Agroforestry systems have multifunctional roles in enhancing agronomic productivity, co-production of diversity of food and non-food products and provision of ecosystem services. The knowledge of the performance of agroforestry systems compared with monoculture is scarce and scattered. Hence, the objective of the study was to analyze the agronomic productivity and economic viability of diverse agroforestry systems in Europe. A network of five agroforestry systems integrating arable crops, livestock and biomass trees was investigated to assess the range of agricultural products in each agroforestry system. Land Equivalent Ratio (LER) was used to measure the agronomic productivity, whereas gross margin was used as an indicator for economic viability assessment. LER values ranged from 1.36–2.00, indicating that agroforestry systems were more productive by 36–100% compared to monocultures. Agroforestry gross margin was lower in Denmark (€112 ha⁻¹ year⁻¹) compared to United Kingdom (€5083 ha⁻¹ year⁻¹) and the crop component yielded higher returns compared to negative returns from the tree component in agroforestry. Hence, the study provided robust field-based evidence on agronomic productivity and economic viability assessment of agroforestry systems in diverse contexts for informed decision making by land managers, advisory services, farmers and policymakers.

Given the environmental footprints of the conventional agriculture, it is imperative to test and validate alternative production systems, with lower environmental impacts to mitigate and adapt our production systems. In this study, we identified six production systems, four in Italy and two in Denmark, to assess the environmental footprint for comparison among the production systems and additionally with conventional production systems. SimaPro 8.4 software was used to carry out the life cycle impact assessment. Among other indicators, three significantly important indicators, namely global warming potential, acidification, and eutrophication, were used as the proxy for life cycle impact assessment. In Italy, the production systems compared were silvopastoral, organic, traditional, and conventional olive production systems, whereas in Denmark, combined food and energy production system was compared with the conventional wheat production system. Among the six production systems, conventional wheat production system in Denmark accounted for highest global warming potential, acidification, and eutrophication. In Italy, global warming potential was highest in traditional agroforestry and lowest in the silvopastoral system whereas acidification and eutrophication were lowest in the traditional production system with high acidification effects from the silvopastoral system. In Italy, machinery use contributed the highest greenhouse gas emissions in silvopastoral and organic production systems, while the large contribution to greenhouse gas emissions from fertilizer was recorded in the traditional and conventional production systems. In Denmark, the combined food and energy system had lower environmental impacts compared to the conventional wheat production system according to the three indicators. For both systems in Denmark, the main contribution to greenhouse gas emission was due to fertilizer and manure application. The study showed that integrated food and non-food systems are more environmentally friendly and less polluting compared to the conventional wheat production system in Denmark with use of chemical fertilizers and irrigation. The study can contribute to informed decision making by the land managers and policy makers for promotion of environmentally friendly food and non-food production practices, to meet the European Union targets of providing biomass-based materials and energy to contribute to the bio-based economy in Europe and beyond.

Olive tree is a vector of cultural heritage in Mediterranean. This study explored the biocultural geography of extra virgin olive oil (EVOO) from the cultivar Ogliarola campana in Campania region, Italy. Here, the rich cultural elements related to olive tree and oil represent a suitable case study for a biocultural analysis. We joined analytical techniques, based on stable isotopes and trace elements of EVOOs, with humanistic analyses, based on toponymy and historical data. In order to provide a science-based assessment of the terroir concept, we set up a new method of data analysis that inputs heterogeneous data from analytical and anthropic variables and outputs an original global evaluation score, named terroir score, as a measure of biocultural distinctiveness of the production areas. The analysis highlighted two distinct cultural sub-regions in the production area of Ogliarola campana: a continental cluster in the inner area of Irpinia and a coastal one around Salerno province. Finally, a biocultural map displays the diversity of heterogeneous variables and may support science-based decision making for territory valorisation. This novel biocultural analysis is a promising approach to substantiate the terroir concept with science-based elements and appears suitable to characterize local agri-food products with old tradition and historical data.

We developed a novel approach in the field of spatiotemporal modeling, based on the spatialisation of time, the Timescape algorithm. It is especially aimed at sparsely distributed datasets in ecological research, whose spatial and temporal variability is strongly entangled. The algorithm is based on the definition of a spatiotemporal distance that incorporates a causality constraint and that is capable of accommodating the seasonal behavior of the modeled variable as well. The actual modeling is conducted exploiting any established spatial interpolation technique, substituting the ordinary spatial distance with our Timescape distance, thus sorting, from the same input set of observations, those causally related to each estimated value at a given site and time. The notion of causality is expressed topologically and it has to be tuned for each particular case. The Timescape algorithm originates from the field of stable isotopes spatial modeling (isoscapes), but in principle it can be used to model any real scalar random field distribution.

Italy is the second largest extra-virgin olive oil (EVOO) producer within the European Union. Despite its importance in preserving rural landscape and in supporting household economy, the EVOO sector faces several constraints due to high management costs, small farm size, lack of cooperation and investment, production vulnerability, and farmers’ ageing. Such a number of weak points suggests the need to identify and adopt innovative approaches, at both the farm and oil mill levels. In order to address these priorities, a fuzzy cognitive mapping (FCM) survey was carried out in Umbria region, central Italy, involving key local stakeholders of the EVOO value chain in the Orvieto district. Based on stakeholders’ perception and knowledge, this paper aims to identify and evaluate the most relevant components of the local olive oil value chain, and predict scenarios responding to hypothetical changes of the same components. These stakeholders were firstly invited to each build an individual fuzzy cognitive map and then, grouped all together, build a joint fuzzy cognitive map. Finally, the maps represented both the individual and the grouped stakeholders’ perceptions. The maps were translated into adjacency matrices in order to create an FCM model by applying the software “Mental Modeler”. In total, 24 participants, including practitioners, multipliers, researchers, suppliers and members of local administration, participated in the survey. The component analysis and the scenario analysis highlighted several priority issues: to preserve the ecosystem functioning, to implement cooperation, innovation and education, to adapt and mitigate climate change. The main novelty of this study is that all stakeholders’ categories in the EVOO sector recognize several challenges to sustain the EVOO value chain, in particular, climate change adaptation and mitigation.