‘FIGS’ – or the Focused Identification of Germplasm Strategy – is a pioneering scientific technique that helps crop breeders achieve faster and better-targeted pinpointing of improved crops for the future.
Never before has the planet had so many hungry human mouths to feed. Nine billion at 2050 - and counting. Demand for food is also increasing with economic development - as people get richer they tend to eat more meat, which indirectly raises demand for grain.
Meanwhile, the threat of climate change is becoming clearer with preliminary studies showing that rising global temperatures alone could reduce the productivity of the world’s main crops by over 25 percent. Climate change will also increase the number of droughts and floods that can wipe out an entire season of crops.
Rising to these challenges requires new high-yielding food crops with heat, cold, or drought resistance. Achieving this, however, will not be an easy task. The development of these crops will depend on effective processes for searching genebanks, and the processes available today – to identify the best traits of resistance and higher performance that can be bred into new food crop varieties – are largely hit and miss.
They tend to range from ‘lucky dip’ efforts to a core collection concept, which aims to capture as much genetic diversity as possible, but using only a small subset of 5-10% of a total collection.
Previous simulations on crop improvement show that the benefits of using improved crop varieties can be substantial provided that the traits of interest are identified early. FIGS enables us to do this.
In several hundred searches delivered to date, FIGS has demonstrated that it can identify specific traits for breeders rapidly and precisely. In some cases it has identified traits that researchers have been looking for, unsuccessfully, for a number of years.
FIGS – how it works
At its core is a powerful algorithm that matches plant traits with geographic and agro-climatic information about where the samples were collected. This allows the rapid searching of thousands of plant samples conserved in gene banks to pinpoint a number of high potential types that can meet the breeder’s strategy.
FIGS is based on the premise that the environment strongly influences natural selection, and consequently the geographical distribution of organisms. It creates ‘best-bet’ trait-specific subsets of material by passing accession-level information, especially agro-climatic site information, through a series of filters that increase the chances of finding the adaptive trait of interest.
Accessions from these areas have a higher probability to contain the traits and genes of interest. From this calculation are assembled smaller subsets of genetic material that have a high potential to contain the plant traits that breeders need to develop their robust new varieties.
Put simply, ‘FIGS’ improves the effectiveness of breeding for national breeding programs and international research centers. It speeds the work of scientists looking for specific plant traits. In doing this, it helps improve the quality of crop improvement programs, and over the coming decades, can contribute to enhanced food security in the world’s low-income countries.