Pub Date : 2022-01-18DOI: 10.1093/insilicoplants/diab038
T. De Swaef, Olivier Pieters, S. Appeltans, I. Borra‐Serrano, Willem Coudron, V. Couvreur, S. Garré, P. Lootens, B. Nicolaï, L. Pols, Clément Saint Cast, J. Šalagovič, Maxime Van Haeverbeke, Michiel Stock, F. Wyffels
Water potential explains water transport in the Soil-Plant-Atmosphere Continuum (SPAC), and is gaining interest as connecting variable between ‘pedo-, bio- and atmosphere’. It is primarily used to simulate hydraulics in the SPAC, and is thus essential for studying drought effects. Recent implementations of hydraulics in large-scale Terrestrial Biosphere Models (TBMs) improved their performance under water-limited conditions, while hydraulic features of recent detailed Functional-Structural Plant Models (FSPMs) open new possibilities for dissecting complex traits for drought tolerance. These developments in models across scales deserve a critical appraisal to evaluate its potential for wider use in FSPMs, but also in crop systems models (CSMs), where hydraulics are currently still absent. After refreshing the physical basis, we first address models where water potential is primarily used for describing water transport along the transpiration pathway from the soil to the leaves, through the roots, the xylem and the leaf mesophyll. Then, we highlight models for three ecophysiological processes, which have well-recognised links to water potential: phloem transport, stomatal conductance and organ growth. We identify water potential as the bridge between soil, root and shoot models, as the physiological variable integrating below- and above-ground abiotic drivers, but also as the link between water status and growth. Models making these connections enable identifying crucial traits for ecosystem resilience to drought and for breeding towards improved drought tolerance in crops. Including hydraulics often increases model complexity, and thus requires experimental data on soil and plant hydraulics. Nevertheless, modelling hydraulics is insightful at different scales (FSPMs, CSMs and TBMs).
{"title":"On the pivotal role of water potential to model plant physiological processes","authors":"T. De Swaef, Olivier Pieters, S. Appeltans, I. Borra‐Serrano, Willem Coudron, V. Couvreur, S. Garré, P. Lootens, B. Nicolaï, L. Pols, Clément Saint Cast, J. Šalagovič, Maxime Van Haeverbeke, Michiel Stock, F. Wyffels","doi":"10.1093/insilicoplants/diab038","DOIUrl":"https://doi.org/10.1093/insilicoplants/diab038","url":null,"abstract":"\u0000 Water potential explains water transport in the Soil-Plant-Atmosphere Continuum (SPAC), and is gaining interest as connecting variable between ‘pedo-, bio- and atmosphere’. It is primarily used to simulate hydraulics in the SPAC, and is thus essential for studying drought effects. Recent implementations of hydraulics in large-scale Terrestrial Biosphere Models (TBMs) improved their performance under water-limited conditions, while hydraulic features of recent detailed Functional-Structural Plant Models (FSPMs) open new possibilities for dissecting complex traits for drought tolerance. These developments in models across scales deserve a critical appraisal to evaluate its potential for wider use in FSPMs, but also in crop systems models (CSMs), where hydraulics are currently still absent. After refreshing the physical basis, we first address models where water potential is primarily used for describing water transport along the transpiration pathway from the soil to the leaves, through the roots, the xylem and the leaf mesophyll. Then, we highlight models for three ecophysiological processes, which have well-recognised links to water potential: phloem transport, stomatal conductance and organ growth. We identify water potential as the bridge between soil, root and shoot models, as the physiological variable integrating below- and above-ground abiotic drivers, but also as the link between water status and growth. Models making these connections enable identifying crucial traits for ecosystem resilience to drought and for breeding towards improved drought tolerance in crops. Including hydraulics often increases model complexity, and thus requires experimental data on soil and plant hydraulics. Nevertheless, modelling hydraulics is insightful at different scales (FSPMs, CSMs and TBMs).","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2022-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45835485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-14DOI: 10.1093/insilicoplants/diab040
Jan Vaillant, I. Grechi, F. Normand, F. Boudon
Functional-Structural Plant Models (FSPMs) are powerful tools to explore the complex interplays between plant growth, underlying physiological processes and the environment. Various modeling platforms dedicated to FSPMs have been developed with limited support for collaborative and distributed model design, reproducibility and dissemination. With the objective to alleviate these problems, we used the Jupyter project, an open-source computational notebook ecosystem, to create virtual modeling environments for plant models. These environments combined Python scientific modules, L-systems formalism, multidimensional arrays and 3D plant architecture visualization in Jupyter notebooks. As a case study, we present an application of such an environment by reimplementing V-Mango, a model of mango tree development and fruit production built on interrelated processes of architectural development and fruit growth that are affected by temporal, structural and environmental factors. This new implementation increased model modularity, with modules representing single processes and the workflows between them. The model modularity allowed us to run simulations for a subset of processes only, on simulated or empirical architectures. The exploration of carbohydrate source-sink relationships on a measured mango branch architecture illustrates this possibility. We also proposed solutions for visualization, distant distributed computation and parallel simulations of several independent mango trees during a growing season. The development of models on locations far from computational resources makes collaborative and distributed model design and implementation possible, and demonstrates the usefulness and efficiency of a customizable virtual modeling environment.
{"title":"Towards virtual modeling environments for functional structural plant models based on Jupyter notebooks: Application to the modeling of mango tree growth and development","authors":"Jan Vaillant, I. Grechi, F. Normand, F. Boudon","doi":"10.1093/insilicoplants/diab040","DOIUrl":"https://doi.org/10.1093/insilicoplants/diab040","url":null,"abstract":"\u0000 Functional-Structural Plant Models (FSPMs) are powerful tools to explore the complex interplays between plant growth, underlying physiological processes and the environment. Various modeling platforms dedicated to FSPMs have been developed with limited support for collaborative and distributed model design, reproducibility and dissemination. With the objective to alleviate these problems, we used the Jupyter project, an open-source computational notebook ecosystem, to create virtual modeling environments for plant models. These environments combined Python scientific modules, L-systems formalism, multidimensional arrays and 3D plant architecture visualization in Jupyter notebooks. As a case study, we present an application of such an environment by reimplementing V-Mango, a model of mango tree development and fruit production built on interrelated processes of architectural development and fruit growth that are affected by temporal, structural and environmental factors. This new implementation increased model modularity, with modules representing single processes and the workflows between them. The model modularity allowed us to run simulations for a subset of processes only, on simulated or empirical architectures. The exploration of carbohydrate source-sink relationships on a measured mango branch architecture illustrates this possibility. We also proposed solutions for visualization, distant distributed computation and parallel simulations of several independent mango trees during a growing season. The development of models on locations far from computational resources makes collaborative and distributed model design and implementation possible, and demonstrates the usefulness and efficiency of a customizable virtual modeling environment.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47657156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1093/insilicoplants/diab039
M. Cieslak, N. Khan, Pascal Ferraro, R. Soolanayakanahally, S. J. Robinson, I. Parkin, Ian McQuillan, P. Prusinkiewicz
Artificial neural networks that recognize and quantify relevant aspects of crop plants show great promise in image-based phenomics, but their training requires many annotated images. The acquisition of these images is comparatively simple, but their manual annotation is time-consuming. Realistic plant models, which can be annotated automatically, thus present an attractive alternative to real plant images for training purposes. Here we show how such models can be constructed and calibrated quickly, using maize and canola as case studies.
{"title":"L-system models for image-based phenomics: case studies of maize and canola","authors":"M. Cieslak, N. Khan, Pascal Ferraro, R. Soolanayakanahally, S. J. Robinson, I. Parkin, Ian McQuillan, P. Prusinkiewicz","doi":"10.1093/insilicoplants/diab039","DOIUrl":"https://doi.org/10.1093/insilicoplants/diab039","url":null,"abstract":"\u0000 Artificial neural networks that recognize and quantify relevant aspects of crop plants show great promise in image-based phenomics, but their training requires many annotated images. The acquisition of these images is comparatively simple, but their manual annotation is time-consuming. Realistic plant models, which can be annotated automatically, thus present an attractive alternative to real plant images for training purposes. Here we show how such models can be constructed and calibrated quickly, using maize and canola as case studies.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42666334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-05DOI: 10.1093/insilicoplants/diab032
Megan L. Matthews, Amy Marshall-Colón, J. McGrath, E. Lochocki, S. Long
Soybean is a major global source of protein and oil. Understanding how soybean crops will respond to the changing climate and identifying the responsible molecular machinery, are important for facilitating bioengineering and breeding to meet the growing global food demand. The BioCro family of crop models are semi-mechanistic models scaling from biochemistry to whole crop growth and yield. BioCro was previously parameterized and proved effective for the biomass crops miscanthus, coppice willow, and Brazilian sugarcane. Here, we present Soybean-BioCro, the first food crop to be parameterized for BioCro. Two new module sets were incorporated into the BioCro framework describing the rate of soybean development and carbon partitioning and senescence. The model was parameterized using field measurements collected over the 2002 and 2005 growing seasons at the open air [CO2] enrichment (SoyFACE) facility under ambient atmospheric [CO2]. We demonstrate that Soybean-BioCro successfully predicted how elevated [CO2] impacted field-grown soybean growth without a need for re-parameterization, by predicting soybean growth under elevated atmospheric [CO2] during the 2002 and 2005 growing seasons, and under both ambient and elevated [CO2] for the 2004 and 2006 growing seasons. Soybean-BioCro provides a useful foundational framework for incorporating additional primary and secondary metabolic processes or gene regulatory mechanisms that can further aid our understanding of how future soybean growth will be impacted by climate change.
{"title":"Soybean-BioCro: A semi-mechanistic model of soybean growth","authors":"Megan L. Matthews, Amy Marshall-Colón, J. McGrath, E. Lochocki, S. Long","doi":"10.1093/insilicoplants/diab032","DOIUrl":"https://doi.org/10.1093/insilicoplants/diab032","url":null,"abstract":"\u0000 Soybean is a major global source of protein and oil. Understanding how soybean crops will respond to the changing climate and identifying the responsible molecular machinery, are important for facilitating bioengineering and breeding to meet the growing global food demand. The BioCro family of crop models are semi-mechanistic models scaling from biochemistry to whole crop growth and yield. BioCro was previously parameterized and proved effective for the biomass crops miscanthus, coppice willow, and Brazilian sugarcane. Here, we present Soybean-BioCro, the first food crop to be parameterized for BioCro. Two new module sets were incorporated into the BioCro framework describing the rate of soybean development and carbon partitioning and senescence. The model was parameterized using field measurements collected over the 2002 and 2005 growing seasons at the open air [CO2] enrichment (SoyFACE) facility under ambient atmospheric [CO2]. We demonstrate that Soybean-BioCro successfully predicted how elevated [CO2] impacted field-grown soybean growth without a need for re-parameterization, by predicting soybean growth under elevated atmospheric [CO2] during the 2002 and 2005 growing seasons, and under both ambient and elevated [CO2] for the 2004 and 2006 growing seasons. Soybean-BioCro provides a useful foundational framework for incorporating additional primary and secondary metabolic processes or gene regulatory mechanisms that can further aid our understanding of how future soybean growth will be impacted by climate change.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45876118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-04DOI: 10.1093/insilicoplants/diab037
N. Gaudio, G. Louarn, R. Barillot, Clémentine Meunier, Rémi Vezy, M. Launay
Promoting plant diversity through crop mixtures is a mainstay of the agroecological transition. Modelling this transition requires considering both plant-plant interactions and plants’ interactions with abiotic and biotic environments. Modelling crop mixtures enables designing ways to use plant diversity to provide ecosystem services, as long as they include crop management as input. A single modelling approach is not sufficient, however, and complementarities between models may be critical to consider the multiple processes and system components involved at different and relevant spatial and temporal scales. In this article, we present different modelling solutions implemented in a variety of examples to upscale models from local interactions to ecosystem services. We highlight that modelling solutions (i.e. coupling, metamodelling, inverse or hybrid modelling) are built according to modelling objectives (e.g. understand the relative contributions of primary ecological processes to crop mixtures, quantify impacts of the environment and agricultural practices, assess the resulting ecosystem services) rather than to the scales of integration. Many outcomes of multispecies agroecosystems remain to be explored, both experimentally and through the heuristic use of modelling. Combining models to address plant diversity and predict ecosystem services at different scales remains rare but is critical to support the spatial and temporal prediction of the many systems that could be designed.
{"title":"Exploring complementarities between modelling approaches that enable upscaling from plant community functioning to ecosystem services as a way to support agroecological transition","authors":"N. Gaudio, G. Louarn, R. Barillot, Clémentine Meunier, Rémi Vezy, M. Launay","doi":"10.1093/insilicoplants/diab037","DOIUrl":"https://doi.org/10.1093/insilicoplants/diab037","url":null,"abstract":"\u0000 Promoting plant diversity through crop mixtures is a mainstay of the agroecological transition. Modelling this transition requires considering both plant-plant interactions and plants’ interactions with abiotic and biotic environments. Modelling crop mixtures enables designing ways to use plant diversity to provide ecosystem services, as long as they include crop management as input. A single modelling approach is not sufficient, however, and complementarities between models may be critical to consider the multiple processes and system components involved at different and relevant spatial and temporal scales. In this article, we present different modelling solutions implemented in a variety of examples to upscale models from local interactions to ecosystem services. We highlight that modelling solutions (i.e. coupling, metamodelling, inverse or hybrid modelling) are built according to modelling objectives (e.g. understand the relative contributions of primary ecological processes to crop mixtures, quantify impacts of the environment and agricultural practices, assess the resulting ecosystem services) rather than to the scales of integration. Many outcomes of multispecies agroecosystems remain to be explored, both experimentally and through the heuristic use of modelling. Combining models to address plant diversity and predict ecosystem services at different scales remains rare but is critical to support the spatial and temporal prediction of the many systems that could be designed.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42968471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-29DOI: 10.1093/insilicoplants/diab036
A. Masson, Y. Caraglio, E. Nicolini, P. Borianne, J. Barczi
Tree structural and biomass growth studies mainly focus on the shoot compartment. Tree roots usually have to be taken apart due to the difficulties involved in measuring and observing this compartment, particularly root growth. In the context of climate change, the study of tree structural plasticity has become crucial and both shoot and root systems need to be considered simultaneously as they play a joint role in adapting traits to climate change (water availability for roots and light or carbon availability for shoots). We developed a botanically accurate whole-plant model and its simulator (RoCoCau) with a linkable external module (TOY) to represent shoot and root compartment dependencies and hence tree structural plasticity in different air and soil environments. This paper describes a new deep neural network calibration trained on simulated datasets computed from a set of more than 360 000 random TOY parameter values and random climate values. These datasets were used for training and for validation. For this purpose, we chose Voxnet, a convolutional neural network designed to classify 3D objects represented as a voxelized scene. We recommend further improvements for Voxnet inputs, outputs, and training. We were able to teach the network to predict the value of environment data well (mean error < 2%), and to predict the value of TOY parameters for plants under water stress conditions (mean error < 5% for all parameters), and for any environmental growing conditions (mean error < 20%).
{"title":"Modelling the functional dependency between root and shoot compartments to predict the impact of the environment on the architecture of the whole plant. Methodology for model fitting on simulated data using Deep Learning techniques","authors":"A. Masson, Y. Caraglio, E. Nicolini, P. Borianne, J. Barczi","doi":"10.1093/insilicoplants/diab036","DOIUrl":"https://doi.org/10.1093/insilicoplants/diab036","url":null,"abstract":"\u0000 Tree structural and biomass growth studies mainly focus on the shoot compartment. Tree roots usually have to be taken apart due to the difficulties involved in measuring and observing this compartment, particularly root growth. In the context of climate change, the study of tree structural plasticity has become crucial and both shoot and root systems need to be considered simultaneously as they play a joint role in adapting traits to climate change (water availability for roots and light or carbon availability for shoots). We developed a botanically accurate whole-plant model and its simulator (RoCoCau) with a linkable external module (TOY) to represent shoot and root compartment dependencies and hence tree structural plasticity in different air and soil environments. This paper describes a new deep neural network calibration trained on simulated datasets computed from a set of more than 360 000 random TOY parameter values and random climate values. These datasets were used for training and for validation. For this purpose, we chose Voxnet, a convolutional neural network designed to classify 3D objects represented as a voxelized scene. We recommend further improvements for Voxnet inputs, outputs, and training. We were able to teach the network to predict the value of environment data well (mean error < 2%), and to predict the value of TOY parameters for plants under water stress conditions (mean error < 5% for all parameters), and for any environmental growing conditions (mean error < 20%).","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42422438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-05DOI: 10.1093/insilicoplants/diab034
Marion Gauthier, R. Barillot, B. Andrieu
Phenotypic plasticity - the ability of one genotype to produce different phenotypes depending on growth conditions - is a core aspect of the interactions between plants and the environment. The model CN-Wheat simulates the functioning of a grass culm and the construction of traits as properties emerging from the feedback loops between morphogenesis, the environmental factors and source–sink activities. The plant is seen as a self-regulated system where leaf growth is driven by carbon and nitrogen metabolism within each leaf and by coordination rules between successive leaves. Here, we investigated the ability of this approach to simulate realistic grass phenotypic plasticity and explored plant behaviour in a wide range of growth conditions.The growth of grass monoculms, with traits similar to a wheat stem, was simulated for highly contrasting conditions of soil nitrogen concentration, incident light and planting density. The monoculms were kept vegetative and produced ~15 mature leaves at the end of the simulations. The model simulated highly contrasting phenotypes. Overall, the simulated trends and the magnitude of responses of leaf and plant traits to growth conditions were consistent with the literature on grass species. These results demonstrate that integrating plant functioning at organ scale can simulate, as an emergent property, the phenotypic plasticity of plants in contrasting light and nitrogen conditions. Besides, simulations of the internal variables of plants gave access to plant trophic status across plant ontogeny and plant environments. In conclusion, this framework is a significant step towards better integration of the genotype-environment interactions.
{"title":"Simulating grass phenotypic plasticity as an emergent property of growth zone responses to carbon and nitrogen metabolites","authors":"Marion Gauthier, R. Barillot, B. Andrieu","doi":"10.1093/insilicoplants/diab034","DOIUrl":"https://doi.org/10.1093/insilicoplants/diab034","url":null,"abstract":"\u0000 Phenotypic plasticity - the ability of one genotype to produce different phenotypes depending on growth conditions - is a core aspect of the interactions between plants and the environment. The model CN-Wheat simulates the functioning of a grass culm and the construction of traits as properties emerging from the feedback loops between morphogenesis, the environmental factors and source–sink activities. The plant is seen as a self-regulated system where leaf growth is driven by carbon and nitrogen metabolism within each leaf and by coordination rules between successive leaves. Here, we investigated the ability of this approach to simulate realistic grass phenotypic plasticity and explored plant behaviour in a wide range of growth conditions.The growth of grass monoculms, with traits similar to a wheat stem, was simulated for highly contrasting conditions of soil nitrogen concentration, incident light and planting density. The monoculms were kept vegetative and produced ~15 mature leaves at the end of the simulations. The model simulated highly contrasting phenotypes. Overall, the simulated trends and the magnitude of responses of leaf and plant traits to growth conditions were consistent with the literature on grass species. These results demonstrate that integrating plant functioning at organ scale can simulate, as an emergent property, the phenotypic plasticity of plants in contrasting light and nitrogen conditions. Besides, simulations of the internal variables of plants gave access to plant trophic status across plant ontogeny and plant environments. In conclusion, this framework is a significant step towards better integration of the genotype-environment interactions.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2021-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46631205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-14DOI: 10.1093/insilicoplants/diab031
Yi-Chen Pao, K. Kahlen, Tsu-Wei Chen, Dirk Wiechers, H. Stützel
One-dimensional light models using the Beer-Lambert equation (BL) with the light extinction coefficient k are simple and robust tools for estimating light interception of homogeneous canopies. Functional-structural plant models (FSPMs) are powerful to capture light-plant interactions in heterogeneous canopies, but they are also more complex due to explicit descriptions of three-dimensional plant architecture and light models. For choosing an appropriate modelling approach, the trade-offs between simplicity and accuracy need to be considered when canopies with spatial heterogeneity are concerned. We compared two light modelling approaches, one following BL and another using ray tracing (RT), based on a framework of a dynamic FSPM of greenhouse cucumber. Resolutions of hourly-step (HS) and daily-step (DS) were applied to simulate light interception, leaf-level photosynthetic acclimation and plant-level dry matter production over growth periods of two to five weeks. Results showed that BL-HS was comparable to RT-HS in predicting shoot dry matter and photosynthetic parameters. The k used in the BL approach was simulated using an empirical relationship between k and leaf area index established with the assistance of RT, which showed variation up to 0.2 in k depending on canopy geometry under the same plant density. When a constant k value was used instead, a difference of 0.2 in k resulted in up to 27% loss in accuracy for shoot dry matter. These results suggested that, with the assistance of RT in k estimation, the simple approach BL-HS provided efficient estimation for long-term processes.
{"title":"How does structure matter? Comparison of canopy photosynthesis using one- and three-dimensional light models: a case study using greenhouse cucumber canopies","authors":"Yi-Chen Pao, K. Kahlen, Tsu-Wei Chen, Dirk Wiechers, H. Stützel","doi":"10.1093/insilicoplants/diab031","DOIUrl":"https://doi.org/10.1093/insilicoplants/diab031","url":null,"abstract":"\u0000 One-dimensional light models using the Beer-Lambert equation (BL) with the light extinction coefficient k are simple and robust tools for estimating light interception of homogeneous canopies. Functional-structural plant models (FSPMs) are powerful to capture light-plant interactions in heterogeneous canopies, but they are also more complex due to explicit descriptions of three-dimensional plant architecture and light models. For choosing an appropriate modelling approach, the trade-offs between simplicity and accuracy need to be considered when canopies with spatial heterogeneity are concerned. We compared two light modelling approaches, one following BL and another using ray tracing (RT), based on a framework of a dynamic FSPM of greenhouse cucumber. Resolutions of hourly-step (HS) and daily-step (DS) were applied to simulate light interception, leaf-level photosynthetic acclimation and plant-level dry matter production over growth periods of two to five weeks. Results showed that BL-HS was comparable to RT-HS in predicting shoot dry matter and photosynthetic parameters. The k used in the BL approach was simulated using an empirical relationship between k and leaf area index established with the assistance of RT, which showed variation up to 0.2 in k depending on canopy geometry under the same plant density. When a constant k value was used instead, a difference of 0.2 in k resulted in up to 27% loss in accuracy for shoot dry matter. These results suggested that, with the assistance of RT in k estimation, the simple approach BL-HS provided efficient estimation for long-term processes.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48969159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-09DOI: 10.1093/insilicoplants/diab030
D. Holloway, C. Wenzel
The growth regulator auxin plays a central role in the phyllotaxy, shape, and venation patterns of leaves. The auxin spatial localization underlying these phenomena involves polar auxin transport (PAT) at the cellular level, particularly the preferential allocation of PIN efflux proteins to certain areas of the plasma membrane. Two general mechanisms have been studied: an up-the-gradient (UTG) allocation dependent on neighbouring-cell auxin concentrations, and a with-the-flux (WTF) allocation dependent on the flow of auxin across walls. We have developed a combined UTG+WTF model to quantify the observed auxin flows both towards (UTG) and away from (WTF) auxin maxima during primary and secondary vein patterning in leaves. The model simulates intracellular and membrane kinetics and intercellular transport, and is solved for a 2D leaf of several hundred cells. In addition to normal development, modelling of increasing PAT inhibition generates, as observed experimentally: a switch from several distinct vein initiation sites to many less-distinct sites; a delay in vein canalization; inhibited connection of new veins to old; and finally loss of patterning in the margin, loss of vein extension, and confinement of auxin to the margin. The model generates the observed formation of discrete auxin maxima at leaf vein sources and shows the dependence of secondary vein patterning on the efficacy of auxin flux through cells. Simulations of vein patterning and leaf growth further indicate that growth itself may bridge the spatial scale from the cell-cell resolution of the PIN-auxin dynamics to vein patterns on the whole-leaf scale.
{"title":"Polar auxin transport dynamics of primary and secondary vein patterning in dicot leaves","authors":"D. Holloway, C. Wenzel","doi":"10.1093/insilicoplants/diab030","DOIUrl":"https://doi.org/10.1093/insilicoplants/diab030","url":null,"abstract":"\u0000 The growth regulator auxin plays a central role in the phyllotaxy, shape, and venation patterns of leaves. The auxin spatial localization underlying these phenomena involves polar auxin transport (PAT) at the cellular level, particularly the preferential allocation of PIN efflux proteins to certain areas of the plasma membrane. Two general mechanisms have been studied: an up-the-gradient (UTG) allocation dependent on neighbouring-cell auxin concentrations, and a with-the-flux (WTF) allocation dependent on the flow of auxin across walls. We have developed a combined UTG+WTF model to quantify the observed auxin flows both towards (UTG) and away from (WTF) auxin maxima during primary and secondary vein patterning in leaves. The model simulates intracellular and membrane kinetics and intercellular transport, and is solved for a 2D leaf of several hundred cells. In addition to normal development, modelling of increasing PAT inhibition generates, as observed experimentally: a switch from several distinct vein initiation sites to many less-distinct sites; a delay in vein canalization; inhibited connection of new veins to old; and finally loss of patterning in the margin, loss of vein extension, and confinement of auxin to the margin. The model generates the observed formation of discrete auxin maxima at leaf vein sources and shows the dependence of secondary vein patterning on the efficacy of auxin flux through cells. Simulations of vein patterning and leaf growth further indicate that growth itself may bridge the spatial scale from the cell-cell resolution of the PIN-auxin dynamics to vein patterns on the whole-leaf scale.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2021-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49141313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-27DOI: 10.1093/insilicoplants/diab026
R. Janoutová, L. Homolová, J. Novotný, B. Navrátilová, M. Pikl, Z. Malenovský
This study presents a method for three-dimensional (3D) reconstruction of forest tree species that are, for instance, required for simulations of 3D canopies in radiative transfer modelling. We selected three forest species of different architecture: Norway spruce (Picea abies) and European beech (Fagus sylvatica), representatives of European production forests, and white peppermint (Eucalyptus pulchella), a common forest species of Tasmania. Each species has a specific crown structure and foliage distribution. Our algorithm for 3D model construction of a single tree is based on terrestrial laser scanning (TLS) and ancillary field measurements of leaf angle distribution, percentage of current-year and older leaves, and other parameters that could not be derived from TLS data. The algorithm comprises four main steps: i) segmentation of a TLS tree point cloud separating wooden parts from foliage, ii) reconstruction of wooden parts (trunks and branches) from TLS data, iii) biologically genuine distribution of foliage within the tree crown, and iv) separation of foliage into two age categories (for spruce trees only). The reconstructed 3D models of the tree species were used to build virtual forest scenes in the DART model and to simulate canopy optical signals, specifically: angularly anisotropic top-of-canopy reflectance (for retrieval of leaf biochemical compounds from nadir canopy reflectance signatures captured in airborne imaging spectroscopy data) and solar-induced chlorophyll fluorescence signal (for experimentally unfeasible sensitivity analyses).
{"title":"Detailed reconstruction of trees from terrestrial laser scans for remote sensing and radiative transfer modelling applications","authors":"R. Janoutová, L. Homolová, J. Novotný, B. Navrátilová, M. Pikl, Z. Malenovský","doi":"10.1093/insilicoplants/diab026","DOIUrl":"https://doi.org/10.1093/insilicoplants/diab026","url":null,"abstract":"This study presents a method for three-dimensional (3D) reconstruction of forest tree species that are, for instance, required for simulations of 3D canopies in radiative transfer modelling. We selected three forest species of different architecture: Norway spruce (Picea abies) and European beech (Fagus sylvatica), representatives of European production forests, and white peppermint (Eucalyptus pulchella), a common forest species of Tasmania. Each species has a specific crown structure and foliage distribution. Our algorithm for 3D model construction of a single tree is based on terrestrial laser scanning (TLS) and ancillary field measurements of leaf angle distribution, percentage of current-year and older leaves, and other parameters that could not be derived from TLS data. The algorithm comprises four main steps: i) segmentation of a TLS tree point cloud separating wooden parts from foliage, ii) reconstruction of wooden parts (trunks and branches) from TLS data, iii) biologically genuine distribution of foliage within the tree crown, and iv) separation of foliage into two age categories (for spruce trees only). The reconstructed 3D models of the tree species were used to build virtual forest scenes in the DART model and to simulate canopy optical signals, specifically: angularly anisotropic top-of-canopy reflectance (for retrieval of leaf biochemical compounds from nadir canopy reflectance signatures captured in airborne imaging spectroscopy data) and solar-induced chlorophyll fluorescence signal (for experimentally unfeasible sensitivity analyses).","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44681925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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