Andrea Casson

The study compares the overall sustainability of two organic and two conventional rice farming systems during the 2022 drought. The research aimed to develop an experiment exploring the ability of an integrated methodological approach to identify tradeoffs and provide actionable insights for a sustainable agricultural transition under extreme climate stress. To this aim, the study employed economic analysis, Life Cycle Assessment (LCA) for environmental impact, and the OASIS framework for broader social and resilience indicators. The study revealed tradeoffs between the economic efficiency of conventional rice farming and the ecological resilience of organic systems, a conclusion made possible only through its integrated assessment methodology. By combining different methods, the research suggested that while conventional farms achieved clear financial superiority and greater efficiency per ton of rice, organic systems showcased superior ecological performance per hectare, greater biodiversity, and enhanced resilience. This highlights a crucial research frontier focused on designing hybrid systems or new economic models that can translate the environmental resilience of organic methods into tangible market value, effectively resolving the very tradeoffs this comprehensive assessment suggested.

During the last two decades, hyperspectral imaging (HSI) has been one of the most studied and applied techniques in the field of nondestructive monitoring systems for the fruit and vegetable supply chain. This review provides HSI technical aspects (i.e., device features) and data analysis approaches (i.e., data processing and qualitative/quantitative modeling) for fresh-cut products, focusing on the different applications which the literature offers and the possible scale-up for process monitoring. Moreover, new frontiers in the development of possible process analytical technologies of cost-effective and hand-held HSI devices are presented and discussed. Even though the performance of these new proximal sensing tools needs to be carefully evaluated, new applicative research perspectives in the development of a proximal sensing approach based on HSI sensor networks are ready to be studied and developed for finding field applications (i.e., precision agriculture, food processing, and more) and enabling faster and more convenient analysis while maintaining the accuracy and capabilities of traditional HSI systems.

Background and Aims. The composition of grapes (Vitis vinifera L.) at harvest is a key factor that determines the future quality of wine. The work aimed to evaluate and compare the environmental impact of the most evaluated technological parameters using three approaches: the wet-chem method, the optical method using benchtop devices, and the optical method using a prototype smart and cost-effective device (Technology Readiness Level: 5). Methods and Results. The life cycle assessment (LCA) methodology was used to identify the most environmentally sustainable solution in a from-cradle-to-grave approach. The functional unit was identified by the execution of the analyses necessary to measure the three technological parameters: TSS, pH, and TA. The findings show that the optical analysis carried out with the prototype is the most suitable and green solution (i.e., with the lowest environmental impact in all the impact categories analysed); in fact, the same technology has proved to be 3.2 times more sustainable than the wet-chem method for the best average environmental performance. Conclusions. The research demonstrated the environmental impact advantages of the optical analyses for assessment of grape composition. Significance of the Study. Innovations in agriculture and the development of smart solutions represent advantages for managing and monitoring the composition of agri-food products that are a green solution for the industry.

In this paper, a cost-effective visible/near infrared optical prototype was tested for grape maturity monitoring. The device was used to quantify the qualitative parameters of Chardonnay grapes, based on the combination of spectroscopic data and the creation of predictive models. The optical acquisitions were performed directly in the field through the use of 12 wavelengths in the vis/NIR range, i.e., 450, 500, 550, 570, 600, 610, 650, 680, 730, 760, 810 and 860 nanometers. The prediction of the qualitative parameters was carried out through a multivariate model, partial least square (PLS) regression technique and built knowing the real values of the parameters, i.e., total soluble solids (TSS), titratable acidity (TA) and pH measured through the reference laboratory analyses. Sampling included two harvest years. The most efficient model was the one for TSS evaluation that gave a R² = 0.87 (independent test set validation). The results demonstrated that the optical device is able to provide useful information about the ripening parameters of Chardonnay grapes directly in the field in order to predict its correct maturation stage and, therefore, support operators in rapid and objective decision making. Overall, the use of the prototype promotes a sustainable approach and viticulture 4.0.

In recent years, the use of automated machine tools in the wine industry has increasingly gained ground to simplify and optimize winemaking, complying with Industry 4.0 requirements. This work aimed to analyse a system for the automatic management of yeast nutrition in alcoholic fermentation in terms of environmental, management, and economic performance in comparison with traditional fermentation management. The automated system is a transportable and easily installable place and start system, equipped with a control unit and rods for the dosage of nutrients, and it works with a memory unit in which fermentative kinetics curves are loaded. The curves are predefined or customized according to oenologists’ needs. Hence, fermentation time, manpower, nutrients, oxygen, water, and energy consumption were evaluated concerning the alcoholic fermentation process. The analysis was carried out considering two different Italian wineries with different working capacities. Furthermore, life cycle assessment methodology and variable costs analysis was performed. Overall, the automated system reveals to be a promising investment, especially if applied to wineries characterized by high-volume tanks, where scale factor played a crucial role. Nutrients used by the automated system are more expensive but more environmentally sustainable than traditional ones.