Precision management of the pernicious weed Eriochloa villosa (Thunb.) Kunth in maize: an anchor‐point calibration framework integrating economic thresholds and species distribution models

Abstract BACKGROUND Eriochloa villosa is a destructive pernicious weed threatening maize production in Northeast China. While species distribution models (SDMs) identify potential habitats, they often overestimate risk areas, leading to inefficient resource allocation. Bridging the gap between macro‐scale prediction and field‐level management remains a critical challenge for precision agriculture. RESULTS We developed an integrated risk navigation framework combining a stacked ensemble SDM (AUC = 0.932) with field‐verified economic parameters. Through controlled field experiments, we established a management‐oriented economic threshold (ET) of 6.2 plants/m 2 and a critical period for weed control (CPWC) of 32 days. Utilizing a novel ‘anchor‐point calibration’ method, we translated ecological probabilities into a three‐tiered risk zonation. This approach compressed the priority management area by over 2200‐fold compared to traditional statistical thresholds, identifying a High Economic Risk Zone of just 66 km 2 . CONCLUSION The proposed framework concentrates management efforts on high‐impact zones, drastically reducing the priority management footprint and optimizing resource allocation compared to conventional strategies. By explicitly incorporating prediction uncertainty, this study provides a highly efficient, scientifically robust decision support tool for pernicious weed management and pesticide reduction in maize systems. © 2026 Society of Chemical Industry.