Co-Expression Modules and Core Regulatory Factors Linked to Maize Abiotic Stress Resistance Under the Compound Agroecological Stress Index in Southwest China.

Regionally, compound agroecological stress arising from both natural and anthropogenic emergy inputs may influence maize transcriptomic responses; however, evidence across multiple scales remains limited. We developed a reproducible five-step framework integrating a macro-level compound stress index, molecular response modules, cross-scale coupling, spatial continuity, and independent field validation. Nine variables (emergy indicators ELR, Fn, and NEYR; climate; soil; and terrain) were PCA-weighted into a Composite Abiotic Stress Intensity Index (CASI; first three PCs = 83.7%; and prefecture-level Moran's I = 0.463). Across 15 public RNA-seq datasets (286 samples), WGCNA identified five separable modules (drought-heat, reproductive stage heat, low nitrogen/phosphorus, osmotic salt, and chronic compound), 270 core genes, and four cross-module hubs (ZmDREB2A, ZmHSFA2, ZmWRKY33, and ZmNRT2.1). With n = 21, the sCCA (r1 = 0.81, permutation p = 0.003; LOO-CV r = 0.71), random forest, and spatial error model all confirmed coupling between ELR and the drought-heat module (β = 0.51, p = 0.008). PLS-DA four-zone partitioning (Q2 = 0.548) and a county-level second-order trend surface (R2 = 0.67) verified spatial continuity. GSVA on five independent field RNA-seq datasets yielded 74.4 to 82.8% core gene directional consistency and Cliff's δ of 0.59 to 0.68 (large effect), avoiding circular reasoning. The framework enables molecular analysis for precision agriculture and climate-resilient breeding.