Responses of wheat kernel weight to diverse allelic combinations under projected climate change conditions
In wheat, kernel weight (KW) is a key determinant of grain yield (GY). However, it is often overlooked when improving wheat productivity under climate warming. Moreover, little is known about the complex effects of genetic and climatic factors on KW. Here, we explored the responses of wheat KW to di...
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Published in | Frontiers in plant science Vol. 14; p. 1138966 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Media S.A
14.03.2023
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Subjects | |
Online Access | Get full text |
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Summary: | In wheat, kernel weight (KW) is a key determinant of grain yield (GY). However, it is often overlooked when improving wheat productivity under climate warming. Moreover, little is known about the complex effects of genetic and climatic factors on KW. Here, we explored the responses of wheat KW to diverse allelic combinations under projected climate warming conditions.
To focus on KW, we selected a subset of 81 out of 209 wheat varieties with similar GY, biomass, and kernel number (KN) and focused on their thousand-kernel weight (TKW). We genotyped them at eight kompetitive allele-specific polymerase chain reaction markers closely associated with TKW. Subsequently, we calibrated and evaluated the process-based model known as Agricultural Production Systems Simulator (APSIM-Wheat) based on a unique dataset including phenotyping, genotyping, climate, soil physicochemistry, and on-farm management information. We then used the calibrated APSIM-Wheat model to estimate TKW under eight allelic combinations (81 wheat varieties), seven sowing dates, and the shared socioeconomic pathways (SSPs) designated SSP2-4.5 and SSP5-8.5, driven by climate projections from five General Circulation Models (GCMs) BCC-CSM2-MR, CanESM5, EC-Earth3-Veg, MIROC-ES2L, and UKESM1-0-LL.
The APSIM-Wheat model reliably simulated wheat TKW with a root mean square error (RMSE) of < 3.076 g TK
and R
of > 0.575 (
< 0.001). The analysis of variance based on the simulation output showed that allelic combination, climate scenario, and sowing date extremely significantly affected TKW (
< 0.001). The impact of the interaction allelic combination × climate scenario on TKW was also significant (
< 0.05). Meanwhile, the variety parameters and their relative importance in the APSIM-Wheat model accorded with the expression of the allelic combinations. Under the projected climate scenarios, the favorable allelic combinations (TaCKX-D1b + Hap-7A-1 + Hap-T + Hap-6A-G + Hap-6B-1 + H1g + A1b for SSP2-4.5 and SSP5-8.5) mitigated the negative effects of climate change on TKW.
The present study demonstrated that optimizing favorable allelic combinations can help achieve high wheat TKW. The findings of this study clarify the responses of wheat KW to diverse allelic combinations under projected climate change conditions. Additionally, the present study provides theoretical and practical reference for marker-assisted selection of high TKW in wheat breeding. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Reviewed by: Vikas Gupta, Indian Institute of Wheat and Barley Research (ICAR), India; Jose Miguel Soriano, Institute of Agrifood Research and Technology (IRTA), Spain Edited by: Ilaria Marcotuli, University of Bari Aldo Moro, Italy These authors share first authorship This article was submitted to Plant Abiotic Stress, a section of the journal Frontiers in Plant Science |
ISSN: | 1664-462X 1664-462X |
DOI: | 10.3389/fpls.2023.1138966 |