Real-time data may improve food insecurity crisis prediction
More accurate and timely warnings could greatly improve the global response to food crisis and help alleviate acute problems, according to a team of agricultural economists at the University of Illinois, working in collaboration with the University of Texas–Austin.
When international aid organizations respond to hunger crises around the world, they rely on food security early warning and monitoring systems. However, assessments from those systems can lag months behind the actual situation on the ground. More accurate and timely warnings could greatly improve the global response to food crisis and help alleviate acute problems, according to a team of agricultural economists at the University of Illinois, working in collaboration with the University of Texas–Austin. The results of the study are published in World Development.
The researchers developed a prediction model that uses readily available weather, market, and demographic data to forecast village-level food security status. They trained and tested the model using data from Malawi, a country with frequent food insecurity problems. The researchers used data from 2010–2011 to predict village food security in 2013, and then compared the forecasts with measured 2013 food security outcomes
The research team then used the information to estimate and forecast village food security using three different internationally recognized food security measures. When comparing their predictions with the actual food security status for Malawi households in 2013, they found that their results ranged in accuracy from 83% to 99%, depending on the type of food security measure. They compared their results with the ability of current assessments to predict the same villages’ food security and found that the current assessment method had an accuracy close to or below 10%.
The goal of the project is to provide national stakeholders and global aid organizations with a timely, precise, and transparent model they can use to quickly identify regions that are likely to experience hunger crises. This model can provide an important contribution to an early warning system that would allow for immediate action as soon as a problem is identified.
The next step is to systematize the information and build a model that can run in real-time, providing constant updates on food security status. The National Center for Supercomputing Applications at University of Illinois is collaborating on these extensions, including a public website that monitors circumstances in real-time. They will start with data from Malawi, then gradually expand to other countries, as well as longer time horizons.