Your search found 2 records
1 Vermeulen, S. J.; Challinor, A. J.; Thornton, P. K.; Campbell, B. M.; Eriyagama, Nishadi; Vervoort, J; Kinyangi, J.; Jarvis, A.; Laderach, P.; Ramirez-Villegas, J.; Nicklin, K. J.; Hawkins, E.; Smith, D. R. 2013. Addressing uncertainty in adaptation planning for agriculture. Proceedings of the National Academy of Sciences of the United States of America, 110(21): 8357-8362.
Climate change ; Adaptation ; Uncertainty ; Agriculture ; Food security ; Developing countries ; Coffee ; Models ; Case studies ; Stakeholders ; Decision making ; Greenhouse gases / Sri Lanka / East Africa / Central America
(Location: IWMI HQ Call no: e-copy only Record No: H045835)
(0.90 MB) (921.17KB)
We present a framework for prioritizing adaptation approaches at a range of timeframes. The framework is illustrated by four case studies from developing countries, each with associated characterisation of uncertainty. Two cases, on near-term adaptation planning in Sri Lanka and on stakeholder scenario exercises in East Africa, show how the relative utility of ‘capacity’ versus ‘impact’ approaches to adaptation planning differ with level of uncertainty and associated lead time. A further two cases demonstrate that it is possible to identify uncertainties that are relevant to decision-making in specific timeframes and circumstances. The case on coffee in Latin America identifies altitudinal thresholds at which incremental versus transformative adaptation pathways are robust options. The final case uses three crop-climate simulation studies to demonstrate how uncertainty can be characterised at different time horizons to discriminate where robust adaptation options are possible. We find that ‘impact’ approaches, which use predictive models, are increasingly useful over longer lead times and at higher levels of greenhouse gas emissions. We also find that extreme events are important in determining predictability across a broad range of timescales. The results demonstrate the potential for robust knowledge and actions in the face of uncertainty.
2 Dinesh, D.; Hegger, D.; Vervoort, J.; Campbell, B. M.; Driessen, P. P. J. 2021. Learning from failure at the science-policy interface for climate action in agriculture. Mitigation and Adaptation Strategies for Global Change, 26(1):2. [doi: https://doi.org/10.1007/s11027-021-09940-x]
Research programmes ; CGIAR ; Climate change ; Agriculture ; Adaptation ; Mitigation ; Policies ; Decision making ; Food security ; Funding ; Learning ; Institutions ; Strategies ; Innovation ; Uncertainty ; Political aspects
(Location: IWMI HQ Call no: e-copy only Record No: H050281)
https://link.springer.com/content/pdf/10.1007/s11027-021-09940-x.pdf
https://vlibrary.iwmi.org/pdf/H050281.pdf
https://vlibrary.iwmi.org/pdf/H050281.pdf
(0.61 MB) (629 KB)
Science–policy engagement efforts to accelerate climate action in agricultural systems are key to enable the sector to contribute to climate and food security goals. However, lessons to improve science–policy engagement efforts in this context mostly come from successful efforts and are limited in terms of empirical scope. Moreover, lessons have not been generated systematically from failed science–policy engagement efforts. Such analysis using lessons from failure management can improve or even transform the efficacy of efforts. To address this knowledge gap, we examined challenges and failures faced in science–policy engagement efforts of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). We developed an explanatory framework inspired by Cash et al.’s criteria for successful knowledge systems for sustainable development: credibility, salience, and legitimacy, complemented with insights from the wider literature. Using this framework in a survey, we identified factors which explain failure. To effectively manage these factors, we propose a novel approach for researchers working at the science–policy interface to fail intelligently, which involves planning for failure, minimizing risks, effective design, making failures visible, and learning from failures. This approach needs to be complemented by actions at the knowledge system level to create an enabling environment for science–policy interfaces.
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