Time-Series Similarity and Clustering of Producer Share Dynamics in Agrifood Markets: Evidence from Origin–Destination Price Relationships

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.