Improvement of the NorFor feed evaluation system for cattle with special emphasis on feed passage kinetics in the rumen (184240)

Feed evaluation systems are used for describing the nutritive value of feeds and the nutritional requirements of the animal. Therefore, they are vital for understanding of nutrition and for developing and implementing nutritional strategies in animal prod uction. The practical goal of feed evaluation is to optimize the efficiency of feed utilization, animal performance and ultimately the financial return to the producer. In 2002, the farmers? advisory organizations in Denmark, Iceland, Norway and Sweden s tarted a project named NorFor, where the objective was to introduce a new feed evaluation system for cattle. NorFor decided that NorFor Plan, which is based on the Norwegian AAT-model, should be implemented to practice from 2006 as the new system used for ration formulation and optimization. Sensitivity analysis in NorFor Plan has shown that the passage rate of feed fractions out of the rumen has a large impact on the prediction of feed nutritive value. In NorFor Plan fractional passage rates are calcula ted for the individual feed carbohydrate and protein fractions. These fractions are further separated, depending on whether they are in liquid soluble or are classified as concentrate or roughage. Passage rates are also regulated by total dry matter intak e and intake of NDF. The same approaches have also been used in other semi-mechanistic feed evaluation systems, CNCPS and NRC. However, a major weakness in these models is that feedstuffs must be classified either as roughage or concentrate in the passage rate equations. Moreover, intrinsic properties of feedstuffs, such as particle size and density, functional specific gravity, and concentrate processing are not considered by the passage equations used in the new feed evaluation systems, and these intrin sic feed factors seems to have a high importance for feed passage rates According to NRC 2001 these factors are difficult to take into account because data are too sparse to make adjustments for these factors.