<p>The agricultural data landscape is largely dysfunctional because of the industry’s highvariability  in  scale,  scope,  technological  adoption,  and  relationships.   Integrated  data  andmodels of agricultural sub-systems could be used to advance decision-making, but interoperability  challenges  prevent  successful  innovation.   In  this  work,  temporal  and  geospatial indexing  strategies  and  aggregation  were  explored  toward  the  development  of  functional data  structures  for  soils,  weather,  solar,  and  machinery-collected  yield  data  that  enhance data context, scalability, and sharability.</p> <p>The data structures were then employed in the creation of decision support tools including web-based  applications  and  visualizations.   One  such  tool  leveraged  a  geospatial  indexing technique called geohashing to visualize dense yield data and measure the outcomes of on-farm yield trials.  Additionally, the proposed scalable, open-standard data structures were used to drive a soil water balance model that can provide insights into soil moisture conditions critical to farm planning, logistics, and irrigation.  The model integrates SSURGO soil data,weather data from the Applied Climate Information System, and solar data from the National Solar Radiation Database in order to compute a soil water balance, returning values including runoff, evaporation, and soil moisture in an automated, continuous, and incremental manner.</p> <p>The approach leveraged the Open Ag Data Alliance framework to demonstrate how the data structures can be delivered through sharable Representational State Transfer Application Programming Interfaces and to run the model in a service-oriented manner such that it can be operated continuously and incrementally, which is essential for driving real-time decision support tools.  The implementations rely heavily on the Javascript Object Notation data schemas leveraged by Javascript/Typescript front-end web applications and back-end services delivered through Docker containers.  The approach embraces modular coding concepts and several levels of open source utility packages were published for interacting with data sources and supporting the service-based operations.</p> <p>By making use of the strategies laid out by this framework, industry and research canenhance data-based decision making through models and tools.  Developers and researchers will  be  better  equipped  to  take  on  the  data  wrangling  tasks  involved  in  retrieving  and parsing unfamiliar datasets, moving them throughout information technology systems, and understanding those datasets down to a semantic level.</p>