H Chica, M Fernández, J Caicedo and F Garcés
Estación Experimental Cenicaña, Vía Cali-Florida km 26. Corregimiento San Antonio de los Caballeros, Valle del Cauca, Colombia
To reduce climate-related risks through informed decision-making based on reliable information, Cenicaña classified the Automated Meteorological Network (RMA) information into homogeneous groups based on the behavior of important key climatic variables. The study sought to determine the differential productivity behavior of sugarcane within each of the identified groups. To achieve this, historical daily climatic data were processed, generating 80 variables summarizing the average and cumulative seasonal behavior of climatic conditions for each season within the Cauca River Valley’s annual rainfall calendar. The Ward clustering method was applied after standardizing the variables using the maximum absolute value method to identify the station groups. The hierarchical classification of the stations into seven groups explained 74% of the climatic variability in the region. The groups exhibited distinct climatic and productive behavior. Groups with higher levels of solar radiation and elevated temperatures were associated with greater cane yield (t/ha). The groups with higher precipitation levels displayed more variable production outcomes. The differences observed between the groups regarding climatic conditions and productivity provided a basis for optimizing crop management strategies and tailoring them to local conditions. These results are being applied in implementing climate forecasting services and decision-making models with the deployment of applications. These tools support science-based, data-driven decisions for a resilient and sustainable sugarcane industry.