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The purpose of this paper is to explain why the high quality cassava flour (HQCF) value chain in Nigeria has not performed as well as expected. The specific objectives are to: analyse important sources of uncertainty influencing HQCF value chains; explore stakeholders’ strategies to respond to uncertainty; and highlight the implications of different adaptation strategies for equity and the environment in the development of the value chain.

The authors used a conceptual framework based on complex adaptive systems to analyse the slow development of the value chain for HQCF in Nigeria, with a specific focus on how key stakeholders have adapted to uncertainty. The paper is based on information from secondary sources and grey literature. In particular, the authors have drawn heavily on project documents of the Cassava: Adding Value for Africa project (2008 to present), which is funded by the Bill & Melinda Gates Foundation, and on the authors’ experience with this project.

Policy changes; demand and supply of HQCF; availability and price of cassava roots; supply and cost of energy are major sources of uncertainty in the chain. Researchers and government have shaped the chain through technology development and policy initiatives. Farmers adapted by selling cassava to rival chains, while processors adapted by switching to rival cassava products, reducing energy costs and vertical integration. However, with uncertainties in HQCF supply, the milling industry has reserved the right to play. Vertical integration offers millers a potential solution to uncertainty in HQCF supply, but raises questions about social and environmental outcomes in the chain.

The use of the framework of complex adaptive systems helped to explain the development of the HQCF value chain in Nigeria. The authors identified sources of uncertainty that have been pivotal in restricting value chain development, including changes in policy environment, the demand for and supply of HQCF, the availability and price of cassava roots, and the availability and cost of energy for flour processing. Value chain actors have responded to these uncertainties in different ways. Analysing these responses in terms of adaptation provides useful insights into why the value chain for HQCF in Nigeria has been so slow to develop.

Recent developments suggest that the most effective strategy for the milling industry to reduce uncertainty in the HQCF value chain is through vertical integration, producing their own cassava roots and flour. This raises concerns about equity. Until now, it has been assumed that the development of the value chain for HQCF can combine both growth and equity objectives. The validity of this assumption now seems to be open to question. The extent to which these developments of HQCF value chains can combine economic growth, equity and environmental objectives, as set out in the sustainable development goals, is an open question.

The originality lies in the analysis of the development of HQCF value chains in Nigeria through the lens of complex adaptive systems, with a particular focus on uncertainty and adaptation. In order to explore adaptation, the authors employ Courtney et al.’s (1997) conceptualization of business strategy under conditions of uncertainty. They argue that organisations can assume three strategic postures in response to uncertainty and three types of actions to implement that strategy. This combination of frameworks provides a fresh means of understanding the importance of uncertainty and different actors’ strategies in the development of value chains in a developing country context.

Present a method to impute missing data from a chaotic time series, in this case lightning prediction data, and then use that completed dataset to create lightning prediction forecasts.

Using the technique of spatiotemporal kriging to estimate data that is autocorrelated but in space and time. Using the estimated data in an imputation methodology completes a dataset used in lightning prediction.

The techniques provided prove robust to the chaotic nature of the data, and the resulting time series displays evidence of smoothing while also preserving the signal of interest for lightning prediction.

The research is limited to the data collected in support of weather prediction work through the 45th Weather Squadron of the United States Air Force.

These methods are important due to the increasing reliance on sensor systems. These systems often provide incomplete and chaotic data, which must be used despite collection limitations. This work establishes a viable data imputation methodology.

Improved lightning prediction, as with any improved prediction methods for natural weather events, can save lives and resources due to timely, cautious behaviors as a result of the predictions.

Based on the authors’ knowledge, this is a novel application of these imputation methods and the forecasting methods.