Designing oil palm architectural ideotypes for optimal light interception and carbon assimilation through a sensitivity analysis of leaf traits

The functional diversity-productivity relationship of woody plants is climatically sensitive

Plot-scale experiments indicate that functional diversity (FD) plays a pivotal role in sustaining ecosystem functions such as net primary productivity (NPP). However, the relationships between functional diversity and NPP across larger scale under varying climatic conditions are sparsely studied, despite its significance for understanding forest-atmosphere interactions and informing policy development. Hence, we examine the relationships of community-weighted mean (CWM) and functional dispersion (FDis) of woody plant traits on NPP across China and if such relationships are modulated by climatic conditions at the national scale. Using comprehensive datasets of distribution, functional traits, and productivity for 9120 Chinese woody plant species, we evaluated the distribution pattern of community-weighted mean and functional dispersion (including three orthogonal trait indicators: plant size, leaf morphology, and flower duration) and its relationships with NPP. Finally, we tested the effects of climatic conditions on community-weighted mean/functional dispersion-NPP relationships. We first found overall functional diversity-NPP relationships, but also that the magnitude of these relationships was sensitive to climate, with plant size community-weighted mean promoting NPP in warm regions and plant size functional dispersion promoting NPP in wet regions. Second, warm and wet conditions indirectly increased NPP by its positive effects on community-weighted mean or functional dispersion, particularly through mean plant size and leaf morphology. Our study provides comprehensive evidence for the relationships between functional diversity and NPP under varying climates at a large scale. Importantly, our results indicate a broadening significance of multidimensional plant functional traits for woody vegetation NPP in response to rising temperatures and wetter climates. Restoration, reforestation actions and natural capital accounting need to carefully consider not only community-weighted mean and functional dispersion but also their interactions with climate, to predict how functional diversity may promote ecosystem functioning under future climatic conditions.

Efficient Bayesian automatic calibration of a functional-structural wheat model using an adaptive design and a metamodelling approach

Functional-structural plant models are increasingly being used by plant scientists to address a wide variety of questions. However, the calibration of these complex models is often challenging, mainly because of their high computational cost, and, as a result, error propagation is usually ignored. Here we applied an automatic method to the calibration of WALTer: a functional-structural wheat model that simulates the plasticity of tillering in response to competition for light. We used a Bayesian calibration method to jointly estimate the values of five parameters and quantify their uncertainty by fitting the model outputs to tillering dynamics data. We made recourse to Gaussian process metamodels in order to alleviate the computational cost of WALTer. These metamodels are built from an adaptive design that consists of successive runs of WALTer chosen by an efficient global optimization algorithm specifically adapted to this particular calibration task. The method presented here performed well on both synthetic and experimental data. It is an efficient approach for the calibration of WALTer and should be of interest for the calibration of other functional-structural plant models.

Crop/Plant Modeling Supports Plant Breeding: I. Optimization of Environmental Factors in Accelerating Crop Growth and Development for Speed Breeding

The environmental conditions in customered speed breeding practice are, to some extent, empirical and, thus, can be further optimized. Crop and plant models have been developed as powerful tools in predicting growth and development under various environments for extensive crop species. To improve speed breeding, crop models can be used to predict the phenotypes resulted from genotype by environment by management at the population level, while plant models can be used to examine 3-dimensional plant architectural development by microenvironments at the organ level. By justifying the simulations via numerous virtual trials using models in testing genotype × environment × management, an optimized combination of environmental factors in achieving desired plant phenotypes can be quickly determined. Artificial intelligence in assisting for optimization is also discussed. We admit that the appropriate modifications on modeling algorithms or adding new modules may be necessary in optimizing speed breeding for specific uses. Overall, this review demonstrates that crop and plant models are promising tools in providing the optimized combinations of environment factors in advancing crop growth and development for speed breeding.

More than 1000 genotypes are required to derive robust relationships between yield, yield stability and physiological parameters: a computational study on wheat crop

Using in silico experiment in crop model, we identified different physiological regulations of yield and yield stability, as well as quantify the genotype and environment numbers required for analysing yield stability convincingly. Identifying target traits for breeding stable and high-yielded cultivars simultaneously is difficult due to limited knowledge of physiological mechanisms behind yield stability. Besides, there is no consensus about the adequacy of a stability index (SI) and the minimal number of environments and genotypes required for evaluating yield stability. We studied this question using the crop model APSIM-Wheat to simulate 9100 virtual genotypes grown under 9000 environments. By analysing the simulated data, we showed that the shape of phenotype distributions affected the correlation between SI and mean yield and the genotypic superiority measure (P_i) was least affected among 11 SI. P_i was used as index to demonstrate that more than 150 environments were required to estimate yield stability of a genotype convincingly and more than 1000 genotypes were necessary to evaluate the contribution of a physiological parameter to yield stability. Network analyses suggested that a physiological parameter contributed preferentially to yield or P_i. For example, soil water absorption efficiency and potential grain filling rate explained better the variations in yield than in P_i; while light extinction coefficient and radiation use efficiency were more correlated with P_i than with yield. The high number of genotypes and environments required for studying P_i highlight the necessity and potential of in silico experiments to better understand the mechanisms behind yield stability.

Light Interception, Photosynthetic Performance, and Yield of Oil Palm Interspecific OxG Hybrid (Elaeis oleifera (Kunth) Cortés x Elaeis guineensis Jacq.) under Three Planting Densities

Environmental conditions are crucial for crops’ growth, development, and productivity. One of the most important physiological factors associated with the production of crops is the use of solar radiation for the photosynthesis process, which determines the amount of assimilates available for crop growth and yield. Three age classes (4, 6, and 14 years) and three planting densities (143, 128, and 115 palms ha⁻¹) were evaluated in a commercial interspecific Elaeis Oleifera x Elaeis guineensis hybrid Coari x La Mé. The light interception patterns and the photosynthetic performance were determined. Measurements were taken of the leaf area, the number of leaves, and incident and photosynthetically transmitted active radiation. Also, photosynthetic rates, light, and yield were measured. The canopy extinction coefficient (Kc) was estimated using the Monsi and Saeki model. Under the evaluated conditions, the average Kc value for 4-year-old palms was 0.44; for the 6-year-old group of palms, the average value was 0.40, and 0.32 for the 14-year-old palms, with coefficients of determination (R²) greater than 0.8. A pattern associated with the age of the crop was observed, where the Kc decreased in groups of adult palms. The results showed increased Kc as the planting density decreased. No statistically significant differences were observed between planting densities or ages in the light and CO₂ curves regarding photosynthesis. The leaf level in which the measurement was made influenced photosynthesis. Thus, the highest values of the photosynthesis parameters were observed in leaf 17. The crop yield tended to stabilize 8 years after planting under 143 and 128 palms per hectare, but 14 years after planting, the Fresh fruit bunch (FFB) production was still growing under 115 palms per hectare. The results showed that, up to year 14 after planting, the highest cumulative yield was achieved with 115 palms per hectare. This was partly caused by a sharp decline in production observed under 128 palms per hectare, which could indicate that in the long production cycle of the OxG hybrids, the 115-palms-per-hectare planting density would result in higher cumulative FFB production. Furthermore, the results showed that the optimum planting density for the hybrids of the present study would be 120 palms ha⁻¹, corresponding to a planting distance of 9.8 m between plants.

Geographic Variation in the Petiole-Lamina Relationship of 325 Eastern Qinghai-Tibetan Woody Species: Analysis in Three Dimensions

The petiole-lamina relationship is central to the functional tradeoff between photosynthetic efficiency and the support/protection cost. Understanding environmental gradients in the relationship and its underlying mechanisms remains a critical challenge for ecologists. We investigated the possible scaling of the petiole-lamina relationships in three dimensions, i.e., petiole length (PL) vs. lamina length (LL), petiole cross sectional area (PCA) vs. lamina area (LA), and petiole mass (PM) vs. lamina mass (LM), for 325 Qinghai-Tibetan woody species, and examined their relation to leaf form, altitude, climate, and vegetation types. Both crossspecies analysis and meta-analysis showed significantly isometric, negatively allometric, and positively allometric scaling of the petiole-lamina relationships in the length, area, and mass dimensions, respectively, reflecting an equal, slower, and faster variation in the petiole than in the lamina in these trait dimensions. Along altitudinal gradients, the effect size of the petiole-lamina relationship decreased in the length and mass dimensions but increased in the area dimension, suggesting the importance of enhancing leaf light-interception and nutrient transport efficiency in the warm zones in petiole development, but enhancing leaf support/protection in the cold zones. The significant additional influences of LA, LM, and LA were observed on the PL-LL, PCA-LA, and PM-LM relationships, respectively, implying that the single-dimension petiole trait is affected simultaneously by multidimensional lamina traits. Relative to simple-leaved species, the presence of petiolule in compound-leaved species can increase both leaf light interception and static gravity loads or dynamic drag forces on the petiole, leading to lower dependence of PL variation on LL variation, but higher biomass allocation to the petiole. Our study highlights the need for multidimension analyses of the petiole-lamina relationships and illustrates the importance of plant functional tradeoffs and the change in the tradeoffs along environmental gradients in determining the relationships.

Functional-Structural Plant Modeling Highlights How Diversity in Leaf Dimensions and Tillering Capability Could Promote the Efficiency of Wheat Cultivar Mixtures

Increasing the cultivated diversity has been identified as a major leverage for the agroecological transition as it can help improve the resilience of low input cropping systems. For wheat, which is the most cultivated crop worldwide in terms of harvested area, the use of cultivar mixtures is spreading in several countries, but studies have seldom focused on establishing mixing rules based on plant architecture. Yet, the aerial architecture of plants and the overall canopy structure are critical for field performance as they greatly influence light interception, plant interactions and yield. The very high number of trait combinations in wheat mixtures makes it difficult to conduct experimentations on this issue, which is why a modeling approach appears to be an appropriate solution. In this study, we used WALTer, a functional structural plant model (FSPM), to simulate wheat cultivar mixtures and try to better understand how differences between cultivars in key traits of the aerial architecture influence mixture performance. We simulated balanced binary mixtures of cultivars differing for different critical plant traits: final height, leaf dimensions, leaf insertion angle and tillering capability. Our study highlights the impact of the leaf dimensions and the tillering capability on the performance of the simulated mixtures, which suggests that traits impacting the plants' leaf area index (LAI) have more influence on the performance of the stand than traits impacting the arrangement of the leaves. Our results show that the performance of mixtures is very variable depending on the values of the explored architectural traits. In particular, the best performances were achieved by mixing cultivars with different leaf dimensions and different tillering capability, which is in agreement with numerous studies linking the diversity of functional traits in plant communities to their productivity. However, some of the worst performances were also achieved by mixing varieties differing in their aerial architecture, which suggests that diversity is not a sufficient criterion to design efficient mixtures. Overall, these results highlight the importance of simulation-based explorations for establishing assembly rules to design efficient mixtures.

Crop breeding for a changing climate: integrating phenomics and genomics with bioinformatics

Safeguarding crop yields in a changing climate requires bioinformatics advances in harnessing data from vast phenomics and genomics datasets to translate research findings into climate smart crops in the field. Climate change and an additional 3 billion mouths to feed by 2050 raise serious concerns over global food security. Crop breeding and land management strategies will need to evolve to maximize the utilization of finite resources in coming years. High-throughput phenotyping and genomics technologies are providing researchers with the information required to guide and inform the breeding of climate smart crops adapted to the environment. Bioinformatics has a fundamental role to play in integrating and exploiting this fast accumulating wealth of data, through association studies to detect genomic targets underlying key adaptive climate-resilient traits. These data provide tools for breeders to tailor crops to their environment and can be introduced using advanced selection or genome editing methods. To effectively translate research into the field, genomic and phenomic information will need to be integrated into comprehensive clade-specific databases and platforms alongside accessible tools that can be used by breeders to inform the selection of climate adaptive traits. Here we discuss the role of bioinformatics in extracting, analysing, integrating and managing genomic and phenomic data to improve climate resilience in crops, including current, emerging and potential approaches, applications and bottlenecks in the research and breeding pipeline.

Marker-based crop model-assisted ideotype design to improve avoidance of abiotic stress in bread wheat

Wheat phenology allows escape from seasonal abiotic stresses including frosts and high temperatures, the latter being forecast to increase with climate change. The use of marker-based crop models to identify ideotypes has been proposed to select genotypes adapted to specific weather and management conditions and anticipate climate change. In this study, a marker-based crop model for wheat phenology was calibrated and tested. Climate analysis of 30 years of historical weather data in 72 locations representing the main wheat production areas in France was performed. We carried out marker-based crop model simulations for 1019 wheat cultivars and three sowing dates, which allowed calculation of genotypic stress avoidance frequencies of frost and heat stress and identification of ideotypes. The phenology marker-based crop model allowed prediction of large genotypic variations for the beginning of stem elongation (GS30) and heading date (GS55). Prediction accuracy was assessed using untested genotypes and environments, and showed median genotype prediction errors of 8.5 and 4.2 days for GS30 and GS55, respectively. Climate analysis allowed the definition of a low risk period for each location based on the distribution of the last frost and first heat days. Clustering of locations showed three groups with contrasting levels of frost and heat risks. Marker-based crop model simulations showed the need to optimize the genotype depending on sowing date, particularly in high risk environments. An empirical validation of the approach showed that it holds good promises to improve frost and heat stress avoidance.

V-Mango: a functional-structural model of mango tree growth, development and fruit production

BACKGROUND AND AIMS

Mango (Mangifera indica L.) is the fifth most widely produced fruit in the world. Its cultivation, mainly in tropical and sub-tropical regions, raises a number of issues such as the irregular fruit production across years, phenological asynchronisms that lead to long periods of pest and disease susceptibility, and the heterogeneity of fruit quality and maturity at harvest. To address these issues, we developed an integrative functional-structural plant model that synthesizes knowledge about the vegetative and reproductive development of the mango tree and opens up the possible simulation of cultivation practices.

METHODS

We designed a model of architectural development in order to precisely characterize the intricate developmental processes of the mango tree. The appearance of botanical entities was decomposed into elementary stochastic events describing occurrence, intensity and timing of development. These events were determined by structural (position and fate of botanical entities) and temporal (appearance dates) factors. Daily growth and development of growth units and inflorescences were modelled using empirical distributions and thermal time. Fruit growth was determined using an ecophysiological model that simulated carbon- and water-related processes at the fruiting branch scale.

KEY RESULTS

The model simulates the dynamics of the population of growth units, inflorescences and fruits at the tree scale during a growing cycle. Modelling the effects of structural and temporal factors makes it possible to simulate satisfactorily the complex interplays between vegetative and reproductive development. The model allowed the characterization of the susceptibility of mango tree to pests and the investigatation of the influence of tree architecture on fruit growth.

CONCLUSIONS

This integrative functional-structural model simulates mango tree vegetative and reproductive development over successive growing cycles, allowing a precise characterization of tree phenology and fruit growth and production. The next step is to integrate the effects of cultivation practices, such as pruning, into the model.

Light, Not Age, Underlies the Maladaptation of Maize and Miscanthus Photosynthesis to Self-Shading

Zea mays and Miscanthus × giganteus use NADP-ME subtype C₄ photosynthesis and are important food and biomass crops, respectively. Both crops are grown in dense stands where shaded leaves can contribute a significant proportion of overall canopy productivity. This is because shaded leaves, despite intercepting little light, typically process light energy very efficiently for photosynthesis, when compared to light-saturated leaves at the top of the canopy. However, an apparently maladaptive loss in photosynthetic light-use efficiency as leaves become shaded has been shown to reduce productivity in these two species. It is unclear whether this is due to leaf aging or progressive shading from leaves forming above. This was resolved here by analysing photosynthesis in leaves of the same chronological age in the centre and exposed southern edge of field plots of these crops. Photosynthetic light-response curves were used to assess maximum quantum yield of photosynthesis; the key measure of photosynthetic capacity of a leaf in shade. Compared to the upper canopy, maximum quantum yield of photosynthesis of lower canopy leaves was significantly reduced in the plot centre; but increased slightly at the plot edge. This indicates loss of efficiency of shaded leaves is due not to aging, but to the altered light environment of the lower canopy, i.e., reduced light intensity and/or altered spectral composition. This work expands knowledge of the cause of this maladaptive shade response, which limits productivity of some of the world's most important crops.

Changes in the vertical distribution of leaf area enhanced light interception efficiency in maize over generations of selection

Breeders select for yield, thereby indirectly selecting for traits that contribute to it. We tested if breeding has affected a range of traits involved in plant architecture and light interception, via the analysis of a panel of 60 maize hybrids released from 1950 to 2015. This was based on novel traits calculated from reconstructions derived from a phenotyping platform. The contribution of these traits to light interception was assessed in virtual field canopies composed of 3D plant reconstructions, with a model tested in a real field. Two categories of traits had different contributions to genetic progress. (a) The vertical distribution of leaf area had a high heritability and showed a marked trend over generations of selection. Leaf area tended to be located at lower positions in the canopy, thereby improving light penetration and distribution in the canopy. This potentially increased the carbon availability to ears, via the amount of light absorbed by the intermediate canopy layer. (b) Neither the horizontal distribution of leaves in the relation to plant rows nor the response of light interception to plant density showed appreciable trends with generations. Hence, among many architectural traits, the vertical distribution of leaf area was the main indirect target of selection.

Optimal Design of Plant Canopy Based on Light Interception: A Case Study With Loquat

Canopy architecture determines the light distribution and light interception in the canopy. Reasonable shaping and pruning can optimize tree structure; maximize the utilization of land, space and light energy; and lay the foundation for achieving early fruiting, high yield, health and longevity. Due to the complexity of loquat canopy architecture and the multi-year period of tree growth, the variables needed for experiments in canopy type training are hardly accessible through field measurements. In this paper, we concentrated on exploring the relationship between branching angle and light interception using a three-dimensional (3D) canopy model in loquat (Eriobotrya japonica Lindl). First, detailed 3D models of loquat trees were built by integrating branch and organ models. Second, the morphological models of different loquat trees were constructed by interactive editing. Third, the 3D individual-tree modeling software LSTree integrated with the OpenGL shadow technique, a radiosity model and a modified rectangular hyperbola model was used to calculate the silhouette to total area ratio, the distribution of photosynthetically active radiation within canopies and the net photosynthetic rate, respectively. Finally, the influence of loquat tree organ organization on the light interception of the trees was analyzed with different parameters. If the single branch angle between the level 2 scaffold branch and trunk is approximately 15° and the angles among the level 2 scaffold branches range from 60 to 90°, then a better light distribution can be obtained. The results showed that the branching angle has a significant impact on light interception, which is useful for grower manipulation of trees, e.g., shoot bending (scaffold branch angle). Based on this conclusion, a reasonable tree structure was selected for intercepting light. This quantitative simulation and analytical method provides a new digital and visual method that can aid in the design of tree architecture.

Morphological acclimation to agronomic manipulation in leaf dispersion and orientation to promote "Ideotype" breeding: Evidence from 3D visual modeling of "super" rice (Oryza sativa L.)

Food security is confronted by major threats from crop yield stagnation and global climate change. The benefits of phenotypic plasticity across environments for given crop genotypes are thought to be imperative for high-yielding cropping systems. Given that 3D modeling is increasingly recognized for dissecting crop phenotypic plasticity, it requires an assessment of the potential benefits of architectural adaptation of super rice to different agronomic practices. In this study, we focused on a comprehensive evaluation of the phenotypic plasticity of super rice on the aspects of 3D architectural "reoptimization," photosynthetic productivity, nitrogen economy, and grain yield. A super rice phenotype in superhigh-yielding practice (SH) displays a "reoptimized" morphogenesis in the leaf vertical dispersion and orientation in comparison to that in Farmer's practice (FP). Specifically, a super rice phenotype in SH is provided with a high cumulative rate and peaks of leaf area, increasing the distribution of high leaf inclination angles in comparison to that in FP, particularly in the upper parts of the canopy. These "reoptimizations" sustained profits in light environment within a canopy, leaf area duration, photosynthetic light harvest, and light utilization efficiency and were coordinated with improving nitrogen uptake and assimilation. The current literature indicates that the agronomic plasticity of super rice in architectural "reoptimization" is a promising perspective for high yield formation. Our results suggest that more emphasis should be placed upon agronomic adaptation strategies for super rice across diverse genotypes and environments to further improve crop establishment and photosynthetic productivity.

Estimating Sink Parameters of Stochastic Functional-Structural Plant Models Using Organic Series-Continuous and Rhythmic Development

Functional-structural plant models (FSPMs) generally simulate plant development and growth at the level of individual organs (leaves, flowers, internodes, etc.). Parameters that are not directly measurable, such as the sink strength of organs, can be estimated inversely by fitting the weights of organs along an axis (organic series) with the corresponding model output. To accommodate intracanopy variability among individual plants, stochastic FSPMs have been built by introducing the randomness in plant development; this presents a challenge in comparing model output and experimental data in parameter estimation since the plant axis contains individual organs with different amounts and weights. To achieve model calibration, the interaction between plant development and growth is disentangled by first computing the occurrence probabilities of each potential site of phytomer, as defined in the developmental model (potential structure). On this basis, the mean organic series is computed analytically to fit the organ-level target data. This process is applied for plants with continuous and rhythmic development simulated with different development parameter sets. The results are verified by Monte-Carlo simulation. Calibration tests are performed both in silico and on real plants. The analytical organic series are obtained for both continuous and rhythmic cases, and they match well with the results from Monte-Carlo simulation, and vice versa. This fitting process works well for both the simulated and real data sets; thus, the proposed method can solve the source-sink functions of stochastic plant architectures through a simplified approach to plant sampling. This work presents a generic method for estimating the sink parameters of a stochastic FSPM using statistical organ-level data, and it provides a method for sampling stems. The current work breaks a bottleneck in the application of FSPMs to real plants, creating the opportunity for broad applications.

A new methodology based on sensitivity analysis to simplify the recalibration of functional-structural plant models in new conditions

Background and Aims

Functional-structural plant models (FSPMs) describe explicitly the interactions between plants and their environment at organ to plant scale. However, the high level of description of the structure or model mechanisms makes this type of model very complex and hard to calibrate. A two-step methodology to facilitate the calibration process is proposed here.

Methods

First, a global sensitivity analysis method was applied to the calibration loss function. It provided first-order and total-order sensitivity indexes that allow parameters to be ranked by importance in order to select the most influential ones. Second, the Akaike information criterion (AIC) was used to quantify the model's quality of fit after calibration with different combinations of selected parameters. The model with the lowest AIC gives the best combination of parameters to select. This methodology was validated by calibrating the model on an independent data set (same cultivar, another year) with the parameters selected in the second step. All the parameters were set to their nominal value; only the most influential ones were re-estimated.

Key Results

Sensitivity analysis applied to the calibration loss function is a relevant method to underline the most significant parameters in the estimation process. For the studied winter oilseed rape model, 11 out of 26 estimated parameters were selected. Then, the model could be recalibrated for a different data set by re-estimating only three parameters selected with the model selection method.

Conclusions

Fitting only a small number of parameters dramatically increases the efficiency of recalibration, increases the robustness of the model and helps identify the principal sources of variation in varying environmental conditions. This innovative method still needs to be more widely validated but already gives interesting avenues to improve the calibration of FSPMs.

Computational botany: advancing plant science through functional–structural plant modelling

The need to integrate the ever-expanding body of knowledge in the plant sciences has led to the development of sophisticated modelling approaches. This special issue focuses on functional–structural plant (FSP) models, which are the result of cross-fertilization between the domains of plant science, computer science and mathematics. FSP models simulate growth and morphology of individual plants that interact with their environment, from which complex plant community properties emerge. FSP models can be used for a broad range of research questions across disciplines related to plant science. This special issue presents the latest developments in FSP modelling, including the novel incorporation of plant ecophysiological concepts and the application of FSP models to address new scientific questions. Additionally, it illustrates the breadth of model evaluation approaches that are performed. FSP modelling is a very active domain of plant research which brings together a wide range of scientific disciplines. It offers the opportunity to address questions in complex plant systems that cannot be addressed by empirical approaches alone, including questions on fundamental concepts related to plant development such as regulation of morphogenesis, as well as on applied concepts such as the relationship between crop performance and plant competition for resources.