Application of the factor analytic model to assess wheat falling number performance and stability in multienvironment trials

Abstract

A factor analytic model was used to characterize data generated with the Hagberg–Perten falling number (FN) method, a measure of wheat (Triticum aestivum L.) quality influenced by genotype‐by‐environment interactions. The FN method detects starch degradation due to the presence of the enzyme α‐amylase in wheat grain such that a low FN indicates high α‐amylase activity and high risk of poor end‐product quality. Because farmers receive severe discounts for low FN, FN data have been collected over multiple years for the Washington State University multilocation variety trials to help farmers and breeders identify lower risk varieties. Analysis of these data to objectively rank varieties is challenging because the dataset is unbalanced and because FN is subject to complex genotype‐by‐environment interactions. Low FN can result from environmental differences at multiple stages in grain development because there are two major causes of α‐amylase accumulation in grain, late‐maturity α‐amylase (LMA) and preharvest sprouting (PHS). A five‐factor analytic model extracted explicit measures of overall performance and of stability in variable environments from historical FN data from the multilocation trial, providing a basis for breeding and planting decisions. Whereas a linear model explained 70.3% of the variation, the five‐factor analytic model accounted for 92.5% of variation in the data. Examination of factor loadings enabled us to separate environments and genotype response to either PHS or LMA, specifically. This is the first application of a factor analytic model to evaluate the end‐use quality trait FN, providing a method to rank varieties for grower decisions and breeder selections.

Environment characterization and genomic prediction for end‐use quality traits in soft white winter wheat

“…Extracting valuable information from unbalanced and multienvironment trial datasets is challenging in breeding programs (Sjoberg et al, 2021). In this study we examined the pattern of G × E interaction in soft white wheat end-use quality traits using methods that can accommodate unbalanced and multi-environment data.…”

“…variables into a few unobserved variables or latent factors and showed its utility in capturing the variance structure of G × E in multi-environment trials (Cullis et al, 2010;Dias et al, 2018;Oliveira et al, 2020;Sjoberg et al, 2021;Smith et al, 2015). Since end-use quality traits are predominately controlled by genetic factors (Carter et al, 2012;Smith et al, 2011;Souza et al, 2012), the use of molecular markers for marker-assisted selection or genomic selection can optimize breeding for enduse quality traits (Kiszonas & Morris, 2018;Sandhu et al, 2021).…”

“…Multiple methods have been used to study G × E interaction including linear models and multivariate models such as additive main effects and multiplicative interaction (Gauch, 1988) and the genotype plus G × E models (Yan et al, 2000). However, these methods have their limitations to accommodate multi-environment and unbalanced datasets (Dias et al, 2018;Sjoberg et al, 2021;Smith et al, 2001). Thus, multiplicative mixed models such as the factor analytic (FA) model have been proposed as a more robust method to improve the understanding of G × E interaction in unbalanced multi-environment datasets that are common in applied breeding programs (Beeck et al, 2010;Burgueño et al, 2008Burgueño et al, , 2011Kelly et al, 2007Kelly et al, , 2009Piepho, 1998).…”

Registration of ‘ARS Crescent’ soft white winter club wheat

“…Falling number tests were performed on whole grain meal with the Hagberg–Perten Falling Number Apparatus 1800 using the ICC 107/1 method as described by Sjoberg et al. (2020).…”

“…Grain samples were collected after harvest from the Washington State Cereal Variety Testing Nurseries. Falling number tests were performed on whole grain meal with the Hagberg-Perten Falling Number Apparatus 1800 using the ICC 107/1 method as described by Sjoberg et al (2020).…”