Can we harness digital technologies and physiology to hasten genetic gain in U.S. maize breeding?

Abstract

Genetic gain in breeding programs depends on the predictive skill of genotype-to-phenotype algorithms and precision of phenotyping, both integrated with well-defined breeding objectives for a target population of environments (TPE). The integration of physiology and genomics could improve predictive skill by capturing additive and non-additive interaction effects of genotype (G), environment (E), and management (M). Precision phenotyping at managed stress environments (MSEs) can elicit physiological expression of processes that differentiate germplasm for performance in target environments, thus enabling algorithm training. Gap analysis methodology enables design of GxM technologies for target environments by assessing the difference between current and attainable yields within physiological limits. Harnessing digital technologies such as crop growth model-whole genome prediction (CGM-WGP) and gap analysis, and MSEs, can hasten genetic gain by improving predictive skill and definition of breeding goals in the U.S. maize production TPE. A half-diallel maize experiment resulting from crossing 9 elite maize inbreds was conducted at 17 locations in the TPE and 6 locations at MSEs between 2017 and 2019. Analyses over 35 families represented by 2367 hybrids demonstrated that CGM-WGP offered a predictive advantage (y) compared to WGP that increased with occurrence of drought as measured by decreasing whole-season evapotranspiration (ET; log(y) = 0.80({+/-}0.6) - 0.006({+/-}0.001) x ET; r2 = 0.59; df = 21). Predictions of unobserved physiological traits using the CGM, akin to digital phenotyping, were stable. This understanding of germplasm response to ET enables predictive design of opportunities to close productivity gaps. We conclude that enabling physiology through digital methods can hasten genetic gain by improving predictive skill and defining breeding objectives bounded by physiological realities.