Flowering time is a key adaptive trait, responding to environmental and endogenous signals that ensure reproductive growth and development occurs under favorable environmental conditions. Under a climate change scenario, temperature and water conditions are forecast to change and/or fluctuate, while photoperiods will remain constant at any given latitude. By assessing the current knowledge of the flowering-time pathways in both model (Arabidopsis thaliana) and key cereal (rice, barley, wheat, maize), temperate forage and biofuel grasses (perennial ryegrass, Miscanthus, sugarcane), root (sugar beet), and tree (poplar) crop species, it is possible to define key breeding targets for promoting adaptation and yield stability under future climatic conditions. In Arabidopsis, there are four pathways controlling flowering time, and the genetic and/or epigenetic control of many of the steps in these pathways has been well characterized. Despite this, even in this model species, there is little published information on the molecular basis of adaptation to the environment. In contrast, in crop and tree species, flowering time has been continually selected, either directly or indirectly as breeders and growers have selected the material that best suits a particular location. Understanding the genetic basis of this adaptive selection is now being facilitated via cloning of major genes, the mapping of QTL, and the use of marker-assisted breeding for specific flowering targets. In crop species where the genetic basis of flowering is not well understood (i.e., in the emerging biofuel grass, Miscanthus), such work is in its infancy. In cases where the genetic basis is well established, however, there are still grounds for important discovery, via new and emerging methods for mapping and selecting for flowering-time traits (i.e., QTL mapping in MAGIC populations, RABID selection), as well as methods for creating new genetic combinations with potentially novel flowering-time phenotypes (i.e., via targeted mutagenesis). In the future it is likely that computational modeling approaches which incorporate gene networks and the range of phenological response to measurable environmental conditions will play a central role in predicting the resilience of crop and tree species under climate change scenarios.