What Drives Green Fodder Supply in China?—A Nerlovian Analysis with LASSO Variable Selection

Shengying Zhai, Qihui Chen () and Wenxin Wang
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Shengying Zhai: College of Economics and Management, China Agricultural University, Beijing 100083, China
Wenxin Wang: College of Economics and Management, China Agricultural University, Beijing 100083, China

Sustainability, 2019, vol. 11, issue 23, 1-17

Abstract: To understand key factors that drive China’s green fodder supply, this study estimates a Nerlovian partial-adjustment model, using provincial-level panel data spanning two decades (1997–2016). Based on a set of explanatory variables selected by the LASSO (Least Absolute Shrinkage and Selection Operator) method, estimation of the Nerlovian model by the system-GMM (Generalized Method of Moments) method yields three key findings. First, while farmers’ previous cultivation decisions on green fodder supply strongly predict their current decisions, without the influence of other drivers, China’s green fodder supply tends to decline over time. Second, among the identified drivers, government policy plays the most significant role—the availability of subsidies for cultivation of green fodder crops raises the sown area of green fodder crops by more than 30 percent. In contrast, farmer’s sown-area decision is only modestly responsive to price incentives. Finally, while the stock of fixed capital inputs (e.g., number of combine harvesters) and natural disasters (e.g., floods) both affect green fodder supply, their impacts are small.

Keywords: green fodder; supply response; LASSO; China; panel data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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