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

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.

New Evidence Using a Dynamic Panel Data Approach: Cereal Supply Response in Smallholder Agriculture in Ethiopia

“…The results of estimating the Nerlovian partial-adjustment model shed new light onto the supply dynamics of major cereal crops in Ethiopian agriculture. In particular, the estimated one-year lagged-area elasticity of these crops indicates a solid influence of farmers' previous allocation decisions on current decisions of teff and wheat, which is consistent with previous findings (Zhai et al 2019). Considering these results, I can make the following points.…”

“…The Nerlovian supply response model (the Nerlovian adaptive expectation and the partial-adjustment model) is the most commonly used (Askari and Cummings 1977;Khan et al 2019;Magrini et al 2018;Ogundari 2018;Shonkwiler and Hinckley 1985;Wickens and Greenfield 1973;Zhai et al 2019).…”

“…Imai et al (2011) studied the extent to which the production of different agricultural commodities, namely, maize, wheat, rice, fruit, vegetables, and oilseeds, responds to price changes in 10 Asian countries and reported a short-term elasticity ranging from 0.25 to 0.31 for these commodities. Zhai et al (2019) used the system GMM estimator using provincial-level panel data (1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) and reported partial adjustments (i.e., At-1 and Pt-1) of 0.21 and 0.87, respectively, for the green fodder supply in China. Similarly, Brockhaus et al (2015) presented new evidence using provincial panel data over the period 1995-2012 for rice, wheat, and corn supply responses in China.…”

Cooling effects of increased green fodder area on native grassland in the northeastern Tibetan Plateau

“…Forage production that merely relies on native grassland is becoming unsustainable for growing forage demands, especially in the current state of practices regarding native grassland degradation [1,2]. These current practices require supplementation of native grasslands to complement forage and protein requirements, among which green fodder stands out, with richer nutrients and higher water content compared to native grass [3].…”