A Bayesian approach towards facilitating climate change adaptation research on the South African agricultural sector
Evaluating the range of proposed adaptation measures to combat the sensitivity of agriculture to climate change effects involves evaluating complex interactions between human and natural systems. Integrated strategy-making and implementation in the agricultural sector to reduce the risks posed by climate change requires the consideration of multiple, interdisciplinary factors and the sensitivities of their inter-relationships. Lack of information on the sensitivity of agricultural activities to climate change in Africa hampers climate change adaptation research on the region. In water scarce South Africa, the growth of the agricultural sector is threatened by projected decreases in water availability due to climate change. This paper shows how Bayesian networks may be used to facilitate cross-disciplinary participation in elucidating these sensitivities. Bayesian networks provide a graphical framework for mixing quantitative and qualitative information and can be characterised using information associated with varying degrees of uncertainty. This enables a variety of domain experts to test key driver-response interactions through sensitivity analysis and enables visualisation of the complex inter-relationships between inter-disciplinary variables resulting from the impacts of climate change scenarios on South African agriculture. The ability to represent the sensitivities between key variables for which varying degrees of data-scarcity and uncertainty occur provides agricultural sector researchers with a facilitation tool that may helps visualise and formulate climate change mitigation strategies. The results presented here illustrates the extreme sensitivity of water-scarce South African agricultural sector to projected climate change impacts and provides a framework in which tradeoffs between activities can be preliminarily assessed in strategy-making for adaptation.
When requesting a correction, please mention this item's handle: RePEc:ags:agreko:10121. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (AgEcon Search)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.