Climatic Change

, Volume 128, Issue 1–2, pp 57–70 | Cite as

Greenhouse gas intensity of three main crops and implications for low-carbon agriculture in China

  • Wen Wang
  • Liping Guo
  • Yingchun Li
  • Man Su
  • Yuebin Lin
  • Christian de Perthuis
  • Xiaotang Ju
  • Erda LinEmail author
  • Dominic Moran


China faces significant challenges in reconciling food security goals with the objective of becoming a low-carbon economy. Agriculture accounts for approximately 11 % of China’s national greenhouse gas (GHG) emissions with cereal production representing a large proportion (about 32 %) of agricultural emissions. Minimizing emissions per unit of product is a policy objective and we estimated the GHG intensities (GHGI) of rice, wheat and maize production in China from 1985 to 2010. Results show significant variations of GHGIs among Chinese provinces and regions. Relative to wheat and maize, GHGI of rice production is much higher owing to CH4 emissions, and is more closely related to yield levels. In general, the south and central has been the most carbon intensive region in rice production while the GHGI of wheat production is highest in north and northwest provinces. The southwest has been characterized by the highest maize GHGI but the lowest rice GHGI. Compared to the baseline scenario, a 2 % annual reduction in N inputs, combined with improved water management in rice paddies, would mitigate 17 % of total GHG emissions from cereal production in 2020 while sustaining the required yield increase to ensure food security. Better management practices will entail additional gains in soil organic carbon further decreasing GHGI. To realize the full mitigation potential while maximizing agriculture development, the design of appropriate policies should accommodate local conditions.


Soil Organic Carbon Rice Paddy Soil Organic Carbon Content Cereal Production Maize Production 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This study is funded by the Chinese Ministry of Science and Technology (2013BAD11B03), the Climate Economics Chair of Paris-Dauphine University and the Agricultural Science and Technology Innovation Program of CAAS. D.M. acknowledges support from the SmartSOIL EU FP7 project (grant number 289694).

Supplementary material

10584_2014_1289_MOESM1_ESM.docx (68 kb)
ESM 1 (DOCX 68.3 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Wen Wang
    • 1
    • 2
  • Liping Guo
    • 1
  • Yingchun Li
    • 1
  • Man Su
    • 3
  • Yuebin Lin
    • 1
  • Christian de Perthuis
    • 2
  • Xiaotang Ju
    • 4
  • Erda Lin
    • 1
    Email author
  • Dominic Moran
    • 5
  1. 1.Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences/Key Laboratory of Agricultural EnvironmentMinistry of Agriculture of P.R. CBeijingChina
  2. 2.Climate Economics ChairParis-Dauphine UniversityParisFrance
  3. 3.Graduate School, Chinese Academy of Agricultural SciencesBeijingChina
  4. 4.College of Resources and Environmental SciencesChina Agricultural UniversityBeijingChina
  5. 5.Land Economy and Environment Research GroupScotland’s Rural CollegeEdinburghScotland

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