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Predicting climate and geoengineering effects on rice in India

Dr Steven Dobbie (SEE), Prof Andy Challinor (SEE), Prof Sat Ghosh (VIT, India) and Dr Huiyi Yang (Exeter, UK)

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Project Summary

Future climate change is predicted to give rise to further increases in global temperature accompanied by more frequent extremes especially since atmospheric carbon dioxide is continuing to rise to unprecedented levels. It may be necessary in the future for humans to intentionally intervene in the Earth-Atmosphere system to counteract climate change. There have been a number of possible ways suggested and they generally focus on either directly removing carbon dioxide from the atmosphere or by increasing the reflectance of the Earth-atmosphere system thereby reducing the amount of sunlight absorbed.

If geoengineering is ever to be used, then it is important that scientists consider the wider effects of such actions, and not only whether it regulates global temperature. For instance, it is vitally important to know if geoengineering will have undesirable effects like impacting on the food supply. This is especially important for developing countries where, for example, Carleton (2017) found that fluctuations in climate, especially temperature, over the last 47 years for India was correlated with suicide rates and strongly linked with temperatures above 20oC during the growing seasons.


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This project will focus on the rice crop in India which is a stable food for over half of the Indian population. Much of the rice is grown in the Eastern part of the country with a significant concentration in the Southern peninsula. Agriculture in India accounts for almost 20% of the GDP and along with allied sectors employs half the total workforce.

The research project will build on past experience evaluating crops in India (Koehler et al., 2013; Ramirez-Villegas and Challinor, 2016; Yang et al., 2017) but this research will utilise the Oryza model that models the rice crop. As in the work of Yang et al. (2017) we will make use of CMIP climate predictions (Andrews et al., 2012; Taylor et al., 2012) as well as GeoMIP (Kravitz, B., et al., 2013) geoengineering predictions to drive the crop model simulations. The study period will be for the current century with a 50-year solar dimming geoengineering period imposed.


  1. Model rice crop yields and failures for India using the Oryza crop model including the crop timing and growth for historical, future climate and climate geoengineered scenarios.
  2. Evaluate the future projections relative to historical levels and conclude about the future trajectory of rice for India, highlighting and explaining important temporal and spatial patterns.
  3. Assess the various climate geoengineering implementations of GeoMIP to determine if any have positive effects on rice crop yields and failure rates.
  4. Evaluate how geoengineering affects the rice crop yield, for example through changes to the patterns or frequency etc. of monsoon, levels of precipitation, temperature changes, etc.
  5. Evaluate the response of the crop yields and failures to climatically related adaptation such as removing water and heat stress and highlight if certain adaptation approaches are particularly effective for the future scenarios.

Potential for high impact outcomes

The results of this research will conclude about the impact of climate and geoengineered climate on rice crop yields and failure rates in India. Rice is a staple food for over half the Indian population and consequently any considerations of way forward regarding climate geoengineering must take account of the projected impact on this population, especially since recent work has shown agricultural suicide rates are on the rise and linked with climate change in recent years.


You will work under the supervision of Dr Steven Dobbie ( and Prof Andrew Challinor ( at the University of Leeds, together with Prof Sat Ghosh from the Vellore Institute of Technology, India and in collaboration with Dr Huiyi Yang (University of Exeter). 

The project therefore provides both a high level of specialist scientific training in (i) climate-crop modelling, (ii) analysis of climate data and (iii) socio-economic methods for assessing financial and livelihood impacts of crop losses for farmers. Co-supervision will involve regular meetings between both supervisors and email and visits by collaborators with the potential to visit India.    You will have access to a broad spectrum of training workshops put on by the Faculty including statistical and numerical modelling, through to managing your degree, to preparing for your viva (


You should have an interest in global environmental change, including land use and climate change. A strong quantitative background is essential, as is the motivation to work with large datasets and computer models.  Programming in any language is desirable, but not essential.


  1. Yang H; Dobbie S; Ramirez-Villegas J; Feng K; Challinor AJ; Chen B; Gao Y; Lee L; Yin Y; Sun L; Watson J; Koehler AK; Fan T; Ghosh S (2016) Potential negative consequences of geoengineering on crop production: A study of Indian groundnut, Geophys. Res. Lett., 43, pp.11-795. doi:10.1002/2016GL071209.
  2. Koehler A-K; Challinor AJ; Hawkins E; Asseng S (2013) Influences of increasing temperature on Indian wheat: quantifying limits to predictability, Environmental Research Letters, 8, pp.034016. doi:10.1088/1748-9326/8/3/034016.
  3. Ramirez-Villegas J; Challinor AJ (2016) Towards a genotypic adaptation strategy for Indian groundnut cultivation using an ensemble of crop simulations, Climatic Change, 138, pp.223-238. doi: 10.1007/s10584-016-1717-y.
  4. Carleton, (2017) Crop-damaging temperatures increase suicide rates in India, PNAS, 114, 33, pp.8746–8751, doi: 10.1073/pnas.1701354114.
  6. Kravitz, B., et al. (2013), Climate model response from the Geoengineering Model Intercomparison Project (GeoMIP), J. Geophys. Res. Atmos., 118, 8320–8332, doi:10.1002/jgrd.50646.
  7. Andrews, T., J. M. Gregory, M. J. Webb, and K. E. Taylor (2012), Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere–ocean climate models, Geophys. Res. Lett., 39, L09712, doi:10.1029/2012GL051607.
  8. Taylor, K. E., R. J. Stouffer, and G. A. Meehl (2012), An overview of CMIP5 and the experiment design, Bull. Am. Meteorol. Soc., 93(4), 485–498, doi:10.1175/BAMS-D-11-00094.1.

Related undergraduate subjects:

  • Agriculture
  • Applied mathematics
  • Atmospheric science
  • Computer science
  • Computing
  • Earth system science
  • Ecology
  • Environmental biology
  • Environmental science
  • Geography
  • Hydrology
  • Mathematics
  • Meteorology
  • Natural sciences
  • Physical geography
  • Physical science
  • Physics
  • Plant science
  • Soil science
  • Statistics