Optimizing multi‐trait selection for profitability in Illinois wheat using a factor analytic mixed model and an economic selection index

Abstract Economic selection indices can improve the efficiency of selection for multiple traits, but they are underutilized in plant breeding due to limited research on the economic values of traits and a lack of well‐documented case examples employing economic indices. This study evaluated a genomic‐based Smith–Hazel index incorporating empirically derived economic weights for grain yield, test weight, and maturity in soft red winter wheat ( Triticum aestivum L.) in Illinois. Economic weights used in the index were estimated using a function describing expected profit considering the predominant cropping system and grain quality pricing scheme of the region. Data from 2995 genotypes, five traits, and 12 environments (2022–2024) were analyzed together with genome‐wide marker data using a factor analytic (factor analytic model with k factors [Fa k ]) model fitted to the environment‐by‐trait combinations to obtain predictions of breeding value. The optimal multi‐trait multi‐environment (MTME) model (FA5) explained over 91% of the additive genetic variance and achieved high prediction accuracy (≥0.95). Compared with a single‐trait multi‐environment (STME) model, use of an MTME model for index selection increased predicted economic return by 3.4% ($120 ha −1 vs. $116 ha −1 for STME). Furthermore, index‐based selection under MTME yielded a predicted net merit gain of $120 ha −1 , compared with $97.5 ha −1 when selecting on grain yield alone, demonstrating the economic value of multi‐trait selection. Covariance estimates from the MTME model indicated that the genetic correlations between traits depended upon the environment, likely due to environment‐specific stressors. Overall, we determined that an optimal economic selection index can be constructed for a regionally focused plant breeding program using an FA k model and economic weights.