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

Unlocking dynamic root phenotypes for simultaneous enhancement of water and phosphorus uptake

Phosphorus (P) and water are crucial for plant growth, but their availability is challenged by climate change, leading to reduced crop production and global food security. In many agricultural soils, crop productivity is confronted by both water and P limitations. The diminished soil moisture decreases available P due to reduced P diffusion, and inadequate P availability diminishes tissue water status through modifications in stomatal conductance and a decrease in root hydraulic conductance. P and water display contrasting distributions in the soil, with P being concentrated in the topsoil and water in the subsoil. Plants adapt to water- and P-limited environments by efficiently exploring localized resource hotspots of P and water through the adaptation of their root system. Thus, developing cultivars with improved root architecture is crucial for accessing and utilizing P and water from arid and P-deficient soils. To meet this goal, breeding towards multiple advantageous root traits can lead to better cultivars for water- and P-limited environments. This review discusses the interplay of P and water availability and highlights specific root traits that enhance the exploration and exploitation of optimal resource-rich soil strata while reducing metabolic costs. We propose root ideotype models, including 'topsoil foraging', 'subsoil foraging', and 'topsoil/subsoil foraging' for maize (monocot) and common bean (dicot). These models integrate beneficial root traits and guide the development of water- and P-efficient cultivars for challenging environments.

Location: root architecture structures rhizosphere microbial associations

Root architectural phenotypes are promising targets for crop breeding, but root architectural effects on microbial associations in agricultural fields are not well understood. Architecture determines the location of microbial associations within root systems, which, when integrated with soil vertical gradients, determines the functions and the metabolic capability of rhizosphere microbial communities. We argue that variation in root architecture in crops has important implications for root exudation, microbial recruitment and function, and the decomposition and fate of root tissues and exudates. Recent research has shown that the root microbiome changes along root axes and among root classes, that root tips have a unique microbiome, and that root exudates change within the root system depending on soil physicochemical conditions. Although fresh exudates are produced in larger amounts in root tips, the rhizosphere of mature root segments also plays a role in influencing soil vertical gradients. We argue that more research is needed to understand specific root phenotypes that structure microbial associations and discuss candidate root phenotypes that may determine the location of microbial hotspots within root systems with relevance to agricultural systems.

Development of an assessment-based planting structure optimization model for mitigating agricultural greenhouse gas emissions

Optimization of crop structure is an efficient way to reduce greenhouse gas (GHGs) from agriculture production. However, carbon footprint have rarely been incorporated into previous planting structure optimization models due to the challenges of assessing the spatial and temporal distribution of agricultural carbon footprint for multiple crops in irrigated districts. In addition, previous planting structure models suffered from strong subjectivity in objective function determination, and the obtained non-dominated solution set offered difficulties to decision-makers in selecting specific implementation options. To fill such gaps, an integrated accounting-assessment-optimization-decision making (AAODM) approach was proposed, which remedies the shortcomings of previous crop planting structure optimization models in carbon footprint mitigation, and overcomes the subjectivity of objective function determination and the difficulty in selecting specific implementation options. Firstly, life cycle assessment (LCA) method was used to account for the multi-year agricultural carbon footprints of multiple crops in the irrigation district. The optimization objective functions of planting structure optimization models can then be determined based on the assessment method of carbon footprint influencing factors. Next, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was used to generate a non-dominated solution set of the optimization model. The optimal planting structure can be finally obtained based on decision making methods by determining the maximum harmonic mean (HM) and knee points (KPs) of the non-dominated solution set. The developed AAODM approach was then applied to a case study of agricultural crop management in Bayan Nur City, China. The results showed that the level of economic development was a key factor influencing the increase in carbon footprint in Bayan Nur City over the past 20 years. The regulation of the level of economic development would significantly influence the agricultural carbon footprint in Bayan Nur City. Moreover, two optimal crop cultivation patterns were provided for decision-makers by selecting solutions from the Pareto front with decision making methods. The comparison results with other methods showed that the solutions obtained by NSGA-II were superior to MOPSO in terms of carbon reduction. The developed AAODM approach for agricultural GHG mitigation could help agricultural production systems in achieving low carbon emissions and high efficiency.

Response to gallium (Ga) exposure and its distribution in rice plants

Root architecture is the temporal and spatial configuration of root system in the heterogeneous matrix of soil that is prone to chemical stresses. Gallium (Ga) is among the emerging chemical pollutants that are mostly associated with high-tech industries, specifically associated with semiconductors. In view of its potential risk and increasing distribution in the environment, this study was designed to evaluate the inhibition rate, Ga distribution in different tissues, and root architecture of rice seedlings under different concentrations of Ga. We observed that 2.59, 46.7, and 168.2 mg Ga/L were minimum (EC20), medium (EC50), and maximum (EC75) effective concentrations for rice plants that corresponded to the 20, 50, and 75% inhibition on the relative growth rate, respectively. Distribution of Ga in rice tissues showed that accumulation of Ga was much higher in roots than shoots of rice seedlings, and it increased with an increase in Ga doses. Evan blue staining technique reveals that the number of damaged/dead cell was dose-dependent on Ga. Moreover, several traits associated with root system architecture demonstrating that rice root system architecture altered in response to Ga stress. Collectively, the results reveal that Ga exposure inhibited the growth and development of rice plants. This study will enhance our understanding that how different concentrations of Ga in the environment can affect plants; however, more comprehensive studies are essential to further determine plant response against Ga stress.

Tapping into the plasticity of plant architecture for increased stress resilience

Plant architecture develops post-embryonically and emerges from a dialogue between the developmental signals and environmental cues. Length and branching of the vegetative and reproductive tissues were the focus of improvement of plant performance from the early days of plant breeding. Current breeding priorities are changing, as we need to prioritize plant productivity under increasingly challenging environmental conditions. While it has been widely recognized that plant architecture changes in response to the environment, its contribution to plant productivity in the changing climate remains to be fully explored. This review will summarize prior discoveries of genetic control of plant architecture traits and their effect on plant performance under environmental stress. We review new tools in phenotyping that will guide future discoveries of genes contributing to plant architecture, its plasticity, and its contributions to stress resilience. Subsequently, we provide a perspective into how integrating the study of new species, modern phenotyping techniques, and modeling can lead to discovering new genetic targets underlying the plasticity of plant architecture and stress resilience. Altogether, this review provides a new perspective on the plasticity of plant architecture and how it can be harnessed for increased performance under environmental stress.

RootSlice-A novel functional-structural model for root anatomical phenotypes

Root anatomy is an important determinant of root metabolic costs, soil exploration, and soil resource capture. Root anatomy varies substantially within and among plant species. RootSlice is a multicellular functional-structural model of root anatomy developed to facilitate the analysis and understanding of root anatomical phenotypes. RootSlice can capture phenotypically accurate root anatomy in three dimensions of different root classes and developmental zones, of both monocotyledonous and dicotyledonous species. Several case studies are presented illustrating the capabilities of the model. For maize nodal roots, the model illustrated the role of vacuole expansion in cell elongation; and confirmed the individual and synergistic role of increasing root cortical aerenchyma and reducing the number of cortical cell files in reducing root metabolic costs. Integration of RootSlice for different root zones as the temporal properties of the nodal roots in the whole-plant and soil model OpenSimRoot/maize enabled the multiscale evaluation of root anatomical phenotypes, highlighting the role of aerenchyma formation in enhancing the utility of cortical cell files for improving plant performance over varying soil nitrogen supply. Such integrative in silico approaches present avenues for exploring the fitness landscape of root anatomical phenotypes.

Large root cortical cells and reduced cortical cell files improve growth under suboptimal nitrogen in silico

Suboptimal nitrogen availability is a primary constraint to plant growth. We used OpenSimRoot, a functional-structural plant/soil model, to test the hypothesis that larger root cortical cell size (CCS), reduced cortical cell file number (CCFN), and their interactions with root cortical aerenchyma (RCA) and lateral root branching density (LRBD) are useful adaptations to suboptimal soil nitrogen availability in maize (Zea mays). Reduced CCFN increased shoot dry weight over 80%. Reduced respiration, reduced nitrogen content, and reduced root diameter accounted for 23%, 20%, and 33% of increased shoot biomass, respectively. Large CCS increased shoot biomass by 24% compared with small CCS. When simulated independently, reduced respiration and reduced nutrient content increased the shoot biomass by 14% and 3%, respectively. However, increased root diameter resulting from large CCS decreased shoot biomass by 4% due to an increase in root metabolic cost. Under moderate N stress, integrated phenotypes with reduced CCFN, large CCS, and high RCA improved shoot biomass in silt loam and loamy sand soils. In contrast, integrated phenotypes composed of reduced CCFN, large CCS and reduced lateral root branching density had the greatest growth in silt loam, while phenotypes with reduced CCFN, large CCS and high LRBD were the best performers in loamy sands. Our results support the hypothesis that larger CCS, reduced CCFN, and their interactions with RCA and LRBD could increase nitrogen acquisition by reducing root respiration and root nutrient demand. Phene synergisms may exist between CCS, CCFN, and LRBD. CCS and CCFN merit consideration for breeding cereal crops with improved nitrogen acquisition, which is critical for global food security.