In Life Cycle Assessment (LCA), the Life Cycle Inventory (LCI) provides emission data for the various environmental compartments and subsequently Life Cycle Impact Assessment (LCIA) determines the final distribution, fate and effects of substances such as pesticides. Given the overlap between the Technosphere (the studied anthropogenic system) and the Ecosphere (the natural environment) in agricultural case studies, it is, difficult to establish what LCI needs to capture with respect to degradation and partitioning of the pesticides in air, water and soil at the local scale. While evaluating the partitioning of the emitted substances, LCA practitioners must keep in mind that human toxicity and ecotoxicity models used in LCIA also include inter compartment transfers, fate, and degradation mechanisms at larger temporal and spatial scales such as long-range transmission of air pollutants at regional, continental, and global scale. Up to now, LCA practitioners have been using several hypotheses to build agricultural inventories. For example, the application of a regional or global scale model of substance transfers in the LCI phase (inducing an overlapping with LCIA models), or the application of a simplified approach assuming that pesticide emissions are entirely emitted to the soil compartment, or 85% is emitted to soil, 5% to crops and 10% to air (Audsley et al. 1997; Margni et al. 2002) are commonly used. To date, no clear distinction nor guidance are provided on how to combine LCI and LCIA models with respect to toxicological assessments of pesticides applied in agriculture. This paper aims to provide guidance to better define the boundaries in space and time between what should be included in LCI and where LCIA takes over. A literature review was undertaken on available methods and models for both LCI (e.g. Birkved and Hauschild, 2006) and LCIA (such as Rosenbaum et al. 2008) with a special focus on toxicological assessments of pesticides used in crop production. The relevant biophysical phenomena are identified and guidelines are proposed to overcome the gaps between LCI and LCIA as well as to harmonise further comparisons of agricultural LCA results. To complement these recommendations a case study on bananas is presented to i) characterise the current gap between LCI and LCIA, and ii) demonstrate the application of the proposed solution to the current LCA approach. From this case study, it is clear that impact assessment results for both human health and ecosystems are strongly influenced by the LCI hypotheses. The LCI hypotheses involving either inadequate model scales, or a too simplistic LCI, or non-equilibrated balances lead to an underestimation of up to a factor of 5.