Plant roots exhibit distinct architectural organization and overall shape. Current concepts to quantify architectural variation assume a homogeneous phenotype for a given genotype. However, this assumption neglects the observable variation in root architecture for two reasons: (i) sampling strategies are designed to capture architectural variation only for the most common phenotype, and (ii) traits are often measured locally within a root system and ignore the architectural organization. Here, we introduce a new concept: the phenotypic spectrum of crop roots to quantify architectural variation as the number of architecture types for one genotype in a specific environment. We use the shape descriptor DS-curve to characterize the whole root system architecture. Using DS curves as a core, we developed a computing pipeline that combines Kmeans++ clustering, outlier filtering and the Fréchet distance as a similarity metric to classify types of root architectures. Subsequently, we applied this pipeline to analyze a field dataset including three common bean (Phaseolus vulgaris) genotypes DOR364 (n=797), L88_57 (n=1772), and SEQ7 (n=768) under non-limiting and water-stressed conditions in 2015 and 2016. We found DOR364 showed five different root architecture types across environments, while L88_57 and SEQ7 showed four. The total variation within classified root architecture types of DOR364, L88_57, and SEQ reduced by 58.59%, 50.19% and 53.01%, compared to the variation of the complete data sets. DOR364 had stable fractions of root architecture types across environments. In contrast, L88_57 and SEQ7 showed more variation in their fractions. There was no significant biomass difference among root architecture types for all studied genotypes within each environment. As such, we hypothesize that the phenotypic spectrum might buffer the impact of environmental stresses as an acclimatization strategy by changing the composition of root architecture types at the population level.

The vertical distribution of plant roots in the soil profile is a key trait modulating plant contributions to soil carbon storage, drought and nutrient stress resistance, yield, and fitness. However, direct sampling of deep roots requires massive effort, so existing data are sparse and many researchers have adopted modeling approaches to fill data gaps and generate hypotheses about how soil properties change the biogeochemical, agricultural, ecological, and hydrologic consequences of root depth. Such models are useful only if they correctly represent the processes of interest and give accurate predictions of the root systems they simulate. Most current root growth models represent soil as a uniform and unrestrictive medium. This is often a reasonable simplification when modeling roots grown in pots or artificial media, but is less so for field soils which often increase in density, hardness, and heterogeneity with depth. To better predict the effect of soil hardness on root distribution, we updated the structural-functional root growth model OpenSimRoot to explicitly predict soil hardness from soil bulk density, water content, porosity, and depth. Root growth impedance is curently represented by linear scaling of the root elongation rate according to soil hardness. Future work will incorporate configurable growth responses and allow hardness to control changes in root diameter and growth direction, thus allowing the model to examine the fitness implications of carbon reallocation in complex structured soils. Our updated OpenSimRoot captured >50% of observed variation in penetrometer resistance from field soils. When we incorporated soil hardness into simulations of maize growth, we observed a substantial reduction in the predicted root:shoot ratio that overwhelmed previous model predictions of increased water uptake from steeper root angles. These findings reinforce that models considering costs and benefits of deep rooting should routinely consider soil hardess.

Abstract: At the genus and species level, variation in root anatomy and architecture may interact to affect strategies of drought avoidance. To investigate this idea, root anatomy and architecture of the drought-sensitive common bean (Phaseolus vulgaris) and drought-adapted tepary bean (Phaseolus acutifolius) were analyzed in relation to water use under terminal drought. Intraspecific variation for metaxylem anatomy and axial conductance was found in the roots of both species. Genotypes with high-conductance root metaxylem phenotypes acquired and transpired more water per unit leaf area, shoot mass, and root mass than genotypes with low-conductance metaxylem phenotypes. Interspecific variation in root architecture and root depth was observed where P. acutifolius has a deeper distribution of root length than P. vulgaris. In the deeper-rooted P. acutifolius, genotypes with high root conductance were better able to exploit deep soil water than genotypes with low root axial conductance. Contrastingly, in the shallower-rooted P. vulgaris, genotypes with low root axial conductance had improved water status through conservation of soil moisture for sustained water capture later in the season. These results indicate that metaxylem morphology interacts with root system depth to determine a strategy of drought avoidance and illustrate synergism among architectural and anatomical phenotypes for root function.