Back to the future 2: the implications of germplasm structure on the balance between short- and long-term genetic gain in a changing target population of environments

Plant breeding operates within a complex genetic landscape determined by genes interacting within biological networks and with the environment. This environment is not constant but subject to short-term fluctuations and long-term shifts. This makes finding a balance between adapting germplasm for short- and long-term objectives challenging. We previously investigated the implications of genetic complexity on breeding program design. Here we build on this work by adding an environmental dimension in the form of the E(NK) model to the simulation framework. We found that the addition of environmental interactivity and change creates greater uncertainty associated with pursuing any specific selection trajectory. This advantages preserving genetic variability and genetic landscape exploration over quickly exposing additive variation by constraining genetic space around a particular and temporary local optimum. Nonetheless, also in a dynamically changing environment, a distributed breeding program structure finds the best balance between short and long-term objectives. In this structure, several breeding programs explore genetic space while maintaining constant germplasm exchange. This is in contrast to isolated programs or one large undifferentiated program, which exclusively emphasize short respectively long-term objectives. We furthermore highlight the difficulty of exchanging germplasm to restore genetic variability with non-stationary and germplasm context dependent genetic effects. In summary, also under environmental complexity and change, the structural features that characterized breeding operations hitherto and allowed them to navigating biological complexity apply. Namely the necessity to constrain genetic space in order for heritable additive variation to emerge. We end by arguing that optimal breeding program design depends on the level of genetic and environmental complexity. This complexity should be appropriately reflected when modeling the long-term behavior of selection programs and the implications of specific interventions into these.