It’s been a tumultuous year for Planet Earth with heatwaves, droughts, floods, and forest fires dominating headlines. Extreme weather-related events have increasingly become the norm and a landmark UN report out last week paints a stark picture of the risks associated with rising temperatures across the globe.
It’s clear we live in volatile times, and as food crops are particularly sensitive to variations in growing conditions, our increasingly erratic weather is hitting soft commodities and their supply chains hard. A poor potato crop this year in Europe, for example, led to strained retail relationships and caused havoc for factory planners and buyers. While there were ominous signs during the season, it was difficult to forecast precisely what the yields would be until the harvest season began. As a result, planning was both painful and stressful for all concerned.
Meanwhile, in the longer term, the rising frequency of extreme weather events means farmers will need to adapt their operations to ensure their land remains productive (for example through irrigation or drought resistant seeds) – important decisions that will demand considerable thought and investment.
However, there is good news. Advances in machine learning are enabling us to help those producing food and managing our food supply make better climate-smart decisions for the future, and regenerate Earth’s natural capital in the process.
Cervest’s next-generation software has been developed by some of the world’s leading scientists and AI experts. Our pioneering platform combines statistical science, computational sustainability, and agronomy with data from multiple sources – climatic, scientific, satellite, biophysical – and decodes it into genuinely useful and actionable intelligence.
We can deliver field-level personalised yield predictions anywhere in the world, earlier in the season than ever before without expensive on-farm equipment. And by tracking and predicting crop productivity, growers and buyers are able to plan earlier, saving scarce natural and financial resources.
Beyond watching the current season unfold in real time, our platform’s machine learning capabilities have also supercharged our ability to predict what will happen next, by continuously learning from billions of data points, across multiple crops and time periods.
Using these to simulate agricultural scenarios into the future for the first time, our approach also enables us to recommend more climate-smart farming practices. From new planting techniques to alternative seed types or crops, scenario modelling can help growers understand how to adapt for future productivity, and create a more resilient food supply ecosystem for everyone.
Leading companies, such as Mars are already embracing more sustainable ways of buying, committing $1bn to further support its growers and the land from which they source – technology has the power to enhance this even further.
Beyond industry, policy makers and NGOs can use AI to arm themselves with early predictions to help the world’s 570 million farms around the world adapt to climate change.
Artificial intelligence is enabling us to learn from nature, in order to protect it. And by doing so, we believe humans and machines together are now able to solve some of the world’s most complex food, agriculture and supply chain challenges – securing food supplies and sustaining the planet for future generations where volatility is the new normal.
This article originally appeared on techUK.org on 15th October as part of Green Week. See #techUKGreenweek for more content.
Photography is by Hal Gatewood on Unsplash.