Abstract

To address the challenge of balancing food production, groundwater protection, and economic sustainability in agricultural systems, this study developed a novel multi-objective optimization framework integrating an Extreme Learning Machine (ELM)-based surrogate model. An irrigation–fertilization–economy (IFE) model was established to evaluate region-specific agricultural adjustment strategies under four scenarios: baseline adjustment, flexible restructuring, production reduction and profit-oriented expansion scenarios. The optimization results were subsequently used to assess the impacts of agricultural production regulation on groundwater nitrate concentrations in the study area. The results showed that the IFE model consistently favored the conversion from wheat to maize cultivation across all regions, because maize requires less irrigation and fertilizer inputs while maintaining relatively high net profits. However, the optimal strategies varied among regions: Pingdu and Jiaozhou showed greater economic potential for production expansion, whereas regions with lower economic potential were more suitable for reducing high-input crop cultivation. During the 2020–2030 prediction period, nitrate concentrations exhibited substantial interannual variations. Reducing the overall production scale shifted nitrate concentrations approximately 6% closer to the 30–50 mg/L range over the decade, but this limited environmental benefit was accompanied by substantial short-term economic losses. Considering both environmental impacts and economic viability, the optimization results suggest that moderate agricultural expansion could be acceptable provided that environmental burdens are effectively managed.

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