Linking genetic markers and crop model parameters using neural networks to enhance genomic prediction of integrative traits.

Introduction: Predicting the performance (yield or other integrative traits) of cultivated plants is complex because it involves not only estimating the genetic value of the candidates to selection, the interactions between the genotype and the environment (GxE) but also the epistatic interactions between genomic regions for a given trait, and the interactions between the traits contributing to the integrative trait. Classical Genomic Prediction (GP) models mostly account for additive effects and are not suitable to estimate non-additive effects such as epistasis. Therefore, the use of machine learning and deep learning methods has been previously proposed to model those non-linear effects.

Modelling tiller growth and mortality as a sink-driven process using Ecomeristem: implications for biomass sorghum ideotyping.

Background And Aims: Plant modelling can efficiently support ideotype conception, particularly in multi-criteria selection contexts. This is the case for biomass sorghum, implying the need to consider traits related to biomass production and quality. This study evaluated three modelling approaches for their ability to predict tiller growth, mortality and their impact, together with other morphological and physiological traits, on biomass sorghum ideotype prediction.