Integrating genomics with crop modelling to predict maize yield and component traits: Towards the next generation of crop models

Conventional breeding of ideotypes for target environments is quite challenging because of the genotype by environment interaction and the nature of the genetic complexity for economic traits. Simulation of the adaptive capacity of existing and new germplasms using crop model and genetic information can efficiently assist in determining the potential of well-adapted genotypes for target environments. This study aimed to design a marker-based model by detecting associated markers for target traits associated with model input parameters and incorporating the genetic effects into the CERES-Maize model. To achieve this goal, a two-year trial with 282 maize genotypes across five locations in Northern China was conducted for phenotypic and genotypic data collection. The marker effects on target traits were integrated with crop model to develop a marker-based model. The performance of the integrated model was tested using four independent sub-datasets, (i) observed genotypes grown in observed environments; (ii) observed genotypes phenotyped in new environments; (iii) new genotypes in characterized environments; and (iv) new genotypes in new environments. The model simulated the anthesis date, kernel number, kernel weight and yield reasonably well across 282 genotypes. The marker-based prediction performance of simpler morphological traits, such anthesis date and kernel number were generally improved compared to highly complex quantitative traits, such as kernel weight and yield. The performance of the model was affected by new genotypes or new environments depending on the types of traits being simulated. Maker-based simulation of maize yield and its component traits across five locations and 37 years in Northern China was used as a case study to demonstrate the model applications for studying genotype–environment interactions. The biplot revealed the top yielding genotypes and most ideal environment by comparing yield performance and stability of 282 genotypes in five phenotyping sites under both water-limited and well-water conditions. Breeding programs could further exploit marker-based modelling to predict adaptation in diverse environmental and management conditions for new genotypes before they are globally distributed for multilocation yield testing.