytechnic Institute of Training and Applied Research of Katibougou
U.S. Partner: Laura Schmitt Olabisi, Michigan State University
Project Dates: December 2017-November 2018

Project Overview:

Research on technology in a developing world context, particularly in Africa, is sparse, with studies on technology adoption and diffusion focusing mainly on descriptive statistics. However, such statistics do not offer much insight into the process of technology adoption or productivity growth. This project will contribute to theoretical and methodological perspectives on the processes of innovation and technology development in the context of Mali. On one side, the research will contribute to understanding not only what digital technologies fit into which socioeconomic context but also what enabling conditions are required to allow their inclusive use across scales. On the other side, the NSF-funded project headed by the U.S. partner is aimed at studying the drivers of food insecurity in West Africa using a participatory modeling approach. Stakeholders at national and local levels are involved in the modeling process and in identifying drivers of food security and coping mechanisms for food insecurity. Digital technologies are being proposed in Mali as partial solutions to food insecurity. For example, mobile phones are proposed to guide farmer decision-making through expert advice on the issues of concern farmers may have, including the right period for sowing crops and market information like the price and demand for different crops. Digital technologies are also used through the combination of mobile phones and remote sensing to provide extension services, to improve agriculture statistics compilation, and to help define and document land rights, thereby reducing conflict and improving tenure security. The NSF-supported project will incorporate data collected under this PEER project to determine how effective these technologies are under different conditions. This PEER project will also yield important insights into how farmers alter their decisions after receiving enhanced information.This will help to refine how the agent-based models under the U.S. partner’s project work.

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