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Spatio-Temporal Dynamics of Feed Grain Demand of Dairy Cows in China

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  • Chao Yang Dong

    (School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China)

  • Bei Bei Ma

    (School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China)

  • Chun Xia LU

    (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

As the income of urban and rural residents has increased in recent decades in China, dairy products have become an important part of the Chinese diet. Therefore, keeping up with the growing demand for feed grain for dairy cows is a critical issue of feed grain security. Utilizing traditional statistical and spatial statistical methods, this study analyzes the spatio-temporal dynamics of dairy cow feed grain (DCFG) demand on the provincial, regional, and national levels across China from 1990 to 2016. Additionally, this paper explores the impacts of various factors on the spatio-temporal dynamics of DCFG demand using the Geo-Detector method. The results demonstrate that: (1) the temporal dynamics of DCFG demand can be divided into three stages of slow growth, rapid growth, and high-level stability, and the relative level of DCFG demand in the whole animal husbandry tends to decline; (2) at the regional and national levels, the spatial concentration of high DCFG demand has intensified; in particular, North China was the region where the largest demand for DCFG was localized and was increasing at the highest rate; (3) based on the hot spot analysis of provincial DCFG demand, the high and low demand provinces of DCFG have sharp characteristic contrast from north to south China; (4) the spatio-temporal dynamics of DCFG demand in China were essentially co-affected by the four groups of factors (e.g., resource endowment, feeding scale, feeding technology, and market environment), of which resource endowment and feeding scale were the dominant factors. Therefore, in the future, dairy cow feeding in China should promote grain-saving feeding technology, improve the utilization of forage, expand large-scale feeding, and create a good market environment to ensure the reasonable development and sustainability of DCFG demand.

Suggested Citation

  • Chao Yang Dong & Bei Bei Ma & Chun Xia LU, 2020. "Spatio-Temporal Dynamics of Feed Grain Demand of Dairy Cows in China," Sustainability, MDPI, vol. 12(2), pages 1-17, January.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:2:p:663-:d:309471
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    References listed on IDEAS

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    1. Li, Jau-Rong & Barrett, Christopher B., 1999. "Distinguishing Between Equilibrium And Integration In Markets Analysis," Working Papers 14607, International Agricultural Trade Research Consortium.
    2. Manfred M. Fischer & Arthur Getis (ed.), 2010. "Handbook of Applied Spatial Analysis," Springer Books, Springer, number 978-3-642-03647-7, January.
    3. Mergos, George J, 1989. "Feed Use of Grain: On Trends and Determinants," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 16(1), pages 1-17.
    4. Cheng Fang & Frank H. Fuller, 1998. "Feed-Grain Consumption by Traditional Pork-Producing Households in China," Center for Agricultural and Rural Development (CARD) Publications 98-wp203, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    5. Cheng Fang & Frank H. Fuller, 1998. "Feed-Grain Consumption by Traditional Pork-Producing Households in China," Food and Agricultural Policy Research Institute (FAPRI) Publications (archive only) 98-wp203, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    6. Allan Rae, 2008. "China's agriculture, smallholders and trade: driven by the livestock revolution? ," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 52(3), pages 283-302, September.
    7. Wei-Ming Tian & John Chudleigh, 1999. "China's feed grain market: Development and prospects," Agribusiness, John Wiley & Sons, Ltd., vol. 15(3), pages 393-409.
    8. Rae, Allan N., 2008. "China’s agriculture, smallholders and trade: driven by the livestock revolution?," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 52(3), pages 1-20.
    9. Zhang Y. Zhou & Wei M. Tian & Bill Malcolm, 2008. "Supply and demand estimates for feed grains in China," Agricultural Economics, International Association of Agricultural Economists, vol. 39(1), pages 111-122, July.
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