Linking climate forecasts to rural livelihoods: Mapping decisions, information networks and value chains

• Published in: Weather and climate extremes 2020-03, Vol.27, p.100174, Article 100174
• Main Authors: Nidumolu, Uday, Lim-Camacho, Lilly, Gaillard, Estelle, Hayman, Peter, Howden, Mark
• Format: Article
• Language: English
• Subjects: Climate risk; Decision analysis; Seasonal climate forecasts; Social network analysis; Value chains
• ISSN: 2212-0947; 2212-0947
• DOI: 10.1016/j.wace.2018.06.001

Climate variability is a key source of livelihood risks faced by smallholder farmers in drier environments in many developing countries. Climate information provided on seasonal time-scales can sometimes improve agricultural decision-making. However, there are many barriers to the effective dissemination, communication and use of such information on farm and across the value chain. We used a case study in southern India to explore ways of overcoming some of these barriers such as those limiting access to information and effective communication of probabilistic forecast information. Firstly, we used social network analysis at the village level to identify particular individuals, groups or/and institutions who are central in information networks so as to be able to support them to increase the efficiency, effectiveness, equity and robustness of information transfers. This allowed us to identify potential opportunities and challenges around access, communication and forecast use. Close linking of formal and informal networks appeared to be a common, positive influencing factor. Secondly, we used value chain analysis to assess how pre-and post-farm decision-makers could use seasonal climate forecasts (SCF) in their own businesses and how this may propagate up and down the value chain. We found that the motivation for using SCF varied across the value chain and was likely of limited use to smaller, off-farm value chain players who take a short-term adaptive management approach to planning. However, it was seen as having significant potential for larger businesses who take a more strategic approach. This identified a possible risk of increased competitive inequality between businesses of different sizes. Thirdly, we addressed the challenge of translating probabilistic climate forecast information into support for decision making by using decision analysis with intermediaries enabling them to structure clearly problems with embedded climate probabilities. The construction of decision-trees enabled farm advisers and local researchers to explore the potential value of SCF by using the decision trees as a “boundary object” around which farmers and other decision makers, agricultural scientists, climate scientists, economists, social scientists and policy-makers could have thoughtful discussion leading to useful strategies to better manage climate risk. This paper outlines approaches and outcomes associated with these three activities as a way of exploring effective