Precision Agriculture for Smallholder Systems in Africa
Lead Institution: Michigan State University
Award Amount: $518,400
Focus Country: Malawi
Principal Investigator: Joe Messina - jpm@msu.edu
International Collaborating Institution(s): Oakland University
Summary: The purpose of this project is to collect and integrate multi-scale data and analytic techniques to generate inter- and intra-seasonal decision support guidance capable of improving Malawian smallholder farmers’ agricultural productivity. Recent advances in data management and modeling and in the commercialization of remote sensing tools are leading to dramatic improvements in our understanding of the multi-scale spatial patterns of soils, weather and climate, and agricultural productivity. These types of data are now increasingly available in Africa to combine with farm survey data on farm management, input use, and soil characteristics to provide inter- and intra-seasonal decision support guidance for agricultural extension systems for the benefit of smallholder farmers. In essence, the project seeks to demonstrate how to make agriculture in developing countries “smarter” through newly available tools for collecting and and analyzing large volumes of data on productivity, suitability, and producer behavior.
Project Objectives:
This project will gather, analyze, and translate data into actionable, timely, scaled, and localized farm level guidance. The effort will be guided by three overarching objectives:
- Develop and test a model for precision farming in the small-holder agriculture context.
- Using domain and context specific data, multidimensional models, and theoretical frameworks from agronomy, economics, and geography design and deploy inter- and intra-seasonal tools for farm level, site-specific decision making.
- Integrate and use proven and new analytic and dissemination tools for scalable decision support.
2019 Project Updates - Project Completed
- The Precision Agriculture for Smallholder Systems in Africa, with support from the USAID, has mapped much of Malawi and provided information to the USAID Mission. The project completed its work on building tools to integrate data sets and began work with CIAT to push forward a pilot project related to their work, and writing a scaling proposal along with CIAT to Google. All objectives have been achieved and the project was completed in FY 2019.