Mathematica is conducting an impact and process evaluation of Stellapps, an Indian end-to-end dairy technology solutions company, to understand whether their innovative technology, extension, and financial services affect the productivity and livelihoods of smallholder farmers.
The Gates Foundation posits that livestock productivity empowers women and improves income and food security for smallholder farmers. Our evaluation will test this assumption and provide evidence to guide scale-up of the dairy digitization program.
- Academy of Management Studies
- International Food Policy Research Institute
Bill & Melinda Gates Foundation
Uttar Pradesh is among the poorest states in India, and one of the biggest producers of milk nationwide. Yet, smallholder dairy farmers in Uttar Pradesh face persistent challenges that limit their productivity and returns. In particular, limited access to markets, dairy infrastructure, timely advisory services, affordable technologies, and financial solutions result in a substantial productivity gap and low dairy incomes. The Bill & Melinda Gates Foundation is seeking to test promising solutions to these longstanding challenges, leveraging innovative technological platforms. It has partnered with Stellapps, an Indian machine learning start-up, to pilot a dairy digitization program in Uttar Pradesh. Over the course of three years, Stellapps will establish a network of milk chilling and collection centers and provide innovative technology, extension services, and financial services that aim to improve the productivity and livelihoods of smallholder farmers, particularly women.
The Foundation seeks evidence of the effectiveness of Stellapps’ program. To provide that evidence, Mathematica will conduct a rigorous evaluation to measure the impact of digital interventions on the livelihoods of smallholder dairy farmers. We will also conduct a process evaluation to interpret the magnitude of impacts, the extent to which impact pathways and assumptions manifested (and why), and any unintended consequences. Through these efforts, we will generate learnings about how to effectively roll out, implement, and scale these technologies and services, and guide possible scale-up decisions and the design of future investments.