LLM-Enabled Reconstruction of Farmer Fertilizer-Reduction Responses Under Policy Scenarios: Evidence from Sparse Stated-Preference Data

Agricultural fertilizer reduction depends on farmers’ responses to policy incentives, but such responses are often observed only at a few subsidy levels and under hypothetical conditions. Using survey-based stated-preference data from 15 counties in China, this study examines whether large language model (LLM)-based methods can reconstruct fertilizer-reduction response intervals under alternative subsidy scenarios. Three LLM-based inference strategies were designed and compared with 14 conventional methods within an exploratory evaluation framework covering interval recovery, extrapolation behavior, and curve-shape plausibility. LLM-based methods were competitive in this sparse-anchor reconstruction task. The incremental inference strategy, which reconstructs target intervals through local changes between subsidy anchors, produced the most stable results. DeepSeek V3.2 Increment obtained the highest IO (0.528) and a high EIO (0.602), while Qwen3-8B Increment achieved the lowest MAME (1.291) and the highest EIO (0.636). SHAP analysis showed that reconstruction difficulty was mainly associated with fertilizer bags per mu (0.2414), annual fertilizer cost (0.1808), and fertilization training (0.1473). Overall, this study explores the potential of LLM-based inference as a flexible approach for fertilizer-reduction policy-response analysis from limited stated-preference data.