z-logo
open-access-imgOpen Access
Marker-based crop model-assisted ideotype design to improve avoidance of abiotic stress in bread wheat
Author(s) -
Matthieu Bogard,
Delphine Hourcade,
Benoît Piquemal,
David Gouache,
Jean-Charles Deswartes,
Mickaël Throude,
JeanPierre Cohan
Publication year - 2020
Publication title -
journal of experimental botany
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.616
H-Index - 242
eISSN - 1460-2431
pISSN - 0022-0957
DOI - 10.1093/jxb/eraa477
Subject(s) - frost (temperature) , ideotype , phenology , abiotic component , crop , sowing , environmental science , abiotic stress , climate change , agronomy , cultivar , heat stress , biology , ecology , geography , meteorology , biochemistry , zoology , gene
Wheat phenology allows escape from seasonal abiotic stresses including frosts and high temperatures, the latter being forecast to increase with climate change. The use of marker-based crop models to identify ideotypes has been proposed to select genotypes adapted to specific weather and management conditions and anticipate climate change. In this study, a marker-based crop model for wheat phenology was calibrated and tested. Climate analysis of 30 years of historical weather data in 72 locations representing the main wheat production areas in France was performed. We carried out marker-based crop model simulations for 1019 wheat cultivars and three sowing dates, which allowed calculation of genotypic stress avoidance frequencies of frost and heat stress and identification of ideotypes. The phenology marker-based crop model allowed prediction of large genotypic variations for the beginning of stem elongation (GS30) and heading date (GS55). Prediction accuracy was assessed using untested genotypes and environments, and showed median genotype prediction errors of 8.5 and 4.2 days for GS30 and GS55, respectively. Climate analysis allowed the definition of a low risk period for each location based on the distribution of the last frost and first heat days. Clustering of locations showed three groups with contrasting levels of frost and heat risks. Marker-based crop model simulations showed the need to optimize the genotype depending on sowing date, particularly in high risk environments. An empirical validation of the approach showed that it holds good promises to improve frost and heat stress avoidance.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom