Feed represent one of the major costs in modern dairy production. The real feed value of the diet, the animal products, and the partial efficiency of feed utilization for the particular livestock product, all influence total efficiency, and thus overall feed costs. Continues development of our common Nordic feed evaluation system, NorFor, is an important step towards implementing more sophisticated nutritional strategies to control production responses from dairy cows and improve feed utilization. In a context of a higher volatility of feed and milk prices, quantification of animals multiple responses to dietary changes is of particular interest to help dairy farmers in optimizing the feeding and reducing the environmental impact. The main objective of this project is to improve the NorFor system by developing a dynamic dairy cow nutrition approach. This means moving from a diet formulation based on a pre-defined production level to a response approach, adjusting the diet based on the response to changes in diet composition. This should improve feed efficiency and improve financial return by a more precise feeding. Improved feed efficiency will also reduce greenhouse gas emissions. This new approach will be implemented to the NorFor software system. Farm variation has been one of the biggest challenges for adaprtation from R&D into the commercial area. Thus, a big challenge for this project is the intention to bring the "Information and Communication Technologies" (ICTs) into the Animal Science Field, like Machine Learning (ML). With ML, the goal is to bring the data collected from farms by sensing technologies (body changes, milk prodcution changes, etc.) for accounting farm variation. Other big Challenge is validation of the models. The Project will test the dynamic modelling approach on farms in Denmark, Norway and Sweden by implementing the models into national NorFor Client software Tools.
Project leader: Tone Roalkvam
Institution: TINE Rådgiving