JOURNAL ARTICLE

A Nationwide Survey in Japan to Identify the Factors Associated With Rodent Infestation on Livestock Farms.

  • Published In: Animal Science Journal, 2025, v. 96, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Kiyokawa, Yasushi; Koizumi, Ryoko; Yamada, Ryoko; Frye, Matthew; Parsons, Michael H.; Hirata, Shigeki; Tanikawa, Tsutomu 3 of 3

Abstract

Brown rats (Rattus norvegicus), roof rats (Rattus rattus), and house mice (Mus musculus) are major pests on livestock farms. Identifying the factors associated with rodent infestation is considered the first step to control rodents effectively on livestock farms. Here, we performed a nationwide survey in Japan to elucidate the factors associated with rodent infestation. We found that 82.5% of the 401 valid responses indicated that rodent infestation occurred on their farms, suggesting that the presence of rodents is a common occurrence. An ordinal logistic regression analysis suggests that livestock type, farm size, and implementation of rodent control measures contribute to differences in rodent infestation between farms. Comparisons between the most‐ and least‐infested barns on each farm among the 237 valid responses suggest that the number of livestock in the barn on farms keeping cows or pigs, the feeding method on farms keeping pigs, and the age of the barn on farms keeping cows, chickens, or pigs contribute to differences in rodent infestation within a farm. Taken together, the results of this study provide valuable information for understanding rodent infestation on livestock farms. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Animal Science Journal. 2025/01, Vol. 96, Issue 1, p1
  • Document Type:Article
  • Subject Area:Anatomy and Physiology
  • Publication Date:2025
  • ISSN:1344-3941
  • DOI:10.1111/asj.70057
  • Accession Number:190446160
  • Copyright Statement:Copyright of Animal Science Journal is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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