Geographic distribution of livestock products in China- an application of spatial autocorrelation analysis

Abstract

The rapid increase in the livestock industry in China in recent two decades has played an important role in the livelihoods of people and has become a very significant issue in terms of sustainable animal food supply chains. Knowledge gaps in the geographic distribution may hinder the sustainable development of livestock industry. This paper investigates the spatial distribution in the outputs of livestock products (meat, milk and egg, respectively) in China using exploratory spatial data analysis. This method is a set of GIS spatial statistical techniques that are useful in describing and visualizing the spatial distribution, detecting patterns of hot-spots, and suggesting spatial regimes. The global Moran’s I statistics for the three products reveal strong positive and significant spatial autocorrelation. Furthermore, the Moran significance maps indicate four hot-spots (North-eastern cluster, Northern Coast cluster, Central Inland cluster and Southern cluster) and one cold-spot (Western cluster) in the meat product distribution, one large hot-spot (Northern cluster) and one large cold-spot (South-central cluster) for the milk product, four relatively small hot-spots (North-eastern cluster, Northern Coast cluster, Eastern Coast cluster and Central Inland cluster) and one large cold-spot (Western cluster) for the egg product. Based on the results, we show that livestock products are polarized into clusters and the outputs of the products tend to be reducing from east to west and from north to south China. Implications are drawn, such as priority of resource allocations for hot-spot area in terms of animal-source food security and the utilization of spillover effects from hot-spots.