Inventory of field water flows for agri-food LCA: critical review and recommendations of modelling options

Purpose: In a context of flourishing eco-labelling programs and environment policy for food products, LCA application to agricultural systems faces the challenges of being operational, accurate and exhaustive. This is particularly challenging for the newly developing LCA and ISO-compliant water footprinting, with many LCIA methods only recently developed, but no dedicated inventory method. To support the inventory of elementary water flows, LCA practitioners have a variety of tools available, ranging from databases (e.g. World Food LCA Database) to complex agro-hydrological models. To allow all LCA practitioners to fulfil their diverse agri-food LCA objectives, a review of available inventory tools for field water flows and recommendations are needed. Methods: The selection of the appropriate method and tool for the inventory of field water flows in agri-food LCA studies depends on the objectives of the LCA study, data and resources available (time and skills). We analysed water inventory and agri-food LCA databases by evaluating the models on which they rely and their input data. Then, we explored the use of agro-hydrological models for LCA aiming at discriminating between different cropping system practices (LCA-based eco-design). Results and discussion: Water inventory and agri-food LCA databases provide estimates of theoretical water consumed by a crop and rely on data and methods that have limitations, making them suitable only for background agricultural LCAs. In addition, databases do not support the application of water availability footprint indicators (assessing quantitative water use and water quality alteration). For the LCA-based eco-design of cropping systems, the inventory of water flows should be based on a model simulating evapotranspiration, deep percolation and runoff accounting for crop specificities, pedo-climatic conditions and agricultural management. In particular, the model should account for possible water, salinity and nutrient stresses; assess evaporation and transpiration separately; and estimate runoff and drainage according to the system specificities. Yield should not be estimated with a model but with primary data. Recommended and default data sources are provided for each input parameter. Conclusions: The FAO AquaCrop model represents a good trade-off between accuracy, simplicity and robustness for LCA-based eco-design of cropping systems. However, this model is not yet applicable for perennial crops. Beyond a single model selection, this is a modelling approach that we characterised in this work.