Our Research Improving smallholder farmers’ welfare with AI-driven technologies

Principal Investigator

Y. Karen Zheng

  • Sloan School Career Development Professor
  • Associate Professor of Operations Management
  • Sloan School of Management

Y. Karen Zheng is a Sloan School Career Development Professor and an Assistant Professor of Operations Management at the MIT Sloan School of Management. Karen’s research adopts a behavior-centric, data-driven, field-based approach. Her recent efforts focus on two main topics: (I) the design and impact of digital platforms to enable efficient physical supply chains in resource constrained environments, and (II) the role of information transparency in driving environmentally and socially responsible behaviors. In her research, Karen closely collaborates with partners on the ground to ensure that her research leads to positive impacts to society and practice.


Digital platforms have arisen as a key intervention to help improve the livelihood of millions of smallholder farmers around the world. How can we utilize these platforms to develop practical, data-driven decision support tools to improve farmers' decisions and their welfare?

Research Strategy

  • Develop AI-driven market intelligence tools based on a predict-then-optimize approach to enhance farmers’ market selection decisions
  • Leverage large-scale transaction and product quality data from digital platforms to prescribe recommendations for farmers' quality investment decisions
  • Collaborate with organizations on the ground to devise and implement practical solutions to generate positive impacts for smallholder farmers

Project description

Millions of smallholder farmers around the world persistently struggle with low productivity and high poverty. To improve the welfare of these farmers, many countries and organizations have invested heavily in digital or mobile platforms to improve farmers’ market and information access. However, two significant gaps persist and prevent the full benefit of these platforms from being realized. First, while these platforms enable large-scale data collection, there have been insufficient efforts in translating this into data-driven decision support tools for farmers. Second, the data collected through these platforms are often “censored” in a non-random fashion. Hence, advanced machine learning and statistical methods must be developed to generate unbiased prediction and prescription. In this project, Karen, together with her collaborators Retsef Levi (MIT Sloan) and Somya Singhvi (USC Marshall), seek to fill these gaps by developing advanced analytics and optimization tools to enhance farmers’ quality investment and market selection decisions. The work will build on close collaborations with organizations on the ground to ensure that the research findings will be fully transferred to actual practice and bring material benefits to the farmers.

Additional Details

Impact Areas

  • Food

Research Themes

  • Economics, Policy, & Practices
  • Transforming Food Systems
  • Modeling & Data Analytics
  • Equity & Access

Year Funded

  • 2021

Grant Type

  • Seed Grant


  • Ongoing