Advancing crop growth models using genotype-specific parameters

Plant breeders face an urgent mission: of adapting crops to climate change and feeding an increased world population. Crop models can help breeders select cultivars and cultivar traits for different target environments. For instance, models can be used to evaluate traits (e.g., life cycles, daylength sensitivities, productivities, heat tolerance, and seed size/growth rate) to improve yield. 

In a recent review article, Dr. Kenneth Boote and coauthors from the University of Florida urge modelers to represent cultivars in models using genetics to better evaluate these environmental effects. According to the authors, modelers must go beyond species-specific models that simulate cultivars using fixed parameters to represent different phenotypic traits and instead incorporate the molecular genetics information of each cultivar to evaluate how the phenotypic traits respond to the environment.

According to the authors, the time is ripe for these advancements in genetic modelling. The rapid development of molecular genetics has allowed gene actions to be simulated at the level of interactions of regulators, gene-products, and other metabolites. The way that the environment affects the expression of genes that differ between cultivars can therefore be simulated by linking QTL markers associated with genes to cultivar-specific phenotypes.

Importantly, models must also incorporate realistic trait physiology to environmental factors to better understand how genetic variation affects the processes of crop carbon, water, and nutrient balance. The authors highlight studies to illustrate emergent outcomes simulated as a result of single and multiple combinations of genotype-specific parameters and to illustrate genotype by environment interactions that may occur in different target environments. 

According to Boote, “much work is needed to link genetic effects to the physiological processes for in-silico modeling applications. Additionally, we need much more phenotyping and performance data from growth in multiple environments.”

Note to educators: this article provides an excellent review of the general principles of crop simulation models, and the steps for simulating genetic improvement with a crop model.