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- ItemThe MiLA tool: Modeling greenhouse gas emissions and cumulative energy demand of energy crop cultivation in rotation(Amsterdam : Elsevier, 2016) Peter, Christiane; Specka, Xenia; Aurbacher, Joachim; Peter Kornatz, Peter Kornatz; Herrmann, Christiane; Heiermann, Monika; Müller, Janine; Nendel, ClaasCrop rotations are part of current agricultural practice, since they and their effects can contribute to a sustainable agricultural cropping system. However, in current Life Cycle Assessment (LCA) studies, crop rotation effects are insufficiently considered, since these effects are difficult to measure. LCA studies from crop production typically take only one vegetation period into account. As a result, the consideration of how the assessed crop is influenced by the previous crop (crop rotation effects) including: (1) nutrient carryover, (2) reduction in operational requirements and (3) different intensity and timing of farming activities, is outside of the system boundary. However, ignoring these effects may lead to incorrect interpretation of LCA results and consequently to poor agricultural management as well as poor policy decisions. A new LCA tool called the “Model for integrative Life Cycle Assessment in Agriculture (MiLA)” is presented in this work. MiLA has been developed to assess GHG emissions and cumulative energy demands (CED) of cropping systems by taking the characteristics of crop cultivation in rotation into account. This tool enables the user to analyze cropping systems at farm level in order to identify GHG mitigation options and energy-efficient cropping systems. The tool was applied to a case study, including two crop rotations in two different regions in Germany with the goal of demonstrating the effectiveness of this tool on LCA results. Results show that including crop rotation effects can influence the GHG emission result of the individual crop by − 34% up to + 99% and the CED by − 16 up to + 89%. Expanding the system boundary by taking the whole crop rotation into account as well as providing the results based on different functional units improves LCA of energy crop production and helps those making the assessment to draw a more realistic picture of the interactions between crops while increasing the reliability of the LCA results.