A prototype phenology-driven Bayesian belief network (BBN) model, named BxNET, was developed to represent the relationship between fresh market yield (U.S. #1 grade) and agroclimatic variables known to influence the critical storage root initiation stages in 'Beauregard' sweetpotato. This data-driven model was developed from experimental data collected over 3 years of field trials in which management variables were kept as uniform as possible. The BBN was developed assuming that soil moisture measured at the 15-cm depth was not a limiting variable during the first 20 days after transplanting, during which the onset of storage root initiation determined the majority of storage root yield at harvest. The absence of influence from weeds, disease, insect pests, and chemical injury was also assumed. Accuracy of the fully parameterized working prototype was estimated through leave-one-out cross-validation (14% error rate), validation on an independent test data set (20% error rate), and area under the receiving operator characteristic curve (0.59) analysis. As a result of its empirical nature, BxNET is only applicable to the cultivar, location, and the limited set of environmental (air temperature, soil temperature, relative humidity, solar radiation) and management variables as defined in the 3-year study. This beta-level model can serve as a foundation for the development of a final working model through further testing and validation. Additional validation data may require revision of the current model structure and conditional probabilities. These validation studies will also allow the model to be used in other locations. BxNET can be expanded to include other causal variables such as weed incidence, disease presence, insects, and chemical injury. Such an expansion can lead to the development of a model-based decision support system for sweetpotato production. Such a system can help model alternative management scenarios and determine the most reasonable management interventions to achieve optimum yield outcomes under different agroclimatic conditions.