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Abstract

Highlight Soil water capture potential of maize has not changed over 50 years of single-cross breeding. Changes in resource use efficiency and allocation to reproductive organs must underpin yield improvement.

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Breeding drought-tolerant maize hybrids for the US corn-belt: discovery to product.

Mark Cooper, Carla Gho, Roger Leafgren … (2014)

Germplasm, genetics, phenotyping, and selection, combined with a clear definition of product targets, are the foundation of successful hybrid maize breeding. Breeding maize hybrids with superior yield for the drought-prone regions of the US corn-belt involves integration of multiple drought-specific technologies together with all of the other technology components that comprise a successful maize hybrid breeding programme. Managed-environment technologies are used to enable scaling of precision phenotyping in appropriate drought environmental conditions to breeding programme level. Genomics and other molecular technologies are used to study trait genetic architecture. Genetic prediction methodology was used to breed for improved yield performance for drought-prone environments. This was enabled by combining precision phenotyping for drought performance with genetic understanding of the traits contributing to successful hybrids in the target drought-prone environments and the availability of molecular markers distributed across the maize genome. Advances in crop growth modelling methodology are being used to evaluate the integrated effects of multiple traits for their combined effects and evaluate drought hybrid product concepts and guide their development and evaluation. Results to date, lessons learned, and future opportunities for further improving the drought tolerance of maize for the US corn-belt are discussed.

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Evolution of US maize (Zea mays L.) root architectural and anatomical phenes over the past 100 years corresponds to increased tolerance of nitrogen stress

Larry M. York, Tania Galindo-Castañeda, Jeffrey Schussler … (2015)

Highlight Comprehensive analysis of maize root phenotypes over the past century indicates that they have evolved to be more efficient in acquiring nitrogen.

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Yield-trait performance landscapes: from theory to application in breeding maize for drought tolerance.

Carlos D. Messina, Dean Podlich, Zhanshan Dong … (2011)

The effectiveness of breeding strategies to increase drought resistance in crops could be increased further if some of the complexities in gene-to-phenotype (G → P) relations associated with epistasis, pleiotropy, and genotype-by-environment interactions could be captured in realistic G → P models, and represented in a quantitative manner useful for selection. This paper outlines a promising methodology. First, the concept of landscapes was extended from the study of fitness landscapes used in evolutionary genetics to the characterization of yield-trait-performance landscapes for agricultural environments and applications in plant breeding. Second, the E(NK) model of trait genetic architecture was extended to incorporate biophysical, physiological, and statistical components. Third, a graphical representation is proposed to visualize the yield-trait performance landscape concept for use in selection decisions. The methodology was demonstrated at a particular stage of a maize breeding programme with the objective of improving the drought tolerance of maize hybrids for the US Western Corn-Belt. The application of the framework to the genetic improvement of drought tolerance in maize supported selection of Doubled Haploid (DH) lines with improved levels of drought tolerance based on physiological genetic knowledge, prediction of test-cross yield within the target population of environments, and their predicted potential to sustain further genetic progress with additional cycles of selection. The existence of rugged yield-performance landscapes with multiple peaks and intervening valleys of lower performance, as shown in this study, supports the proposition that phenotyping strategies, and the directions emphasized in genomic selection can be improved by creating knowledge of the topology of yield-trait performance landscapes.