Data on global agricultural production are usually available as statistics at administrative units, which does not give any diversity and spatial patterns; thus they are less informative for subsequent spatially explicit agricultural and environmental analyses. In the second part of the two-paper series, we introduce SPAM2010 – the latest global spatially explicit datasets on agricultural production circa 2010 – and elaborate on the improvement of the SPAM (Spatial Production Allocation Model) dataset family since 2000. SPAM2010 adds further methodological and data enhancements to the available crop downscaling modeling, which mainly include the update of base year, the extension of crop list, and the expansion of subnational administrative-unit coverage. Specifically, it not only applies the latest global synergy cropland layer (see Lu et al., submitted to the current journal) and other relevant data but also expands the estimates of crop area, yield, and production from 20 to 42 major crops under four farming systems across a global 5 arcmin grid. All the SPAM maps are freely available at the MapSPAM website (http://mapspam.info/, last access: 11 December 2020), which not only acts as a tool for validating and improving the performance of the SPAM maps by collecting feedback from users but is also a platform providing archived global agricultural-production maps for better targeting the Sustainable Development Goals. In particular, SPAM2010 can be downloaded via an open-data repository (DOI: https://doi.org/10.7910/DVN/PRFF8V; IFPRI, 2019).

Justifiable usage of large-scale crop model simulations requires transparent, comprehensive and spatially extensive evaluations of their performance and associated accuracy. Simulated crop yields of a Pan-European implementation of the Environmental Policy Integrated Climate (EPIC) crop model were satisfactorily evaluated with reported regional yield data from EUROSTAT for four major crops, including winter wheat, rainfed and irrigated maize, spring barley and winter rye. European-wide land use, elevation, soil and daily meteorological gridded data were integrated in GIS and coupled with EPIC. Default EPIC crop and biophysical process parameter values were used with some minor adjustments according to suggestion from scientific literature. The model performance was improved by spatial calculations of crop sowing densities, potential heat units, operation schedules, and nutrient application rates. EPIC performed reasonable in the simulation of regional crop yields, with long-term averages predicted better than inter-annual variability: linear regression R2 ranged from 0.58 (maize) to 0.91 (spring barley) and relative estimation errors were between +-30% for most of the European regions. The modelled and reported crop yields demonstrated similar responses to driving meteorological variables. However, EPIC performed better in dry compared to wet years. A yield sensitivity analysis of crop nutrient and irrigation management factors and cultivar specific characteristics for contrasting regions in Europe revealed a range in model response and attainable yields. We also show that modelled crop yield is strongly dependent on the chosen PET method. The simulated crop yield variability was lower compared to reported crop yields. This assessment should contribute to the availability of harmonised and transparently evaluated agricultural modelling tools in the EU as well as the establishment of modelling benchmark as a requirement for sound and ongoing policy evaluations in the agricultural and environmental domains