We conclude that the utility of a root architectural phenotype is determined by whether the constituent phenes are synergistic or antagonistic. Competition for internal resources and trade-offs for external resources result in multiple phenotypes being optimal under a given nutrient regime. We also find that no single phenotype is optimal across contrasting environments. These results have implications for understanding plant evolution and also for the breeding of more stress-tolerant crop phenotypes.

Suboptimal water and P availability are primary limitations to grain legume production. Root architecture influences water and P acquisition, but tradeoffs need to be better understood and mitigated. We hypothesized that tradeoffs in root class investment and resource acquisition strategy would be observable in a variety of grain legumes. Diversity panels of common bean (Phaseolus vulgaris L.), tepary bean (Phaseolus acutifolius A. Gray), cowpea [Vigna unguiculata (L.) Walp.], soybean [Glycine max (L.) Merr.], chickpea (Cicer arietinum L.), groundnut (Arachis hypogaea L.), and single accessions of other legumes were phenotyped in the field. We identified inverse relationships among investments in different root classes in most species, and between indicators of deep and shallow exploration in all species. Bean and tepary bean showed particularly strong tradeoffs in investment patterns among root classes, whereas chickpea and groundnut show less pronounced tradeoffs. We found that legume root architectural phenotypes can be placed on a root system architecture (RSA) spectrum, and that root phenotypes of epigeal and hypogeal taxa present distinct adaptive mechanisms. These life strategies integrating resource acquisition, use, and phenology are exemplified by contrasting chickpea, with many root axes, to tepary bean with few root axes and a contrasting water use strategy. We propose several RSA ideotypes and highlight how dimorphic root architecture may co‐optimize resource acquisition.

High throughput phenotyping is important to bridge the gap between genotype and phenotype. The methods used to describe the phenotype therefore should be robust to measurement errors, relatively stable over time, and most importantly, provide a reliable estimate of elementary phenotypic components. In this study, we use functional-structural modeling to evaluate quantitative phenotypic metrics used to describe root architecture to determine how they fit these criteria. Our results show that phenes such as root number, root diameter, and lateral root branching density are stable, reliable measures and are not affected by imaging method or plane. Metrics aggregating multiple phenes such as total length, total volume, convex hull volume, and bushiness index estimate different subsets of the constituent phenes; they however do not provide any information regarding the underlying phene states. Estimates of phene aggregates are not unique representations of underlying constituent phenes: multiple phenotypes having phenes in different states could have similar aggregate metrics. Root growth angle is an important phene which is susceptible to measurement errors when 2D projection methods are used. Metrics that aggregate phenes which are complex functions of root growth angle and other phenes are also subject to measurement errors when 2D projection methods are used. These results support the hypothesis that estimates of phenes are more useful than metrics aggregating multiple phenes for phenotyping root architecture. We propose that these concepts are broadly applicable in phenotyping and phenomics.

Background and Aims The utility of root hairs for nitrogen (N) acquisition is poorly understood. Methods We explored the utility of root hairs for N acquisition in the functional-structural model SimRoot and with maize genotypes with variable root hair length (RHL) in greenhouse and field environments. Key Results Simulation results indicate that long, dense root hairs can improve N acquisition under varying N availability. In the greenhouse, ammonium availability had no effect on RHL and low nitrate availability increased RHL, while in the field low N reduced RHL. Longer RHL was associated with 216% increase in biomass and 237% increase in plant N content under low N conditions in the greenhouse and a 250% increase in biomass and 200% increase in plant N content in the field compared with short RHL phenotypes. In a low N field environment, genotypes with long RHL had 267% greater yield than those with short RHL. We speculate that long root hairs improve N capture by increased root surface area and expanded soil exploration beyond the N depletion zone surrounding the root surface. Conclusions We conclude that root hairs play an important role in nitrogen acquisition. We suggest that root hairs merit consideration as a breeding target for improved N acquisition in maize and other crops.

SUMMARY Root phenotypes are avenues to the development of crop cultivars with improved nutrient capture, which is an important goal for global agriculture. The fitness landscape of root phenotypes is highly complex and multidimensional. It is difficult to predict which combinations of traits (phene states) will create the best performing integrated phenotypes in various environments. Brute force methods to map the fitness landscape by simulating millions of phenotypes in multiple environments are computationally challenging. Evolutionary optimization algorithms may provide more efficient avenues to explore high dimensional domains such as the root phenotypic space. We coupled the three‐dimensional functional–structural plant model, SimRoot, to the Borg Multi‐Objective Evolutionary Algorithm (MOEA) and the evolutionary search over several generations facilitated the identification of optimal root phenotypes balancing trade‐offs across nutrient uptake, biomass accumulation, and root carbon costs in environments varying in nutrient availability. Our results show that several combinations of root phenes generate optimal integrated phenotypes where performance in one objective comes at the cost of reduced performance in one or more of the remaining objectives, and such combinations differed for mobile and non‐mobile nutrients and for maize (a monocot) and bean (a dicot). Functional–structural plant models can be used with multi‐objective optimization to identify optimal root phenotypes under various environments, including future climate scenarios, which will be useful in developing the more resilient, efficient crops urgently needed in global agriculture.