This study examined the effects of meteorological and farm input cost factors on the onion yields in Korea by employing a panel regression analysis. Also, it investigated the variables’ interdependencies and their relations to the onion yields by using a Bayesian network model. We collected the panel data from 1991 to 2019 for our analysis. More specifically, we used the panel data of the regional meteorological factors by month (cumulative precipitation, cumulative sunshine duration, average relative humidity, average temperature), farm input cost factors of the onion (the cost of fertilizer and the cost of agricultural medicines), and the onion yield of three regions in Korea. We used STATA 14.0 and Hugin Expert for descriptive analysis, panel regression analysis, and the Bayesian network model. Our analysis can be summarized in two significant ways. First, we chose the fixed-effect model based on the Hausman test. The results based on the fixed-effect model confirmed that the average relative humidity (October, -), the cumulative precipitation (January, -; March, +; May, +), the cumulative sunshine duration (April, +), the average temperature (June, -), the cost of fertilizer(+) and the cost of agricultural medicines(+) were the significant variables of the model. Second, we analyzed the relationship between meteorological factors and farm input cost factors on the onion yields through the Bayesian network model and showed that the Bayesian network model is a promising analysis method that is useful in the agricultural sector to predict the yield. We also discussed the implications and limitations of this study and future research directions at the end of the paper.