Management-oriented modeling: Optimizing nitrogen management with artificial intelligence

Increasing nitrate levels in groundwater have been attributed to inappropriate nitrogen (N) management. The application rates, timing, and methods of both N fertilization and irrigation are important management tools that determine and control the fate and behavior of N in soil–plant systems. For example, multiple applications with small amounts of fertilizer (e.g. split application) usually enhance plant uptake and reduce potential nitrate leaching, although increasing costs. Precision N management requires that the models evaluate so many alternatives that traditional N models are challenged beyond their intended use. The objective of this study was to construct and test a model that searches for optimal N management to: (1) minimize nitrate leaching; (2) maximize production; and (3) maximize profits. Management-oriented modeling (MOM), a dynamic simulation modeling with artificial intelligence optimization techniques, was developed for these purposes. MOM consists of a generator that generates a set of plausible management alternatives, a simulator that evaluates each alternative, and an evaluator that determines which alternative meets the user-weighted multiple criteria. MOM uses ‘hill-climbing’ as a strategic search method that uses ‘best-first’ as a tactical search method to find the shortest path from start nodes to goals. In a maize production scenario, MOM found an optimal management solution that would have increased the profit from $570 to $935 ha⁻¹ and reduced the nitrate leaching from 36 to 7 kg N ha⁻¹. Goal-driven simulation of MOM offers new opportunities to balance N and irrigation water management while meeting multiple objectives.