The relationship between species richness and productivity (SRPR) has been a long-studied and hotly debated topic in ecology. Different studies have reported different results with variable shapes (i.e. unimodal, linear) and directions (i.e. positive, negative) of SRPRs depending on spatial grain (i.e. size of sampling unit for species richness), productivity estimates, and study extent. In this study, we quantified the effect of multiple estimates of productivity (aboveground, belowground and total biomass, and various measures of soil fertility) on species richness across three spatial grains (0.04 m², 1 m², and 25 m²) across temperate grasslands from two regions in Central Europe. We analyzed SRPR in each of the two regional datasets separately, as well as the two datasets pooled together. Our results have revealed that differences caused by spatial grain were unexpectedly small, and the direction of the SRPR was consistent within each productivity estimate, but differed between regions. Productivity estimates (across all spatial scales) had different, sometimes contrasting effects on SRPR (together with predictive power) within a region, and this pattern was more pronounced when compared between regions. The combination of different datasets led to very different results than when these were analyzed separately. We did not find any evidence for a unimodal response. This study points to the necessity of careful assessing when combining datasets from different regions, even if the plant communities belong to the same vegetation type. The dataset combination may blur the role of different drivers, which likely determine the shape and strength of SRPR. We suggest that data and study comparability may be enhanced by consistently using the same productivity estimates, which would allow for more robust interpretation of possible ecological drivers underlying the SRPR.