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Articles

Individual level spatial-temporal modelling of exposure potential of livestock in the Cove Wash watershed, Arizona

, ORCID Icon, ORCID Icon, ORCID Icon, &
Pages 87-107 | Received 02 Apr 2021, Accepted 02 May 2022, Published online: 30 May 2022

Figures & data

Figure 1. Exposure assessment in spatial-temporal dimension (Example equations are listed to demonstrate exposure assessment approaches under different scenarios).

Figure 1. Exposure assessment in spatial-temporal dimension (Example equations are listed to demonstrate exposure assessment approaches under different scenarios).

Figure 2. Research workflow including data collection, cleaning and analysis.

Figure 2. Research workflow including data collection, cleaning and analysis.

Figure 3. Distribution of AUMs in Cove.

Figure 3. Distribution of AUMs in Cove.

Table 1. Fuzzy rules.

Figure 4. Membership functions.

a). The blue curve shows the fuzzy membership function of low speed, while the red curve represents that of high speed. b). The blue curve shows the fuzzy membership function of inactive status, while the red curve represents that of active status.
Figure 4. Membership functions.

Table 2. Calculation of cumulative risk and probability.

Figure 5. Livestock location and proximity to AUMs.

Figure 5. Livestock location and proximity to AUMs.

Figure 6. Daily exposure of flock A and flock B.

Figure 6. Daily exposure of flock A and flock B.

Figure 7. Geographic distribution of area associated with grazing, resting, and travelling for flock A and flock B.

Figure 7. Geographic distribution of area associated with grazing, resting, and travelling for flock A and flock B.

Figure 8. Daily exposure without considering behaviour patterns.

Figure 8. Daily exposure without considering behaviour patterns.

Figure 9. Daily exposure considering behaviour patterns but without probability/uncertainty.

Figure 9. Daily exposure considering behaviour patterns but without probability/uncertainty.

Figure 10. Points before and after the data preprocessing. (Note: Base map is excluded to protect livestock owner’s privacy).

Figure 10. Points before and after the data preprocessing. (Note: Base map is excluded to protect livestock owner’s privacy).

Figure 11. Histograms of livestock distance to AUMs.

Figure 11. Histograms of livestock distance to AUMs.

Table 3. Fuzzy membership of flock B on August 12th.

Table 4. Frequency of dominant behaviour.

Table 5. GPS data sample.

Table 6. Basic information of datasets.

Table 7. Sample result of fuzzy logic for behaviour classification.

Table 8. Frequency of resting, grazing, and travelling of flock A.

Table 9. Frequency of resting, grazing, and travelling of flock B.

Table 10. T-test of daily cumulative exposure potential of flock B comparison of the current method with those not considering probability/uncertainty.