Climatic Change

, Volume 116, Issue 2, pp 357–372 | Cite as

Impacts of a nuclear war in South Asia on rice production in Mainland China

  • Lili Xia
  • Alan Robock


A regional nuclear war between India and Pakistan with a 5 Tg black carbon injection into the upper troposphere would produce significant climate changes for a decade, including cooling, reduction of solar radiation, and reduction of precipitation, which are all important factors controlling agricultural productivity. We used the Decision Support System for Agrotechnology Transfer agricultural simulation model to simulate regional nuclear war impacts on rice yield in 24 provinces in China. We first evaluated the model by forcing it with daily weather data and management practices for the period 1980–2008 for 24 provinces in China, and compared the results to observations of rice yields in China. Then we perturbed observed weather data using climate anomalies for a 10-year period from a nuclear war simulation. We perturbed each year of the 30-year climate record with anomalies from each year of the 10-year nuclear war simulations for different regions in China. We found that rice production would decline by an average of 21 % for the first 4 years after soot injection, and would slowly recover in the following years. For the next 6 years, the reduction in rice production was about 10 %. Different regions responded differently to climate changes from nuclear war. Rice production in northern China was damaged severely, while regions along the south and east coasts showed a positive response to regional nuclear war. Although we might try to adapt to a perturbed climate by enhancing rice planting activity in southern and eastern China or increasing fertilizer usage, both methods have severe limitations. The best solution to avoid nuclear war impacts on agriculture is to avoid nuclear war, and this can only be guaranteed with a nuclear-weapon-free world.


Rice Production Rice Yield Late Rice Increase Rice Production Rice Planting Area 
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.



We thank Luke Oman for providing us with the climate model output for Mainland China. We also thank Xinyi Zhao for providing weather observations in China. This work is partially supported by the Switzerland Federal Department of Foreign Affairs and NSF grant ATM-0730452.


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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Department of Environmental SciencesRutgers UniversityNew BrunswickUSA

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