<p>The rapid transition from backyard to industrial livestock production has profoundly altered the geochemical characteristics of agricultural wastes, yet conventional risk assessment frameworks remain predominantly concentration-oriented and lack the capacity to resolve structural imbalances within the heavy metal suite. This study proposes a novel two-dimensional geometric fingerprinting framework to quantify the decoupling between pollution magnitude and structural distortion in livestock manure. A total of 204 manure samples were collected from a representative intensive farming region in the Sichuan Basin, Southwest China. Eight heavy metals and pH were analyzed, and concentrations were dynamically normalized using the pH-dependent thresholds defined in the Ministry of Ecology and Environment of the People’s Republic of China standard GB 15618-2018. Two geometric descriptors were derived from radar projections of risk quotients: the comprehensive risk area (<i>S</i>_area), representing cumulative pollution magnitude, and the coefficient of variation (CV), quantifying fingerprint distortion. Results revealed a significant expansion of pollution magnitude under industrial farming, accompanied by intensified structural imbalance, primarily driven by excessive Cu and Zn inputs. Principal component analysis further confirmed a clear structural divergence between backyard and industrial systems. Critically, no significant linear correlation was observed between <i>S</i>_area and CV, demonstrating a stochastic decoupling between total load and geochemical structure. This dual-indicator framework reveals that pollution magnitude does not inherently predict structural distortion, highlighting the inadequacy of single-metric assessments. By integrating dynamic regulatory normalization with geometric topology, the study establishes a structural early warning paradigm for manure management.</p>