Management-oriented modeling: optimizing nitrogen management with artificial intelligence

Increasing nitrate levels in groundwater have been attributed to inappropri ate nitrogen (N) management. The application rates, timing, and methods of both N fertilization and irrigation are important management tools that det ermine and control the fate and behavior of N in soil-plant systems. For ex ample, multiple applications with small amounts of fertilizer (e.g. split a pplication) usually enhance plant uptake and reduce potential nitrate leach ing, although increasing costs. Precision N management requires that the mo dels evaluate so many alternatives that traditional N models are challenged beyond their intended use. The objective of this study was to construct an d test a model that searches for optimal N management to: (1) minimize nitr ate leaching; (2) maximize production; and (3) maximize profits. Management -oriented modeling (MOM), a dynamic simulation modeling with artificial int elligence optimization techniques, was developed for these purposes. MOM co nsists of a generator that generates a set of plausible management alternat ives, a simulator that evaluates each alternative, and an evaluator that de termines which alternative meets the user-weighted multiple criteria. MOM u ses '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 goal s, 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 MO M offers new opportunities to balance N and irrigation water management whi le meeting multiple objectives. (C) 2000 Published by Elsevier Science Ltd.