Plants undergo several key developmental transitions, such as the decision to flower, that farmers would like to synchronise to maximise their yields. In this talk I will describe (i) a novel experimental design to understand how these transitions happen and (ii) a novel application of functional data analysis to help farmers breed more synchronised crops. To understand the biological regulation that leads to these transition points, a high temporal resolution of sampling would be required\DSEMIC however, the degree of developmental asynchrony makes such an experiment difficult to design. Instead, we sample a large collection of individual plants at the transition point and then estimate their age retroactively with a bootstrapping strategy, enabling us to order the plants along a pseudotime, giving us an unprecedented level of detail of the cascade of biological events that lead to the initiation of flowering. We then hypothesised that plants that are more sensitive to changes in day length (as occur in the spring and autumn) would have more synchronised development. Using functional data analysis approaches, we developed a predictive model of flowering synchrony on the basis of how the circadian rhythms of plants respond to changes in day length, in a population of plants with parents adapted from different latitudes. We are further adapting FDA methods to identify genetic loci that are significantly associated with these clock-related traits, which can be used to direct crop breeding for synchronised development.