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Sea fennel is a minor crop of emerging socioeconomic importance in the Mediterranean region. Despite its potential, there has been no assessment of its environmental impacts to support its perceived sustainability for broader promotion. Using a life cycle assessment (LCA), we evaluated the environmental performance of sea fennel production in an open field. A cradle-to-farm gate assessment was conducted using a functional unit (FU) of 1 kg of fresh sea fennel at the farm gate. The system boundaries encompassed the nursery, cultivation, and waste management phases. Primary data was collected from farms in the Marche region of Italy. The environmental impacts were assessed using the Environmental Footprint (EF) 3.0 midpoint life cycle impact assessment method. From the results, the primary hotspot input was plastic mulch, with a relative contribution of over 50% across most impact categories. Increasing the recycling rate from 30% to 70% substantially improved the environmental performance of sea fennels, reiterating the need for further investment in plastic recycling. This study provides insights into the environmental sustainability of sea fennel production. Findings demonstrate that sea fennel cultivation offers a promising path toward sustainable biomass production, underscoring the prospects for its commercial exploitation.

Biofuel quality control plays an important role considering the recent European policy about renewable energy source promotion. Origin and source of the raw material are often required to be stated by biofuel chain operators for traceability and sustainability issues. Being fast, non-destructive, and low-cost, infrared spectroscopy coupled with chemometrics is already applied to several sectors and could also be employed in the solid biomass sector. The result of this work is a tool for the prediction of hardwood and softwood contents in blend samples by means of Fourier transform infrared spectroscopy coupled with partial least squares regression. A total of 61 samples of fir, pine, sessile oak, and beech and four series of binary blends (28 samples) from wood powders of one hardwood species and one softwood species were analyzed. The infrared prediction model was full cross-validated. The results of this work showed the good performance of the model with a standard error of a few percentage points (3.8%). As a consequence, the development of an analytical instrument based on such techniques could be useful to support the bioenergy chain stakeholders, such as solid biofuel producers, traders, and customers, for traceability, process tuning, and quality control issues.