A data-driven simulation platform to predict cultivars’ performances under uncertain weather conditions

Abstract

In most crops, genetic and environmental factors interact in complex ways giving rise to substantial genotype-by-environment interactions (G×E). We propose that computer simulations leveraging field trial data, DNA sequences, and historical weather records can be used to tackle the longstanding problem of predicting cultivars’ future performances under largely uncertain weather conditions. We present a computer simulation platform that uses Monte Carlo methods to integrate uncertainty about future weather conditions and model parameters. We use extensive experimental wheat yield data (n = 25,841) to learn G×E patterns and validate, using left-trial-out cross-validation, the predictive performance of the model. Subsequently, we use the fitted model to generate circa 143 million grain yield data points for 28 wheat genotypes in 16 locations in France, over 16 years of historical weather records. The phenotypes generated by the simulation platform have multiple downstream uses; we illustrate this by predicting the distribution of expected yield at 448 cultivar-location combinations and performing means-stability analyses.

Spatial Models With Inter-Tree Competition From Airborne Laser Scanning Improve Estimates of Genetic Variance

“…The use of ACE models including known genetics in analysis of a genetics trial was shown to substantially reduce model residuals and improve heritabilities, potentially improving breeding values, tree selection, and future breeds. The benefits of improved tree breed selection for forest sites are accentuated in the context of climate change ( Dungey et al, 2018 ; D’odorico et al, 2020 ; De Los Campos et al, 2020 ). The ACE method is also advocated for use in general research trial analyses to improve accuracy and precision of results by accounting for environmental variation.…”

The Modern Plant Breeding Triangle: Optimizing the Use of Genomics, Phenomics, and Enviromics Data

“…Thus, enviromics acts as a central bottleneck for the application of modern genomics-assisted prediction tools, especially for use across multiple environments. Novel approaches have integrated field trial data with DNA sequences using different sources of enviromics, such as linear and nonlinear reaction-norm models (e.g., Jarquín et al, 2014;Morais-Júnior et al, 2018;Millet et al, 2019;Monteverde et al, 2019;Costa-Neto et al, 2020a), crop growth model (CGM) outputs (Heslot et al, 2014;Rincent et al, 2017Rincent et al, , 2019, CGM integrated with GS (Cooper et al, 2016;Messina et al, 2018;Robert et al, 2020) and historical weather records to predict cultivars in years to come (de los Campos et al, 2020).…”

“…Progress toward the modernization of the statistical and quantitative genetic models for the analysis of plant breeding in multi-environment trials has become clearer as the availability of genomics, phenomics, and environments information has increased (see, among others, Vargas et al, 1998;Crossa et al, 2010;Burgueño et al, 2012;Heslot et al, 2014;Jarquín et al, 2014;Montesinos-López et al, 2017;Millet et al, 2019;Costa-Neto et al, 2020a;de los Campos et al, 2020;Robert et al, 2020). Thus, we see that all the elements described above offer a clear potential for the acceleration of genetic gains in plant breeding.…”

An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions

“…This choice is properly arbitrary and depends on the model creator. A grid search including cross-validation was used to determine the values of the ML model hyperparameters [81]. The range of parameter values for the cross-validation procedure was selected based on [75].…”