Climate change’s impact on China’s wheat productivity: empirical analysis based on Huang-Huai-Hai Plain*

CHEN Shuai1

(1.Peking University)

【Abstract】This thesis adopts the statistics of wheat’s planting structure, irrigation situation and sunshine from 2000 to 2009 in Huang-Huai-Hai Plain at county level. Combining the statistics with wheat’s regional growing cycle and social-economic information, this thesis examines the impact of climate change on China’s wheat productivity. The research finds out that though the increase of temperature has different effects on wheat productivity according to different growth stages, generally speaking, the overall temperature’s variation has an obvious negative impact. The drop of sunshine will further restrain the increase of wheat production. Similarly, the increasingly uneven distribution of precipitation also has a negative effect. After excluding the impact of economic factors and human behaviors, climate change’s influence on Huang-Huai-Hai Plain’s wheat productivity is around a drop of 0.68% per decade. Nevertheless, it is still not certain whether it will deteriorate in the long-term.

【Keywords】 climate change; Huang-Huai-Hai Plain; wheat production;


【Funds】 * This thesis is supported by the Surface Project of The National Natural Science Foundation (70773001) and the Project For Young Scholars of The National Natural Science Foundation of China (71403291).

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    [1]. ① According to State Council’s report on national food security, China’s cultivated area is only 1.826 billion Mu, which decreases by 123 million Mu compared with 1.949 billion in 1997. The per capita possession is only 40% of the global average statistics. China is on the boundary of the 1.8 billion Mu red lines. The size of reclaimable wasteland in China is only 13.6 million hectare. (Source of data:, 2010). [^Back]

    [2]. ②In 2012, the international consumption ratio of grain stock has reached its lowest point in the last 30 years. (see FAO database, 2012:*/E). [^Back]

    [3]. ③Food security index is the ratio of end-of-term stock to annual consumption. In 2004, as the national wheat growing area and production both severely decreased, the amount of the imported wheat took up 7% of China’s consumption in that year. Since 2008, China’s end-of-year wheat stock has gradually fallen. By 2012, the stock amount represented less than 20% of the global consumption, while the import has been gradually rising. (see China’s wheat supply-demand balance sheet,, 2002–2012). [^Back]

    [4]. ①In 2008, China’s emission of greenhouse gas represented 25.4% of that of the globe. (According to United Nations Climate Change Framework Convention; China surpassed the U.S and has become the country with the largest greenhouse gas emission. In the past 100 years, the increasing range of China’s average ground surface temperature has been from 0.5°C to 0.8°C, higher than the international statistics (0.6°C±0.2°C). In the last 50 years, China’s ground surface temperature has increased 1.1°C, with an increasing rate of 0.22°C/10 years, clearly higher than global even North Hemisphere’s average rate (Li et al., 2007). [^Back]

    [5]. ②See FAO database, 2012. */E [^Back]

    [6]. ③China’s wheat planting area accounts for 15% of the national total grain sown area, while production is 20% of that of the country. (see the website of National Bureau of Statistics of China., 2000–2012). [^Back]

    [7]. ①Source: National Bureau of Statistics of the People’s Republic of China. (, 2000–2010. [^Back]

    [8]. ②Source: National Bureau of Statistics of the People’s Republic of China ( , 2000–2010. [^Back]

    [9]. ③The county-level crop production data in this thesis originates from the county-level crop database provided by the website of Department of Crop Production of the Ministry of Agriculture of the People’s Republic of China. ( [^Back]

    [10]. ④The sample area in this thesis is the semi humid warm irrigation collective double cropping area (Huang-Huai-Hai Plain) in the Chinese farming system zoning, which is slightly larger than Huang-Huai-Hai area in the geographical sense (see [^Back]

    [11]. ⑤The unit yield of crops is an important index to value the productivity. This thesis calculates wheat unit yield by dividing wheat production by its planting area. Strictly speaking however, this may slightly differ from the authentic wheat unit yield (production divided by the harvest area), as the planting area cannot equal the harvest area once there is an agricultural disaster. Because of the limitation of data, we cannot get the information of county-level wheat harvest area. Even so, Schlenker et al. (2006) proved that once the spatial calculation was adopted, there would not be significant differences in the final empirical result, no matter whether the planting area or the harvest area was adopted to calculate the unit yield, because the spatial econometric model can cover errors of missing variables at the spatial level to a great extent (including regional natural disasters). [^Back]

    [12]. ①The meteorological data adopted in the empirical analysis of this thesis originates from the data set of Chinese daily data of ground climate on China Meteorological Science Data Sharing Service Network (V3.0). [^Back]

    [13]. ②See [^Back]

    [14]. ③See Basic Knowledge on the Growth and Development of Wheat, from Baidu ( [^Back]

    [15]. ④All the climate change tendencies in Table 1 is proven by meteorological studies (Pan et al., 2011; Jiang et al., 2008). [^Back]

    [16]. ①The basic idea and deduction of spatial residual model refers to the study of Elhorst (2014). [^Back]

    [17]. ②This thesis’s empirical analysis model is a function that determines the unit yield of wheat. The input of wheat’s growing includes all the variables represented by climate variable matrix Zr.t and economic variable matrix Ar, t. Spatial weighted matrix Wr, r defines the scope of spatial impact. This thesis uses spatial adjacent matrix, which argues there is a spatial correlation between neighboring counties. [^Back]

    [18]. ③The price and price index in this thesis are all from the province-level panel data, downloaded from the annual regional database of provinces of National Bureau of Statistics (see the website of National Bureau of Statistics, The wheat’s real price of each province is from China Yearbook of Agricultural Price Survey (each year from 2004 to 2009), compiled by the countryside socio-economic investigation office of National Bureau of Statistics, published by China Statistics Press). [^Back]

    [19]. ④Apparently, the economic factor and human behavior in the economic variable matrix Ar, t are both endogenous variables. The approach to the endogenous issue of panel spatial residual model is included in Chen et al. (2014, 2015) and Fisher et al. (2012). Their idea is to use one-period lagged variables of extreme weather as instrumental variables of economic factors and human behaviors in the current period. The conduction system of the instrumental variables is: the extreme weather from last year may be very likely to result in the fluctuation of the price of agricultural products, which further impacts farmers’ expectation on the cost and revenue of planting in that year. In the end, the crop unit yield in that year will be influenced. In addition to that, if there were some extreme rainfall or extreme drought in the previous year, the local irrigation condition may be changed as well, which can influence the unit yield of that year. [^Back]

    [20]. ①Nearly all the research literature on the relationship between agriculture and climate change argues that the sample spatial correlation is significant and needed to be treated with cautions (e.g. Schlenker et al., 2006; Schlenker and Roberts, 2009). The spatial correlation test values of Chinese county-level samples are shown in Moran’s I index, LM, LR and so on (Chen et al. 2015). Besides, the benchmark regression result also shows that the degree of the spatial correlation of samples is between 0.336 and 0.382 (see ρ’s estimated value in Table 3). Hence, this thesis does not need to describe the test result of samples’ spatial correlation. [^Back]

    [21]. ①At this point expectations are no longer needed on equation (4) at the regional level, which can ensure that each sample county has its own predictive value, and enable the comparison of regional differences in impacts of future climate change. [^Back]

    [22]. ① Schlenker and Roberts (2009) and Chen et al. (2013) verified that no matter in the US or in China, for maize and soybeans, there was a nonlinear relationship that increased first and then decreased between climate factors (temperature, precipitation, and sunshine hours) and the level of crop yield. Therefore, if the climate condition exceeds the optimal demand of crops (inflection point), the damage will increase. [^Back]


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This Article


CN: 11-1262/F

Vol , No. 07, Pages 4-16

July 2015


Article Outline


  • 1 Introduction
  • 2 Sample distribution and data source
  • 3 Empirical strategies and regression results
  • 4 The impact assessment and regional forecast
  • 5 Conclusion
  • Appendix
  • Footnote