During the last century in the Netherlands milk production per cow has almost tripled. Accordingly, the amount of concentrates yearly fed per cow strongly increased. Furthermore, automation and robotisation has changed dairy management, especially by the introduction of automatic concentrate feeders and milking systems. A new management concept, emerging in the last decades, is Precision Livestock Farming (PLF). The objective of PLF is to optimize livestock production, by on-line monitoring and control of the production process, utilizing the technical possibilities of automation and robotisation. Nowadays, individual settings for daily concentrate supply and milking frequency are based on standards, ignoring individual variation in milk yield response on concentrate intake and milking frequency. This leads to the main hypothesis for this thesis research that profitability of dairy farming can be improved by utilizing information on individual variation in response. The first objective of this research was to quantify the individual variation in milk yield response to concentrate intake and milking interval length, in order to assess the economic prospects of applying individual optimal settings for concentrate supply and milking frequency. The second objective was the development and testing of adaptive models for on-line estimation of the actual individual response in milk yield to concentrate intake and milking interval length. The conclusion is that on-line estimation of the actual individual response in milk yield and milking duration is possible following a Bayesian approach for time series using an adaptive dynamic model. Besides estimation of the actual response the Bayesian approach adequately detects process deteriorations. Therefore, adaptive dynamic models provide a useful tool for control and monitoring of the dairy production process.