Agricultural systems are an essential and growing aspect of our society.  Not only is agriculture going through a data-centric revolution (Smith and Katz, 2013), the methods and analysis approaches for such efforts are also becoming much more complex (Gray, 2006, Bell, 2009, Clarke, 2009, Maimon and Rokach, 2010).  Precision agriculture systems, cloud-based data assembly for farmers, and machine learning algorithms for predictive analytics, are all example areas of scientific discovery that are pushing efficient agricultural systems forward. This research builds upon this data growth, through the development of a modular data mining and machine learning methodology, initially focused on agricultural systems. The proposed methodology will  be applied to  1) irrigated and 2) dryland agricultural systems in the Pacific Northwest region, stepping thru the processes of data assembly and geographic characterization, feature transformation and engineering, classifier/regressor selection, optimization, tuning, and finally, incorporation into a custom application programming interface (API).  Each model and API will use climate outcomes to predict agricultural crop loss, estimating the influence of these changing conditional relationships over time. (e.g. how influential is drought on crop loss for a particular county, and does that influence change into the future?).  Finally, the API, models, and analytics are integrated into a technology platform for access by land managers, farmers, or scientists, with the added capability of extending the methodology to other climate impact areas, such as health or land subsidence.