Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-09T05:53:53.878Z Has data issue: false hasContentIssue false
  • English
  • Français

Weather effects on maize yields in northern China

Published online by Cambridge University Press:  27 March 2013

B. J. SUN  and
G. C. VAN KOOTEN
B. J. SUN*
Affiliation:
College of Economics and Management, Northwest A&F University, No. 3 Taicheng Road, Yangling, Shaanxi 712100, China Department of Economics, University of Victoria, PO Box 1700, STN CSC, Victoria, BC V8W 2Y2, Canada
G. C. VAN KOOTEN
Affiliation:
Department of Economics, University of Victoria, PO Box 1700, STN CSC, Victoria, BC V8W 2Y2, Canada
*
*To whom all correspondence should be addressed. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

In the present study, the effect of weather on maize yields in northern China was examined using data from 10 districts in Inner Mongolia and two in Shaanxi province. A regression model with a flexible functional form was specified on the basis of agronomic considerations. Explanatory variables included in the model were seasonal growing degree days, precipitation, technological change (e.g. adoption of new crop varieties, improved equipment, better management, etc.) and dummy variables to account for regional fixed effects. Results indicated that a fractional polynomial model in growing degree days could explain variability in maize yields better than a linear or quadratic model. Growing degree days, precipitation in July, August and September, and technological changes were important determinants of maize yields. The results could be used to predict potential maize yields under future climate change scenarios, to construct financial weather products and for policy makers to incentivize technological changes and construction of infrastructure (e.g. irrigation works) that facilitate adaptation to climate change in the agricultural sector.

Type
Climate Change and Agriculture Research Papers
Information
The Journal of Agricultural Science , Volume 152 , Issue 4 , August 2014 , pp. 523 - 533
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Almaraz, J. J., Mabood, F., Zhou, X., Gregorich, E. G. & Smith, D. L. (2008). Climate change, weather variability and corn yield at a higher latitude locale: Southwestern Quebec. Climate Change 88, 187–197.Google Scholar
Bai, C. Y., Li, S. K., Bai, J. H., Zhang, H. B. & Xie, R. Z. (2011). Characteristics of accumulated temperature demand and its utilization of maize under different ecological conditions in Northeast China. Chinese Journal of Applied Ecology 22, 2337–2342.Google Scholar
Bunting, E. S. (1979). The relationship between mean temperature and accumulated temperature totals for maize in the central lowlands of England. Journal of Agricultural Science, Cambridge 93, 157–169.CrossRefGoogle Scholar
Chen, C., Lei, C., Deng, A., Qian, C., Hoogmoed, W. & Zhang, W. (2011). Will higher minimum temperature increase corn production in northeast China? An analysis of historical data over 1965–2008. Agricultural and Forestry Meteorology 151, 1580–1588.Google Scholar
Data Sharing Infrastructure of Earth System Science (1977–96). China 1:4,000,000 Basic Data. Beijing: Institute of Geographic Sciences and Natural Resources Research, CAS.Google Scholar
Dupuis, I. D. & Dumas, C. (1990). Influence of temperature stress on in vitro fertilization and heat shock protein synthesis in maize (Zea mays L.) reproductive tissues. Plant Physiology 94, 665–670.Google Scholar
FAO (2010). Food and Agricultural Commodities Production. Rome: FAO. Available online at: http://faostat.fao.org/site/339/default.aspx (verified 20 June 2012).Google Scholar
Government of Inner Mongolia (2010). Overview. Inner Mongolia, China. Available online at: http://intonmg.nmg.gov.cn/channel/zjnmg/col6722f.html (verified 20 June 2012).Google Scholar
Government of Yanan (2012). Overview. Yanan, China. Available online at: http://www.yanan.gov.cn/structure/zjya/zjyax (verified 28 October 2012).Google Scholar
Government of Yulin (2012). Overview. Yulin, China. Available online at: http://www.yl.gov.cn/site/1/html/zjyl/list/list_18.htm (verified 28 October 2012).Google Scholar
Huang, J., Rozelle, S., Martin, W. & Liu, Y. (2009). China. In Distortions to Agricultural Incentives in Asia (Eds Anderson, K. & Martin, W.), pp. 117–161. Washington, DC: The International Bank of Reconstruction and Development/World Bank.Google Scholar
Inner Mongolia Bureau of Statistics (1990–2000). Inner Mongolia Statistical Yearbooks. Beijing: China Statistics Press.Google Scholar
Li, X., Takahashi, T., Suzuki, N. & Kaiser, H. M. (2011). The impact of climate change on maize yields in the United States and China. Agricultural Systems 104, 348–353.Google Scholar
Lin, J. Y. (1988). The household responsibility system in China's agriculture reform: a theoretical and empirical study. Economic Development and Cultural Change 36, S199–S224.CrossRefGoogle Scholar
Lin, J. Y. (1992). Rural reforms and agricultural growth in China. American Economic Review 82, 34–51.Google Scholar
Liu, B. C., Li, M. S., Guo, Y. & Shan, K. (2010). Analysis of the demand for weather index agricultural insurance on household level in Anhui, China. Agriculture and Agricultural Science Procedia 1, 179–186.Google Scholar
National Bureau of Statistics of China (2011). China Statistical Yearbook. Beijing: China Statistical Press.Google Scholar
Pan, W. (2001). Akaike's information criterion in generalized estimating equations. Biometrics 57, 120–125.Google Scholar
Royston, P. & Altman, D. G. (1994). Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling. Journal of the Royal Statistical Society Series C: Applied Statistics 43, 429–467.Google Scholar
Schlenker, W. & Roberts, M. J. (2006). Nonlinear effects of weather on corn yields. Applied Economic Perspectives and Policy 28, 391–398.Google Scholar
Schlenker, W. & Roberts, M. J. (2008). Estimating the Impact of Climate Change on Crop Yields: The Importance of Non-linear Temperature Effects. NBER Working Paper 13799. Cambridge, MA, USA: National Bureau of Economic Research. Available online at: http://www.nber.org/papers/w13799 (verified 2 May 2012).Google Scholar
Shaanxi Bureau of Statistics (1990–2002). Shaanxi Statistical Yearbooks. Beijing: China Statistics Press.Google Scholar
Turvey, C. G., Kong, R. & Belltawn, B. C. (2009). Weather risk and the viability of weather insurance in western China. In Annual Meeting of the American Agricultural Economics Association, 26–28 July 2009, Milwaukee, WI (Ed. AAEA), pp. 1–33. Milwaukee, WI: American Agricultural Economics Association. Available online at: http://purl.umn.edu/49362 (verified 14 February 2013).Google Scholar
Vučetić, V. (2011). Modelling of maize production in Croatia: present and future climate. Journal of Agricultural Science, Cambridge 149, 145–157.Google Scholar