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.
| Main Authors: | , |
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| Format: | Journal Article biblioteca |
| Language: | English |
| Published: |
Springer
2016-09
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| Subjects: | yields, climate change, improvement, climate change adaptation, heat stress, crop modelling, simulation models, rendimiento, cambio climático, adaptación al cambio climático, estrés térmico, modelización de los cultivos, modelos de simulación, |
| Online Access: | https://hdl.handle.net/10568/75716 https://doi.org/10.1007/s10584-016-1717-y |
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