Stochastic simulation of rumen degradable protein surplus in grazing dairy cows

In grazing systems, the quality of the pasture is an ever-changing scenario: weather, forage variety, level of fertilisation and age of the plant are some of many factors influencing the quality of the cows’ diet. Furthermore, accurate dry matter intake measurements are difficult to obtain under grazing conditions. As a result, the use of mathematical models to describe aspects of pasture digestion has been limited in practice. Stochastic modelling might overcome this limitation. In the current example, a static model of cow digestion (National Research Council (NRC), 2001. Nutrient Requirements of Dairy Cattle. National Academy Press, Washington, DC, USA) was coded into a software package for probabilistic simulation (GoldSim v. 9.2) using stochastic variables for pasture chemical composition and dry matter intake. Partitioning of crude protein (nitrogen) in the rumen of cows was simulated over a period of 7 weeks in early spring to estimate the potential losses of nitrogen due to high ruminal degradability of protein when different levels and types of supplementary feeds were offered. More than 98% of the simulations resulted in estimated excess of rumen degradable protein of up to 561 g N/d. By comparison, the magnitude of deficit was small (up to −42 g N/d) in the simulations with negative values for rumen degradable protein balance. Pasture crude protein concentration was the stochastic variable with the biggest influence on the amount of rumen degradable protein. Higher levels of supplementation (60 g DM supplements per 100 g DMI) resulted in estimated excess rumen degradable protein that was 0.3 of that obtained from simulations with lower levels of supplementation (35 g DM supplements per 100 g DMI). Stochastic simulation may be useful to explore the likelihood of responses to management scenarios designed to increase the efficiency of dietary nitrogen utilisation in pastoral systems characterised by uncertainty and variability.