Functional-structural Plant Model Research Articles

Light Distribution Models for Tree Growth Simulation

AbstractThe simulation and modelling of tree growth is a complex subject with a long history and an important area of research in both computer graphics and botany. For more than 50 years, new approaches to this topic have been presented frequently, including several aspects to increase realism. To further improve these achievements, we present a compact and robust functional‐structural plant model (FSPM) that is consistent with botanical rules. While we show several extensions to typical approaches, we focus mainly on the distribution of light as a resource in three‐dimensional space. We therefore present four different light distribution models based on ray tracing, space colonization, voxel‐based approaches and bounding volumes. By simulating individual light sources, we were able to create a more specified scene setup for plant simulation than it has been presented in the past. By taking into account such a more accurate distribution of light in the environment, this technique is capable of modelling realistic and diverse tree models.

Identifying and quantifying the contribution of maize plant traits to nitrogen uptake and use through plant modelling

Abstract Breeding for high nitrogen use efficient crops can contribute to maintaining or even increasing yield with less nitrogen. Nitrogen use is co-determined by N uptake and physiological use efficiency (PE, biomass per unit of N taken up), to which soil processes as well as plant architectural, physiological and developmental traits contribute. The relative contribution of these crop traits to N use is not well known but relevant to identify breeding targets in important crop species like maize. To quantify the contribution of component plant traits to maize N uptake and use, we used a functional-structural plant model. We evaluated the effect of varying both shoot and root traits on crop N uptake across a range of nitrogen levels. Root architectural traits were found to play a more important role in root N uptake than physiological traits. Phyllochron determined the structure of the shoot through changes in source: sink ratio over time which, in interaction with light and temperature, resulted in a significant effect on PE and N uptake. Photosynthesis traits were more relevant to biomass accumulation rather than yield, especially under high nitrogen conditions. The traits identified in this study are potential targets in maize breeding for improved crop N uptake and use.

Unravelling drivers of local adaptation through evolutionary functional-structural plant modelling.

Local adaptation to contrasting environmental conditions along environmental gradients is a widespread phenomenon in plant populations, yet we lack a mechanistic understanding of how individual agents of selection contribute to this evolutionary process. Here, we developed a novel evolutionary functional-structural plant (E-FSP) model that recreates local adaptation of virtual plants along an environmental gradient. First, we validate the model by testing if it can reproduce two elevational ecotypes of Dianthus carthusianorum occurring in the Swiss Alps. Second, we use the E-FSP model to disentangle the relative contribution of abiotic (temperature) and biotic (competition and pollination) selection pressures to elevational adaptation in D. carthusianorum. Our results suggest that elevational adaptation in D. carthusianorum is predominantly driven by the abiotic environment. The model reproduced the qualitative differences between the elevational ecotypes in two phenological (germination and flowering time) and one morphological trait (stalk height), as well as qualitative differences in four performance variables that emerge from G × E interactions (flowering time, number of stalks, rosette area and seed production). Our approach shows how E-FSP models incorporating physiological, ecological and evolutionary mechanisms can be used in combination with experiments to examine hypotheses about patterns of adaptation observed in the field.

Optimizing radiation capture in machine-harvested cotton: A functional-structural plant modelling approach to chemical vs. manual topping strategies

Topping and planting density are key agronomic management practices to optimize cotton plant structure for machine harvesting and light capture. However, modelling the effects of these practices on canopy light utilization in the field, in order to improve cotton management, remains challenging. Functional-structural plant modelling is a computational approach to explore the effects of agronomic practices on shaping plant architecture and thus light interception. This study, for the first time, utilizes the CottonXL model to quantify the significant impact of chemical topping compared to manual topping on radiation interception on machine-harvested cotton in China at different planting densities, providing new management strategies for cotton production. A more compact plant structure is shaped by chemical topping through inhibiting leaf expansion and shortening internodes of both the main stem and fruiting branches, thereby allowing more PAR interception by middle and lower leaves. Simulation results showed that the total PAR intercepted by the canopy over entire growth season was increased by 11.3 % under chemical topping compared to manual topping. This positive effect became even more pronounced with increasing plant density. These results indicate that chemical topping could be beneficial for optimizing canopy structure, enhancing light interception and lint yield, especially at higher plant densities. The results illustrate the importance of shaping plant structure for improving radiation resource capture and exemplify the potential of optimizing topping strategy and plant density to enhance crop performance. They also demonstrate the utility of a functional-structural plant model for guiding crop management.

Are we focusing on the right parameters? Insights from Global Sensitivity Analysis of a Functional-Structural Plant Model

Abstract Performing global sensitivity analysis on functional-structural plant models (FSP models) can greatly benefit both model development and analysis by identifying the relevance of parameters for specific model outputs. Setting unimportant parameters to a fixed value decreases dimensionality of the typically large model parameter space. Efforts can then be concentrated on accurately estimating the most important input parameters. In this work, we apply the Elementary Effects method for dimensional models with arbitrary input types, adapting the method to models with inherent randomness. Our FSP model simulated a maize stand for 160 days of growth, considering three outputs, namely yield, peak biomass and peak leaf area index (LAI). Of 52 input parameters, 12 were identified as important for yield and peak biomass and 14 for LAI. Over 70 % of parameters were deemed unimportant for the outputs under consideration, including most parameters relating to crop architecture. Parameters governing shade avoidance response and leaf appearance rate (phyllochron) were also unimportant; variations in these physiological and developmental parameters do lead to visible changes in plant architecture but not to significant changes in yield, biomass or LAI. Some inputs identified as unimportant due to their low sensitivity index have a relatively high standard deviation of effects, with high fluctuations around a low mean, which could indicate non-linearity or interaction effects. Consequently, parameters with low sensitivity index but high standard deviation should be investigated further. Our study demonstrates that global sensitivity analysis can reveal which parameter values have the most influence on key outputs, predicting specific parameter estimates that need to be carefully characterized.

Correction to: Are we focusing on the right parameters? Insights from Global Sensitivity Analysis of a Functional-Structural Plant Model

Correction to: Are we focusing on the right parameters? Insights from Global Sensitivity Analysis of a Functional-Structural Plant Model

Mixed particle swarm optimization algorithm-based approach to optimize spatial distribution of virtual maize

Increasing crop yields is one potential way to solve the problem of food shortage and limited field resources. Optimization of crop spatial distribution is an efficient method to increase yield. However, extensive field experiments are costly and time consuming, which renders conventional optimization studies difficult if not prohibitive. In this study, a new optimization strategy was proposed, integrating a Functional-Structural Plant Model (FSPM) of maize with a workflow based on a Mixed Particle Swarm Optimization (MPSO) algorithm. The maize FSPM applies a dynamic interactive feedback method between plant morphology and plant growth dynamics on the one hand, and simultaneously between plant spatial distribution and plant growth, and furthermore can respond to different environmental factors. MPSO is a new mixed particle swarm optimization algorithm with multistage disturbance, which enhances the ability of traversing the solution space and jumping out of local extrema. This study performs a plant spatial distribution optimization of monocropping maize to maximize light interception. Simulation results indicate that the optimized row spacing can increase maize light interception comparing to the random row spacing within the spacing range (with maximum of 36.44% increment), and that the optimization results satisfy the optimal interval provided by previous field experiments.

Modeling cassava root system architecture and the underlying dynamics in shoot–root carbon allocation during the early storage root bulking stage

Abstract Background and aims Plants store carbohydrates for later use during, e.g., night, drought, and recovery after stress. Carbon allocation presents the plant with tradeoffs, notably between growth and storage. We asked how this tradeoff works for cassava (Manihot esculenta) pre- and post-storage root (SR) formation and if manipulation of the number of storage organs and leaf growth rate might increase yield. Methods We developed a functional-structural plant model, called MeOSR, to simulate carbon partitioning underlying cassava growth and SR formation in conjunction with the root system's three-dimensional (3D) architecture (RSA). We compared the model results to experimental data and simulated phenotypes varying in the number of SR and leaf growth rate. Results The simulated 3D RSA and the root mass closely represented those of field-grown plants. The model simulated root growth and associated carbon allocation across developmental stages. Substantial accumulation of non-structural carbohydrates (NSC) preceded SR formation, suggesting sink-limited growth. SR mass and canopy photosynthesis might be increased by both increasing the number of SR and the leaf growth rate. Conclusion MeOSR offers a valuable tool for simulating plant growth, its associated carbon economy, and 3D RSA over time. In the first month, the specific root length increased due to root branching, but in the third month, it decreased due to secondary root growth. The accumulation of NSC might initiate SR development in cassava. Cassava growth is relatively slow during the first 3 months, and a faster crop establishment combined with a greater SR growth might increase yield.

Parametrization of biological assumptions to simulate growth of tree branching architectures.

Modeling and simulating the growth of the branching of tree species remains a challenge. With existing approaches, we can reconstruct or rebuild the branching architectures of real tree species, but the simulation of the growth process remains unresolved. First, we present a tree growth model to generate branching architectures that resemble real tree species. Secondly, we use a quantitative morphometric approach to infer the shape similarity of the generated simulations and real tree species. Within a functional-structural plant model, we implement a set of biological parameters that affect the branching architecture of trees. By modifying the parameter values, we aim to generate basic shapes of spruce, pine, oak and poplar. Tree shapes are compared using geometric morphometrics of landmarks that capture crown and stem outline shapes. Five biological parameters, namely xylem flow, shedding rate, proprioception, gravitysense and lightsense, most influenced the generated tree branching patterns. Adjusting these five parameters resulted in the different tree shapes of spruce, pine, oak, and poplar. The largest effect was attributed to gravity, as phenotypic responses to this effect resulted in different growth directions of gymnosperm and angiosperm branching architectures. Since we were able to obtain branching architectures that resemble real tree species by adjusting only a few biological parameters, our model is extendable to other tree species. Furthermore, the model will also allow the simulation of structural tree-environment interactions. Our simplifying approach to shape comparison between tree species, landmark geometric morphometrics, showed that even the crown-trunk outlines capture species differences based on their contrasting branching architectures.

Combining modeling and experimental approaches for developing rice-oil palm agroforestry systems.

Monoculture systems in South East Asia are facing challenges due to climate change-induced extreme weather conditions, leading to significant annual production losses in rice and oil palm. To ensure the stability of these crops, innovative strategies like resilient agroforestry systems need to be explored. Converting oil palm (Elaeis guineensis) monocultures to rice (Oryza sativa)-based intercropping systems shows promise, but achieving optimal yields requires adjusting palm density and identifying rice varieties adapted to changes in light quantity and diurnal fluctuation. This paper proposes a methodology that combines a model of light interception with indoor experiments to assess the feasibility of rice-oil palm agroforestry systems. Using a functional-structural plant model of oil palm, the planting design was optimized to maximize transmitted light for rice. Simulation results estimated the potential impact on oil palm carbon assimilation and transpiration. In growth chambers, simulated light conditions were replicated with adjustments to intensity and daily fluctuation. Three light treatments independently evaluated the effects of light intensity and fluctuation on different rice accessions. The simulation study revealed intercropping designs that significantly increased light transmission for rice cultivation with minimal decrease in oil palm densities compared with conventional designs. The results estimated a loss in oil palm productivity of less than 10%, attributed to improved carbon assimilation and water use efficiency. Changes in rice plant architecture were primarily influenced by light quantity, while variations in yield components were attributed to light fluctuations. Different rice accessions exhibited diverse responses to light fluctuations, indicating the potential for selecting genotypes suitable for agroforestry systems.

Estimating the Light Interception and Photosynthesis of Greenhouse-Cultivated Tomato Crops under Different Canopy Configurations

Understanding the spatial heterogeneity of light and photosynthesis distribution within a canopy is crucial for optimizing plant growth and yield, especially in the context of greenhouse structures. In previous studies, we developed a 3D functional-structural plant model (FSPM) of the Chinese solar greenhouse (CSG) and tomato plants, in which the greenhouse was reconstructed as a 3D mockup and implemented in the virtual scene. This model, which accounts for various environmental factors, allows for precise calculations of radiation, temperature, and photosynthesis at the organ level. This study focuses on elucidating optimal canopy configurations for mechanized planting in greenhouses, building upon the commonly used north–south (N–S) orientation by exploring the east–west (E–W) orientation. Investigating sixteen scenarios with varying furrow distance (1 m, 1.2 m, 1.4 m, 1.6 m) and row spacing (0.3 m, 0.4 m, 0.5 m, 0.6 m), corresponding to 16 treatments of plant spacing, four planting patterns (homogeneous row, double row, staggered row, incremental row) and two orientations were investigated. The results show that in Shenyang city, an E–W orientation with the path width = 0.5 (furrow distance + row distance) = 0.8 m (homogeneous row), and a plant distance of 0.32 m, is the optimal solution for mechanized planting at a density of 39,000 plants/ha. Our findings reveal a nuanced understanding of how altering planting configurations impacts the light environment and photosynthesis rate within solar greenhouses. Looking forward, these insights not only contribute to the field of CSG mechanized planting, but also provide a basis for enhanced CSG planting management. Future research could further explore the broader implications of these optimized configurations in diverse geographic and climatic conditions.

Modelling the architecture of hazelnut (Corylus avellana) Tonda di Giffoni over two successive years

Abstract Hazelnut (Corylus avellana) cultivation is increasing worldwide. A 3D model of its structure could improve managerial techniques such as pruning. This study aims to analyse, over two successive years, hazelnut architectural development to implement a functional structural plant model. One hundred and four one-year-old shoots of own-rooted hazelnut trees were selected and analysed in winter 2020 and 2021. Exploratory analyses, generalized linear models and multinomial regression models were used to describe the architectural processes. The existence of sylleptic shoots on hazelnut one-year-old shoots, characterized by the presence of the male inflorescence on apical position, was detected. Along proleptic shoots, the branching pattern was described by (i) blind nodes located in the proximal part (ii) sylleptic shoots and mixed buds in the median part (iii) vegetative buds in the distal part. Apical bud died during the growing season, suggesting that Tonda di Giffoni has a sympodial branching. The models revealed dependencies among buds located at the same node, in the case of proleptic shoots. Especially, the probability of a bud bursting depended on both its type (i.e. mixed or vegetative) and the presence of other buds, either mixed or vegetative. Based on these local models and on a flow diagram, which defines the steps that led to the construction of hazelnut tree architecture, the first functional–structural plant model of hazelnut tree architecture was built. Further experiments will be needed and should be repeated over the following years to extend this study towards the juvenile phase and tree architecture over time.

A scalable pipeline to create synthetic datasets from functional–structural plant models for deep learning

Abstract In plant science, it is an established method to obtain structural parameters of crops using image analysis. In recent years, deep learning techniques have improved the underlying processes significantly. However, since data acquisition is time and resource consuming, reliable training data are currently limited. To overcome this bottleneck, synthetic data are a promising option for not only enabling a higher order of correctness by offering more training data but also for validation of results. However, the creation of synthetic data is complex and requires extensive knowledge in Computer Graphics, Visualization and High-Performance Computing. We address this by introducing Synavis, a framework that allows users to train networks on real-time generated data. We created a pipeline that integrates realistic plant structures, simulated by the functional–structural plant model framework CPlantBox, into the game engine Unreal Engine. For this purpose, we needed to extend CPlantBox by introducing a new leaf geometrization that results in realistic leafs. All parameterized geometries of the plant are directly provided by the plant model. In the Unreal Engine, it is possible to alter the environment. WebRTC enables the streaming of the final image composition, which, in turn, can then be directly used to train deep neural networks to increase parameter robustness, for further plant trait detection and validation of original parameters. We enable user-friendly ready-to-use pipelines, providing virtual plant experiment and field visualizations, a python-binding library to access synthetic data and a ready-to-run example to train models.

CPlantBox: a fully coupled modelling platform for the water and carbon fluxes in the soil–plant–atmosphere continuum

Abstract A plant’s development is strongly linked to the water and carbon flows in the soil–plant–atmosphere continuum. Expected climate shifts will alter the water and carbon cycles and will affect plant phenotypes. Comprehensive models that simulate mechanistically and dynamically the feedback loops between a plant’s three-dimensional development and the water and carbon flows are useful tools to evaluate the sustainability of genotype–environment–management combinations which do not yet exist. In this study, we present the latest version of the open-source three-dimensional Functional–Structural Plant Model CPlantBox with PiafMunch and DuMu${}^{\text{x}}$ coupling. This new implementation can be used to study the interactions between known or hypothetical processes at the plant scale. We simulated semi-mechanistically the development of generic C3 monocots from 10 to 25 days after sowing and undergoing an atmospheric dry spell of 1 week (no precipitation). We compared the results for dry spells starting on different days (Day 11 or 18) against a wetter and colder baseline scenario. Compared with the baseline, the dry spells led to a lower instantaneous water-use efficiency. Moreover, the temperature-induced increased enzymatic activity led to a higher maintenance respiration which diminished the amount of sucrose available for growth. Both of these effects were stronger for the later dry spell compared with the early dry spell. We could thus use CPlantBox to simulate diverging emerging processes (like carbon partitioning) defining the plants’ phenotypic plasticity response to their environment. The model remains to be validated against independent observations of the soil–plant–atmosphere continuum.

Studying spatial variability of fruit size within the tree canopy with a functional-structural plant model

Studying spatial variability of fruit size within the tree canopy with a functional-structural plant model

Consequences of interplant trait variation for canopy light absorption and photosynthesis.

Plant-to-plant variation (interplant variation) may play an important role in determining individual plant and whole canopy performance, where interplant variation in architecture and photosynthesis traits has direct effects on light absorption and photosynthesis. We aimed to quantify the importance of observed interplant variation on both whole-plant and canopy light absorption and photosynthesis. Plant architecture was measured in two experiments with fruiting tomato crops (Solanum lycopersicum) grown in glasshouses in the Netherlands, in week 16 (Exp. 1) or week 19 (Exp. 2) after transplanting. Experiment 1 included four cultivars grown under three supplementary lighting treatments, and Experiment 2 included two different row orientations. Measured interplant variations of the architectural traits, namely, internode length, leaf area, petiole angle, and leaflet angle, as well as literature data on the interplant variation of the photosynthesis traits alpha, J max28, and V cmax28, were incorporated in a static functional-structural plant model (FSPM). The FSPM was used to analyze light absorption and net photosynthesis of whole plants in response to interplant variation in architectural and photosynthesis traits. Depending on the trait, introducing interplant variation in architecture and photosynthesis traits in a functional-structural plant model did not affect or negatively affected canopy light absorption and net photosynthesis compared with the reference model without interplant variation. Introducing interplant variation of architectural and photosynthesis traits in FSPM results in a more realistic simulation of variation of plants within a canopy. Furthermore, it can improve the accuracy of simulation of canopy light interception and photosynthesis although these effects at the canopy level are relatively small (<4% for light absorption and<7% for net photosynthesis).

Consequences of intra-canopy and top LED lighting for uniformity of light distribution in a tomato crop.

In the past decade, the potential of positioning LED lamps in between the canopy (intra-canopy) to enhance crop growth and yield has been explored in greenhouse cultivation. Changes in spatial heterogeneity of light absorption that come with the introduction of intra-canopy lighting have not been thoroughly explored. We calibrated and validated an existing functional structural plant model (FSPM), which combines plant morphology with a ray tracing model to estimate light absorption at leaflet level. This FSPM was used to visualize the light environment in a tomato crop illuminated with intra-canopy lighting, top lighting or a combination of both. Model validation of light absorption of individual leaves showed a good fit (R2 = 0.93) between measured and modelled light absorption of the canopy. Canopy light distribution was then quantified and visualized in three voxel directions by means of average absorbed photosynthetic photon flux density (PPFD) and coefficient of variation (CV) within that voxel. Simulations showed that the variation coefficient within horizontal direction was higher for intra-canopy lighting than top lighting (CV=48% versus CV= 43%), while the combination of intra-canopy lighting and top lighting yielded the lowest CV (37%). Combined intra-canopy and top lighting (50/50%) had in all directions a more uniform light absorption than intra-canopy or top lighting alone. The variation was minimal when the ratio of PPFD from intra-canopy to top lighting was about 1, and increased when this ratio increased or decreased. Intra-canopy lighting resulted in 8% higher total light absorption than top lighting, while combining 50% intra-canopy lighting with 50% top lighting, increased light absorption by 4%. Variation in light distribution was further reduced when the intra-canopy LEDs were distributed over strings at four instead of two heights. When positioning LED lamps to illuminate a canopy both total light absorption and light distribution have to be considered.

Correlating Coffea canephora 3D architecture to plant photosynthesis at a daily scale and vegetative biomass allocation.

Coffea canephora (C. canephora) has two botanical varieties, Robusta and Conilon. Intraspecific variability was hypothesized and projected for the selection of C. canephora plants able to maintain production in the context of global climate changes. For that, architectural, C-assimilation and biomass analyses were performed on 17-month-old Robusta (clones 'A1' and '3V') and Conilon (clones '14' and '19') varieties grown in non-limiting soil, water and mineral nutrient conditions. Nondestructive coffee plant architecture coding, reconstruction and plant photosynthesis estimations were performed using a functional-structural plant modeling platform OpenAlea. 3D reconstructions and inclusion of parameters calculated and estimated from light response curves, such as dark respiration (Rd), maximum rate of carboxylation of RuBisCO and photosynthetic electron transport allowed the estimation of instantaneous and daily plant photosynthesis. The virtual orchard leaf area index was low, and light was not a limiting factor in early C. canephora development stages. Under such conditions, Robusta assimilated more CO2 at the plant and orchard scale and produced higher total biomass than Conilon. Lower plant daily photosynthesis and total biomass were correlated to higher Rd in Conilon than in Robusta. Among the architectural traits, leaf inclination, size and allometry were most highly correlated with plant assimilation and biomass. Relative allocation in leaf biomass was higher in '19' Conilon than in young Robusta plants, indicating intraspecific biomass partitioning. Similarly, variation in relative distribution of the root biomass and the root volume reflected clonal variation in soil occupation, indicating intraspecific variability in space occupation competitiveness. Coffea canephora denoted high root allocation in both Conilon and Robusta clones. However, relevant differences at subspecific levels were found, indicating the high potential of C. canephora to cope with drought events, which are expected to occur more frequently in the future, because of climate changes. The methodology developed here has the potential to be used for other crops and tree species. Highlights Functional-structural plant model was used to estimate photosynthesis on a plant and daily scales in Coffea canephora (C. canephora). Among the architectural traits, leaf shape and inclination had the most impact on photosynthesis and biomass. Under non-limiting conditions, Robusta had higher plant photosynthesis and biomass than Conilon. A higher leaf biomass allocation in Conilon clone '19' than in Robusta suggested variety-specific partitioning. Variation in the relative distribution of the root biomass indicated C. canephora intraspecific soil occupation variability.

Stronger wind, smaller tree: Testing tree growth plasticity through a modeling approach.

Plants exhibit plasticity in response to various external conditions, characterized by changes in physiological and morphological features. Although being non-negligible, compared to the other environmental factors, the effect of wind on plant growth is less extensively studied, either experimentally or computationally. This study aims to propose a modeling approach that can simulate the impact of wind on plant growth, which brings a biomechanical feedback to growth and biomass distribution into a functional-structural plant model (FSPM). Tree reaction to the wind is simulated based on the hypothesis that plants tend to fit in the environment best. This is interpreted as an optimization problem of finding the best growth-regulation sink parameter giving the maximal plant fitness (usually seed weight, but expressed as plant biomass and size). To test this hypothesis in silico, a functional-structural plant model, which simulates both the primary and secondary growth of stems, is coupled with a biomechanical model which computes forces, moments of forces, and breakage location in stems caused by both wind and self-weight increment during plant growth. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to maximize the multi-objective function (stem biomass and tree height) by determining the key parameter value controlling the biomass allocation to the secondary growth. The digital trees show considerable phenotypic plasticity under different wind speeds, whose behavior, as an emergent property, is in accordance with experimental results from works of literature: the height and leaf area of individual trees decreased with wind speed, and the diameter at the breast height (DBH) increased at low-speed wind but declined at higher-speed wind. Stronger wind results in a smaller tree. Such response of trees to the wind is realistically simulated, giving a deeper understanding of tree behavior. The result shows that the challenging task of modeling plant plasticity may be solved by optimizing the plant fitness function. Adding a biomechanical model enriches FSPMs and opens a wider application of plant models.

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

SUMMARYRoot 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.