Since the 1980s, expert decision support systems (DSSs) have been explored for enhancement of agricultural decision-making. Combinations of expert DSSs and cyber-age technology, such as mobile devices, is increasing adoption and accuracy of these systems and will allow DSSs to be easily modified to incorporate new information and web-based resources as they become available. Using barley yellow dwarf (BYD), a disease complex caused by several aphid-vectored viruses, as a model system we created a DSS for winter wheat growers based on dependency networks. At key points throughout the growing season the networks interpret how field conditions may affect management recommendations for BYD in winter wheat. To address nine possible management recommendations the networks analyze 72,387 combinations of input field conditions. This method of decision modeling can potentially be used to provide support to enable the efficient management of other crop pests and diseases and enable a more sustainable agroecosystem. The DSS was created for use in a mobile device app which will produce real-time recommendations, emulating disease management experts. Coupling this expert DSS with high resolution weather, pest, and disease forecasts will prove to be a powerful management tool in the future.
All Science Journal Classification (ASJC) codes
- Agronomy and Crop Science
- Computer Science Applications