FOOD SECURITYPredicting Threats to Food Security

Published 20 March 2023

Pests and diseases remain one of the biggest threats to food production, increasingly destabilizing food security and livelihoods across climate-vulnerable regions around the world,” says one expert. Mathematical modelling can prevent crop devastation and preserve livelihoods.

“Pests and diseases remain one of the biggest threats to food production, increasingly destabilizing food security and livelihoods across climate-vulnerable regions around the world,” says Professor Chris Gilligan, Director of Research in the Department of Plant Sciences, University of Cambridge.

“While farmers in these countries are struggling to control familiar pests and pathogens that are endemic, new pests and pathogens are appearing for the first time. Until now, these newly emerging threats have not been possible to predict.”

So, what has changed? The answer lies, in thinking big.

“In the past there’s been a tendency to focus on an individual crop when controlling a particular disease using genetic resistance or fungicides.” But, as Gilligan explains, when it comes to diseases that have no regard for boundaries and borders, that is not enough.

“Some diseases can spread very rapidly – these are known as transboundary pests and pathogens – in the context of the African continent you could have a disease that spreads through many countries in Sub-Saharan Africa within days or weeks.”

“As epidemiologists we realized that what your neighbor does, and their neighbor does and so on, really matters. We needed to think on a bigger scale.”

Multiple factors determine the spread of a disease: the crops themselves and how they are distributed across the landscape; the environment, which is now further complicated by climate change; the pathogens and their propensity to multiply and spread, and finally, there’s the influence of farmers’ behavior in controlling disease.

Gilligan realized that what was needed was an “epidemiological toolkit” that could account for all the varying factors. The resource built with his team of modelers, biologists and computer scientists in the Department of Plant Sciences not only enables the tracking of diseases in real-time, it also predicts how an outbreak will unfold and how cost-effective interventions are for halting its spread.

The idea is to pull the epidemic below a threshold of prevalence in a landscape so that it fades out, explains Gilligan.

Key to the success of the models is collaboration with in-country partners. “We meet with agricultural extension workers and research scientists in Sub-Saharan Africa and South Asia to discuss what is feasible. Our aim is always to provide practical guidance in managing disease.

“Yes, models can be interesting for academic reasons, but we want to help people on the ground. When a grower in Kenya says, “I don’t want to know what it is; I want to know what to do about it,” that’s very telling.”

The modelling technology that Gilligan and his team have developed has been used by governments across the world and is helping to preserve livelihoods.

“Ethiopia is the largest wheat producer in Sub-Saharan Africa providing a source of food and income for an estimated five million farming households,” says Gilligan. It’s also vital to the economy. This means that diseases affecting wheat production can have devastating consequences.

One of the deadliest is a fungal disease called wheat rust that can result in “explosive epidemics” due to rapid wind dispersal. Gilligan explains that in the past there have been severe epidemics with fungicide being applied “too late or in the wrong place at the wrong time” resulting in a “double failure” with crops lost and foreign currency spent on fungicide.

Gilligan and his team adapted their epidemiological toolkit to predict the timing and dispersal range of wheat rust spores, based upon weather forecasts generated by the UK Met Office. This allowed for an early warning system (EWS) in Ethiopia, forecasting up to seven days in advance.

They worked in partnership with the Ethiopian Agricultural Transformation Agency, the Ethiopian Institute of Agricultural Research, the International Maize and Wheat Improvement Centre (CIMMYT), and the UK Met Office to deploy the EWS.

This allowed policymakers to make timely and informed decisions about the allocation of limited stocks of fungicide. To date, it is estimated that the models to forecast wheat rust disease have enabled up to 500,000 smallholder farmers in a region of Ethiopia to take timely action reducing risks to food supplies.

In 2021 all conditions were favorable for an epidemic of a new strain of wheat rust – meaning there was no natural resistance making chemical control the only option. Thanks to the EWS, fungicides were used effectively and the epidemic prevented.

Since the application of the wheat rust EWS in Ethiopia, the forecasting service has been expanded across Bangladesh, Nepal and Kenya.