Helping analysts identify food security crises and rapidly test interventions
Significant increases in interdependencies and interactions dominate modern societies, fundamentally transforming critical systems like the food network.
The pace and magnitude of these changes has severely limited the capacity of decision-makers to respond effectively to new and unanticipated events.
A new model coupling platform to support decision-making
We have coupled a range of different models to forecast food security at local, national, and regional scales.
These include supply chain logistics, cropping, household and rangeland models.
This model integration allows us to explore questions about food system and supply chain dynamics in response to shock events such as disasters. The integration also exposes longer term risks due to factors such as climate change.
The tool links different models to represent complex socio-ecological interactions from food production to consumption. In doing so, it aims to enhance the predictive power of individual models and to provide new policy insights that cannot be captured by individual models.
Scenarios that can be assessed include:
- climate change and climate extremes
- pest outbreaks
- changes in crop and livestock farming practices
- provision of agricultural inputs and food to households
- transport network improvements
- changes in fuel and labour costs and road closures.
The platform can provide the following analysis:
- grass, crop and livestock production
- household consumption
- storage, sale and purchase dynamics
- food transportation volumes and costs
- transport bottlenecks.
An open-source platform with broad applications
The open-source food security model coupling platform was initially developed for the Horn of Africa as part of the DARPA-funded World Modelers Project.
It can be applied to a broad range of contexts and geographies, including data-poor environments.
The platform is a valuable tool for policy makers, aid agencies, non-governmental organisations and military analysts.
For example, it can be used to inform decision-making on responsive socio-political or economic interventions to improve food security.