Enhancing ecosystem services to mitigate agro-environmental pressures: integrating participatory mapping and land suitability analysis for crop-livestock mixed farming and agroforestry systems - Alberto Mantino; Sara Burbi; Martina Re; Matteo Finocchi; Alice Ripamonti; Manuel Pulido Fernández; Susanne Schnabel; Joaquín Francisco Lavado-Contador; Mareike Jäger; Matthias Baumann; Clémence Berne; Ulrich Schmutz; et al. (Agroforestry Systems, 2026)
Stakeholders from six European pilot sites engaged in participatory mapping and land suitability assessments to co-design climate-smart strategies for sustainable land management. The mixed methodology applied combined GIS based landscape vulnerability analysis, stakeholder knowledge, and assessments of ecosystem services. Key phases included preliminary assessment of environmental pressures, participatory SWOT analysis, and collaborative mapping exercises to identify suitable mixed farming (MF) and agroforestry (AF) practices. This approach empowered local communities, enhanced knowledge exchange, and integrated socio-ecological dimensions into land-use planning. Participatory mapping proved effective in capturing spatial perceptions, guiding context-specific transitions, and building consensus on landscape-scale interventions. Based on environmental pressure indicators, the scaling-up analysis showed that, depending on local conditions, the proportion of areas suitable for MF and AF ranged from 2 to 61% of the total area analysed across the six pilot sites. All the stakeholders agreed on the introduction of MF and AF and expressed differing views on their potential to reduce the environmental pressures of agricultural practices and enhance ecosystem services. Practitioners, such as farmers and advisors, emphasised the need for greater knowledge and stronger policy support to implement the transition toward more agroecological farming systems. While results highlight the large potential for MF and AF (up to 61% of the land use, in certain cases), it also showed the importance of participatory tools in bridging scientific research and practice, reinforcing the role of stakeholder engagement in designing resilient and multifunctional agricultural systems. While this might help to bridge the gap between planning and the implementation of agroecological practices across diverse European contexts, further research on the implementations and the socio-economic assessment of MF and AF at landscape scale is needed. Graphical abstract

Development of a machine learning tool for the enhancement of carbon footprint prediction for cattle milk production - Lorena Foschi; Diego Pennino; Giulia Gislon; Giampiero Grossi; Alberto Mantino; Marco Barbanera; Marco Marconi; Francesco Cesarini; Chiara Rossi; Andrea Vitali; Anna Sandrucci; Maddalena Zucali; et al. (The International Journal of Life Cycle Assessment, 2025)
Purpose The combination of machine learning (ML) and life cycle-based methodologies is a promising strategy to overcome some of the most relevant issues regarding carbon footprint (CF) calculation/prediction. This research presents an approach and a software tool to apply ML techniques with the aim of solving issues related to data availability in the life cycle inventory (LCI) and improving the accuracy of carbon footprint predictions. The final purpose is to streamline the CF calculation process in the dairy cattle farming sector. Methods The methodology used in this study consists of three steps: dataset creation, data optimization, and enhanced prediction. Initially, a dataset was compiled from primary sources and literature on LCA studies related to dairy cattle farming. This dataset contained missing data that could affect prediction accuracy. ML techniques were then applied to improve the quality of the data. A ML tool implementing 11 different regression algorithms was developed. This user-friendly tool, easily accessible also to non-ML experts, automatically optimizes data quality by determining the most appropriate algorithm for predicting missing values on a case-by-case basis. In the final step, the same tool was used on the optimized dataset to identify and use the best model for accurate CO 2 -eq emissions prediction. Results and discussion An initial assessment was conducted to evaluate the accuracy of a ML model. The results of the test demonstrated that the Gaussian kernel regression model exhibited the highest performance among the available models, with a root mean square error (RMSE) of 18.87%. This value will be used as a threshold for the assessment of the efficacy of the described approach. The tool was then employed to optimize the dataset by predicting missing values. Lastly, the tool was utilized to predict CO 2 -eq emissions once more, but this time using the optimized dataset for training. As with the previous test, the Gaussian model exhibited the highest performance, but this time with a RMSE of 14.65%. The RMSE was 4% lower, thereby reflecting an increased accuracy in the prediction. Conclusions The results demonstrate that the presented ML-based approach and tool are effective in predicting lacking inventory data, as well as in improving the accuracy and reliability of CO 2 -eq emissions prediction in life cycle-based studies. The developed tool makes ML more accessible to non-expert users and facilitates usability. This integration of ML and life cycle-based methodologies shows promising avenues for more accurate and efficient environmental impact assessments in agriculture.

Effects of tree presence on forage yield and nutritive value in agroforestry livestock systems: a global systematic review - Alice Ripamonti; Matteo Finocchi; Antonio Pulina; Antonello Franca; Giovanna Seddaiu; Luca Turini; Marcello Mele; Alberto Mantino (Agroforestry Systems, 2025)

Carotid artery stenting during endovascular thrombectomy for acute ischemic stroke with tandem occlusion: the Italian Registry of Endovascular Treatment in Acute Stroke - Fabrizio Sallustio; Giovanni Pracucci; Manuel Cappellari; Valentina Saia; Alfredo Paolo Mascolo; Federico Marrama; Roberto Gandini; Giacomo Koch; Marina Diomedi; Federica D’Agostino; Alessandro Rocco; Valerio Da Ros; et al. (Acta Neurologica Belgica, 2023)

Human sperm motility: a molecular study of mitochondrial DNA, mitochondrial transcription factor A gene and DNA fragmentation - Fabiana Faja; Tania Carlini; Giulia Coltrinari; Federica Finocchi; Matteo Nespoli; Francesco Pallotti; Andrea Lenzi; Francesco Lombardo; Donatella Paoli (Molecular Biology Reports, 2019)