Nitrous oxide (N₂O) emissions from soils have been widely studied in the literature — mostly with the chamber method — due to the importance of this gas to climate change. Emissions of N₂O derive from biological reactions and are controlled by soil parameters, which are by nature heterogeneous (i.e., “hot spots” for N₂O emissions) — a source of uncertainty in chamber-based studies. Spatial variation in N₂O fluxes has been assessed in the literature, but the information is still needed for contrasting soil management practices (e.g., tillage) and for specific bioclimatic situations [e.g., non-growing seasons (NGS) under cold weather]. Here, we subsampled daily N₂O data to assess within-plot and between-block spatial variation from an agronomic experiment under conventional tillage (CT) and no-tillage (NT), identifying if patterns differ between growing seasons (GS) and NGS datasets. Within-plot spatial variation in N₂O fluxes was a small source of uncertainties, but half of the comparisons in GS datasets presented a slope different from 1 for the regression of N₂O averages from two vs. one chamber per plot — a source of uncertainty mitigated when within-plot duplication occurred during N₂O “hot moments”. Between-block spatial variation in N₂O emissions was much larger than within-plot errors — an effect more accentuated for NGS and CT than GS and NT datasets. Decreasing the number of sampled blocks resulted in averages that did not represent the N₂O daily average of the whole field, but exceptions occurred. The methodology proposed here may be used in other locations, after appropriate verification, for improved planning and maximization of the resources associated with N₂O measurements.