Agricultural management practices have multiple impacts on farming systems, including crop yield, soil fertility parameters such as soil organic carbon (SOC), and environmental quality. Agricultural decision support tools (DSTs) are key in sustainable farm strategies to optimize yield and minimize environmental losses because both the current agroecosystem properties as well as the effectiveness of management practices are highly variable in time and space. Here, we introduce a highly data-driven framework focusing on the evaluation of agronomic measures to reach agronomic and environmental targets. We demonstrate the potential of this approach by a proof of principle for 81 selected farm types across Europe, focusing on measures with respect to crop rotation, fertilization and soil tillage. Synthesizing data from long-term experiments and meta-analytical models, we estimated the impact of these measures on crop yield, SOC and N surpluses, while accounting for site-specific properties for the current and desired situation. The impacts of these measures on all farm types have been quantified, and optimum sets of agronomic measures have been selected in order to maximize crop yield and SOC levels and minimize N surpluses to reach the critical values for NO3− concentrations in groundwater. Our results, quantifying trade-offs among sustainability indicators that have traditionally been analyzed separately, illustrate that the suitability of measures varies by soil, climate and crop types within Europe. Our approach is promising for mapping region-specific management recommendations and evaluating the effectiveness of agronomic measures over multiple environmental goals and targets. Highlights: We find a lack of empirical-based DSTs holistically assessing agronomic practices and indicators. Meta-analytical models were used to assess impacts of best fertilizer, tillage and crop measures. Our multi-criteria analysis shows impacts vary with crop, soil and climate in European regions. We demonstrate our developed framework focusing on crop yield, soil carbon and nitrogen losses.