A FRAMEWORK FOR IMPROVED DATA FLOW AND INTEROPERABILITY THROUGH DATA STRUCTURES, AGRICULTURAL SYSTEM MODELS, AND DECISION SUPPORT TOOLS
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.
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.
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.
- Doctor of Philosophy
- Agricultural and Biological Engineering
- West Lafayette