When Do Prices Matter Most? Rice, Wheat, and Corn Supply Response in China

Abstract

Unexpected high and volatile food prices during the 2007-2008 world food crisis and thereafter have reemphasized the question of how countries can protect themselves from supply shortages. In view of the various trade restrictions imposed by some major exporting countries, governments tend once again to focus more on self-sufficiency and food storage. Additionally, emerging economies like China aim at increasing their yields. This is because the possibilities of expanding agricultural land are limited, while population, total grain demand, and meat consumption are rising.The primary purposes of analyzing the supply response are threefold in this chapter. First, this work aims to identify the different factors that can affect production, such as market prices, biophysical conditions, and infrastructure. The second objective is to analyze the differences in the effects of these factors on the different crops. The third aim is to evaluate how the predictive power of prices evolves over time and therefore to understand when farmers react most strongly to prices. Hence, a clear understanding of the farmers' planting and production behavior is needed.

Analisis Penawaran Beras Di Kabupaten Sukoharjo

“…Lahan irigasi teknis pada tahun t secara individu berpengaruh positif terhadap penawaran beras di Kabupaten Sukoharjo. Hasil tersebut sejalan dengan penelitian dari Brockhaus et al (2015) yang menyatakan bahwa variabel irigasi pada tahun t mempengaruhi penawaran beras. Irigasi teknis merupakan salah satu faktor teknologi dalam menunjang ketersediaan air untuk produksi padi.…”

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

“…Both price and nonprice factors such as the real producer prices of their own and substitute crops, rainfall, farm size, land quality, and education are included. Empirical evidence shows that the dynamics of supply can be better estimated using system GMM (Blundell and Bond 1998;Brockhaus et al 2015;Roodman 2009). System GMM does not assume normality and controls for heteroscedasticity, unlike the conventional OLS method (Jaunky 2013).…”

“…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. They used the system GMM estimator and reported price elasticities ranging from 0.16 (rice) to 0.34 (wheat).…”

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“…The literature on modelling and forecasting commodity prices tends to rely on particular model specifications to draw conclusions concerning in-sample and out-ofsample predictive accuracy. Egelkraut et al (2003) examine the accuracy of corn and soybean production forecasts provided by the USDA, while Brockhaus et al (2016) analyze the response of different factors (such as prices) on production of rice, wheat and corn in China. Algieri (2014) investigates the main drivers of wheat price using vector error correction models.…”