In the context of the globalization process, the interplay between geopolitical dynamics and international trade fluctuations has had significant effects on global economic and business stability. Recent crises, such as the US–China trade war, the invasion of Ukraine, and the COVID-19 pandemic, have highlighted how changes in the structure of international trade can amplify the risks of business failure and reshape global competitiveness. This study aims to analyze in depth the transmission of business failure risk within the global trade network by assessing the sensitivity of industrial sectors in different countries to disruptive/critical/significant events. Through the integration of data from sources such as the World Trade Organization, national customs, and international relations research centers, a quantitative, exploratory, and descriptive approach based on graph theory, random forest, multivariate regression models, and neural networks is developed. This quantitative system makes it possible to identify patterns of risk propagation and to evaluate the degree of vulnerability of each country according to its commercial and financial structure. The mechanisms that relate geopolitical factors, such as trade sanctions and international conflicts, with the oscillations in the global market are analyzed. This study not only contributes to our understanding of how the macroeconomic environment influences business survival, but also provides analytical tools for strategic decision making. By providing an empirical and theoretical framework for early risk identification, it brings a novel perspective to academia and business, facilitating better adaptation to an increasingly volatile and uncertain business environment.

Parcel size strongly influences agricultural production costs, and combining spatial and economic information within a mathematical setting helps to clarify this relationship. In this study, we introduce a Gamma-based stochastic framework to integrate actual parcel size distributions into cost estimates, an approach that, to our knowledge, has not been applied in this context. Using a representative traditional orchard system as a case study, parcel sizes (characterized by strong right skewness) are modelled with a Gamma distribution; for highly fragmented landscapes, a truncated Gamma on $(0.01,1]$ ha yields a mean parcel area of about $0.255$ ha. Results show that parcel-size heterogeneity substantially affects expected per-parcel costs; for example, calibrating ploughing at 800 EUR/ha leads to an average of ∼160 EUR/parcel, whereas intensive vegetable harvesting at 5000 EUR/ha reaches ∼2100 EUR/parcel. In our simulation, in which the main parameters have been roughly fixed with the aim of showing the methodology, results are given on an expected costs scale relative to parcel area and operation intensity. Overall, the framework shows how parcel-size distributions condition cost estimates and provides a transferable basis for comparative analyses, while acknowledging limitations related to the area-only specification.

The management of non-industrial farming sites often lacks context-specific indicators that allow effective planning. Identifying reliable measures suited to small-scale agriculture (especially in settings with unique features such as suburban orchards) is therefore crucial for their viability. In this paper, we systematically review the indicators that can be applied in such contexts and compare them with those used in similar environments. Because the number of possible indices is as wide as the dimensions involved (social, economic, geographical, water, etc.), we narrow our scope to indicators related to sustainability and water resources. By analyzing several published time series, we show that many of these indices are strongly correlated, so tracking just one or two of them is normally sufficient, a simplification that greatly eases management in low-resource settings. We also point out that many variables can be obtained from open-data repositories or other easily accessible sources, so that producers with limited budgets can monitor the most relevant information without investing in costly data-collection systems. The literature suggests that this reduction could likewise apply to other sustainability factors.

Producer share indicators summarize how value is distributed along agrifood supply chains, yet their temporal dynamics remain difficult to compare across products and periods. This paper proposes a reproducible time-series analytics framework to characterize and group producer-share trajectories derived from paired origin–destination price series. We compute producer share time series for a set of agrifood products and quantify similarity using complementary measures capturing co-movement and shape, including Pearson-correlation-based proximity and Euclidean distance on standardized representations. To reduce dimensionality and mitigate noise, we apply principal component analysis and perform unsupervised clustering (k-means) to identify classes of products exhibiting comparable producer-share dynamics. The resulting clusters provide an interpretable typology of market behaviors, highlighting homogeneous groups that may share structural drivers (e.g., commercialization patterns or intermediation margins). We further discuss how cluster membership can support decision-making in crop substitution and market monitoring by revealing products with analogous temporal responses. The proposed pipeline is simple to implement, fully data-driven, and adaptable to other commodity-price settings.

La iniciativa de la apertura de datos de las administraciones públicas es aparentemente independiente del sesgo ideológico de los gobiernos en los países democráticos. Sin embargo, esta práctica se justifica de forma distinta según el país donde se desarrolla, siendo en muchos casos una pieza más del sistema económico, plenamente compatible con los planteamientos del neoliberalismo. Así, los sistemas de información pueden ser diferentes dependiendo del enfoque de los gobiernos y de sus fundamentos ideológicos, y las iniciativas de apertura de datos pueden sustentarse tanto en planteamientos liberales de apoyo a las empresas como en términos de acciones de mejora social. Aunque habitualmente se asume que los proyectos de apertura de datos son apolíticos, lo cierto es que las ideologías que los justifican pueden ser muy distintas en relación a los rendimientos económicos o a los beneficios sociales que producen. La cuestión es determinar quiénes son los beneficiarios finales de la información que proporcionan.