The research compiled in this thesis aims to explore a few core questions related to the perceptions, management and measurement of climate risk within the context of smallholders in Kenya. It makes use of a unique data set comprising of nearly 200,000 adoptions of a pioneering weather index insurance that was bundled with a hybrid seed product and available over several seasons. The first empirical chapter econometrically explores the role of basis risk in the continued uptake of the bundled index insurance product. The findings conclude that localised experience of both positive and negative basis risk compound an uncertainty of the product and consequently stifle demand. This presents evidence to suggest that smallholders do indeed view these products risk mitigating mechanisms rather than investments under balanced reciprocity. The second empirical study, using the same dataset, explores the effect that ‘experimentation’ and ‘learning’, by repeat users of the bundled index insurance product, has on another adaptive agricultural behaviour (changes to sowing timings). This work provides suggestive evidence of ‘learning’ that consequently leads to improved sowing decisions and lower insurance pay-outs for repeated users. This offers some valuable policy insights for the structuring of insurance premiums within these products. The final research project used a sample of the same database of farmers to conduct a large scale field experiment which sought to collect risk preference data in a novel way (via mobile phone). This data is analysed within a pioneering Bayesian modelling framework to track how risk preferences update given changing environmental contexts. The study identifies a notable reduction in risk aversion post-harvest as well as evidence of increased levels of localised risk aversion in more challenging climates and following weather shocks. This work supports an evolutionary model of risk preferences and may also offer applied insights for development practitioners pursuing the effective delivery of risk mitigation mechanisms.