Use of Crop Growth Models with Whole‐Genome Prediction: Application to a Maize Multienvironment Trial

Abstract

High throughput genotyping, phenotyping, and envirotyping applied within plant breeding multienvironment trials (METs) provide the data foundations for selection and tackling genotype × environment interactions (GEIs) through whole‐genome prediction (WGP). Crop growth models (CGM) can be used to enable predictions for yield and other traits for different genotypes and environments within a MET if genetic variation for the influential traits and their responses to environmental variation can be incorporated into the CGM framework. Furthermore, such CGMs can be integrated with WGP to enable whole‐genome prediction with crop growth models (CGM‐WGP) through use of computational methods such as approximate Bayesian computation. We previously used simulated data sets to demonstrate proof of concept for application of the CGM‐WGP methodology to plant breeding METs. Here the CGM‐WGP methodology is applied to an empirical maize (Zea mays L.) drought MET data set to evaluate the steps involved in reduction to practice. Positive prediction accuracy was achieved for hybrid grain yield in two drought environments for a sample of doubled haploids (DHs) from a cross. This was achieved by including genetic variation for five component traits into the CGM to enable the CGM‐WGP methodology. The five component traits were a priori considered to be important for yield variation among the maize hybrids in the two target drought environments included in the MET. Here, we discuss lessons learned while applying the CGM‐WGP methodology to the empirical data set. We also identify areas for further research to improve prediction accuracy and to advance the CGM‐WGP for a broader range of situations relevant to plant breeding.

Genetic Covariance of Environments in the Potato National Chip Processing Trial

“…Endelman et al (2018) estimated prediction accuracy at 0.53 for yield using only the data for the Wisconsin location. Other promising directions include using environmental variables as covariates in the prediction (Heslot et al, 2014;Jarquín et al, 2014) or combining crop development models with whole-genome prediction (Technow et al, 2015;Cooper et al, 2016). Other promising directions include using environmental variables as covariates in the prediction (Heslot et al, 2014;Jarquín et al, 2014) or combining crop development models with whole-genome prediction (Technow et al, 2015;Cooper et al, 2016).…”

Grain Yield and Nitrogen Accumulation in Maize Hybrids Released during 1934 to 2013 in the US Midwest

“…As the first step in the analysis, combinations of year (2013 and 2014) ´ location (Marion and Sciota) ´ N treatment (HN and LN) were considered as eight environments (siteyears). The method has been used by Cooper et al (2016) and Gaffney et al (2015) to quantify total soil moisture available to the crop during the season. This model allowed for assessment of the hybrid ´ environment interaction (which included year, location, and N treatment effects).…”

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

“…This comprehensive approach combines experimental data, physiological understanding of a trait from multiple genotypes, model simulation, and breeding trial validation to determine what traits to measure (Hammer et al, 2016). The current cutting edge of phenomics/CGM application in breeding is best described by the CGM-whole-genome prediction approach of Cooper et al (2016), who show how a relatively simple CGM can be inserted between genomic and phenomic data to impose biological pathway constraints to the statistical prediction models.…”