The frequency and severity of crop damage by wildlife in rural Beijing, China

Liang Fang, Yiping Hong, Zaiying Zhou, Wenhui Chen

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


With a continuous expansion of the number and activity of wild animals induced by ecological conservation and restoration efforts, the human-wildlife conflict is becoming more prominent across China. There have been frequent and severe incidents of crop damage caused by wildlife. In this paper, we investigate the crop losses caused by wildlife in the rural districts of Beijing, using a unique dataset of 31,573 observations from 2009 to 2017. Through statistical tests on the individual coefficients and the overall fitness, we find that a negative binomial generalized regression model describes the pattern of crop loss events more accurately, compared to an alternative Poisson model. The frequency of crop loss events is positively related to a village's distance from the river system but negatively associated with the distance from woodland, population density, and protective measures taken. The predicted frequencies of crop damage events are then used to correlate with the amounts of losses at the village level. Based on these results, we propose solutions for effective reduction of future crop losses and practical assessment of the likely damage compensation or insurance premium.
Original languageEnglish (US)
Pages (from-to)102379
JournalForest Policy and Economics
StatePublished - Dec 31 2020

Bibliographical note

KAUST Repository Item: Exported on 2021-01-14
Acknowledgements: The authors are grateful for the experts' and scholars' constructive comments and suggestions during the first international forum on forest policy and economics held at Beijing Forestry University. This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant BLX2019446, the National Natural Science Foundation of China under Grant 71573018, and the Beijing Forestry University Education and Teaching Research Project under Grant BJFU2020JY023. Our deepest gratitude goes to the journal editors and reviewers for their insightful comments and suggestions that have substantially improved this paper.


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