Integrating modelling and phenotyping approaches to identify and screen complex traits: transpiration efficiency in cereals

Abstract

Following advances in genetics, genomics, and phenotyping, trait selection in breeding is limited by our ability to understand interactions within the plant and with the environment, and to identify traits of most relevance to the target population of environments. We propose an integrated approach that combines insights from crop modelling, physiology, genetics, and breeding to characterize traits valuable for yield gain in the target population of environments, develop relevant high-throughput phenotyping platforms, and identify genetic controls and their value in production environments. This paper uses transpiration efficiency (biomass produced per unit of water used) as an example of a complex trait of interest to illustrate how the approach can guide modelling, phenotyping, and selection in a breeding programme. We believe that this approach, by integrating insights from diverse disciplines, can increase the resource use efficiency of breeding programmes for improving yield gains in target populations of environments.

Progress in understanding drought tolerance: from alleles to cropping systems

“… Chenu et al (2018) integrate modelling and phenomic approaches for addressing complex traits in cereals and cover advances in cereal genomics using integrated approach including the identification of a number of significant QTLs for transpiration efficiency (biomass produced for unit of water used) in sorghum. Despite of progress in genetics, genomics and phenotyping, trait selection in breeding is limited by our ability to understand interactions within the plant and with the environment and to identify traits of most relevance for the target population of environments.…”

“…Today there is also a recognition of the diversity of drought scenarios in each region of the world in current and future climates, even in a single field over different years, and the importance of fitting plant phenology and traits to the most likely scenarios in a given region ( Olesen et al , 2011 ; Mickelbart et al , 2015 ; Chenu et al , 2018 ). This raises the necessity of adopting probabilistic approaches to drought tolerance based on both crop modelling and genomic prediction in identifying where and when any combination of alleles or traits are beneficial in specific drought scenarios ( Ewert et al , 2015 ; Tardieu et al , 2018 ).…”

“…This raises the necessity of adopting probabilistic approaches to drought tolerance based on both crop modelling and genomic prediction in identifying where and when any combination of alleles or traits are beneficial in specific drought scenarios ( Ewert et al , 2015 ; Tardieu et al , 2018 ). Furthermore, the importance attached to modelling approaches has increased, together with an improved ability of the scientific community to more-accurately measure and forecast environmental conditions, and to simulate the behaviour of genotypes in a range of environmental conditions ( Chenu et al , 2018 ), including those associated with climate change.…”

A low-cost method to rapidly and accurately screen for transpiration efficiency in wheat

“…This can be achieved, in part, through greater crop yield and more efficient use of limited resources, such as water. Transpiration efficiency (TE), which is defined as the amount of biomass produced per unit of water transpired, has been suggested as a trait of interest to improve yield in drought-prone environments, in particular where crops rely on stored soil moisture [ 3 – 5 ]. In such environments, crops that are able to utilise available soil water more efficiently can maintain greater soil water reserves early during the crop cycle to use later in the season, when water can be more effectively used to produce grain yield (e.g.…”

A multi-model Framework for the Arabidopsis life cycle

“…As systems become more complex we need models to describe our understanding and help our thinking when trying to explain and make predictions across scales. This approach has also been proposed in attempts to engineer crop traits starting from genetics or from genomes (Welch et al , 2005; Yin and Struik, 2008, 2010; Parent and Tardieu, 2014; Wu et al , 2016; Chenu et al , 2018), where simpler models have demonstrated both the potential of crop modelling in general and the significant demands of detailed models for empirical data that varies in availability (Hammer et al , 2006; Asseng et al , 2013). For microorganisms, comprehensive models link the metabolic and molecular level with the cellular (Karr et al , 2012) and population growth scales (Weiße et al , 2015), whereas contemporary work in more complex organisms has necessarily focused more narrowly (Buckley and Mott, 2013; Lynch, 2013; Zhu et al , 2013; Klose et al , 2015; Le Novere, 2015; Hepworth et al., 2018).…”