Assessing the environmental efficiency of smart farming via life cycle assessment: a case study in Greece

In the context of achieving environmental and socio-economic sustainability in the agricultural sector, Smart farming (SF) is a promising state-of-the-art technology for decision making in agricultural management practices. By exploiting data of various sources, such as remote sensing, drones and in-situ measurements, and efficiently using field inputs (fertilizers, pesticides and water), SF targets on moderating-related environmental impacts, while enhancing yield. A limited number of life cycle assessment (LCA) studies focusing on the environmental performance of SF are available in scientific literature. Thus, the present study aims at providing LCA-based indications of the environmental profile of SF, using data collected in the frame of LIFE GAIASENSE project. Eleven pilot cases in Greece participated in the study during a 2-year period (2020 and 2021) and provided data for comparing the environmental profiles of conventionally cultivated (reference) fields with the GAIASENSE SF-treated fields (treatment fields). The applied methodology includes the calculation of midpoint and endpoint impacts, as well as single score analysis, based on the aggregated results of all field cases. The calculations show significant reductions in the majority of the impacts examined and indicate a total environmental benefit of SF in the magnitude of 11.4–11.6%. The results are useful in the context of providing realistic evidence of the efficiency of SF on a national level and supporting its application in decision making in the agricultural sector.