Descriptive Analysis and Clustering-Based Productive Scale Segmentation of Colombian Transitory Crop Production: A Departmental-Level Approach

Colombian transitory crop production exhibits marked structural heterogeneity across department–crop combinations, yet empirical characterizations of productive scale at the subnational level remain scarce. This study presents a descriptive analysis and clustering-based productive scale segmentation of Colombian transitory crops at the departmental level for the period 2007–2024. Data from the Evaluaciones Agropecuarias Municipales(EVA) were processed through a structured CRISP-DM pipeline comprising preprocessing of 347,141 records, departmental aggregation, and engineering of five clustering features: average production, average planted area, number of active periods, and temporal and spatial Herfindahl–Hirschman indices. K-Means clustering (k=3)was applied to a final dataset of 490 department–crop pairs and validated based on a global silhouette coefficient of 0.888. The segmentation reveals a markedly asymmetric productive structure: 93.7% small scale (459 pairs), 5.3% medium scale (26 pairs), and 1.0% large scale (5 pairs), with natural breakpoints at approximately 35,386 t and 275,959 t. Large-scale production is concentrated in papa (Cundinamarca, Boyacá, Nariño) and arroz (Casanare, Tolima). Clustering demonstrated quantitative superiority over quartile-based classification, reducing the within-group coefficient of variation from 223.9% to 30.6% for the upper segment. The methodology is replicable across national agricultural statistics systems, and the processed dataset is publicly available under CC BY 4.0.