Boreal old-growth forests are key determinants in the global carbon cycle. It is unknown how the role of persistent old-growth forests will be in the carbon cycle in the face of predicted climatic changes. Eddy-covariance measurements are commonly used to quantify carbon exchange between ecosystems, such as forests, and the atmosphere. Error due to gap-fill method is of particular interest in these datasets. Here we filled a 15-year eddy covariance dataset from the Northern Old-Growth Boreal Black Spruce (Picea mariana) site located near Thompson, in central Manitoba, Canada using four different gap-fill methods. Our objectives were to determine if choice of gap-fill method affected annual NEP and if these errors compounded to even greater differences over the 15-year study period. Most significant differences in NEP among methods occurred from September to December, but variations during the growing season were responsible for most of the annual differences.