Yield maps are commonly collected by producers and precision-agriculture service providers and are accumulating in warehouse scale data-stores. A key goal in analysis of yield maps is to understand how climate interacts with soil landscapes to cause spatial and temporal variability in grain yield. However, there are many issues that limit utilization of yield map data for this purpose including: i) yield-landscape inversion between climate years, ii) sensor system malfunction and inaccuracy, iii) poor data management practices and operator error, iv) field configuration and logistical limitations, v) spatial, temporal, and producer variability in agronomic management, and vi) incomplete target and predictor dataspace. Each of these issues requires a significant effort to understand and then address by the commercial and research precision agriculture community. A key goal of this investigation was to use a regional extent yield map data warehouse to model the effects of soil landscape properties on site specific mean yield and yield risk. Data mining technologies were used to examine relationships between yield map data and soil landscape attributes. Our initial results indicate challenges in training data mining algorithms to produce stable estimates when applied to independent testing data both within and across years. We found the above factors reduce the effectiveness of data mining approaches. To improve this situation, we propose a more stringent data cleansing procedure and a more agronomically complete yield map data model to better populate important predictive information in yield map databases.