数字农科院2.0

Climate Change is Likely to Alter Sheep and Goat Distributions in Mainland China

文献类型： 外文期刊

作者： Yongxiang Zhang;Guogang Wang;Yu Zhang;Sicheng Zhao;Chengji Han

作者机构：

关键词： bayesian hierarchical model;CMIP 6;integrated nest laplace approximation (INLA);market factors;rainfall

期刊名称： Frontiers in Environmental Science

ISSN： 2296-665X

年卷期： 2021 年 9 卷

页码：

收录情况： JCR（2021版）

摘要： Climate change endangers food security worldwide, especially in developing countries. Livestock husbandry is one of the essential livelihoods for farmers and herders in remote arid and semiarid regions. However, it remains unclear how climate change will impact livestock husbandry in the future. This study collected sheep and goat distributions from the “gridded livestock of the world” (GLW) dataset for 1943 counties in Mainland China. Current climate data include precipitation and temperature from the National Meteorological Information Center (NMIC). We disentangled the effects of precipitation and temperature on current distributions of sheep and goats with the Bayesian Hierarchical Model by Integrated Nest Laplace Approximation (INLA). Further, we forecasted the potential sheep and goat distributions in 2030 and 2050 under Coupled Model Intercomparison Project (CMIP) scenarios. Our result showed that sheep distribution is significantly correlated with elevation, slope, market density, and highway distance, with absolute correlation coefficients ranging from 0.019 to 0.411. In addition to elevation, slope, and market density, goat distribution is also affected by gain production, with a correlation coefficient of 0.055. There is a dynamic correlation of temperature and precipitation with sheep and goat density. The sheep density distribution is predicted to increase in Northwest China, while the goat density distribution might increase in farming areas under climate change. Finally, this study suggests for the sheep and goat breeding industry to respond to climate change.

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