Researchers developed a model that accurately predicts dairy products’ shelf life to reduce food waste and loss for consumers and producers.
Food Waste Challenges
About 40% of food in the United States is wasted, often because consumers base their decision to dispose of food on the “best-by” label, which are determined by manufacturers based on food quality rather than safety. Without a way to predict shelf-life, food waste and loss is becoming increasingly common, with milk spoilage alone costing the U.S. $6.4 billion annually.
Digital Tools to Reduce Food Waste
Dairy producers and consumers needed a more effective system than “best-by” labels to predict shelf life of dairy products based on their quality and avoid wasting food that is still safe to eat. Using fluid milk as an example, researchers took a systems approach by exploring interventions at each step in the dairy farm-to-fork continuum. The researchers ultimately generated five predictive models that the dairy industry can use.
Researchers then developed a user-friendly interface for two models and made them publicly available so different dairy stakeholders, like consumers and retailers, can evaluate the shelf-life of a product.
As part of the project, researchers conducted three studies that showed consumers are willing to pay a price premium of $0.08 per day of extra shelf-life and an additional $1.13 if made aware of potential food waste reduction through appropriate messaging. A final online survey revealed that reduced shelf life is associated with poor shopping experience, which can lead to retailers losing consumer loyalty.
Other Applications
The research highlighted the importance of digital solutions, consumer incentives and effective messaging in mitigating food waste and loss. The project also developed predictive models of food spoilage that can be used beyond the dairy industry to improve sustainability in the broader food system, prevent disposal of foods still safe to consume and enhance the consumer shopping experience.
The modelling component is currently being used in Cornell Cooperative Extension and is moving towards end users. Researchers are exploring commercialization.