There are approximately 270 million dairy cows worldwide. Thats 30 people per producing cow. The problem with dairy farming is the impact on the environment in greenhouse gas production from manure, poor handling of manure and fertilizers degrading water resources, and unsustainable farming practices damaging ecological havens.

Nestlé’s dairy category is its largest and the brand purchases close to 7 million tons per year! To counter the negative environmental impact, Nestlé is turning to AI.

Nestlé's AI Implementation in Dairy Farming

We all have Nestlé in our homes, physically or otherwise. Remember Phoebe’s family recipe?

So, the fact that the brand isn’t burying its head in the sand and is tackling the dairy damage head on is pretty great.

How is Nestlé doing it?

·       By leaning on AI algorithms to analyze large amounts of data from satellite imagery, weather forecasts, and soil sensors. The data analysis assists in predicting crop yields, identifying outbreaks of diseases, and optimizes the use of water and fertilizers.

·       The brand is also utilizing predictive analytics to optimize supply chains between farms and consumers.

·       They are integrating AI with blockchain technology for better transparency and traceability across the supply chain to ensure agricultural products are sourced ethically.

It all sounds awesome, but what impact is it having?

AI-integration with dairy farming has resulted in better milk quality and yield, and reductions in their carbon footprint. Nestlé is not the only brand doing good with AI.

Arla Foods uses AI to analyze data from thousands of farms to optimize feeding, breeding, and milking practices resulting in a celebratory reduction in greenhouse gas emissions per liter of milk produced.

Environmental and Economic Benefits

We all love the planet and want the biggest brands to lead by example, particularly when it has benefits such as these.

AI can be used to manage feeding in an optimized way per cow. Algorithms analyze data on cow health, milk production, and feed efficiency for tailored feeding, this ultimately improves quality of milk as the cows are in better health.

Predictive analytics are highly regarded for its ability to identify diseases early on (thus avoiding unnecessary antibiotics) and study genetic data to highlight which breeds will produce better milk.

The farmers are high up in the priority list and when AI can increase efficiency whilst reducing costs then you’re on to a winner winner beef dinner! Thanks to optimized crop production and AI systems managing energy usage, farmers feel the economic impact on their farms as well as in their pockets.

Conclusion

Nestlé is a great example of how CPG brands can use AI for good through predictive analytics and data analysis that ultimately improves the well-being of the cows, improves the milk quality, supports more sustainable farming practices, and enhances economic standing.

If every large brand adopted AI integration across agriculture we would see a significant improvement to the environment, particularly if AI-driven practice became the rule rather than the exception.