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Improving China's alfalfa industry development: an economic analysis

Wenxin Wang

China Agricultural Economic Review, 2020, vol. 13, issue 1, 211-228

Abstract: Purpose - This study analyzes the factors which affect the alfalfa cultivation acreage in China and estimates the development of alfalfa planting by the supply model. Design/methodology/approach - Based on the characteristics and actual conditions of alfalfa cultivation in China, a naïve empirical model was created to analyze the impact of various influencing factors on the cultivation acreage of alfalfa. Findings - The analysis of influential factors shows that China's alfalfa planting conforms to naïve price behavior. The prices of alfalfa and per capita arable land occupancy have a positive effect on the cultivation acreage, while the price of competitive crops and transportation costs have a negative effect on the production of alfalfa. Lastly, the 2012 alfalfa subsidy policy has a significant negative impact on alfalfa cultivation acreage. Research limitations/implications - Due to the limited research on alfalfa supply in China, there is a lack of available research data and statistical data. A large number of data in this study are mainly indirect data derived and calculated from other industrial data. The measurement results may not be fully accurate. Originality/value - This study represents the first empirical analysis of the characteristics of the factors influencing alfalfa cultivation acreage in China. The secondary data were used to analyze the influence of various control variables on the cultivation acreage of alfalfa, which is different from existing research.

Keywords: Alfalfa acreage; Influencing factors; Supply response model; Market structure; Industrial development (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eme:caerpp:caer-07-2019-0128

DOI: 10.1108/CAER-07-2019-0128

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China Agricultural Economic Review is currently edited by Dr Fu Qin, Dr Jikun Huang, Dr Kevin Z Chen, Dr Weiming Tian, Prof Daniel Sumner, Prof Xian Xin and Prof Holly Wang

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