Effect of cropping policy on landowner reactions towards wildlife: a case of Naivasha area, Kenya
AbstractWildlife policy in Kenya has in most part been protectionist with little incentives to private landowners, who host wildlife in their farms to participate in their conservation. However, in recognition of the role of incentives in conservation, the Kenya Wildlife Service (KWS) piloted a wildlife utilization policy in which organized landowners were allowed a cropping quota based on the number of wildlife present within their land. This study investigates the impact of such policy on human-wildlife conflicts using data compiled from a list of complaints lodged at the KWS warden’s office from farms around Lake Naivasha. Using this data, Poisson and negative binomial regression models are employed to estimate the effect of the wildlife cropping and policy and other factors on the frequency of wildlife damage incidences reported at the KWS offices. Results indicate that the policy may not have worked as intended since rather than reducing the number of conflict reports, it had an unexpected effect of increasing problem reports to KWS. The results are discussed and some recommendations provided.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 21308.
Date of creation: 31 Jan 2010
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Wildlife; Cropping; Count data regression; Buffalo; Landowners;
Find related papers by JEL classification:
- C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
- D62 - Microeconomics - - Welfare Economics - - - Externalities
- B40 - Schools of Economic Thought and Methodology - - Economic Methodology - - - General
- Q28 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Government Policy
- C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
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