Seasonality is present in reproductive parameters in dairy farms in the United States but detailed information about the magnitude of it is limited. The objective of this study was to describe the seasonality in calving patterns of dairy farms across the United States. Lactation records were obtained from USDA. The focus was calvings in 2010. We used a model with sine and cosine functions with a period of 14 d to describe seasonality of the year as proposed by Stolwijk et al. (1999). The coefficient of cyclic variation (CCV, %) was chosen to measure the amount of seasonality per farm because it allows the standard error of the measurement to be taken into consideration and it is a very intuitive measure (Fulford, 2014). Proc GENMOD on SAS 9.4 was used to run the model per farm. The number of calving were adjusted by the number of cows present. States with fewer than 20 farms in the data set, farms with fewer than 50 cows present on average and farms that were greatly expanding or shrinking during 2010 were excluded. We calculated average and the 20, 50 and 80 percentiles of CCV per state (mean, P20, P50 and P80, respectively). The states analyzed (and number of farms) were PA (1,210), NY (860), MN (1,368), OH (667), IA (427), WI (216), MI (415), VT (158), IN (103), VA (226), IL (173), MO (85), KY (44), KS (86), TX (80), SD (50), GA (74), NE (52), MA (30), ME (48), FL (33), OK (19), TN (67) and NC (72) . Seasonality in calving was observed in all states studied. Among those, the greater average seasonality for calving was seen in Ohio (Mean = 54%, P20 = 41%, P50 = 53%, and P80 = 67%) followed by Florida (46%, 23%, 42%, 67%). The ones with the least amount of seasonality were Maine (17%, 8%, 14%, 23%) and Wisconsin (15%, 7%, 14%, 21%). On average, the weighted CCV of calving for the US of the farms available in our data set was 25% (18%, 24%, 30%) with the nadir point happening in early summer and the peak in mid-late fall. In conclusion, seasonality in calving was present in all states. Models with sine and cosine functions are able to smooth seasonal patterns, identify the extreme points and allow for inclusion of covariates if needed.