Two decades of harnessing standing genetic variation for physiological traits to improve drought tolerance in maize

被引:9
|
作者
Messina, Carlos D. [1 ,2 ]
Gho, Carla [3 ]
Hammer, Graeme L. [2 ,4 ]
Tang, Tom [5 ]
Cooper, Mark [2 ,4 ]
机构
[1] Univ Florida, Hort Sci Dept, Gainesville, FL 32611 USA
[2] Univ Queensland, ARC Ctr Excellence Plant Success Nat & Agr, Brisbane, Qld 4072, Australia
[3] Univ Queensland, Sch Agr & Food Sci, Brisbane, Qld 4072, Australia
[4] Univ Queensland, Queensland Alliance Agr & Food Innovat, Brisbane, Qld 4072, Australia
[5] Corteva Agrisci, Johnston, IA USA
基金
澳大利亚研究理事会; 美国国家科学基金会;
关键词
Crop growth models; drought tolerance; genomic selection; maize; plant breeding; standing genetic variation; Shannon information theory; ANTHESIS-SILKING INTERVAL; LOWLAND TROPICAL MAIZE; ABIOTIC STRESS TOLERANCE; GRAIN-YIELD; WATER-USE; ENVIRONMENT INTERACTIONS; LEAF GROWTH; 8; CYCLES; SELECTION; GENOTYPE;
D O I
10.1093/jxb/erad231
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
We review approaches to maize breeding for improved drought tolerance during flowering and grain filling in the central and western US corn belt and place our findings in the context of results from public breeding. Here we show that after two decades of dedicated breeding efforts, the rate of crop improvement under drought increased from 6.2 g m(-2) year(-1) to 7.5 g m(-2) year(-1), closing the genetic gain gap with respect to the 8.6 g m(-2) year(-1) observed under water-sufficient conditions. The improvement relative to the long-term genetic gain was possible by harnessing favourable alleles for physiological traits available in the reference population of genotypes. Experimentation in managed stress environments that maximized the genetic correlation with target environments was key for breeders to identify and select for these alleles. We also show that the embedding of physiological understanding within genomic selection methods via crop growth models can hasten genetic gain under drought. We estimate a prediction accuracy differential (Delta r) above current prediction approaches of similar to 30% (Delta r=0.11, r=0.38), which increases with increasing complexity of the trait environment system as estimated by Shannon information theory. We propose this framework to inform breeding strategies for drought stress across geographies and crops.
引用
收藏
页码:4847 / 4861
页数:15
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