Environment characterization as an aid to wheat improvement: interpreting genotype–environment interactions by modelling water-deficit patterns in North-Eastern Australia

Abstract

Genotype-environment interactions (GEI) limit genetic gain for complex traits such as tolerance to drought. Characterization of the crop environment is an important step in understanding GEI. A modelling approach is proposed here to characterize broadly (large geographic area, long-term period) and locally (field experiment) drought-related environmental stresses, which enables breeders to analyse their experimental trials with regard to the broad population of environments that they target. Water-deficit patterns experienced by wheat crops were determined for drought-prone north-eastern Australia, using the APSIM crop model to account for the interactions of crops with their environment (e.g. feedback of plant growth on water depletion). Simulations based on more than 100 years of historical climate data were conducted for representative locations, soils, and management systems, for a check cultivar, Hartog. The three main environment types identified differed in their patterns of simulated water stress around flowering and during grain-filling. Over the entire region, the terminal drought-stress pattern was most common (50% of production environments) followed by a flowering stress (24%), although the frequencies of occurrence of the three types varied greatly across regions, years, and management. This environment classification was applied to 16 trials relevant to late stages testing of a breeding programme. The incorporation of the independently-determined environment types in a statistical analysis assisted interpretation of the GEI for yield among the 18 representative genotypes by reducing the relative effect of GEI compared with genotypic variance, and helped to identify opportunities to improve breeding and germplasm-testing strategies for this region.

Management of crop water under drought: a review

“…Besides classical field experiments, today SPAC models provide efficient tools for in silico experiments to optimise site specific cropping system design and adaptation (e.g. Messina et al 2006;Chenu et al 2011;Jeuffroy et al 2014). Breeding is an immediate option when referring to the choice among existing cultivars differing in drought resistance.…”

The quest for understanding phenotypic variation via integrated approaches in the field environment

“…The generation of environment proxies, such as drought indices, that then can be utilized in statistical analyses of phenotypes collected in one or more trials on a set of genotypes (Chapman et al, 2000;Chenu et al, 2011;Cooper et al, 2014). For this purpose, the models need to be capable of simulating the performance of one or more check genotype(s), with the inputs typically being daily weather data and an accurate trial-level soil characterization.…”

Tolerance to preharvest sprouting and yield of wheat genotypes from different breeding programs

“…This suggests a quantitative action on the components associated with PHS and grain yield. Although genotype contribution is important for the expression of components linked to grain yield, year and location have shown a stronger influence (Chenu et al, 2011). The strong GxE interaction for yield components, such as NFT, WTG, and NGE, indicate that more than one year of evaluation is needed for an effective characterization of performance in wheat genotypes (Valério et al, 2013).…”