Climatic Change

, Volume 138, Issue 1–2, pp 223–238 | Cite as

Towards a genotypic adaptation strategy for Indian groundnut cultivation using an ensemble of crop simulations



Climate change has been projected to significantly affect agricultural productivity and hence food availability in the coming decades. The uncertainty associated with projecting climate change impacts is a barrier to agricultural adaptation. Despite uncertainty quantification becoming more prominent in impact studies, the thorough quantification of more than one uncertainty source is not commonly exercised. This work focuses on Indian groundnut and uses the General Large Area Model for annual crops (GLAM) to investigate the response of groundnut under future climate scenarios, develop a genotypic adaptation strategy, and quantify the main uncertainty sources. Results suggest that despite large uncertainty in yield projections (to which crop- and climate-related sources contribute 46 and 54 %, respectively) no-regret strategies are possible for Indian groundnut. Benefits from genotypic adaptation were robust towards the choice of climate model, crop model parameters and bias-correction methods. Groundnut breeding for 2030 climates should be oriented toward increasing maximum photosynthetic rates, total assimilate partitioned to seeds, and, where enough soil moisture is available, also maximum transpiration rates. No benefit from enhanced heat stress tolerance was observed, though this trait may become important as warming intensifies. Managing yield variability remains a challenge for groundnut, suggesting that an integral approach to crop adaptation that includes year-to-year coping strategies as well as improvements in crop management is needed across all India.


Climate Change Impact Harvest Index Crop Model Yield Gain Transpiration Efficiency 



This study was funded by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output. We thank two anonymous reviewers for their insightful feedback on an earlier version of this manuscript. We thank Dr. Andy Jarvis from CIAT for title suggestion.

Supplementary material

10584_2016_1717_MOESM1_ESM.pdf (3.8 mb)
ESM 1 (PDF 3859 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Institute for Climate and Atmospheric Science, School of Earth and EnvironmentUniversity of LeedsLeedsUK
  2. 2.CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), c/o CIATCaliColombia
  3. 3.International Center for Tropical AgricultureCaliColombia

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