Precision agriculture (PA) is developing rapidly as an effective method to manage heterogeneity within agroecosystems and use farm resources more efficiently. While adoption rates of PA among farmers have been low, firms within the PA industry continue to develop increasingly complex technology and data analytics systems. This complexity begets high costs and reduced decision-making autonomy for participating farmers, who likely cannot make sense of their farm data on their own yet lack sufficient protections for their control of it within corporate partnerships. Open-source technology represents an emerging option to pursue PA which preserves farmer autonomy. In this thesis, I explore the effectiveness of an open-source precision viticulture (PV) system which measures soil moisture to map soil-related heterogeneity within a field of an organic vineyard and to discern the effects of cover crops on soil moisture dynamics. Using static soil characteristics—including soil organic matter (SOM) and texture—in addition to moisture data, I was able to parse 4 distinct clusters of homogeneous soil characteristics from the experimental area. I found no significant connections between cover crop species and soil moisture dynamics; however, I observed relationships between crop cover presence, SOM, and soil moisture dynamics which simultaneously validate the vineyard’s existing cover-cropping regime and warrant continuing the experiment. I evaluate issues of accessibility with open-source PA/PV and discuss the role collaborative networks may have in improving its effectiveness.