Exploring assumptions in crop breeding for climate resilience: opportunities and principles for integrating climate model projections

Stephen Whitfield (), Sarah Chapman, Marcelin Tonye Mahop, Chetan Deva, Kennedy Masamba and Andekelile Mwamahonje
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Stephen Whitfield: University of Leeds
Sarah Chapman: University of Leeds
Marcelin Tonye Mahop: University of Leeds
Chetan Deva: University of Leeds
Kennedy Masamba: Department of Agricultural Research Services-Malawi, Bvumbwe Research Station
Andekelile Mwamahonje: Tanzania Agricultural Research Institute (TARI)-Makutupora Centre

Climatic Change, 2021, vol. 164, issue 3, No 14, 18 pages

Abstract: Abstract Crop breeding for resilience to changing climates is a key area of investment in African agricultural development, but proactively breeding for uncertain future climates is challenging. In this paper, we characterise efforts to breed new varieties of crops for climate resilience in southern Africa and evaluate the extent to which climate model projections currently inform crop breeding activity. Based on a survey of seed system actors, we find that the prioritisation of crops and traits is only informed to a limited extent by modelled projections. We use an ensemble of CORDEX models for mid and end of century for southern Africa to test some of the assumptions that underpin current breeding activity, particularly associated with breeding for reduced durations and drought tolerance in maize, and demonstrate some of the ways in which such projections can help to inform breeding priorities and agenda setting (e.g. through the case of assessing cassava toxicity risk). Based on these examples, we propose five potential applications of climate models in informing breeding priorities. Furthermore, after unpacking the sources of uncertainty within the presented model projections, we discuss general principles for the appropriate use of climate model information in crop breeding.

Keywords: Climate change; Resilience; Crop breeding; Projections; Agriculture; Africa (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10584-021-02997-2

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