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The decarbonisation of energy use in agriculture remains a substantial challenge for energy system transitions. Mobile machinery in agriculture still largely depends on fossil fuels, such as diesel. To develop effective substitution strategies, it is necessary to take into consideration the geospatial and temporal variability and dependencies of energy demands, as well as the potential for substitution. Identifying distributional patterns, limitations, displacement effects and specific opportunities is of particular importance for practical application and the implementation of renewable energy systems.The presentation will demonstrate our approach that has been developed for assessing spatial heterogeneity in agricultural structures, energy demand, renewable resource availability and infrastructure shapes. This approach will be used to identify feasible substitution options and evaluate the CO2 emissions and the climate mitigation potentials. We will demonstrate its application using the Bavarian case study “EigenKraftBayern” for a geospatial and temporal analysis of fuel consumptions and options for substituting fossil diesel in agricultural mobile machinery to enhance renewable energy self-sufficiency in agriculture.We will describe our modelling approach including required data, regionalised consumption modelling, scenario analysis and assessment of GHG (greenhouse gas) emissions to evaluate regional fuel consumption in agricultural mobile machinery. Within this approach we explicitly account for geospatial differences in production systems and heterogeneity of Bavarian agriculture. Based on this assessment, we deduce alternative drive and fuel options, e.g. electrification, vegetable oil fuel, biomethane, biodiesel and hydrotreated vegetable oil. Our structured, multi-step approach links regional fuel consumption with different substitution pathways, estimates self-supply potentials from locally available renewable energy sources, and compares substitution requirements with technical, spatial and resource-related constraints. Scenario analyses have been used to explore how different assumptions regarding renewable energy availability and infrastructure provision influence substitution outcomes and affect the amount of greenhouse gas emissions.We will show that fuel consumption and substitution potentials vary markedly across Bavaria. For the reference year 2024, total diesel consumption in agricultural mobile machinery is estimated at approximately 399 million liters, with around 75 % attributable to crop production and 25 % to cattle farming. We will demonstrate that a substantial share (58 %) of this consumption can, in principle, be met by regionally produced renewable energy, while the remaining share would rely on fuels that are not regionally producible.Finally, we will describe the climate protection effects associated with different substitution scenarios. We will show that the replacement of diesel by renewable alternatives could reduce greenhouse gas emissions by up to 78 %, corresponding to savings of around one million tons of CO₂ equivalents compared to continued fossil fuel use. We will conclude by discussing how spatially explicit and temporally resolved modelling can support the development of resilient, regionally adapted renewable energy systems and policies for the agricultural energy transition.