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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.

Europe has increased its production, processing, and export of vegetables in recent decades due to changing dietary patterns supporting a greater consumption of vegetables high in nutrition. The growing interest in environmental issues has led to advocacy for sustainable vegetable production and consumption. Thus, this study assessed the ecological impacts of producing 1 kg of frozen spinach (functional unit) by a food processor in central Italy (cradle-to-factory gate approach). We evaluated the global warming potential (GWP) for distributing the final to different destinations. We also compare the potential environmental credits for different spinach residue management strategies, residue reduction through improved process efficiency, and as a feedstock for biogas production (avoided maize silage) based on the total volatile solids content. The life cycle assessment was used following the CML_IA impact assessment method based mainly on primary data related to 2019/2020. The GWP was 1.55 kg CO2eq. with respect to the functional unit. Excluding the dominant cultivation phase, packaging, particularly corrugated board boxes, electricity, and wastewater treatment were significant contributors across the midpoint impact categories assessed. The GWP for distributing the packaged frozen to Australia was 24 times more impactful than regional inland distribution. When spinach residue is reduced to 20% and 10%, total impacts for all impact categories also decrease by 12% and 22%, respectively. The benefit of using the current amount of spinach residue to produce biomethane was less than 7% across all impact categories except terrestrial ecotoxicity (13%). Therefore, reducing spinach waste along the processing line and efficient end-of-packaging life management through recycling and reuse by the manufacturer can considerably reduce the environmental impacts of frozen spinach.

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.