Multi‐objective optimization of root phenotypes for nutrient capture using evolutionary algorithms

Abstract

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.

RootSlice—A novel functional‐structural model for root anatomical phenotypes

“…However, the utility of a phene state depends on the environment as well as interactions with other phene states in integrated phenotypes (Ajmera et al, 2022; Klein et al, 2020; Rangarajan et al, 2018). The number of potential combinations involving multiple phene states over multiple environments exceeds the capabilities of empirical research (Rangarajan et al, 2022). For example, if each of five anatomical phenes of interest exists in one of five states (e.g., varying proportions of root cortical aerenchyma formation), there exist 5 5 = 3125 phenotypic combinations that need to be assessed over different environmental conditions.…”

Location: root architecture structures rhizosphere microbial associations

“…More axial roots also means more lateral roots ( Strock et al , 2021 ), which are readily colonized by microbes. In crops such as maize and bean, expressing fewer axial roots is an adaptive phenotype under low nitrogen availability and water deficit, while being deleterious under low phosphorus ( Saengwilai et al , 2014 ; Gao and Lynch, 2016 ; Rangarajan et al , 2018 , 2022 ; Sun et al , 2018 ; Schäfer et al , 2022 ). Under soil impedance, an increased number of axial roots that can penetrate the hardpan in agricultural soils is beneficial, and overall enhances rooting depth and access to mobile resources ( Strock et al , 2021 ).…”

“…Under such conditions, competition for resources can also occur between plants and microbes ( Hill and Jones, 2019 ). Secondly, plants with more axial roots must use carbon more efficiently, given the high metabolic cost of axial roots ( Rangarajan et al , 2022 ). Therefore, carbon exudation in plants with many axial roots might be compromised, with direct consequences for the microbial commensals relying on such exudates.…”

RootSlice– a novel functional-structural model for root anatomical phenotypes

“…However, the utility of a phene state depends on the environment as well as interactions with other phene states in integrated phenotypes (Ajmera et al ., 2022; Klein et al ., 2020; Rangarajan et al ., 2018). The number of potential combinations involving multiple phene states over multiple environments exceeds the capabilities of empirical research (Rangarajan et al ., 2022). For example, if each of five anatomical phenes of interest exists in one of five states (e.g., varying proportions of root cortical aerenchyma formation), there exist 5 5 = 3125 phenotypic combinations that need to be assessed over different environmental conditions.…”

“…The number of potential combinations involving multiple phene states over multiple environments exceeds the capabilities of empirical research (Rangarajan et al, 2022).…”