Atmospheric Δ14CO2 measurements are useful to investigate the regional signals of anthropogenic CO2 emissions, despite the currently scarce observational network for Δ14CO2. Plant samples are an easily attainable alternative, which have been shown to work well as a qualitative measure of the atmospheric Δ14CO2 signals integrated over the time a plant has grown. Here, we present the 14C analysis results for 89 individual maize (Zea mays) plant samples from 51 different locations that were gathered in the Netherlands in the years 2010 to 2012, and from western Germany and France in 2012. We describe our sampling strategy and results, and include a comparison to a model simulation of the Δ14CO2 that would be accumulated in each plant over a growing season. Our model simulates the Δ14CO2 signatures in good agreement with observed plant samples, resulting in a root-mean-square deviation (RMSD) of 3.30‰. This value is comparable to the measurement uncertainty, but still relatively large (20–50%) compared to the total signal. It is also comparable to the spread in Δ14CO2 values found across multiple plants from a single site, and to the spread found when averaging across larger regions. We nevertheless find that both measurements and model capture the large-scale (>100 km) regional Δ14CO2 gradients, with significant observation-model correlations in all three countries in which we collected samples. The modeled plant results suggest that the largest gradients found in the Netherlands and Germany are associated with emissions from energy production and road traffic, while in France, the 14CO2 enrichment from nuclear sources dominates in many samples. Overall, the required model-based interpretation of plant samples adds additional uncertainty to the already relatively large measurement uncertainty in Δ14CO2, and we suggest that future fossil fuel monitoring efforts should prioritize other strategies such as direct atmospheric sampling of CO2 and Δ14CO2.