This year’s scorching summer of heatwaves has yet again underlined the dramatic impact of climate volatility on global crops. And analysis of the latest season has highlighted the rising pressure on the global brewing industry in particular.

At the end of August, UK barley prices were up 37% year on year, while in France they spiked 23%, according to a report from analysts at investment bank Berenberg. The report also cites US Department of Agriculture data, which puts world barley stocks at their lowest levels since 1984.

Meanwhile, although prices and supply of the other core beer ingredient, hops, were broadly stable this year, a drought in the Czech Republic reduced prized Saaz hops production by more than 30%. And with on-trend craft beer typically using 30 times more hops that traditional brews, the shortage has been especially keenly felt by this flourishing sector.

Developing El Niño conditions are likely to contribute to warmer global average temperatures again next year – and lead to a repeat of summer 2018. While, in the longer-term, projections are that climate change and increases in extreme weather events are likely to lead to more dramatic price hikes and supply chain challenges. Indeed, extreme weather could reduce global barley yield by up to 17%, according to research published in the journal Nature Plants in October.

This is naturally a significant concern for growers keen to protect their livelihoods and buyers struggling to secure their supply each season.

But advances in machine learning and statistical science make it possible to create powerful solutions that address fears at both ends of the supply chain. By drawing on the relevant data points from the billions available, AI can help provide early and accurate signals that can enable more informed decisions to be made much earlier each season.

Here’s how it works using a real-life scenario:

Global brewer feels the heat when severe drought affects multi-million dollar hops order

In 2015, high temperatures and low rainfall led to drought conditions in key growing areas for Saaz hops. In August, a fortnight before scheduled delivery, a hop producer informed one of its brewery buyers of a 31% supply loss and a 140% price increase. This led to significant supply issues and cost implications that had to be passed onto their customers. It also affected the future relationship between the producer and buyer.

In this scenario the shortfall was highlighted only two weeks out – leaving little time to make contingency plans.

We have developed technology that enables us to predict yields and potential disruptions to crop growth early and accurately, so we put our science to the test. We asked our machine learning and statistical scientists to run the hops scenario through our platform to see how much earlier we could have surfaced early warnings about the shortfall. We then analysed the difference the early warning information would have made to growers and buyers.

Combining statistical science, computational sustainability, geospatial, and agronomic data, our platform would have predicted this disruption an invaluable six times earlier and three months out from expected crop delivery – in May, rather than August – with 91% accuracy.

This would have given buyers significantly more time to secure additional supply, at a lower price. They would have been informed in time to make changes to their own purchasing plans, promotions, and factory production settings, and recipes could have been changed according to the predicted supply. Difficult decisions could be made earlier and with more confidence, leading to far greater business efficiencies.

Meanwhile for growers, it would have exponentially increased their ability to plan; for crop management, financially and for harvesting. Early yield disruption warnings also enable growers to manage relationships with their customers; buyers or intermediate processors, adding value and giving all parties confidence to continue working together in the future.

In an alternative scenario, where there was a surplus of supply: this type of early signal is equally useful for farmers to optimise their harvest and minimise waste.

***

With climate volatility growing, livelihoods are increasingly vulnerable to extreme weather, but embracing new technology is a powerful way to manage the risks better. We are already putting this into practice in real time for a number of forward-thinking Fortune 500 companies.

Smart buyers and their growers are starting to capitalise on advances in AI and machine learning, and taking advantage of pioneering software that is empowering them to make better-informed, earlier, and more confident, decisions that drive efficiency and reduce stress. And into the future, long-range predictions and scenario planning tools are enabling them to both protect food (and beer) supplies for generations to come, and help to regenerate our planet.

Photography is by Markus Spiske on Unsplash.