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. 2024 Apr 4;14(7):1102.
doi: 10.3390/ani14071102.

Risk Awareness and Attitude of German Farmers towards Biosecurity Measures

Affiliations

Risk Awareness and Attitude of German Farmers towards Biosecurity Measures

Anna Herrmann et al. Animals (Basel). .

Abstract

The implementation of management activities depends on both the attitude of the people performing the work and their understanding of why the work should be performed. In the context of animal husbandry, the implementation of such practices is crucial for the functionality of biosecurity. Therefore, it is important to know how farmers perceive biosecurity as a whole. An anonymous online survey was conducted among German farmers. In addition to general data about their farm, information about their existing concept of biosecurity, as well as about the assessment of possible introduction routes for animal diseases into the farm with regard to their likelihood, was gathered. Furthermore, information on measures to protect their farm against disease introduction were retrieved. Analysis showed that in general, farmers were aware of the importance of biosecurity and consequently had concepts of biosecurity on their farms. However, awareness about dangerous introduction routes for animal diseases into a farm was associated with a lack of knowledge of how to improve the measures in these areas. The role of the veterinarian in the context of biosecurity was highlighted and further problematic areas were indicated. Overall, the high level of commitment from farmers indicated a good implementation of daily practices.

Keywords: Germany; animal husbandry; biosecurity; farm management; farmer’s attitude; online-survey.

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Conflict of interest statement

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Number of farms per federal state. Four Austrian and two Swiss holdings also took part. Five holdings did not provide any information on their situation.
Figure 2
Figure 2
Univariable linear regression model of participating farms from each German federal state in dependence of farms registered in Germany. Line fits represent linear regression and 95% confidence interval. BB = Brandenburg, BW = Baden-Württemberg, BY = Bavaria, HE = Hesse, MV = Mecklenburg-Western Pomerania, NI = Lower Saxony, NW = North Rhine-Westphalia, RP = Rhineland-Palatinate, SH = Schleswig-Holstein, SL = Saarland, SN = Saxony, ST = Saxony-Anhalt, TH = Thuringia.
Figure 3
Figure 3
Animal species kept on the farms of participants. The number of farms keeping the respective animal species is given on the vertical columns on the right side. The vertical columns give the number of farms that kept animals of a specific species or resp. multiple species, as indicated underneath the bars by the black dot(s) in combination with the black lines in between them, indicating a combination of these species. The number of farms categorized as large in each category is highlighted in blue. Classification for cattle and other animals: S = 1–99 animals, L = more than 100 animals; for swine and chicken: S = 1–999 animals, L = more than 1000 animals.
Figure 4
Figure 4
Likert scale for statements concerning the importance of biosecurity on the farm as stated by the participants.
Figure 5
Figure 5
Spearman’s rank correlation coefficient (ρ) between answers given to statements about biosecurity in a Likert-scale format.
Figure 6
Figure 6
Pareto charts of the possible introduction routes for a disease into the farms (A) as well as the introduction routes perceived to have a high protection status (B). Number of entries are represented as bars.
Figure 7
Figure 7
Risk factors (red), as well as introduction routes that were perceived as well protected (green) by the participants. Gray lines indicate their mutual selection by one participant, with the blue lines highlighting the selection of one introduction route in both categories by one participant. The color gradients of red and green change with increasing percentages (dark red: highest risk, dark green: best protected).

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