Models to estimate genetic gain of soybean seed yield from annual multi-environment field trials

Abstract

Key message Simulations demonstrated that estimates of realized genetic gain from linear mixed models using regional trials are biased to some degree. Thus, we recommend multiple selected models to obtain a range of reasonable estimates. Abstract Genetic improvements of discrete characteristics are obvious and easy to demonstrate, while quantitative traits require reliable and accurate methods to disentangle the confounding genetic and non-genetic components. Stochastic simulations of soybean [Glycine max (L.) Merr.] breeding programs were performed to evaluate linear mixed models to estimate the realized genetic gain (RGG) from annual multi-environment trials (MET). True breeding values were simulated under an infinitesimal model to represent the genetic contributions to soybean seed yield under various MET conditions. Estimators were evaluated using objective criteria of bias and linearity. Covariance modeling and direct versus indirect estimation-based models resulted in a substantial range of estimated values, all of which were biased to some degree. Although no models produced unbiased estimates, the three best-performing models resulted in an average bias of $$\pm\, 7.41$$ ± 7.41 kg/ha$$^{-1}$$ - 1 /yr$$^{-1}$$ - 1 ($$\pm\, 0.11$$ ± 0.11 bu/ac$$^{-1}$$ - 1 /yr$$^{-1}$$ - 1 ). Rather than relying on a single model to estimate RGG, we recommend the application of several models with minimal and directional bias. Further, based on the parameters used in the simulations, we do not think it is appropriate to use any single model to compare breeding programs or quantify the efficiency of proposed new breeding strategies. Lastly, for public soybean programs breeding for maturity groups II and III in North America, the estimated RGG values ranged from 18.16 to 39.68 kg/ha$$^{-1}$$ - 1 /yr$$^{-1}$$ - 1 (0.27–0.59 bu/ac$$^{-1}$$ - 1 /yr$$^{-1}$$ - 1 ) from 1989 to 2019. These results provide strong evidence that public breeders have significantly improved soybean germplasm for seed yield in the primary production areas of North America.

Understanding the Molecular Regulatory Networks of Seed Size in Soybean

“…For instance, soybean is highly sensitive to photoperiod, and the duration as well as intensity of light exposure can affect the process of seed development. In recent years, the influence of environmental factors on soybean seed development has gained widespread attention among researchers [18,19]. As early as around 5000 BC mankind started to cultivate soybeans, and conserved as well as managed the rich source of soybean germplasm resources.…”

“…For instance, soybean is highly sensitive to photoperiod, and the duration as well as intensity of light exposure can affect the process of seed development. In recent years, the influence of environmental factors on soybean seed development has gained widespread attention among researchers [18,19].…”

Q&A: Methods for estimating genetic gain in sub‐Saharan Africa and achieving improved gains

“…While era trials provide better connectivity, historical data are well suited for better TPE coverage. However, the effects of the environment are mostly estimated from the sample of connected checks, often small (Krause et al., 2023), and new breeding programs naturally do not have large historical datasets. Nevertheless, selection accuracy can be improved with the use of advanced statistical analysis methods that employ informative models for genotype‐by‐environment interaction and appropriately accommodate within‐trial error variation.…”

“…Nevertheless, selection accuracy can be improved with the use of advanced statistical analysis methods that employ informative models for genotype‐by‐environment interaction and appropriately accommodate within‐trial error variation. Estimating realized genetic gain over a few years (2–10) can lead to inaccurate results (Krause et al., 2023; Rutkoski, 2019). For that, Krause et al.…”

“…For that, Krause et al. (2023) recommend using the linear mixed models (LMMs; Henderson et al., 1959) with various parameterizations of the G × L × Y, selecting the most appropriate model based on criteria such as the Akaike information criterion. In any case, the gains will only be achieved if the methods are applied with a suitable check strategy.…”