Christopher Gilligan of the University of Cambridge in the United Kingdom will develop a data collection and analysis platform for crop diseases that uses Bayesian modelling frameworks to better integrate data from diverse sources and identifies cost-effective pest and disease control solutions for small-holder farmers. Current crop disease surveillance programs generally collect data from limited sources and lack the capacity to use the data to advise farmers how to manage any disease outbreaks. By integrating a wider variety of data, including meteorological data, and grower and market behaviour such as household nutrition, their approach can predict much broader consequences of crop diseases on individual households and thereby provide more valuable solutions. They will focus on pests and diseases of maize, wheat, and cassava in East Africa and pilot test their SMS and smart phone platform by holding training workshops for participants, testing data analytics and validating the results.
More information about Tools and Technologies for Broad-Scale Disease Surveillance of Crop Plants in Low-Income Countries (Round 21)