Early-stage sparse testing strategies to increase genetic gain in plant breeding programmes

Abstract

Early-stage sparse testing can significantly increase genetic gain in plant breeding programmes, facilitating the development of varieties with high and stable performance across farmers fields. This is achieved through (1) increased selection accuracy, enabled by broader sampling of trial sites across the Target Population of Environments (TPE), and (2) increased selection intensity by testing more selection candidates at a reduced replication rate. Early-stage agronomic testing typically involves evaluating a large number of selection candidates at one or a few experimental sites. Although these sites may poorly represent the TPE, strong selection pressure is generally applied. However, if the genetic correlation between performance at the experimental sites and in the TPE is low, most of the genetic gain achieved through selection will not be expressed under farmers conditions. Sparse testing addresses this challenge by using farm-as-incomplete-block designs, in which different subsets of selection candidates are evaluated across sites. By leveraging a genomic relationship matrix (GRM) to connect genotypes across environments, sparse testing enables early-stage multi-environment trials with broader coverage of the TPE. Here, we used stochastic simulation to compare various partially replicated sparse testing strategies to three fully replicated conventional early-stage testing strategies, with and without GRM. All sparse testing strategies achieved substantially higher and more stable genetic gain than the conventional strategies. Our results show that sparse testing provides breeders with a powerful and flexible framework to rethink early-stage trials and design cost-effective, multi-location experiments that lay the foundation for increased genetic gains in farmers fields. Key messageEarly-stage sparse testing can significantly increase genetic gain by (1) improving selection accuracy through broader sampling of the Target Population of Environments (TPE), and (2) increasing selection intensity.