journal article Open Access Sep 24, 2024

Genotype‐by‐environment interaction for wheat falling number performance due to late maturity α‐amylase

Crop Science Vol. 64 No. 6 pp. 3202-3218 · Wiley
View at Publisher Save 10.1002/csc2.21348
Abstract
AbstractLate maturity α‐amylase (LMA) is known to reduce falling number (FN) in wheat (Triticum aestivum L.), similar to the effect of preharvest sprouting (PHS) and frost, which can result in grain parcels testing below trading thresholds. Hence, Grains Australia mandates that new Australian wheat cultivars must be at a low risk of LMA expression to receive a milling classification. The multi‐environment trial dataset contained 34 environments not affected by PHS or frost and was analyzed using a five‐factor analytic linear mixed model. Factor 1 accounted for 71.4% of the genetic variation in FN, factor 2 accounted for 8.8%, factor 3 accounted for 5.7%, factor 4 accounted for 3.5%, and factor 5 accounted for 3.1%. The interaction class (iClass) summary method was used to assist in the characterization of crossover genotype‐by‐environment interaction (G × E). Poorer FN performance was best observed in the “ppp” iClass, which indicated a minor, but significant, response to crossover G × E. The environment loadings for factor 1 were associated with mild ripening conditions, characterized by fewer days above 28°C, increased rainfall, and increased variation in daily maximum temperature and relative humidity. Factors 2 and 3 were associated with “cool shock” conditions, where the maximum temperature for 1 day was above 24°C followed by at least three consecutive days below 18°C during the grain fill period. This study provides further evidence of the crossover G × E present for FN associated with LMA, poor FN performance in genotypes that express higher levels of LMA, and the environmental conditions that contribute to LMA expression.
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References
46
[1]
AACC International (2022)
[5]
Butler D. G. &Cullis B. R.(2018).NIASRA working paper 04–18: Optimal design under the linear mixed model.National Institute for Applied Statistics Research Australia University of Wollongong.
[6]
Butler D. G. Cullis B. R. Gilmour A. R. Gogel B. J. &Thompson R.(2018).ASReml‐R reference manual version 4.VSN International Ltd.
[9]
Coombes N. E.(2002).The reactive tabu search for efficient experimental designs[ Doctoral dissertation Liverpool John Moores University]. 10.1007/978-3-642-57489-4_34
[11]
On the design of early generation variety trials with correlated data

B. R. Cullis, A. B. Smith, N. E. Coombes

Journal of Agricultural, Biological and Environmen... 10.1198/108571106x154443
[18]
Grains Australia. (2023).Classification guidelines.Grains Australia.
[20]
Hagberg S. "A rapid method for determining alpha‐amylase activity" Cereal Chemistry (1960)
[34]
Perten H. "Application of the falling number method for evaluating alpha‐amylase activity" Cereal Chemistry (1964)
[35]
R Core Team. (2023).R: A language and environment for statistical computing. R Foundation for Statistical Computing.https://www.r‐project.org/
[43]
Tan M. K.(2004).Molecular marker development for LMA determinant in Spica and Cranbrook(VAWCRC Report No. 42).Elizabeth Macarthur Agricultural Institute NSW Agriculture.
[44]
USDA (2017)
Metrics
3
Citations
46
References
Details
Published
Sep 24, 2024
Vol/Issue
64(6)
Pages
3202-3218
License
View
Funding
Australian Government
Grains Research and Development Corporation Award: UW00009
Cite This Article
William Fairlie, David Hughes, Brian Cullis, et al. (2024). Genotype‐by‐environment interaction for wheat falling number performance due to late maturity α‐amylase. Crop Science, 64(6), 3202-3218. https://doi.org/10.1002/csc2.21348
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