Soil and Water Bioengineering (SWB) is an established discipline increasingly reframed within emerging policy concepts such as Nature-Based Solutions (NBS) and Green and Blue Infrastructure (GBI). However, it remains unclear how this conceptual repositioning translates into professional practice, and whether governance and institutional frameworks keep pace with practitioners’ needs. This study investigates the science-policy interface of SWB in Italy by examining how practitioners position the discipline conceptually, what barriers they encounter, and which improvement priorities and innovation pathways they identify. Data were collected through a questionnaire administered in 2024, yielding 1,428 responses classified into specialists (professionally involved in SWB, 26.54%), familiar (aware but not practicing, 50.49%), and unfamiliar respondents (no prior SWB knowledge, 22.97%). Results reveal a specialist core rooted in agriculture/forestry and geology, with familiar respondents recognizing SWB functions and expressing interest in training. While practitioners broadly situate SWB within the NBS framework – treating NBS and GBI as umbrella concepts - this conceptual alignment does not correspond to coherent intervention classification, exposing significant ambiguity at the science-practice interface. Practitioners identify improvement needs more clearly than innovation pathways: training, institutional awareness, regulatory clarity, monitoring, and vegetation management rank as priorities, whereas innovation is primarily conceived through a techno-scientific lens, encompassing materials, plant-based components, and digital tools. These findings suggest that SWB development is constrained less by technical gaps than by governance deficits, weak standardization, and insufficient long-term monitoring frameworks. The study offers actionable insights for policy actors seeking to embed nature-based practices more effectively within regulatory and institutional structures.
The environmental sustainability of livestock production is a well-researched topic. Although there are various ways to reduce the environmental impact of producing milk and meat from dairy cows, enteric methane emissions remain a significant and unavoidable factor. However, improvements in efficiency, greater attention to animal health and welfare, and renewable energy production initiatives can help farmers minimise their environmental footprint. In this context, technology and farm digitalisation can support farmers in managing their farms. This study aims to analyse the environmental performance of a dairy cattle farm in an intensive livestock area in northern Italy using Life Cycle Assessment (LCA). The farm has automatic milking and feeding systems, as well as renewable energy production plants (solar panels and anaerobic digestion), all of which can be monitored remotely. Through an external enterprise, a data collection platform was developed with inputs and outputs of the barn that automatically update daily. To each input/output, a value of carbon footprint was matched, resulting in a daily carbon footprint calculator of the farm, for the whole year 2024. The results of the LCA study show a remarkably low environmental impact, with a carbon footprint of 0.80 kg CO₂eq/kg fat- and protein-corrected milk (FPCM). Instead, the daily carbon footprint calculator consistently yielded lower estimates because of its simplified methodology. However, its purpose is not to replace comprehensive emission assessments but to provide a simple and easily generated indicator that helps increase farmers' awareness of the environmental impacts associated with their daily farming practices.
Lycium barbarum L. (L. barbarum) is a continuous-flowering and continuous-fruiting crop with thin and delicate fruit skin, necessitating multiple harvesting rounds. Currently, the harvesting of L. barbarum primarily relies on manual labor, leading to high labor demands and underscoring the urgent need for efficient, low-damage mechanized harvesting solutions. In this study, an air-suction double-roller harvesting device was designed. The harvesting mechanism operates by guiding L. barbarum into the picking zone via an airflow-induced suction stream, followed by gentle separation through a flexible double-roller structure. The fruit guiding process was modeled based on pneumatic attraction and analyzed through high-speed photography experiments. The results indicate that fruit velocity gradually increases during the guiding phase, with peak acceleration occurring at a specific location within this stage. A response-surface experiment was conducted to evaluate the effects of preloading force and roller speed on the picking rate of ripe fruit, the picking rate of unripe fruit, and the mechanical damage rate of ripe fruit. Parameter optimization resulted in optimal operating conditions with a preloading force of 0.903 N and a roller speed of 49.172 r/min. Under these conditions, validation experiments achieved a picking rate of ripe fruit of 93.7%, a picking rate of unripe fruit of 2.8%, and a damage rate of ripe fruit of 1.1%. This study provides a valuable reference for the design of vacuum-based harvesting devices for L. barbarum.
Vertical container-type smart farms offer an effective way to improve productivity in space-limited environments, but their multi-layer arrangement often creates stagnant airflow and nonuniform microclimates. In this study, computational fluid dynamics (CFD) and the Taguchi method were combined to optimize the layout of air-conditioning components in a container-type strawberry smart farm. Three spatial design factors were considered: inlet position, outlet position, and carbon dioxide (CO2) nozzle position. Nine cases were constructed using a three-level L9 orthogonal array, and spatial standard deviation was used as the uniformity index for temperature, CO2 concentration, and local mean age of air (LMA). The baseline model showed low-velocity and recirculation regions around the cultivation beds and structural frame, producing substantial nonuniformity in CO2 distribution. The middle layer had the highest mean CO2 concentration, whereas the lower layer had the lowest concentration, with a maximum interlayer difference of 53.7 ppm. The Taguchi analysis showed that outlet position was the most influential factor, with a delta value of 8.74 and a contribution ratio of 57.78%. The optimal configuration placed the inlet at 1,200 mm, the outlet at 1,500 mm, and the CO2 nozzle at 1,200 mm. Compared with the baseline model, this configuration reduced the standard deviation of CO2 concentration from 75.85 to 7.08 ppm and improved the standard deviation of LMA from 5.63 to 4.98 s. However, temperature uniformity slightly decreased as the analysis used steady-state conditions, fixed operating flow rates, and a simplified crop geometry; the optimal layout should be revalidated when the container size, crop canopy density, or operating strategy changes. These results demonstrate that spatial microclimate uniformity in multi-layer container-type strawberry smart farms can be improved efficiently by optimizing component layout.
Building information modelling (BIM) has progressively transformed professional practice in building design, landscape analysis, and spatial planning. As architectural and engineering firms have widely embraced BIM over the past two decades, higher education institutions have been pushed to rethink curricula to train future specialists accordingly. Agricultural Sciences, however, represent a discipline where this transition remains largely unexplored, despite its direct relevance to rural building design and agroforestry territory management. This paper presents a cross-sectional analysis of the attitudes and readiness toward BIM integration among Italian university departments of Agricultural Sciences. Data were collected through questionnaires completed by professors affiliated with the scientific sector AGRI-04/C (Rural Buildings and Agroforestry Territory), covering individual familiarity with BIM, perceived curricular relevance, and institutional support for educational innovation. The findings reveal a situation broadly comparable to that of emerging countries in the early stages of BIM adoption: widespread enthusiasm among individual academics is rarely matched by institutional commitment, specialized awareness, or concrete implementation strategies.
