Using radiocarbon (14C) as a tracer for fossil fuel emissions is promising, even as sampling atmospheric 14CO2 for long periods of time is demanding and expensive. An alternative is to use plants to record the atmospheric carbon isotopic abundances, as plants naturally integrate carbon during their growing period by photosynthesis. A main uncertainty in this approach, however, is the unknown time period in which the uptake of CO2 has taken place. How plants “sample” the atmospheric carbon and transport it to their different parts depends strongly on their growth and developmental pattern. We use the Weather Research and Forecast model (WRF) together with a mechanistic crop growth model to quantify the representativeness of plant sampled atmospheric 14C mixing ratios on a regional scale. We compare our modeled results to measured 14C in maize and wheat samples from a region in the north of the Netherlands, affected by urban CO2 plumes as well as a local power plant. We find based on the modeled results that even in the absence of spatial fossil fuel gradients in the atmosphere, differences in plant growth rates can introduce Δ14C gradients of up to 3.5‰ over plants in the Netherlands. We furthermore use the simulated plant growth rates to narrow the period for which a plant sample can be used as a proxy, which will help to lower the uncertainty on estimated fossil fuel emissions. Our work provides first steps towards quantitatively using plant 14C sampling for verification of regional fossil fuel emissions. Map of Δ14C signature (in ‰) of spring wheat at flowering day, with grid resolution of 4x4 km. Plant growth is simulated by mechanistic crop growth model (SUCROS 2) with weather data over the growing season provided by WRF model. The temporal evolution of the 14C signature of the atmosphere is spatially uniform over the domain. The figure shows that differences in daily growth can introduce Δ14C gradients of up to 3.5‰ even in the absence of spatial fossil fuel gradients.