Monday, 10 June 2013

Decision making for climate change adaptation

In a recent talk at the Walker Institute, climate change adaptation specialist Suraje Dessai stressed the need to move away from the linear model of  'predict and provide' which believes that more science = better decisions = successful adaptation, towards an understanding of the limits of what science can provide. Talking in the context of decision making for climate change adaptation in the UK, he emphasised the need to make climate science more useful (matches user needs), usable (user-friendly) and valuable (salient and applicable). 

A smallholder farmer in Rajasthan, India. He decides how much wheat to
grow in the winter based on residual soil moisture from the rains, amount
of seed he has, and family's food requirements.  
This led me to think of similar issues in a very different context. That of rainfed agriculture in semi-arid regions of India. In this landscape, smallholder farmers are highly dependent on agriculture for income and sustenance, making them vulnerable to an increasingly erratic monsoon. In addition to environmental stressors of erratic precipitation (both amount and timing) and fluctuating temperatures (e.g. unusually hot winters), farmers are exposed to market dynamics and changes in the larger institutional landscape (e.g. land tenure reforms or amended public welfare schemes). These factors create a picture of farmers operating in highly uncertain environments, where decision making at the household level is not necessarily driven by profit maximisation. Dennis Wichelns, former IWMI Deputy Director General, explains the levels of uncertainty smallholder farmers deal with:
"farmers dependent on rainfall don't know when its going to rain for sure."..."There is so much uncertainty and risk in rainfed farming that many farmers, particularly at the small scale, have to spend quite a bit of effort managing that risk and uncertainty; often because they have very little freeboard by which to absorb great reductions in yield or sudden shortfalls in output or revenue."
In an attempt to facilitate local adaptation to climate change, policy makers look to science to 'provide answers' to this situation. This points to the belief that 'enough' science will lead to 'appropriate' solutions. A belief that the answers are 'waiting to be found'. Thus, there has been a push for higher resolution models, more accurate rainfall forecasting, as a way to allow for robust decision making. However, while important in its own right, this approach has to be questioned. Why an emphasis on better climate information (and not better market information, welfare schemes information, credit availability information)? Is the climate information given to farmers useful, usable and/or valuable? Are there adequate channels to deliver this information? And finally (perhaps most importantly), will this information help farmers make more robust decisions in their contexts of high uncertainty?

Further resources on the topic:
  1. Dennis Wichelns's video which questions the efficacy of water productivity as a tool to evaluate sustainable strategies of water use.  
  2. Providing climate services that make sense to farmers, a video and blog by CCAFS (CGIAR Research Program on Climate Change, Agriculture and Food Security) documenting their initiatives in trying to find solutions to making climate services more farmer-appropriate. 
  3. Experiences of dealing with uncertainties in practice, a presentation by Suraje Dessai

2 comments:

  1. I think you are right in questioning the approach. As you point out, the current approach is important but only part of the puzzle. Knowledge dissemination for me is probably the big chunk this has been left comparatively untouched. Sure we have TV and radio programmes but how beneficial have they been and to whom?

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    Replies
    1. Developing more engaging methods of information dissemination are definitely important. But first there has to be a clearer idea about what information is required. And also the realisation that more information does not necessarily mean 'better' decisions.

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