ArticlePest management science2026
Precision management of the pernicious weed Eriochloa villosa (Thunb.) Kunth in maize: an anchor-point calibration framework integrating economic thresholds and species distribution models.
Article in Pest management science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundEriochloa 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.
resultsWe 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
conclusionThe 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.
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