|SJOBERG, STEPHANIE - Washington State University
|CARTER, ARRON - Washington State University
Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/5/2020
Publication Date: 8/5/2020
Publication URL: https://handle.nal.usda.gov/10113/7111131
Citation: Sjoberg, S., Carter, A., Steber, C.M., Garland Campbell, K.A. 2020. Application of the factor analytic model to assess wheat falling number performance and stability in multi-environment trials. Crop Science. https://doi.org/10.1002/csc2.20293.
Interpretive Summary: This study examined statistical methods to analyze risk of poor wheat product quality due to low faling numbers. The Hagberg-Perten falling number (FN) test is used to measure starch degradation caused by alpha-amylase enzyme activity in wheat flour. Flour with high alpha-amylase resulting in low falling numbers tends to produce poor quality baked goods, such as cakes that fall and sticky noodles. Thus, farmers receive steeply discounted prices for low FN grain. To help farmers choose varieties with better genetic resistance to low FN, over 12,000 FN data points have been collected by researchers from the USDA-ARS and Washington State University, and made publicly available. While farmers would like to have this data converted into a relative ranking of varieties for risk of low FN, statistical analysis is complicated because FN is influenced by weather during and after grain development, and by the fact that different varieties are grown in different locations and years by Cereal Variety Testing. This paper found that a factor analytic model did a good job of explaining the genetic and environmental variation in FN, providing a good statistical method to objectively rank varieties for risk of low FN over many years and locations. This will be applied to analyze FN in the WSU variety trials over time and over multiple market classes.
Technical Abstract: A factor analytic model was used to characterize a wheat quality trait highly influenced by genotype-by-environment interactions, falling number. The falling number method detects starch degradation due to the presence of the enzyme alpha-amylase in wheat grain such that a low falling number indicates high alpha-amylase and high risk of poor end-product quality. Because farmers receive severe discounts for low falling numbers, falling number data has been collected over multiple years for the Washington State University multilocation variety trials to help farmers and breeders identify lower risk varieties. Analysis of this data to objectively rank varieties is challenging because the dataset is unbalanced and because falling numbers are subject to complex genotype-by-environment interactions. Low falling numbers can result from environmental differences at multiple stages in grain development because there are two major genetic causes of alpha-amylase accumulation in grain, late-maturity alpha-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 falling number 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, falling number, providing a method to rank varieties for grower decisions and for breeder selections.