FIGS uses cutting-edge applied Bayesian mathematics and geographical information data to help gene bank managers more effectively mine the millions of plant seed samples conserved in the world’s agricultural gene banks. It enables the rapid identification of traits that make crop varieties resistant to drought, excessive heat or cold, to insect pests and a variety of crop diseases that reduce farm yields in low-income and developed countries.
FIGS is based on the premise that the environment strongly influences natural selection and consequently the geographic distribution of organisms. FIGS creates ‘best-bet’ trait-specific subsets of material by passing accession-level information, especially agro-climatic sit information, through a series of filters that increase the chances of finding the adaptive trait of interest.
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 rapid searching of thousands of plant samples conserved in genebanks to pinpoint a number of high-potential types that can meet the breeder’s strategy.
A FIG’s approach takes the following path:
· Identify and understand the trait
· Define the environmental parameters that would select for the trait in situ
· Identify accessions that were collected in sites that meet the conditions identified in step 2.