Functional-structural Plant Model Research Articles

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 the managerial techniques such as pruning. This study aims to analyse, over two successive years, hazelnut architectural development to implement a functional structural plant model. 104 one-year-old shoots of own-rooted hazelnut trees were selected and analyzed 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 (1) blind nodes located in the proximal part (2) sylleptic shoots and mixed buds in the median part (3) 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 to burst 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 lead to the construction of hazelnut tree architecture, a first functional-structural plant model of hazelnut tree architecture was built. Further experiments will be needed and should be repeated over following years to extend this study toward the juvenility phase and tree architecture over time.

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

Modeling and simulating the growth of the branching architecture 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. Second, 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 (FSPM), 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, i.e. xylem flow, shedding rate, proprioception, gravitysense, and lightsense, most influenced tree branching and their adjustments led to the generation of different spruce, pine, oak, and poplar shapes. 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 modelling and experimental approaches to assess the feasibility of developing rice-oil palm agroforestry system.

Monoculture systems in SouthEast Asia are facing challenges due to climate change-induced extreme weather conditions, leading to significant annual production losses for rice and oil palm. To ensure the stability of these crops, innovative strategies like resilient agroforestry systems need to be explored. Converting oil palm monocultures to rice-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 (FSPM) 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 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 to 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, suggesting 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.

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.

Waning advantages of CO2 enrichment on photosynthesis and productivity due to accelerated phase transition and source-sink imbalance in sweet pepper

CO2 concentration, which can be raised by CO2 enrichment, has been considered to affect photosynthetic physiology, morphology, and yield of crops in greenhouses. However, inconsistent influence of CO2 enrichment on crop productivity has been demonstrated owing to the complicated and dynamic interaction between physiological and morphological responses of crops. Therefore, integrative analysis on the systemic responses of crops to CO2 enrichment is required for clarifying the reasons for the inconsistency. The objective of this study was to analyze the effect of CO2 enrichment on the photosynthesis and yield of sweet pepper plants according to growth phase and to investigate how photosynthetic physiology and plant morphology contribute to the CO2 enrichment effect via functional structural plant model (FSPM), an integrative plant modeling tool. CO2 was enriched to sweet pepper plants with a daily average concentration of about 900 μmol mol−1. Growth, yield, and photosynthesis were periodically measured during the cultivation. The structural and photosynthetic properties obtained through 3D-phenotyping and gas-exchange measurement were applied to the 3D-framework for FSPM. The change in the CO2 enrichment efficiency for photosynthesis and the contribution of photosynthetic and morphological acclimation to CO2 enrichment effect were calculated. The overall influence of CO2 enrichment on the plants changed by growth stage mostly mediated by photosynthate source-sink balance. In early stages, CO2 enrichment accelerated the growth phase transition and increased the fruit yield through the increase in photosynthate partitioning to fruits. In later stages, however, the efficiency of CO2 enrichment on fruit yield gradually decreased due to the restricted leaf area expansion caused by excessive photosynthate partition to fruits. The systemic influence of CO2 enrichment on the plants including photosynthetic acclimation, structural acclimation, and fruit yield could be reasonably elucidated through the method used in this study. Thus, balancing the photosynthate source-sink balance in the early stages with appropriate cultivation practices could improve the long-term productivity of sweet pepper under CO2 enrichment.

Towards a Stochastic Model to Simulate Grapevine Architecture: A Case Study on Digitized Riesling Vines Considering Effects of Elevated CO2.

Modeling plant growth, in particular with functional-structural plant models, can provide tools to study impacts of changing environments in silico. Simulation studies can be used as pilot studies for reducing the on-field experimental effort when predictive capabilities are given. Robust model calibration leads to less fragile predictions, while introducing uncertainties in predictions allows accounting for natural variability, resulting in stochastic plant growth models. In this study, stochastic model components that can be implemented into the functional-structural plant model Virtual Riesling are developed relying on Bayesian model calibration with the goal to enhance the model towards a fully stochastic model. In this first step, model development targeting phenology, in particular budburst variability, phytomer development rate and internode growth are presented in detail. Multi-objective optimization is applied to estimate a single set of cardinal temperatures, which is used in phenology and growth modeling based on a development days approach. Measurements from two seasons of grapevines grown in a vineyard with free-air carbon dioxide enrichment (FACE) are used; thus, model building and selection are coupled with an investigation as to whether including effects of elevated CO conditions to be expected in 2050 would improve the models. The results show how natural variability complicates the detection of possible treatment effects, but demonstrate that Bayesian calibration in combination with mixed models can realistically recover natural shoot growth variability in predictions. We expect these and further stochastic model extensions to result in more realistic virtual plant simulations to study effects, which are used to conduct in silico studies of canopy microclimate and its effects on grape health and quality.

Understanding the arrangement of Eucalyptus-Marandu palisade grass silvopastoral systems in Brazil

CONTEXTLimited land availability and an increasing global population are challenges faced by the agricultural sector in the coming decades. The silvopastoral system is an efficient alternative to full sun pastures that could be used to sustainably intensify animal and crop production. However, the adoption of this system is being held back by a lack of knowledge on how the trees in such a system should be arranged in order to avoid detrimental effects on pasture growth. OBJECTIVEThe aim of this study was to test different arrangements of trees in the Eucalyptus-Marandu palisade grass silvopastoral system and to evaluate its performance in different regions of Brazil. METHODSA functional-structural plant model was developed to simulate radiation interception by trees and was then coupled to the CROPGRO-Perennial Forage model to simulate the production of forage biomass. Considering solar radiation as the main resource under competition, water interactions that occur under-ground between Eucalyptus and Maranu palisade grass were not considered. The models were calibrated with experimental data collected in silvopastoral systems in Brazil. The silvopastoral systems were selected according to a combination of tree row spacing (15, 30, and 45 m), two row orientations (north-south and east-west), and single or triple rows. Simulations were replicated for different tree ages (2, 4, 6, 8, 10 and 12 years) representing different levels of shading. RESULTS AND CONCLUSIONSOur simulations show that the tree arrangement has a significant effect on the incidence of radiation on the pasture, resulting in different levels of forage production. Row spacing of 30 and 45 m is suitable for providing forage for the animals for several years. In contrast, tree arrangements with a row spacing of 15 m or triple lines demand more careful planning and management, taking into account the soil and climatic characteristics of each region. Lower declines in the forage production were observed in arrangements of 45 m, varying from 1000 and 3000 kg ha−1 after 6 years of system establishment. In this period, the annual production level was lower than 14,000 kg ha−1 for arrangments with row spacing of 15 and 30 m, being recommend pruning and thinning practices aiming to increase the radiation incidence within the system. SIGNIFICANCEThe approach used in this study can be extended to other combinations of species and environments to aid the planning and management of this kind of system.

Theoretical evidence that root penetration ability interacts with soil compaction regimes to affect nitrate capture.

Background and AimsAlthough root penetration of strong soils has been intensively studied at the scale of individual root axes, interactions between soil physical properties and soil foraging by whole plants are less clear. Here we investigate how variation in the penetration ability of distinct root classes and bulk density profiles common to real-world soils interact to affect soil foraging strategies.MethodsWe utilize the functional–structural plant model ‘OpenSimRoot’ to simulate the growth of maize (Zea mays) root systems with variable penetration ability of axial and lateral roots in soils with (1) uniform bulk density, (2) plow pans and (3) increasing bulk density with depth. We also modify the availability and leaching of nitrate to uncover reciprocal interactions between these factors and the capture of mobile resources.Key ResultsSoils with plow pans and bulk density gradients affected overall size, distribution and carbon costs of the root system. Soils with high bulk density at depth impeded rooting depth and reduced leaching of nitrate, thereby improving the coincidence of nitrogen and root length. While increasing penetration ability of either axial or lateral root classes produced root systems of comparable net length, improved penetration of axial roots increased allocation of root length in deeper soil, thereby amplifying N acquisition and shoot biomass. Although enhanced penetration ability of both root classes was associated with greater root system carbon costs, the benefit to plant fitness from improved soil exploration and resource capture offset these.ConclusionsWhile lateral roots comprise the bulk of root length, axial roots function as a scaffold determining the distribution of these laterals. In soils with high soil strength and leaching, root systems with enhanced penetration ability of axial roots have greater distribution of root length at depth, thereby improving capture of mobile resources.

Simulating Crop Root Systems Using OpenSimRoot.

Functional-structural plant models are valuable modeling tools in analyzing plant development. A functional-structural plant model combines a three-dimensional representation of plant structure with models for physiological functions in order to better understand plant development. We present a guide to simulating crop root systems with OpenSimRoot, a feature-rich, highly cited, and open-source functional-structural root architecture model. We describe in detail how to create your own input files in conjunction with some examples. The aim of this guide is to highlight the potential of computational modeling in biology and to make modeling more accessible to the plant science community.

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.

Row orientation affects the uniformity of light absorption, but hardly affects crop photosynthesis in hedgerow tomato crops

Abstract Light distribution within canopies is important for plant growth. We aimed to quantify the influence of row orientation on inter- and within-row variation of light absorption and photosynthesis in a hedgerow crop. An experiment with two row orientations of a tomato crop was conducted which was then used to calibrate a functional–structural plant model (FSPM). The FSPM was used to analyse light absorption and photosynthesis for each of the row facing directions in the double-row trellis system (e.g. north- and south-facing rows for the east–west row orientation). The measured leaf area decreased by 18 % and specific leaf area by 10 %, while fruit dry weight increased by 7 % for south-facing compared to north-facing rows, but total plant dry weight did not significantly differ. Model simulations showed a 7 % higher light absorption for the south-facing rows than north-facing rows, while net photosynthesis was surprisingly −4 % lower, due to local light saturation. When in the model leaf area was kept equal between the rows, light absorption for the south-facing rows was 19 % and net photosynthesis 8 % higher than for north-facing rows. We conclude that although south-facing rows would be expected to have a higher photosynthesis than north-facing rows, plants can adapt their morphology such that differences in light absorption and photosynthesis between north- and south-facing rows are minimal. Rows oriented north–south were more uniform in light absorption and photosynthesis than east–west rows, but the overall crop light absorption and photosynthesis were minimally affected (both 3 % lower compared to east–west orientation).

Leaf removal effects on light absorption in virtual Riesling canopies (Vitis vinifera)

AbstractLeaf removal is a standard vineyard management technique to influence grape composition or to reduce disease pressure; however, the timing and intensity of leaf removal is a widely discussed issue. The interplay of different goals and effects over time does not make experimental studies any easier. To gain insight into positive and negative consequences of leaf removal on grapevine development, a first step can be to study how leaf removal affects the canopy’s light absorption using a dynamic model approach. Functional–structural plant models combine canopy architecture with physiological processes and allow analysing canopy interaction with the environment with great topological detail. The functional–structural plant model Virtual Riesling simulates Riesling vines in a vineyard set-up depending on temperature and plant management. We implemented leaf removal and applied this method in or above the bunch zone to compare the light absorption in canopies. Leaf removal in the bunch zone led to greater loss of absorbed light, but canopies of both scenarios could compensate for most of the loss during the simulation time frame. Compensation was mainly driven by lateral leaves closing the gaps induced by leaf removal and by leaves in the proximity of the leaf removal zones, re-exposed to light. Results showed similar effects as observed in in vivo studies; hence, we suggest extending these simulations to investigate other effects linked to light distribution such as berry sunburn. Simple modifications of implemented leaf removal techniques also allow for testing different application scopes and their impact on canopy light absorption.