Quantification of the effects of architectural traits on dry mass production and light interception of tomato canopy under different temperature regimes using a dynamic functional-structural plant model

Multiscale Mathematical Modeling in Systems Biology: A Framework to Boost Plant Synthetic Biology

Global food insecurity and environmental degradation highlight the urgent need for more sustainable agricultural solutions. Plant synthetic biology emerges as a promising yet risky avenue to develop such solutions. While synthetic biology offers the potential for enhanced crop traits, it also entails risks of extensive environmental damage. This review highlights the complexities and risks associated with plant synthetic biology, while presenting the potential of multiscale mathematical modeling to assess and mitigate those risks effectively. Despite its potential, applying multiscale mathematical models in plants remains underutilized. Here, we advocate for integrating technological advancements in agricultural data analysis to develop a comprehensive understanding of crops across biological scales. By reviewing common modeling approaches and methodologies applicable to plants, the paper establishes a foundation for creating and utilizing integrated multiscale mathematical models. Through modeling techniques such as parameter estimation, bifurcation analysis, and sensitivity analysis, researchers can identify mutational targets and anticipate pleiotropic effects, thereby enhancing the safety of genetically engineered species. To demonstrate the potential of this approach, ongoing efforts are highlighted to develop an integrated multiscale mathematical model for maize (Zea mays L.), engineered through synthetic biology to enhance resilience against Striga (Striga spp.) and drought.

Synthetic data at scale: a development model to efficiently leverage machine learning in agriculture

The rise of artificial intelligence (AI) and in particular modern machine learning (ML) algorithms during the last decade has been met with great interest in the agricultural industry. While undisputedly powerful, their main drawback remains the need for sufficient and diverse training data. The collection of real datasets and their annotation are the main cost drivers of ML developments, and while promising results on synthetically generated training data have been shown, their generation is not without difficulties on their own. In this paper, we present a development model for the iterative, cost-efficient generation of synthetic training data. Its application is demonstrated by developing a low-cost early disease detector for tomato plants (Solanum lycopersicum) using synthetic training data. A neural classifier is trained by exclusively using synthetic images, whose generation process is iteratively refined to obtain optimal performance. In contrast to other approaches that rely on a human assessment of similarity between real and synthetic data, we instead introduce a structured, quantitative approach. Our evaluation shows superior generalization results when compared to using non-task-specific real training data and a higher cost efficiency of development compared to traditional synthetic training data. We believe that our approach will help to reduce the cost of synthetic data generation in future applications.

Branch architecture in relation to canopy positions in three Mediterranean oaks

Branch architecture is a key determinant of plant performance owing to its role in a light interception by photosynthetic tissues. However, under stressed conditions, excess light may be harmful to the photosynthetic apparatus, and plants often present structural mechanisms to avoid photoinhibition. Three-dimensional models were constructed of the aerial parts in different locations within the crown of three co-occurring tree species (Quercus ilex, Q. suber and Q. faginea) growing in a Mediterranean environment. We hypothesized that the species with the shorter leaf life span would exhibit higher leaf display efficiency (silhouette to total leaf area, STAR), maximizing light interception and photosynthesis in the short term. In addition, more exposed positions within a canopy should develop more structural avoidance mechanisms to minimize excessive radiation. Significant differences were detected in architectural traits at both the intra- and interspecific level. Architectural traits promoting greater self-shading were more frequent in the species with longer leaf longevity and in the canopy locations experiencing higher temperatures at the times of maximum sunlight. However, these trends were in part counteracted by the changes in individual leaf area, which tended to be larger in the species with shorter leaf longevity and in the less exposed canopy locations. We conclude that the variation in architectural traits occurs mainly as a means to avoid the excessive self-shading of branches with the largest leaf size.

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).

A digital sensor to measure real-time leaf movements and detect abiotic stress in plants

Plant and plant organ movements are the result of a complex integration of endogenous growth and developmental responses, partially controlled by the circadian clock, and external environmental cues. Monitoring of plant motion is typically done by image-based phenotyping techniques with the aid of computer vision algorithms. Here we present a method to measure leaf movements using a digital inertial measurement unit (IMU) sensor. The lightweight sensor is easily attachable to a leaf or plant organ and records angular traits in real-time for two dimensions (pitch and roll) with high resolution (measured sensor oscillations of 0.36 ± 0.53° for pitch and 0.50 ± 0.65° for roll). We were able to record simple movements such as petiole bending, as well as complex lamina motions, in several crops, ranging from tomato to banana. We also assessed growth responses in terms of lettuce rosette expansion and maize seedling stem movements. The IMU sensors are capable of detecting small changes of nutations (i.e. bending movements) in leaves of different ages and in different plant species. In addition, the sensor system can also monitor stress-induced leaf movements. We observed that unfavorable environmental conditions evoke certain leaf movements, such as drastic epinastic responses, as well as subtle fading of the amplitude of nutations. In summary, the presented digital sensor system enables continuous detection of a variety of leaf motions with high precision, and is a low-cost tool in the field of plant phenotyping, with potential applications in early stress detection.

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.

Seasonal Efficiency of Supplemental LED Lighting on Growth and Photomorphogenesis of Sweet Basil

For decisions on supplemental lighting a quantitative knowledge of the plants' responses to light under varying conditions is fundamental. In this study, we developed light dose-response curves of growth and morphological traits for Ocimum basilicum L. and examined the effects of light color (blue, red, and white plus far-red) and natural environment (season) on these curves. Four greenhouse experiments were conducted throughout the year to determine the efficiencies of the light regimes on growth and their effects on plant morphology. A special aspect was the photosynthetic efficiency of far-red light. Linear and monomolecular relationships were found for the relationships between plant traits and supplemental light dose. Traits related to biomass productivity increased linearly with light dose whereas some morphological characters showed a saturation behavior. Red light and white plus far-red light were more efficient in plant dry weight production than blue light, and the plants adapted differently to the light qualities: higher biomass under red light was related to a plant architecture more favorable for light capture, i.e., taller plants and bigger leaves. White plus far-red light, on the other hand, increased leaf mass per area (LMA) and light use efficiency (LUE). Blue light resulted in lowest plant light interception and LUE. Considering photosynthetic effects of near-infrared light (PPFD800, 400-800 nm) instead of photosynthetic photon flux density (PPFD700, 400-700 nm) led to strongly reduced efficiencies. Traits related to photosynthesis such as dry weight, LMA and LUE were particularly affected by PPFD800. There were no interactions between the efficiencies of the different light colors and the seasons. Efficiencies of all light regimes were significantly lower during summer compared to spring and winter. Higher dry weight production during summer compared to winter and spring were a consequence of increased light interception rather than changes in LUE. The observed differences in seasonal efficiencies were directly linked to the amount of natural light present as indicated by changes in the ratio of supplemental to natural light.

Quantifying the importance of a realistic tomato (Solanum lycopersicum) leaflet shape for 3-D light modelling

BACKGROUND AND AIMS

Leaflet shapes of tomato plants (Solanum lycopersicum) have been reduced to simple geometric shapes in previous functional-structural plant models (FSPMs) in order to facilitate measurements and reduce the time required to reconstruct the plant virtually. The level of error that such simplifications introduce remains unaddressed. This study therefore aims to quantify the modelling error associated with simplifying leaflet shapes.

METHODS

Realistic shapes were implemented in a static tomato FSPM based on leaflet scans, and simulation results were compared to simple geometric shapes used in previous tomato FSPMs in terms of light absorption and gross photosynthesis, for both a single plant and a glasshouse scenario.

KEY RESULTS

The effect of simplifying leaflet shapes in FSPMs leads to small but significant differences in light absorption, alterations of canopy light conditions and differences in photosynthesis. The magnitude of these differences depends on both the type of leaflet shape simplification used and the canopy shape and density. Incorporation of realistic shapes requires a small increase in initial measurement and modelling work to establish a shape database and comes at the cost of a slight increase in computation time.

CONCLUSIONS

Our findings indicate that the error associated with leaflet shape simplification is small, but often unpredictable, and is affected by plant structure but also lamp placement, which is often a primary optimization goal of these static models. Assessment of the cost-benefit of realistic shape inclusion shows relatively little drawbacks for a decrease in model uncertainty.

Quantification of light interception within image-based 3-D reconstruction of sole and intercropped canopies over the entire growth season

BACKGROUND AND AIMS

Light interception is closely related to canopy architecture. Few studies based on multi-view photography have been conducted in a field environment, particularly studies that link 3-D plant architecture with a radiation model to quantify the dynamic canopy light interception. In this study, we combined realistic 3-D plant architecture with a radiation model to quantify and evaluate the effect of differences in planting patterns and row orientations on canopy light interception.

METHODS

The 3-D architectures of maize and soybean plants were reconstructed for sole crops and intercrops based on multi-view images obtained at five growth dates in the field. We evaluated the accuracy of the calculated leaf length, maximum leaf width, plant height and leaf area according to the measured data. The light distribution within the 3-D plant canopy was calculated with a 3-D radiation model. Finally, we evaluated canopy light interception in different row orientations.

KEY RESULTS

There was good agreement between the measured and calculated phenotypic traits, with an R2 >0.97. The light distribution was more uniform for intercropped maize and more concentrated for sole maize. At the maize silking stage, 85 % of radiation was intercepted by approx. 55 % of the upper canopy region for maize and by approx. 33 % of the upper canopy region for soybean. There was no significant difference in daily light interception between the different row orientations for the entire intercropping and sole systems. However, for intercropped maize, near east-west orientations showed approx. 19 % higher daily light interception than near south-north orientations. For intercropped soybean, daily light interception showed the opposite trend. It was approx. 49 % higher for near south-north orientations than for near east-west orientations.

CONCLUSIONS

The accurate reconstruction of 3-D plants grown in the field based on multi-view images provides the possibility for high-throughput 3-D phenotyping in the field and allows a better understanding of the relationship between canopy architecture and the light environment.

Crop Phenomics: Current Status and Perspectives

Reliable, automatic, multifunctional, and high-throughput phenotypic technologies are increasingly considered important tools for rapid advancement of genetic gain in breeding programs. With the rapid development in high-throughput phenotyping technologies, research in this area is entering a new era called 'phenomics.' The crop phenotyping community not only needs to build a multi-domain, multi-level, and multi-scale crop phenotyping big database, but also to research technical systems for phenotypic traits identification and develop bioinformatics technologies for information extraction from the overwhelming amounts of omics data. Here, we provide an overview of crop phenomics research, focusing on two parts, from phenotypic data collection through various sensors to phenomics analysis. Finally, we discussed the challenges and prospective of crop phenomics in order to provide suggestions to develop new methods of mining genes associated with important agronomic traits, and propose new intelligent solutions for precision breeding.

Genetic and environmental dissection of biomass accumulation in multi-genotype maize canopies

Multi-genotype canopies are frequent in phenotyping experiments and are of increasing interest in agriculture. Radiation interception efficiency (RIE) and radiation use efficiency (RUE) have low heritabilities in such canopies. We propose a revised Monteith equation that identifies environmental and genetic components of RIE and RUE. An environmental term, a component of RIE, characterizes the effect of the presence or absence of neighbours on light interception. The ability of a given plant to compete with its neighbours is then identified, which accounts for the genetic variability of RIE of plants having similar leaf areas. This method was used in three experiments in a phenotyping platform with 765 plants of 255 maize hybrids. As expected, the heritability of the environmental term was near zero, whereas that of the competitiveness term increased with phenological stage, resulting in the identification of quantitative trait loci. In the same way, RUE was dissected as an effect of intercepted light and a genetic term. This approach was used for predicting the behaviour of individual genotypes in virtual multi-genotype canopies. A large effect of competitiveness was observed in multi-genotype but not in single-genotype canopies, resulting in a bias for genotype comparisons in breeding fields.

Understanding and optimizing species mixtures using functional-structural plant modelling

Plant species mixtures improve productivity over monocultures by exploiting species complementarities for resource capture in time and space. Complementarity results in part from competition avoidance responses that maximize resource capture and growth of individual plants. Individual organs accommodate to local resource levels, e.g. with regard to nitrogen content and photosynthetic capacity or by size (e.g. shade avoidance). As a result, the resource acquisition in time and space is improved and performance of the community as a whole is increased. Modelling is needed to unravel the primary drivers and subsequent dynamics of complementary growth responses in mixtures. Here, we advocate using functional-structural plant (FSP) modelling to analyse the functioning of plant mixtures. In FSP modelling, crop performance is a result of the behaviour of the individual plants interacting through competitive and complementary resource acquisition. FSP models can integrate the interactions between structural and physiological plant responses to the local resource availability and strength of competition, which drive resource capture and growth of individuals in species mixtures. FSP models have the potential to accelerate mixed-species plant research, and thus support the development of knowledge that is needed to promote the use of mixtures towards sustainably increasing crop yields at acceptable input levels.

Leaf Eh and pH: A Novel Indicator of Plant Stress. Spatial, Temporal and Genotypic Variability in Rice (Oryza sativa L.)

A wealth of knowledge has been published in the last decade on redox regulations in plants. However, these works remained largely at cellular and organelle levels. Simple indicators of oxidative stress at the plant level are still missing. We developed a method for direct measurement of leaf Eh and pH, which revealed spatial, temporal, and genotypic variations in rice. Eh (redox potential) and Eh@pH7 (redox potential corrected to pH 7) of the last fully expanded leaf decreased after sunrise. Leaf Eh was high in the youngest leaf and in the oldest leaves, and minimum for the last fully expanded leaf. Leaf pH decreased from youngest to oldest leaves. The same gradients in Eh-pH were measured for various varieties, hydric conditions, and cropping seasons. Rice varieties differed in Eh, pH, and/or Eh@pH7. Leaf Eh increases and leaf pH decreases with plant age. These patterns and dynamics in leaf Eh-pH are in accordance with the pattern and dynamics of disease infections. Leaf Eh-pH can bring new insight on redox processes at plant level and is proposed as a novel indicator of plant stress/health. It could be used by agronomists, breeders, and pathologists to accelerate the development of crop cultivation methods leading to agroecological crop protection.

High light aggravates functional limitations of cucumber canopy photosynthesis under salinity

Background and Aims

Most crop species are glycophytes, and salinity stress is one of the most severe abiotic stresses reducing crop yields worldwide. Salinity affects plant architecture and physiological functions by different mechanisms, which vary largely between crop species and determine the susceptibility or tolerance of a crop species to salinity.

Methods

Experimental data from greenhouse cucumber (Cucumis sativus), a salt-sensitive species, grown under three salinity levels were interpreted by combining a functional-structural plant model and quantitative limitation analysis of photosynthesis. This approach allowed the quantitative dissection of canopy photosynthetic limitations into architectural and functional limitations. Functional limitations were further dissected into stomatal (Ls), mesophyll (Lm) and biochemical (Lb).

Key Results

Architectural limitations increased rapidly after the start of the salinity treatment and became stronger than the sum of functional limitations (Ls + Lm + Lb) under high salinity. Stomatal limitations resulted from ionic effects and were much stronger than biochemical limitations, indicating that canopy photosynthesis was more limited by the effects of leaf sodium on stomatal regulation than on photosynthetic enzymes. Sensitivity analyses suggested that the relative importance of salinity effects on architectural and functional limitations depends on light conditions, with high light aggravating functional limitations through salinity effects on stomatal limitations.

Conclusions

Salinity tolerance of cucumber is more likely to be improved by traits related to leaf growth and stomatal regulation than by traits related to tissue tolerance to ion toxicity, especially under high light conditions.

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

Background and Aims

Enhancement of light harvesting in annual crops has successfully led to yield increases since the green revolution. Such an improvement has mainly been achieved by selecting plants with optimal canopy architecture for specific agronomic practices. For perennials such as oil palm, breeding programmes were focused more on fruit yield, but now aim at exploring more complex traits. The aim of the present study is to investigate potential improvements in light interception and carbon assimilation in the study case of oil palm, by manipulating leaf traits and proposing architectural ideotypes.

Methods

Sensitivity analyses (Morris method and metamodel) were performed on a functional-structural plant model recently developed for oil palm which takes into account genetic variability, in order to virtually assess the impact of plant architecture on light interception efficiency and potential carbon acquisition.

Key Results

The most sensitive parameters found over plant development were those related to leaf area (rachis length, number of leaflets, leaflet morphology), although fine attributes related to leaf geometry showed increasing influence when the canopy became closed. In adult stands, optimized carbon assimilation was estimated on plants with a leaf area index between 3.2 and 5.5 m2 m-2 (corresponding to usual agronomic conditions), with erect leaves, short rachis and petiole, and high number of leaflets on the rachis. Four architectural ideotypes for carbon assimilation are proposed based on specific combinations of organ dimensions and arrangement that limit mutual shading and optimize light distribution within the plant crown.

Conclusions

A rapid set-up of leaf area is critical at young age to optimize light interception and subsequently carbon acquisition. At the adult stage, optimization of carbon assimilation could be achieved through specific combinations of architectural traits. The proposition of multiple morphotypes with comparable level of carbon assimilation opens the way to further investigate ideotypes carrying an optimal trade-off between carbon assimilation, plant transpiration and biomass partitioning.

Connecting Biochemical Photosynthesis Models with Crop Models to Support Crop Improvement

The next advance in field crop productivity will likely need to come from improving crop use efficiency of resources (e.g., light, water, and nitrogen), aspects of which are closely linked with overall crop photosynthetic efficiency. Progress in genetic manipulation of photosynthesis is confounded by uncertainties of consequences at crop level because of difficulties connecting across scales. Crop growth and development simulation models that integrate across biological levels of organization and use a gene-to-phenotype modeling approach may present a way forward. There has been a long history of development of crop models capable of simulating dynamics of crop physiological attributes. Many crop models incorporate canopy photosynthesis (source) as a key driver for crop growth, while others derive crop growth from the balance between source- and sink-limitations. Modeling leaf photosynthesis has progressed from empirical modeling via light response curves to a more mechanistic basis, having clearer links to the underlying biochemical processes of photosynthesis. Cross-scale modeling that connects models at the biochemical and crop levels and utilizes developments in upscaling leaf-level models to canopy models has the potential to bridge the gap between photosynthetic manipulation at the biochemical level and its consequences on crop productivity. Here we review approaches to this emerging cross-scale modeling framework and reinforce the need for connections across levels of modeling. Further, we propose strategies for connecting biochemical models of photosynthesis into the cross-scale modeling framework to support crop improvement through photosynthetic manipulation.

Quantifying the effect of crop spatial arrangement on weed suppression using functional-structural plant modelling

Suppression of weed growth in a crop canopy can be enhanced by improving crop competitiveness. One way to achieve this is by modifying the crop planting pattern. In this study, we addressed the question to what extent a uniform planting pattern increases the ability of a crop to compete with weed plants for light compared to a random and a row planting pattern, and how this ability relates to crop and weed plant density as well as the relative time of emergence of the weed. To this end, we adopted the functional-structural plant modelling approach which allowed us to explicitly include the 3D spatial configuration of the crop-weed canopy and to simulate intra- and interspecific competition between individual plants for light. Based on results of simulated leaf area development, canopy photosynthesis and biomass growth of the crop, we conclude that differences between planting pattern were small, particularly if compared to the effects of relative time of emergence of the weed, weed density and crop density. Nevertheless, analysis of simulated weed biomass demonstrated that a uniform planting of the crop improved the weed-suppression ability of the crop canopy. Differences in weed suppressiveness between planting patterns were largest with weed emergence before crop emergence, when the suppressive effect of the crop was only marginal. With simultaneous emergence a uniform planting pattern was 8 and 15 % more competitive than a row and a random planting pattern, respectively. When weed emergence occurred after crop emergence, differences between crop planting patterns further decreased as crop canopy closure was reached early on regardless of planting pattern. We furthermore conclude that our modelling approach provides promising avenues to further explore crop-weed interactions and aid in the design of crop management strategies that aim at improving crop competitiveness with weeds.

Sweet Pepper (Capsicum annuum L.) Canopy Photosynthesis Modeling Using 3D Plant Architecture and Light Ray-Tracing

Canopy photosynthesis has typically been estimated using mathematical models that have the following assumptions: the light interception inside the canopy exponentially declines with the canopy depth, and the photosynthetic capacity is affected by light interception as a result of acclimation. However, in actual situations, light interception in the canopy is quite heterogenous depending on environmental factors such as the location, microclimate, leaf area index, and canopy architecture. It is important to apply these factors in an analysis. The objective of the current study is to estimate the canopy photosynthesis of paprika (Capsicum annuum L.) with an analysis of by simulating the intercepted irradiation of the canopy using a 3D ray-tracing and photosynthetic capacity in each layer. By inputting the structural data of an actual plant, the 3D architecture of paprika was reconstructed using graphic software (Houdini FX, FX, Canada). The light curves and A/C i curve of each layer were measured to parameterize the Farquhar, von Caemmerer, and Berry (FvCB) model. The difference in photosynthetic capacity within the canopy was observed. With the intercepted irradiation data and photosynthetic parameters of each layer, the values of an entire plant's photosynthesis rate were estimated by integrating the calculated photosynthesis rate at each layer. The estimated photosynthesis rate of an entire plant showed good agreement with the measured plant using a closed chamber for validation. From the results, this method was considered as a reliable tool to predict canopy photosynthesis using light interception, and can be extended to analyze the canopy photosynthesis in actual greenhouse conditions.

Predicting Plant Performance Under Simultaneously Changing Environmental Conditions-The Interplay Between Temperature, Light, and Internode Growth

Plant performance is significantly influenced by prevailing light and temperature conditions during plant growth and development. For plants exposed to natural fluctuations in abiotic environmental conditions it is however laborious and cumbersome to experimentally assign any contribution of individual environmental factors to plant responses. This study aimed at analyzing the interplay between light, temperature and internode growth based on model approaches. We extended the light-sensitive virtual plant model L-Cucumber by implementing a common Arrhenius function for appearance rates, growth rates, and growth durations. For two greenhouse experiments, the temperature-sensitive model approach resulted in a precise prediction of cucumber mean internode lengths and number of internodes, as well as in accurately predicted patterns of individual internode lengths along the main stem. In addition, a system's analysis revealed that environmental data averaged over the experimental period were not necessarily related to internode performance. Finally, the need for a species-specific parameterization of the temperature response function and related aspects in modeling temperature effects on plant development and growth is discussed.

High temperature and vapor pressure deficit aggravate architectural effects but ameliorate non-architectural effects of salinity on dry mass production of tomato

Tomato (Solanum lycopersicum L.) is an important vegetable crop and often cultivated in regions exposed to salinity and high temperatures (HT) which change plant architecture, decrease canopy light interception and disturb physiological functions. However, the long-term effects of salinity and HT combination (S+HT) on plant growth are still unclear. A dynamic functional-structural plant model (FSPM) of tomato was parameterized and evaluated for different levels of S+HT combinations. The evaluated model was used to quantify the contributions of morphological changes (architectural effects) and physiological disturbances (non-architectural effects) on the reduction of shoot dry mass under S+HT. The model predicted architectural variables with high accuracy (>85%), which ensured the reliability of the model analyses. HT enhanced architectural effects but reduced non-architectural effects of salinity on dry mass production. The stronger architectural effects of salinity under HT could not be counterbalanced by the smaller non-architectural effects. Therefore, long-term influences of HT on shoot dry mass under salinity were negative at the whole plant level. Our model analysis highlights the importance of plant architecture at canopy level in studying the plant responses to the environments and shows the merits of dynamic FSPMs as heuristic tools.