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Ecological theory predicts that predators exert top-down effects on their prey by direct consumption, by imposing risk of predation, or a combination of both. However, empirical evidence for these effects is mixed and inconsistent. Barn owls in agriculture present a system with attributes that should, according to theory, lead to strong top-down effects, but research is limited and the distinction between effects on prey abundance and behavior remains unresolved. Understanding the top-down effect of predators on pests is a cornerstone of integrated pest management and is thus especially important where barn owls may contribute to the control of rodents on farms. Vegetation can enhance or diminish the top-down effects of predators on their prey, but this has received little attention for barn owls and rodent pests, limiting the information farmers can use to manage their fields to harness the effect of barn owls. To help fill these gaps, we surveyed above-ground foraging rodents and monitored barn owl nest boxes at six winegrape vineyards in Napa Valley, California USA in February through July 2023. We deployed chew block grids to determine the direct effect of barn owls on rodent abundance and used giving-up density (GUD) trays and camera traps to determine the effect of barn owls on rodent perceived predation risk and activity, respectively. We found that barn owls exert effects on rodent abundance and behavior and that vegetation cover favors the acoustically attuned barn owls over the visually oriented rodent pests. These results contribute to growing evidence that barn owls can be used as biological control in agricultural systems and that vegetation can be managed to reduce crop damage. We advise that growers maintain the vine canopy of leaves, pruned twigs on the ground, and vegetation cover in a cover crop row grown between vine rows to elevate perceived predation risk in rodent pests. We also recommend that, to the extent feasible, farmers time mowing to reduce rodent activity when crops are particularly vulnerable. chewblock_veg, gud_veg, camera_veg: vegetation variables from the chew block grids, GUD grids, and cameras respectively (predictor variables)chewblock_data, gud_data, camera_data: chewblock weights, gud weights, and number of photos (response variables) + abiotic factors and barn owl hunting pressure (predictor variables)<br>