A dynamic, stochastic, and mechanistic Monte Carlo model, simulating a dair
y herd with focus on the feeding-health-production complex is presented. By
specifying biological parameters at cow level and a management strategy at
herd level, the model can simulate the technical and economic consequences
of scenarios at herd level. The representation of the feeding-health-produ
ction complex is aimed to be sufficiently detailed, to include relationship
s likely to cause significant herd effects, and to be sufficiently simple t
o enable a feasible parameterization of the model and interpretation of the
results from the model. Consequently, diseases are defined as four disease
types: two metabolic disease types, an udder disease type, and a reproduct
ive disease type. Risk factors for the diseases were defined as parity, yie
ld capacity, disease recurrence, disease interrelationships, lactation stag
e, and season. Direct effects of the diseases were defined according to mil
k yield, feed intake, feed utilization, conception, culling, involuntary re
moval, and death.
Scenarios differing in base risks of milk fever and ketosis, heat detection
rate, and culling strategy were simulated for describing the model behavio
r. Annual milk yield per cow was decreased by increased risk of ketosis and
by increased risk of milk fever, even though no direct effect of milk feve
r on milk yield was modeled at the cow level. The indirect effect from milk
fever is a consequence of increased replacement rate (relatively lower mil
k yield from younger cows). By ignoring the history of milk fever in insemi
nation and replacement decisions, a significantly reduced net income per co
w was found in some herds. We concluded that important benefits from using
such a herd model are the capability of accounting for herd management fact
ors and the advantage of avoiding to double count the indirect effects from
disease, such as increased risk of other diseases, poorer reproduction res
ults, and increased risk of culling and death.