COVER STORY Collins and co-founder and CSO
Mariano Alvarez created Avalo in a bid to outrace climate change by breeding crops for future environments. Optimisation of the supply chain is a byproduct of the work. Crop breeding requires a two-to-
three-year development cycle, so focussing on what an environment may be like in 10 or 20 years enables proactive farming that shields crops from immediate environmental damages as well as transportation, processing and packaging challenges. Avalo begins the machine learning
process by choosing 500 seeds representing the most genetic diversity among all the crops in the world. The phenotypes, genetic information and environmental data are all added to the algorithm, which helps to determine which combination of genetic markers could be best suited for a particular desired outcome. “If you’re able to measure all these
different traits and find a genetic basis with them, you can get potentially really interesting outcomes owing to the AI component,” says Collins. Indeed, there are many aspects of
crop growth that can be explored with the algorithm. So far, this technology has led to the production of heat- resistant tomatoes, non-tropical sugar cane, resilient cotton and pesticide-free broccoli that matures in only 37 days.
AVALO AND THE SUPPLY CHAIN Crops with this level of protection are better able to survive the supply chain,
Avalo chose the 500 seeds that would provide most genetic diversity
especially if that supply chain is highly streamlined, according to the company. “We’ve explored the different
places within the supply chain,” says Collins. “One thing that we’re trying to do is work with the mills, garment manufacturers and brands to collect
data on how fibres react to each step in the process – this knowledge will help us create a resilient product that can better withstand the industrial process. Cotton density, for example,
Optimising a crop using AI could lead to farmers using less fertiliser
decreases when touched repeatedly with a physical saw blade while it is being purified and woven into the final garment. “By the time cotton has passed
all the industrial steps to become a t-shirt, more than 50% of the original cotton will have been degraded,” says Collins. Optimising a crop using AI could
lead to farmers using less fertiliser, saving them money or lowering prices for end customers. The prices would also depend on factors, such as the supply chain being used and how competitively farmers sell their crops. While there are many potential
outcomes for introducing a certain level of flexibility into the supply chain, one thing is certain: Breeding resilience is becoming easier.
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