Mathematics helps find food crops' climate-proof genes

Parched farmland (Image: Reuters) The loss of harvests in the world's dry areas could have a significant impact on global food security

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Researchers are developing mathematical models to identify genetic material that could help improve food crops' resilience to climate change.

Impacts - such as drought, pest and disease - could hit harvests and undermine global food security.

Scientists hope the models will speed up the process of identifying traits, such as drought resistance, allowing breeders to grow climate-proof crops.

Dry areas account for 40% of land cover and are home to more than 2.5bn people.

At a recent workshop in Morocco, leading mathematicians and crop scientists met to discuss ways that applied mathematics could be used to speed up the search through agricultural genebanks for climate change resistant traits in the banks' samples.

Dry area characteristics include persistent water scarcity, frequent droughts and land degradation - features that are expected to worsen as a result of future climate change.

Critical need

Experts say there is a critical need for a new generation of crops that have improved tolerance to heat and drought in order to meet the food security needs in the future.

Farmer digs a whole in a cassava field (Getty Images) Improved crop varieties could help improve African agriculture's resilience to future climate changes

"We are seeing the spread of diseases more now than in the past, and heat-related issues are becoming more prevalent than in the past," explained Abdallah Bari, a senior scientist at Syria-based International Center for Agricultural Research in the Dry Area (Icarda).

Globally, there are 1,700 major agricultural genebanks that house in excess of seven million samples - a vast resource that researchers say makes the task of locating the sought-after traits a bit like finding a needle in a haystack.

Dr Bari said that developing mathematical models would help focus the search by "targeting the [samples] with a high probability of finding those traits and reducing the time it takes".

He explained that the Icarda team were developing a technique that used a "learning algorithm" to harvest the necessary data that would allow plant breeders to "zone in on the desired traits, such as tolerance to pests, diseases, drought and heat".

Time saving

Without a model, plant breeders would have to rely on the traditional and time-consuming "trial-and-error" approach, which requires plants to be cross-bred and the progeny being exposed to the conditions they would be expected to encounter in fields during extreme weather events.

Those that display an improved ability to cope with harsh conditions are kept as seed stock, while those without the ability to cope with the conditions often perish or are not used as a seed stock and the plant breeders have to start the process again.

The model - known as Focused Identification of Germplasm Strategy (Figs) - has already recorded a number of successes. Researchers said the technique identified the first-ever sources of resistance to the most virulent biotype of the Russian wheat aphid (Diuraphis noxia), which is a pest responsible for significant yield losses.

Currently, the main focus of the research includes a number of key food crops grown in dry areas: lentils, chickpeas, faba beans (broad beans), durum wheat and barley.

Dr Bari and colleagues published a paper last year that presented how the Figs system successfully identified drought-resistant traits in samples of faba beans.

"From the results we have got so far, we have many requests from plant breeders," he told BBC News.

"We are now working with a number of breeders to develop more subsets."

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