Crop residues may be a viable source for biofuel production and other industrial uses; however their removal from agricultural land may negatively impact productivity through loss of soil organic carbon (SOC), increased soil erosion, reduced available water, and impaired soil quality. Assessing the impact of residue removal on SOC is difficult due to its inherent variability and slow, small rate of change. Models offer a convenient and cost effective means to analyse the effect of residue removal on SOC under a wide range of agricultural management and environmental change scenarios. In this study three prominent process-based models (CENTURY, DAYCENT, and DNDC) and the Campbell empirical model were used to simulate SOC change compared to measured results across 17 residue removal experiments in Canada and the USA.  It was found that 11 of 59 of the experimental treatments showed an unexpected increase in measured soil carbon when residues were removed. This was attributed to the high uncertainty associated with measuring SOC. Measurements of light fraction organic carbon, when available, always declined in response to residue removal even when total SOC increased. The experimental results showed that it was more likely to observe an effect of straw removal on SOC (i) in the less fertile soils, (ii) when greater quantities of residues were removed, and (iii) over longer periods. All models, including the Campbell model which relies on direct inputs of yields and residues, simulated the hypothesized decline in soil carbon when residues were removed. It was difficult to ascertain which model performed best due to the inherent variability in SOC measurements; however, the Campbell empirical model best approximated average rates of removal across all treatments.