Quantifying the relationship between pollen and vegetation is an essential step in the pollen-based quantitative reconstruction of past vegetation cover. In this study, we use the Extended R-Value (ERV) model and a modern dataset of pollen (collected from moss polsters) and related vegetation from 50 sites in the Daba Mountains (subtropical China) to (i) estimate the relevant source area of pollen (RSAP) of the moss samples and the relative pollen productivities (RPPs) of nine major plant taxa-characteristic of the region, and (ii) evaluate the obtained RPPs. The RSAP estimates of moss polsters vary between 225 and 610 m depending on the ERV submodels and models of pollen dispersal and deposition used. The RPP estimates are different from values published in previous studies from temperate and subtropical China. This may be explained by differences in methodology, climate and vegetation (species composition and spatial distribution), of which vegetation is probably the most important factor. The ranking of the RPP estimates for the nine taxa is Pinus > Juglandaceae > D - Quercus (deciduous Quercus) > Poaceae > Rosaceae > Cyperaceae > Anacardiaceae > Castanea > Fabaceae. We use a 'leave-one-out' cross-validation strategy and the Landscape Reconstruction Algorithm (LRA) for pollen-based reconstruction of regional and local plant cover to evaluate the ERV model-based RPP estimates. Both the REVEALS (Regional Estimates of VEgetation Abundance from Large Sites)-based and the LOVE (LOcal Vegetation Estimates)-based plant cover using the RPP estimates are closer to the modern vegetation composition than pollen percentages, thus confirming the applicability of the ERV model and the LRA approach in subtropical China.