The development of a model for the prediction of feed intake and energy partitioning in dairy cows

Balancing the supply of on-farm grown forages with the production targets of the dairy herd is a crucial aspect of the management of a dairy farm. Models which provides a rapid insight of the impact of the ration, feed quality and feeding management on feed intake and performance of dairy cows are indispensable to optimize feeding strategies, allocation of feeds and purchased concentrates, in order to find the best compromise between milk performance, nutrient use efficiency, manure excretion, gaseous emissions and profitability. This thesis describes the development of the Wageningen UR Dairy Cow Model (Wageningen DCM), a model for the prediction of feed intake and performance of dairy cows. The Wageningen DCM is constructed from two modules: a feed intake model and an energy partitioning model which describes the partitioning of the ingested net energy to milk energy output and body reserves. For the development of the feed intake model a calibration dataset was compiled with 38515 weekly records of ration feed composition, diet composition, individual feed intakes, milk yield and composition, parity, days in lactation and days pregnant from 1507 cows. The feed intake model predicts dry matter intake (DMI) from feed and animal characteristics. Data of standard feed analysis were used to estimate the satiety value (SV) of numerous feeds. The SV is the measure of the extent to which a feed limits intake. The cows’ ability to process the intake-limiting satiety value-units is expressed as the feed intake capacity (FIC). The FIC is estimated from parity, days in milk and days of pregnancy which are indicators of the size and physiological state of the cow. An  evaluation of the feed intake model was performed using an independent dataset containing 8974 weekly means of DMI from 348 cows. On the basis of mean square prediction error (MSPE) and relative prediction error (RPE) as criteria, it was concluded that feed intake model was robust and can be applied to various diets and feeding management situations in lactating HF cows. A second model was developed to predict the partitioning of ingested net energy (NEL) to milk energy and body reserves. This energy partitioning model describes the baselines of daily NEL intake and milk energy output (MEO) during successive lactation cycles of a ‘reference’ cow. The MEO and change in body energy of a cow is estimated from deviation of NEL intake from the baseline. A NEL intake above the baselines results in a higher predicted MEO and reduced mobilization of body energy reserves. Whereas, a NEL intake below the baseline results in a lower predicted MEO and increased mobilization. The proportion of ingested NEL partitioned to MEO depends parity number, days in lactation and pregnant, reflecting the changes in priority in energy partitioning during successive lactation cycles of a dairy cow The feed intake model and energy partitioning model are integrated in the Wageningen DCM. Model simulations showed that the Wageningen DCM is able to simulate the effects of diet composition, nutritional strategies and effects of cow characteristics (parity, days in milk and pregnancy) on dry matter and nutrient intake, and the partitioning of ingested NEL into MEO and body energy. The Wageningen DCM requires easily available input data. Validation of the Wageningen DCM with external data indicated a good accuracy of the prediction of intake and milk energy output with relatively low prediction errors ≤ 0.1. The Wageningen DCM enables users to analyse and compare different feeding strategies, identify limitations of feeding strategies, formulate diets, calculate feed budgets and to develop economic and environmental sustainable feeding strategies