Exploring yield stability and the fitness landscape of maize landrace root phenotypes in silico

Abstract

Integrated root phenotypes contribute to environmental adaptation and yield stability. We used the functional-structural plant/soil model OpenSimRoot_v2 to reconstruct the root phenotypes and environments of eight maize landraces to understand the phenotypic and environmental factors associated with broad adaptation. We found that accessions from low phosphorus regions have root phenotypes with shallow growth angles and greater nodal root numbers, allowing them to adapt to their native environments by improved topsoil foraging. We used machine learning algorithms to detect the most important phenotypes responsible for adaptation to multiple environments. The most important phene states responsible for stability across environments are large cortical cell size and reduced diameter of roots in nodes 5 and 6. When we dissected the components of root diameter, we observed that large cortical cell size improved growth by 28%, 23 % and 114%, while reduced cortical cell file number alone improved shoot growth by 137%, 66% and 216%, under drought, nitrogen and phosphorus stress, respectively. Functional-structural analysis of 96 maize landraces from the Americas, previously phenotyped in mesocosms in the greenhouse, suggested that parsimonious anatomical phenotypes, which reduce the metabolic cost of soil exploration, are the main phenotypes associated with adaptation to multiple environments, while root architectural traits were related to adaptation to specific environments. Our results indicate that integrated phenotypes with root anatomical phenes that reduce the metabolic cost of soil exploration will increase tolerance to stress across multiple environments and therefore improve yield stability, regardless of their root architecture.