In the advancing world with urbanization and industrialization, our agro-ecosystems will be under immense pressure often threatening our food security. In fact, climate change, declining water supplies, rising energy costs, the emergence of new pests and diseases, loss of arable land and population growth means that our crop plants will need to yield more on less land and with fewer inputs under increasingly harsh conditions. For this reason, plant breeders will be forced to mine global plant genetic resources collections for variation that can be used to future-proof our crop plants. However, the plant genetic resources collections are large and breeders cannot afford to evaluate every accession in a collection as they hunt for desirable traits.

The Focused Identification of Germplasm Strategy (FIGS) is a gene bank mining tool. It was jointly developed by International Center for Agricultural Research in the Dry Areas (ICARDA), Vavilov Institute, Nordic Genetic Resources Center (NCRC) and Australian Winter Cereals Collection (AWCC) which helps plant breeders in unlocking the variation in Gene banks and make it more accessible to the plant breeding community. FIGS works on the premise that germplasm is likely to reflect the selection pressures of the environment in which it was developed. Environmental parameters describing plant germplasm collection sites are used as selection criteria to improve the probability of uncovering useful variation (Stenberg and Ortiz., 2021).

As FIGS is a gene bank mining tool, it mainly uses two pathways (Classification and filtering of collection sites) to create new subset of accessions which are then used to prepare trial data and environmental data. The approach is based on the principle that environments impose selective pressures that drive the adaptive evolution and consequently, the geographical distribution of plant species. Based on this premise, FIGS capitalizes on the availability of historical climatic and edaphic data (i.e., soil properties such as chemistry, texture and topography that influence organisms and processes occurring in the soil), as well as accurate geographical positioning system to mathematically model the potential relationship between an adaptive trait (i.e., disease resistance or tolerance to extreme weather conditions) and the environment that mediated the natural selection for that specific trait (Shim et al., 2021).

A study was conducted 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, which demonstrates that ecotypic differentiation driven by moisture availability has occurred within the Faba bean gene pool. Both Leaflet and canopy temperatures as well as relative water content (RWC) contributed more than other traits to the discrimination between sets, indicating 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 (Khazaei et al., 2013).

agreement to the FIGS sets, which demonstrates that ecotypic differentiation driven by moisture availability has occurred within the Faba bean gene pool. Both Leaflet and canopy temperatures as well as relative water content (RWC) contributed more than other traits to the discrimination between sets, indicating 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 (Khazaei et al., 2013).

The (FIGS) approach was used to predict stripe rust resistance in a collection of wheat landraces conserved at ICARDA gene bank. Long-term climate data for the collection sites from which these accessions originated and stripe rust evaluation scores for one group of accessions were presented to three different non-linear models to explore the trait × collection site environment interactions. Patterns in the data detected by the models were used to predict stripe rust resistance in a second and different set of accessions. The results of the prediction were then tested against actual evaluation scores of the disease in the field. This study showed that evaluation scores from collection site and from field were similar in nature (Bari et al., 2014).

Genetic resources are like gold mines to the plant breeders and new investigations on PGR needs a substantial long-term investment and specific strategies in order to conserve and create diversity. Recent studies have shown that the FIGS approach was effective when applied in the search of pest, disease and abiotic stress resistance. A meticulous and judicious use of plant genetic resources will be very effective to have advances in crop improvement and agricultural production and to ensure sustainable development.

REFERENCES:

Bari, A., Amri, A., Street, K., Mackay, M., De Pauw, E., Sanders, R., Nazari, K., Humeid, B., Konopka, J. and Alo, F., 2014, Predicting resistance to stripe (yellow) rust (Puccinia striiformis) in wheat genetic resources using focused identification of germplasm strategy. Int. J. Sci., 1: 50-61.

Khazaei, H., Street, K., Bari, A., Mackay, M. and Stoddard, F. L., 2013, The FIGS (Focused Identification of Germplasm Strategy) approach identifies traits related to drought adaptation in Vicia faba genetic resources. Plos one, 8(5): 1-10.

Shim, J., Bandillo, N. B. and Angeles, R. B., 2021, Finding needles in a haystack: using geo-references to enhance the selection and utilization of landraces in breeding for climate-resilient cultivars of upland cotton (Gossypium hirsutum L.). Plants, 10(7): 1-15.

Stenberg, J. A. and Ortiz, R., 2021, Focused identification of germplasm strategy (FIGS): polishing a rough diamond. Curr. Opin. Insect. Sci., 45: 21-26.