Artificial Intelligence for Sustainable Agricultural Forecasting: Predicting Crop–Livestock Spatial Layouts in China’s Industrial Parks

The development of China’s National Modern Agricultural Industrial Parks (NMAIPs) has provided valuable knowledge to guide regional agricultural structural adjustment. To systematically analyze and scale up the successful practices of crop–livestock spatial layouts, this study examines 335 NMAIPs established between 2017 and 2024. Based on seven natural environmental variables, a deep clustering model (VAE-GMM) was applied to classify the parks into representative environmental types, establishing a standardized spatial reference frame. Crucially, the study introduces a spatial discrepancy (Gap) metric—calculated as the difference between model-predicted theoretical suitability and actual occurrence frequencies—to evaluate industrial expansion potential. Results reveal the parks form five distinct environmental types with clear regional patterns. The LightGBM prediction (micro-average AUC = 0.75, macro-average AUC = 0.63; range: 0.37–0.86) effectively captures natural constraints. Discrepancy analysis exposes a structural divergence between environmental suitability and actual agricultural allocation. Quantifying this divergence highlights suitable yet underrepresented industries, offering a pathway for sustainable resource management. By treating existing parks as reference baselines, this AI-driven forecasting framework provides transferable decision support for preliminary ecological screening and early-stage option identification in newly established parks.