ABSTRACT. A mechanistic, dynamic whole farm simulation model was developed to evaluate the effect of farming strategies on the productivity of dairy grazing systems. The model integrates local available information on pasture growth and quality and current knowledge on animal nutrition and metabolism. The pastoral component simulates the pasture rotation structure of the farm, with variable number and size of paddocks, to which the user must assign a pasture type from an available database. Each pasture type is represented by initial herbage mass (HM) and two vectors:monthly dry matter (DM) growth rate values and organic matter digestibility (OMD) values. The model is driven by pasture growth rate (PGR) on a monthly interval step. Several pasture production and management strategies can be defined as a per paddock basis. The cows are defined in terms of their potential for milk production (MPP), body condition score (BCS, scale 1-5), biotype Frame (body weight with BCS of 3), calving date, and contents of fat and protein in milk. These variables are used to characterize the average of up to six groups of adult cows which are defined by the user to represent the current situation of a dairy farm or a theoretical system. Average grazing DM intake (DMI) of each calving group of cows is estimated considering animal factors:Frame, MPP and days in milk (DIM); pasture factors:OMD, pre-grazing HM (pg-HM) and substitution rate (SR) of supplementary feed. The model is based on metabolisable energy (ME) and environmental thermo neutrality is assumed. Total ME intake (MEI) is partitioned among body functions following a defined priority:maintenance, pregnancy, milk production potential and body reserves (BR). One distinct feature of this model is that the approach used implies an active role of BR in defining the partition of MEI. If ME balance for potential milk is not achieved then BR are mobilized at a constant rate (κ) to give an absolute amount which is proportional to the current size of estimated mass of BR, whose initial level is set when inputting the initial BCS. Another feature of this model is that it can manage decisions taken at different system levels (pasture rotation structure, annual DM yield and seasonal distribution, reserves production and supplementation strategies, variables stocking rates, effects of animal size, BCS, milk potential, etc.), to quantitatively assess the impact of these decisions on cows and farm productivity. The model output was initially validated at the "cow biotype level" using published farmlet trials. The relative prediction error (RPE) and concordance correlation coefficient (CCC) were used as measures of fitness; models with values of RPE less than 10% and values of CCC greater than 0.90 were considered to have significant predictive power. Daily milk yield per cow, live weight and BCS change through the lactation were validated using a set of 12 monthly values for each trait, obtained from cows of contrasting body sizes (Heavy and Light).The RPE and CCC were 16% and 0.94 in Heavy, 20% and 0.87 in Light cows for milk yield; 3% and 0.72 in Heavy, 2% and 0.81 in Light cows for live weight; 6% and 0.18 in Heavy and 9% and -0.47 in Light cows for BCS change. Monthly intake of pasture per ha was validated using another independent set of 12 average monthly values for each of 5 farmlet stocking rates treatments (2.2; 2.7; 3.1; 3.7 and 4.3 cows/ha). RPE and CCC were:13% and 0.77; 9% and 0.87; 12% and 0.93; 13% and 0.91; 16% and 0.88 respectively. The model was responsive to contrasting cow type and farming management. These results show that the model has acceptable predictive power and can be used to better understand actual farming systems and also to evaluate the expected productive impact of some technical changes introduced at the farm level.