Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity

Abstract

Climate change and Genotype-by-Environment-by-Management interactions together challenge our strategies for crop improvement. Research to advance prediction methods for breeding and agronomy is opening new opportunities to tackle these challenges and overcome on-farm crop productivity yield-gaps through design of responsive crop improvement strategies. Genotype-by-Environment-by-Management (G × E × M) interactions underpin many aspects of crop productivity. An important question for crop improvement is "How can breeders and agronomists effectively explore the diverse opportunities within the high dimensionality of the complex G × E × M factorial to achieve sustainable improvements in crop productivity?" Whenever G × E × M interactions make important contributions to attainment of crop productivity, we should consider how to design crop improvement strategies that can explore the potential space of G × E × M possibilities, reveal the interesting Genotype-Management (G-M) technology opportunities for the Target Population of Environments (TPE), and enable the practical exploitation of the associated improved levels of crop productivity under on-farm conditions. Climate change adds additional layers of complexity and uncertainty to this challenge, by introducing directional changes in the environmental dimension of the G × E × M factorial. These directional changes have the potential to create further conditional changes in the contributions of the genetic and management dimensions to future crop productivity. Therefore, in the presence of G × E × M interactions and climate change, the challenge for both breeders and agronomists is to co-design new G-M technologies for a non-stationary TPE. Understanding these conditional changes in crop productivity through the relevant sciences for each dimension, Genotype, Environment, and Management, creates opportunities to predict novel G-M technology combinations suitable to achieve sustainable crop productivity and global food security targets for the likely climate change scenarios. Here we consider critical foundations required for any prediction framework that aims to move us from the current unprepared state of describing G × E × M outcomes to a future responsive state equipped to predict the crop productivity consequences of G-M technology combinations for the range of environmental conditions expected for a complex, non-stationary TPE under the influences of climate change.

Fig. 1

Schematic representation of the impact of projected influence of climate change (CC) on changes in the expected frequency of occurrence of five environment-types (ETs) and the associated changes in the distribution of observed crop grain yield productivity levels. Following the characterisation of the US corn-belt Target Population of Environments (TPE) and methodology reported by Cooper et al. (2014b), the depicted scenario represents a projection where there is an increase in frequency of occurrence of flowering and grain-filling water-deficit ETs (ET1 and ET2) and a decrease in frequency of occurrence of favourable ETs with low levels of water-deficit (ET4 and ET5). Characterisation of water-deficit is based on the water supply/demand ratio, relative to flowering time, estimated using a crop growth model

Fig. 2

Schematic representation of an on-farm yield-gap from the perspective of an agronomist and a breeder: a Classical view of a yield-gap. Y_P defines the on-farm yield potential that can be expected when a suitable genotype is selected and all abiotic and biotic stresses are removed from the on-farm environment. Y_80% defines the yield level at 80% of the Y_p. Y_Actual defines the actual on-farm yield that was achieved. The yield difference between the Y_80% and Y_Actual defines the exploitable yield-gap. b On-farm yield-gap depicted as a continuum of differences (as represented in sub-figure (a)), between the actual yield and target exploitable yield along the Yield-Evapotranspiration yield front. The target exploitable yield is defined in the example by the 99% Yield-Evapotranspiration front (Q99, Yield potential) and the 80% Yield-Evapotranspiration front (Q80, Achievable yield). c Classical plant breeding view of crossover Genotype-by-Environment interactions. d Plant breeding view of crossover Genotype-by-Environment interactions superimposed on the agronomist view of yield front

Fig. 3

Schematic representation of plant breeding perspective on grain yield improvement considered in terms of improving Yield Potential, Effective Water Use, and Drought Tolerance in relation to enhancing the yield front. Breeding for yield stability can be considered in terms of breeding for trait combinations that improve genotype yield performance and reduce the yield-gap along the continuum of the yield front. Gap analysis and reduction of yield-gaps can then be investigated in terms of selection of genotypes, management strategies and genotype–management technology combinations to reduce the gap between actual on-farm yields and achievable yield levels along the yield front continuum

Fig. 4

Comparison of grain yield and evapotranspiration BLUPs for maize Genotype-by-Environment-by-Management (G × E × M) interaction case study: a Iowa Management BLUPs (M_BLUPs), b Iowa Genotype BLUPs (G_BLUPs), c Iowa Genotype–Management Technology BLUPs (GM_BLUPs), d Kansas Management BLUPs (M_BLUPs), e Kansas Genotype BLUPs (G_BLUPs), f Kansas Genotype–Management Technology BLUPs (GM_BLUPs)

Fig. 5

Comparison grain yield and evapotranspiration BLUPs between Kansas and Iowa for maize Genotype-by-Environment-by-Management (G × E × M) interaction case study: a Evapotranspiration Genotype BLUPs (G_BLUPs) compared between Kansas and Iowa, b Grain Yield Genotype BLUPs (G_BLUPs) compared between Kansas and Iowa, c Evapotranspiration Genotype–Management Technology BLUPs (GM_BLUPs) compared between Kansas and Iowa, d Grain Yield Genotype–Management Technology BLUPs (GM_BLUPs) compared between Kansas and Iowa