Nitrous oxide (N₂O) is emitted from agricultural soils mostly in transient peaks. Therefore, fixed time interval sampling (e.g. chamber method) does not ensure an accurate estimation of annual cumulative flux due to the non-linear temporal variability of the flux. We propose that a simulation model can be used to estimate the error of different sampling strategies for estimating annual flux. Our objective was to estimate the error and variability associated with regular (varying time intervals) or rule based (i.e. depending on soil water content and precipitation) sampling. Cycles, a daily time step agroecosystem model, was used to simulate soil N₂O flux along with other bio-geochemical processes from three different agroecosystems: Ames; Iowa (corn-soybean), Pullman; Washington (winter wheat), and College Station; Texas (corn)­­­­. Daily N₂O emission, soil temperature and water content were obtained from a multi-year time-span. Then, we “sampled” the daily N₂O emissions from the model outputs using regular time intervals (2, 4… 32 days) and rule-based method. The rule-based annual flux was estimated as the sum of “measured” emission plus and estimated emission for the non-samples days. For regular sampling to remain within ±25% of the annual flux, samplings every 8 days are needed in Ames and Pullman, and every 4 days are needed at College Station. However, because fluxes in Pullman are smaller than at the other locations, the absolute error is small even at larger sampling intervals. Sampling every 8 days rendered a standard deviation of 0.5, 0.12, and 0.51kg N yr^-1 at College Station, Pullman, and Ames, respectively. The rule-based method provided promising results by explaining 50-90% of the modeled annual flux (best in Ames) with a comparatively lower number of samples than weekly sampling. Thus, a simulation model may be used to design a sampling strategy before a costly field campaign.