Climate change’s impact on China’s wheat productivity: empirical analysis based on Huang-Huai-Hai Plain*
【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;
. 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: www.npc.gov.cn, 2010). [^Back]
. ②In 2012, the international consumption ratio of grain stock has reached its lowest point in the last 30 years. (see FAO database, 2012: https://faostat3.fao.org/faostat-gateway/go/to/browse/G1/*/E). [^Back]
. 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, www.chinajci.com, 2002–2012). [^Back]
. ①In 2008, China’s emission of greenhouse gas represented 25.4% of that of the globe. (According to United Nations Climate Change Framework Convention http://unfccc.int/ghg_data/items/3800.php); 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]
. ②See FAO database, 2012. https://faostat3.fao.org/faostat-gateway/go/to/browse/G1/ */E [^Back]
. 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. http://www.stats.gov.cn, 2000–2012). [^Back]
. ①Source: National Bureau of Statistics of the People’s Republic of China. ( http://www.stats.gov.cn/), 2000–2010. [^Back]
. ②Source: National Bureau of Statistics of the People’s Republic of China ( http://www.stats.gov.cn/) , 2000–2010. [^Back]
. 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. (http://zzys.agri.gov.cn/nongqing.aspx). [^Back]
. 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 http://course.cau-edu.net.cn/course/Z0366/ch08/se02/slide/slide01.htm). [^Back]
. 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]
. 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). http://www.escience.gov.cn/metdata/page/index.html [^Back]
. ②See http://www.zzys.gov.cn/nongqingxm.aspx [^Back]
. ③See Basic Knowledge on the Growth and Development of Wheat, from Baidu (http://wenku.baidu.com). [^Back]
. ④All the climate change tendencies in Table 1 is proven by meteorological studies (Pan et al., 2011; Jiang et al., 2008). [^Back]
. ①The basic idea and deduction of spatial residual model refers to the study of Elhorst (2014). [^Back]
. 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]
. 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, http://data.stats.gov.cn). 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]
. 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]
. 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]
. 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]
. 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]
1.Braulke, Michael: A Note on the Nerlove Model of Agricultural Supply Response, International Economic Review, 23(1): 241–244, 1982.
2.Chen, Shuai; Chen, Xiaoguang; and Xu, Jintao: The Economic Impact of Weather Variability on China’s Rice Sector, Ef DDiscussion Paper Series EfD DP 14-13-REV, http://www.efdinitiative.org, 2014.
3. Chen, Shuai; Chen, Xiaoguang; and Xu, Jintao: Impacts of Climate Change on Agriculture: Evidence from China, Journal of Environmental Economics and Management, http://dx.doi.org/10.1016/j.jeem.2015.01.005, forthcoming 2015.
4. Deschênes, Olivier and Greenstone, Michael: The Economic Impacts of Climate Change: Evidence from Agricultural Output and Random Fluctuations in Weather, The American Economic Review, 97(1): 354–385, 2007.
5. Elhorst, J. Paul: Spatial Panel Data Models, Springer Briefs in Regional Science: (3)5: 37–93, 2014.
6. Fischer, Günther, Shah, Mahendra; Tubiello, Francesco N. and van Velhuizen, Harrij: Socio-Economic and Climate Change Impacts on Agriculture: An Integrated Assessment, 1999–2080, Biological Sciences, 360(1463): 2067–2083, 2005.
7. Fisher, Anthony C.; Hanemann, Michael W.; Roberts, Michael J. and Schlenker, Wolfram: The Economic Impacts of Climate Change: Evidence from Agricultural Output and Random Fluctuations in Weather: Comment, The American Economic Review, 102(7): 3749-6370, 2012.
8. Jones, Peter G. and Thomton, Philip K.: The Potential Impacts of Climate Change on Maize Production in Africa and Latin America in 2055，Global Environmental Change, 13(1): 51–59, 2003.
9. Liu, Hui; Li, Xiubin; Fischer, Guenther, and Sun, Laixiang: Study on the Impacts of Climate Change on China’s Agriculture, Climate Change, 65(1–2): 125–148, 2004.
10. Lobell, David B.; Bänziger, Marianne; Magorokosho, Cosmos and Vivek, Bindiganavile: Nonlinear Heat Effects on African Maize as Evidenced by Historical Yield Trials, Nature Climate Change, 1(1): 42–45, 2011.
11. Lobell, David and Field, Christopher B.: Global Scale Climate-Crop Yield Relationships and the Impacts of Recent Warming, Environmental Research Letters, 2(1): 014002, 2007.
12. Lobell, David B.; Orttiz-Monasterio, J. Ivan; Asner, Gregory P.; Matson, Pamela A.; Naylor, Rosamond L. and Falcon, Walter P.: Analysis of Wheat Yield and Climatic Trends in Mexico, Field Crops Research, 94(2): 250-256, 2005.
13. Mendelsohn, Robert; Nordhaus, William D. and Shaw, Daigee: The Impact of Global Warming on Agriculture: A Ricardian Analysis, The American Economic Review, 84(4): 753–771, 1994.
14. Nicholls, Neville: Increased Australian Wheat Yield Due to Recent Climate Trends, Nature, 387(6632): 484–485, 1997.
15. Schlenker, Wolfram; Hanemann, Michael W. and Fisher, Anthony C.: The Impact of Global Warming on Us Agriculture: An Econometric Analysis of Optimal Growing Conditions, Review of Economics and Statistics, 88(1): 113–125, 2006.
16. Schlenker, Wolfram and Roberts, Michael J.: Nonlinear Temperature Effects Indicate Severe Damages to U.S. Crop Yields under Climate Change, Proceedings of the National Academy of Sciences, 106(37): 15594–15598, 2009.
17. Wang, Jinxia; Mendelsohn, Robert; Dinar, Ariel; Huang, Jikun; Lozelle, Scott and Zhang, Lijuan: The Impact of Climate Change on China’s Agriculture, Agricultural Economics, 40(3): 323–337, 2009.
18. Welch, Jarrod R; Vincent, Jeffrey R.; Auffhammer, Maximilian; Moya, Piedad F.; Dobermann, Achim and Dawe, David.: Rice Yields in Tropical/Subtropical Asia Exhibit Large But Opposing Sensitivities to Minimum and Maximum Temperatures, Proceedings of the National Academy of Sciences, 107(33): 14562–14567, 2010.
19. You, Liangzhi; Rosegrant, Mark W.; Wood, Stanley and Sun, Dongsheng: Impact of Growing Season Temperature on Wheat Productivity in China, Agricultural and Forest Meteorology, 149(6): 1009–10014, 2009.
20. Chen, L. & Zhu, W. Acta Meteorologica Sinica (气象学报), (3), (1998).
21. Chun, S. Encyclopedia of agriculture in China: Agricultural Meteorology (中国农业百科全书：农业气象卷). China Agriculture Press, (1986).
22. Cui, J., Wang, X., Xin, X. et al. Chinese Rural Economy (中国农村经济), (9), (2011).
23. Jiang, Z., Zhang, X. & Wang, Y. Geographical Research (地理研究), (4), (2008).
24. Li, X., Qin, D. & Li, J. Climate Change’s National Assessment Report (气候变化国家评估报告). Science Press, (2007).
25. Lin, E. The Simulation of Global Climate Change’s Impact on Chinese Agriculture (全球气候变化对中国农业影响的模拟). Chinese Agricultural Technology Press, (1997).
26. Liu, Y., Liu, Y. & Guo, L. Chinese Journal of Eco-Agriculture (中国生态农业学报), (4), (2010).
27. Pan, G., Gao, M., Hu, G. et al. Journal of Agro-environment Science (农业环境科学学报), (9), (2011).
28. Shao, X. Grains and Meteorology (粮食作物与气象). Beijing Agriculture University Press, (1988).
29. Xiong, W., Xu, Y., Lin, E. et al. Chinese Journal of Agrometeorology (中国农业气象), (1), (2005).
30. Xiong, W., Yang, J., Lin, E. et al. Advances in Earth Science (地球科学进展), (10), (2008).
31. Yao, H. & Li, D. Chinese Journal of Atmospheric Sciences (大气科学), (4), (2013).
32. Zhao, G. Journal of Triticeae Crops, (5), (2010).