Abstract
Efficient methods to explore plant agro-biodiversity for climate change adaptive traits are urgently required. The focused identification of germplasm strategy (FIGS) is one such approach. FIGS works on the premise that germplasm is likely to reflect the selection pressures of the environment in which it developed. Environmental parameters describing plant germplasm collection sites are used as selection criteria to improve the probability of uncovering useful variation. This study was designed to test the effectiveness of FIGS to search a large faba bean (Vicia faba L.) collection for traits related to drought adaptation. Two sets of faba bean accessions were created, one from moisture-limited environments, and the other from wetter sites. The two sets were grown under well watered conditions and leaf morpho-physiological traits related to plant water use were measured. Machine-learning algorithms split the accessions into two groups based on the evaluation data and the groups created by this process were compared to the original climate-based FIGS sets. The sets defined by trait data were in almost perfect agreement to the FIGS sets, demonstrating that ecotypic differentiation driven by moisture availability has occurred within the faba bean genepool. Leaflet and canopy temperature as well as relative water content contributed more than other traits to the discrimination between sets, indicating that their utility as drought-tolerance selection criteria for faba bean germplasm. This study supports the assertion that FIGS could be an effective tool to enhance the discovery of new genes for abiotic stress adaptation.