Short food supply chains (SFSCs) are advocated as strategies to improve the sustainability of the agro-food system. The recent literature claims that SFSCs are examples of social experimentation and innovation. By scaling up the positive elements of niche alternative food networks, SFSCs can serve as a basis for changing the overall agri-food system. However, their economic impact remains a matter of debate. Local multiplier methods have been used to measure the economic impact of economic sectors at a regional scale. In the case of SFSCs, multiplier methods need to be developed at a local scale and are highly data demanding. This analysis measures the local financial flow created by the MercaTiAmo project by applying the Local Multiplier 3 (LM3) method. MercaTiAmo is organized by a group of citizens and farmers and arranges farmers’ markets three days per week in the city of Parma (northern Italy). To measure the financial flow created by the initiative, data were collected from 10 farmers representing 70% of the farmers participating in the project. The method takes into account the total annual turnover (Round 1), the local expenses of the farmers (Round 2), and the local expenses of the farmers’ suppliers (Round 3). The results suggest that farmers with a higher turnover (Round 1) and percentage of direct expenses in the local area (Round 2) also show higher values of LM3, thus suggesting a higher economic impact, while Round 3 does not affect LM3. The novelty of the study relies on the fact that here the local multiplier method is applied for the first time at the farm level, providing precise information and data on farmers’ upstream supply chain, which can be used to inform policymakers and initiators of SFSCs. Moreover, the study provides a detailed description of the main categories of costs for farmers and which categories of costs are more suited to be locally based. For this reason, the study contributes to the debate on the economic impact of SFSCs in the local area. The method can also be used to provide information to consumers on the economic impact of alternative food networks and local food production.

The objective of this study is to perform an ex-ante assessment of the potential impacts of agro-environmental measures included in the post-2020 Common Agricultural Policy (CAP), by estimating farmers’ responsiveness in adopting organic agricultural practices and an eco-scheme that incentivises extensive forage systems. This research is conducted by means of an Agent-Based Model (ABM), based on Positive Mathematical Programming (PMP), implemented in GAMS. The ABM facilitates the simulation of interaction among farmers, allowing for an analysis of farm heterogeneity. The PMP methodology adds a non-rational dimention to the farmers’ economic drivers. The model is calibrated using 2019 Farm Accountancy Data Network (FADN) data specific to the Emilia Romagna region in Italy. Our findings reveal significant impacts on land use, with a notable decrease in cereal cultivation in favour of protein and fodder crops. Moreover, structural shifts are observed, notably a decrease in the number of small-scale farms. We also assess environmental and economic implications, observing a modest reduction in CO2 equivalent emissions per hectare, an increase in water demand, and an overall economic stability among farms, as indicated by changes in gross margin per hectare.

The aim of this work is to assess the structural, production, environmental, and economic impact of an increasing tax on climate change gas emissions related to milk production under the current CAP payment system. The analysis is performed using an Agent-Based Model (ABM) based on Positive Mathematical Programming (PMP). The integration between ABM and PMP makes it possible to simulate farmers’ strategies considering the interaction between them, the territorial specificity, and the heterogeneity of farms in the presence of little information on production costs. It also makes it possible to add a social and cultural perspective to the economic factors. The model is calibrated using FADN data for the Emilia-Romagna region (Italy) from the year 2020. The results show that farmers belonging to different age groups make decisions based on economic profitability, but also on their social and cultural background. To maximise their utility functions, farmers can opt for more efficient agricultural management practices that may result in the exchange of production factors, especially land. The overall impact penalises less efficient farms and agricultural production with higher negative externalities.

Linking computable general or partial equilibrium models with agent-based models can combine the strengths of both modeling concepts. The linking allows to trace global feedback effects while providing a flexible representation of human behavior and social interactions. This linking would facilitate the simulation of interconnected land-use and economic systems while accounting for actors’ heterogeneity, their specializations, and the representation of alternative decision models. In this paper, we examine the challenges that currently hinder the realization of these potential benefits and present a roadmap outlining possible solutions for successful model linking in the context of land use change. The main challenges include: 1) conceptual misalignment between modeling concepts, 2) differences in scales and resolution, 3) difficulties in validation and calibration of linked models, 4) increased complexity in interpreting and communicating results, 5) high demands on compu-tational infrastructure and computational costs, and 6) limited personnel and financial resources. Successfully linking different model concepts and overcoming these challenges requires the modelling communities and stakeholders to engage in long-term, platform-supported dialogue. This dialogue could facilitate the development of a shared framework for model linking, standards for model documentation, validation procedures, and appropriate approaches for communicating and evaluating results. This process will enhance the benefits and sustain the linked models while helping determine circumstances in which the advantages of linking models may outweigh the costs.

Milk quota policies in the European Union have long shaped dairy production, but their transformation into a tool restricted to Geographical Indications (GIs) raises new concerns about their effects on GI agricultural and rural systems. After encompassing the historical reasons that led to the new policy framework this study assesses the economic, environmental, and social impacts of quotas management under the greening measures of the Common Agricultural Policy 2023-2030. This is carried out by applying an ex-ante analysis using the AGRISP model, which combines Positive Mathematical Programming with Agent-Based Modeling. The case study of the Parmigiano Reggiano PDO is considered. Simulation results show that, while quotas increase production volumes and efficiency and do not strongly hinder marginal activities, they worsen farmers’ profits and environmental outcomes compared to a no-quota scenario. This paper provides a comprehensive analysis of the current milk quota policy in a production context underexplored in recent literature.