Abstract

Sheep farmers in the Cape Midlands region of South Africa frequently sustain stock losses through predation by caracal lynx. Further losses are incurred when hyrax compete with sheep for available pasture. Hyrax constitute the natural prey for lynx with the result that culling either hyrax or lynx has complicated feedback effects. In order to investigate the spill-over problems from the natural predator-prey system on farming revenue, a differential equations model was previously formulated, comprising the sectors Hyrax, Lynx, Sheep, Pasture and Revenue and an optimisation procedure was used to determine the optimal culling rate policy for farmers. The purpose of this paper is to investigate the numerical, behavioural and policy sensitivity of this model to parameter uncertainty.

Highlights

  • The hyrax Procavia capensis and lynx Felis caracal that abound in most of these wilderness areas pose a problem to the farmer

  • When the hyrax population exceeds the carrying capacity of the wilderness areas, the hyrax encroach upon farming land and consume pasture in competition with sheep

  • Hyrax constitute the principal food of lynx [Fairall 1980, Grobler 1981] farmers sustain further losses to their flocks through lynx predation

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Summary

INTRODUCTION

When the hyrax population exceeds the carrying capacity of the wilderness areas, the hyrax encroach upon farming land and consume pasture in competition with sheep. To keep the model simple, the populations were divided into a limited number of age classes The different groups, their initial values and the rates determining their levels are shown in table l. Lynx do sometimes kill adult sheep, predation of sheep is confined to the juvenile group (lambs) in the model. Loss of revenue is incurred through lynx culling, hyrax culling and veterinary and other costs (directly proportional to the number of sheep). It is likely that culling costs of hyrax and lynx per head will rise at lower population densities, resulting from higher culling rates, and this has been taken into account in the model.

PARAMETER SENSITIVITY ANALYSIS
Findings
CONCLUSION

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