Pub Date : 2023-01-01DOI: 10.1093/insilicoplants/diad002
Nick Fradgley, Keith A Gardner, Alison R Bentley, Phil Howell, Ian J Mackay, Michael F Scott, Richard Mott, James Cockram
Abstract Cereal crop breeders have achieved considerable genetic gain in genetically complex traits, such as grain yield, while maintaining genetic diversity. However, focus on selection for yield has negatively impacted other important traits. To better understand multi-trait selection within a breeding context, and how it might be optimized, we analysed genotypic and phenotypic data from a genetically diverse, 16-founder wheat multi-parent advanced generation inter-cross population. Compared to single-trait models, multi-trait ensemble genomic prediction models increased prediction accuracy for almost 90 % of traits, improving grain yield prediction accuracy by 3–52 %. For complex traits, non-parametric models (Random Forest) also outperformed simplified, additive models (LASSO), increasing grain yield prediction accuracy by 10–36 %. Simulations of recurrent genomic selection then showed that sustained greater forward prediction accuracy optimized long-term genetic gains. Simulations of selection on grain yield found indirect responses in related traits, involving optimized antagonistic trait relationships. We found multi-trait selection indices could effectively optimize undesirable relationships, such as the trade-off between grain yield and protein content, or combine traits of interest, such as yield and weed competitive ability. Simulations of phenotypic selection found that including Random Forest rather than LASSO genetic models, and multi-trait rather than single-trait models as the true genetic model accelerated and extended long-term genetic gain whilst maintaining genetic diversity. These results (i) suggest important roles of pleiotropy and epistasis in the wider context of wheat breeding programmes, and (ii) provide insights into mechanisms for continued genetic gain in a limited genepool and optimization of multiple traits for crop improvement.
{"title":"Multi-trait ensemble genomic prediction and simulations of recurrent selection highlight importance of complex trait genetic architecture for long-term genetic gains in wheat","authors":"Nick Fradgley, Keith A Gardner, Alison R Bentley, Phil Howell, Ian J Mackay, Michael F Scott, Richard Mott, James Cockram","doi":"10.1093/insilicoplants/diad002","DOIUrl":"https://doi.org/10.1093/insilicoplants/diad002","url":null,"abstract":"Abstract Cereal crop breeders have achieved considerable genetic gain in genetically complex traits, such as grain yield, while maintaining genetic diversity. However, focus on selection for yield has negatively impacted other important traits. To better understand multi-trait selection within a breeding context, and how it might be optimized, we analysed genotypic and phenotypic data from a genetically diverse, 16-founder wheat multi-parent advanced generation inter-cross population. Compared to single-trait models, multi-trait ensemble genomic prediction models increased prediction accuracy for almost 90 % of traits, improving grain yield prediction accuracy by 3–52 %. For complex traits, non-parametric models (Random Forest) also outperformed simplified, additive models (LASSO), increasing grain yield prediction accuracy by 10–36 %. Simulations of recurrent genomic selection then showed that sustained greater forward prediction accuracy optimized long-term genetic gains. Simulations of selection on grain yield found indirect responses in related traits, involving optimized antagonistic trait relationships. We found multi-trait selection indices could effectively optimize undesirable relationships, such as the trade-off between grain yield and protein content, or combine traits of interest, such as yield and weed competitive ability. Simulations of phenotypic selection found that including Random Forest rather than LASSO genetic models, and multi-trait rather than single-trait models as the true genetic model accelerated and extended long-term genetic gain whilst maintaining genetic diversity. These results (i) suggest important roles of pleiotropy and epistasis in the wider context of wheat breeding programmes, and (ii) provide insights into mechanisms for continued genetic gain in a limited genepool and optimization of multiple traits for crop improvement.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":"304 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135126901","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 : 2022-10-21DOI: 10.1093/insilicoplants/diac020
Ana Cristina Zepeda, E. Heuvelink, L. Marcelis
Carbon (C) storage allows a plant to support growth whenever there is a temporal asynchrony between supply (source strength) and demand of carbon (sink strength). This asynchrony is strongly influenced by changes in light and temperature. In most crop models, C storage is included as a passive process that occurs whenever there is an excess of C from photosynthesis compared with the demand of C for metabolism. However, there are numerous studies that challenged this concept, and provided experimental evidence that C storage is an active process that allows buffering of environmental fluctuations and supports long-term plant growth. We propose that an active C pool needs to be included in simulation models for a better understanding of plant growth patterns under fluctuating environment. Specifically, we propose that the two main mechanisms actively regulating C storage in plants are the partitioning of assimilates between soluble sugars and starch and the degradation and remobilization of storage compounds. The insights gained here are important to optimize crop performance under fluctuating conditions and thus for developing more resource-efficient crop production systems.
{"title":"Carbon storage in plants: a buffer for temporal light and temperature fluctuations","authors":"Ana Cristina Zepeda, E. Heuvelink, L. Marcelis","doi":"10.1093/insilicoplants/diac020","DOIUrl":"https://doi.org/10.1093/insilicoplants/diac020","url":null,"abstract":"\u0000 Carbon (C) storage allows a plant to support growth whenever there is a temporal asynchrony between supply (source strength) and demand of carbon (sink strength). This asynchrony is strongly influenced by changes in light and temperature. In most crop models, C storage is included as a passive process that occurs whenever there is an excess of C from photosynthesis compared with the demand of C for metabolism. However, there are numerous studies that challenged this concept, and provided experimental evidence that C storage is an active process that allows buffering of environmental fluctuations and supports long-term plant growth. We propose that an active C pool needs to be included in simulation models for a better understanding of plant growth patterns under fluctuating environment. Specifically, we propose that the two main mechanisms actively regulating C storage in plants are the partitioning of assimilates between soluble sugars and starch and the degradation and remobilization of storage compounds. The insights gained here are important to optimize crop performance under fluctuating conditions and thus for developing more resource-efficient crop production systems.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42498058","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 : 2022-09-21DOI: 10.1093/insilicoplants/diac019
Erin N. Bodine, Caroline J. Bush, A. Capaldi, R. Jabaily
Quantifying reproductive effort (RE), the trade-off between devoting resources to reproduction versus individual growth, in plants presents a number of challenges. Of particular interest is comparing RE between reproductive strategies, such as those in Bromeliaceae: semelparous, where individuals undergo a one-time and subsequently lethal sexual reproductive event, versus iteroparous, where individuals reproduce sexually multiple times by producing clonal offshoots called pups. We introduce a dynamic model of vegetative and reproductive growth in long-lived Bromeliaceae rosettes accounting for the allocation of resources over developmental time. We compare multiple definitions of RE in semelparous and iteroparous Bromeliaceae at critical times during development and over the entire reproductive life of the individual. While others have posited that semelparous taxa exhibit higher RE than comparable iteroparous taxa, our results indicate this will only occur in limited circumstances: when RE is calculated over the lifespan of a rosette started from seed, semelparous RE is greater when pup mass is accounted for as if it were purely vegetative; or when RE is calculated over the lifespan of the genetic individual, semelparous RE is greater when the ratio of vegetative to inflorescence mass in each pup is greater than that of the originating rosette started from seed.
{"title":"Modeling Differences in Reproductive Effort Between Iteroparous and Semelparous Reproductive Strategies in Bromeliaceae","authors":"Erin N. Bodine, Caroline J. Bush, A. Capaldi, R. Jabaily","doi":"10.1093/insilicoplants/diac019","DOIUrl":"https://doi.org/10.1093/insilicoplants/diac019","url":null,"abstract":"\u0000 Quantifying reproductive effort (RE), the trade-off between devoting resources to reproduction versus individual growth, in plants presents a number of challenges. Of particular interest is comparing RE between reproductive strategies, such as those in Bromeliaceae: semelparous, where individuals undergo a one-time and subsequently lethal sexual reproductive event, versus iteroparous, where individuals reproduce sexually multiple times by producing clonal offshoots called pups. We introduce a dynamic model of vegetative and reproductive growth in long-lived Bromeliaceae rosettes accounting for the allocation of resources over developmental time. We compare multiple definitions of RE in semelparous and iteroparous Bromeliaceae at critical times during development and over the entire reproductive life of the individual. While others have posited that semelparous taxa exhibit higher RE than comparable iteroparous taxa, our results indicate this will only occur in limited circumstances: when RE is calculated over the lifespan of a rosette started from seed, semelparous RE is greater when pup mass is accounted for as if it were purely vegetative; or when RE is calculated over the lifespan of the genetic individual, semelparous RE is greater when the ratio of vegetative to inflorescence mass in each pup is greater than that of the originating rosette started from seed.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41871848","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 : 2022-08-25DOI: 10.1093/insilicoplants/diac016
C. Beckford, Montana Ferita, Julie Fucarino, D. Elzinga, Katherine Bassett, A. Carlson, R. Swanson, A. Capaldi
Differences in pollen performance, often revealed during pollen competition, have long been recognized as evolutionarily significant and agriculturally important. Though we have sophisticated models for the growth of individual pollen tubes, we have no robust models for larger scale pollen competition, a process that has been linked with inbreeding avoidance, sexual selection, reproductive barrier reinforcement, and speciation. Here we use existing data on pollen performance traits to develop an agent-based model of pollen competition. We calibrate our model parameters to empirical data found in the literature of seed siring proportions from mixed pollinations and pollen tube length distributions from single accession pollinations. In this model, parameters that influence pollen tube movement and sensing of ovules were found to be primary factors in competition. Our model also demonstrates that interference competition emerges as a property of pollen competition, and suggests a potential mechanism for this phenomenon. This study integrates pollen performance measures with mathematical modeling conducted on a simplified and accessible system. This represents the first mechanistic agent-based model for pollen competition. Our model may be extended to predict seed siring proportions for other accessions of Arabidopsis thaliana given data on their pollen performance traits.
{"title":"Pollen interference emerges as a property from agent-based modeling of pollen competition in Arabidopsis thaliana","authors":"C. Beckford, Montana Ferita, Julie Fucarino, D. Elzinga, Katherine Bassett, A. Carlson, R. Swanson, A. Capaldi","doi":"10.1093/insilicoplants/diac016","DOIUrl":"https://doi.org/10.1093/insilicoplants/diac016","url":null,"abstract":"\u0000 Differences in pollen performance, often revealed during pollen competition, have long been recognized as evolutionarily significant and agriculturally important. Though we have sophisticated models for the growth of individual pollen tubes, we have no robust models for larger scale pollen competition, a process that has been linked with inbreeding avoidance, sexual selection, reproductive barrier reinforcement, and speciation. Here we use existing data on pollen performance traits to develop an agent-based model of pollen competition. We calibrate our model parameters to empirical data found in the literature of seed siring proportions from mixed pollinations and pollen tube length distributions from single accession pollinations. In this model, parameters that influence pollen tube movement and sensing of ovules were found to be primary factors in competition. Our model also demonstrates that interference competition emerges as a property of pollen competition, and suggests a potential mechanism for this phenomenon. This study integrates pollen performance measures with mathematical modeling conducted on a simplified and accessible system. This represents the first mechanistic agent-based model for pollen competition. Our model may be extended to predict seed siring proportions for other accessions of Arabidopsis thaliana given data on their pollen performance traits.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49435870","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 : 2022-08-23DOI: 10.1093/insilicoplants/diad003
Kristine S Bagdassarian, J. Etchells, Natasha S. Savage
The cambium is a secondary meristematic tissue in plant stems, roots, and hypocotyls. Here, cell divisions occur that are required for radial growth. In most species that undergo secondary growth, daughters of cell divisions within the cambium differentiate into woody xylem cells towards the inside of the stem, or phloem towards the outside. As such, a pattern of xylem-cambium-phloem is present along the radial axis of all secondary vascular tissues, whether in stem, hypocotyl, or root. A ligand-receptor pair, TDIF-PXY promotes cell division in the cambium, as do the phytohormones, cytokinin and auxin. An auxin response factor, MP, has been proposed to initiate cambial cell divisions by promoting PXY expression, however, MP has also been reported to repress cambial cell divisions later in development where TDIF-PXY complexes are also reported to suppress MP activity. Here, we used a mathematical modelling approach to investigate how MP cell division-promoting activity and cell division-repressing activity might be integrated into the same network as a negative feedback loop. In our model, this feedback loop improved the ability of the cambium to pattern correctly and was found to be required for normal patterning as the stability of MP was increased. The implications of this model in early and late cambium development are discussed.
{"title":"A mathematical model integrates diverging PXY and MP interactions in cambium development","authors":"Kristine S Bagdassarian, J. Etchells, Natasha S. Savage","doi":"10.1093/insilicoplants/diad003","DOIUrl":"https://doi.org/10.1093/insilicoplants/diad003","url":null,"abstract":"\u0000 The cambium is a secondary meristematic tissue in plant stems, roots, and hypocotyls. Here, cell divisions occur that are required for radial growth. In most species that undergo secondary growth, daughters of cell divisions within the cambium differentiate into woody xylem cells towards the inside of the stem, or phloem towards the outside. As such, a pattern of xylem-cambium-phloem is present along the radial axis of all secondary vascular tissues, whether in stem, hypocotyl, or root. A ligand-receptor pair, TDIF-PXY promotes cell division in the cambium, as do the phytohormones, cytokinin and auxin. An auxin response factor, MP, has been proposed to initiate cambial cell divisions by promoting PXY expression, however, MP has also been reported to repress cambial cell divisions later in development where TDIF-PXY complexes are also reported to suppress MP activity. Here, we used a mathematical modelling approach to investigate how MP cell division-promoting activity and cell division-repressing activity might be integrated into the same network as a negative feedback loop. In our model, this feedback loop improved the ability of the cambium to pattern correctly and was found to be required for normal patterning as the stability of MP was increased. The implications of this model in early and late cambium development are discussed.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44872155","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 : 2022-08-11DOI: 10.1093/insilicoplants/diac017
I. Droutsas, A. Challinor, Chetan Deva, E. Wang
Machine learning (ML) is the most advanced field of predictive modelling and incorporating it into process-based crop modelling is a highly promising avenue for accurate predictions of plant growth, development and yield. Here, we embed ML algorithms into a process-based crop model. ML is used within GLAM-Parti for daily predictions of radiation use efficiency, the rate of change of harvest index and the days to anthesis and maturity. The GLAM-Parti-ML framework exhibited high skill for wheat growth and development in a wide range of temperature, solar radiation and atmospheric humidity conditions, including various levels of heat stress. The model exhibited less than 20% error in simulating the above-ground biomass, grain yield and the days to anthesis and maturity of three wheat cultivars in six countries (USA, Mexico, Egypt, India, the Sudan and Bangladesh). Moreover, GLAM-Parti reproduced around three quarters of the observed variance in wheat biomass and yield. Existing process-based crop models rely on empirical stress factors to limit growth potential in simulations of crop response to unfavourable environmental conditions. The incorporation of ML into GLAM-Parti eliminated all stress factors under high temperature environments and reduced the physiological model parameters down to four. We conclude that the combination of process-based crop modelling with the predictive capacity of ML makes GLAM-Parti a highly promising framework for the next generation of crop models.
{"title":"Integration of machine learning into process-based modelling to improve simulation of complex crop responses","authors":"I. Droutsas, A. Challinor, Chetan Deva, E. Wang","doi":"10.1093/insilicoplants/diac017","DOIUrl":"https://doi.org/10.1093/insilicoplants/diac017","url":null,"abstract":"\u0000 Machine learning (ML) is the most advanced field of predictive modelling and incorporating it into process-based crop modelling is a highly promising avenue for accurate predictions of plant growth, development and yield. Here, we embed ML algorithms into a process-based crop model. ML is used within GLAM-Parti for daily predictions of radiation use efficiency, the rate of change of harvest index and the days to anthesis and maturity. The GLAM-Parti-ML framework exhibited high skill for wheat growth and development in a wide range of temperature, solar radiation and atmospheric humidity conditions, including various levels of heat stress. The model exhibited less than 20% error in simulating the above-ground biomass, grain yield and the days to anthesis and maturity of three wheat cultivars in six countries (USA, Mexico, Egypt, India, the Sudan and Bangladesh). Moreover, GLAM-Parti reproduced around three quarters of the observed variance in wheat biomass and yield. Existing process-based crop models rely on empirical stress factors to limit growth potential in simulations of crop response to unfavourable environmental conditions. The incorporation of ML into GLAM-Parti eliminated all stress factors under high temperature environments and reduced the physiological model parameters down to four. We conclude that the combination of process-based crop modelling with the predictive capacity of ML makes GLAM-Parti a highly promising framework for the next generation of crop models.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45223953","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 : 2022-08-02DOI: 10.1093/insilicoplants/diac015
T. Moon, H. Choi, Dongpil Kim, I. Hwang, Jaewoo Kim, Jiyong Shin, J. Son
Visible traits can be criteria for selecting a suitable crop. Three-dimensional (3D)-scanned plant models can be used to extract visible traits; however, collecting scanned data and physically manipulating point-cloud structures of the scanned models are difficult. Recently, deep generative models have shown high performance in learning and creating target data. Deep generative models can improve the versatility of scanned models. The objectives of this study were to generate sweet pepper (Capsicum annuum L.) leaf models and to extract their traits by using deep generative models. The leaves were scanned, preprocessed, and used to train the deep generative models. The variational autoencoder, generative adversarial network (GAN), and latent space GAN were used to generate the desired leaves. The optimal number of latent variables in the model was selected via the Jensen‒Shannon divergence (JSD). The generated leaves were evaluated by using the JSD, coverage, and minimum matching distance to determine the best model for leaf generation. Among the deep generative models, a modified GAN showed the highest performance. Sweet pepper leaves with various shapes were generated from eight latent variables following a normal distribution, and the morphological traits of the leaves were controlled through linear interpolation and simple arithmetic operations in latent space. Simple arithmetic operations and gradual changes in the latent space modified the leaf traits. Deep generative models can parametrize and generate morphological traits in digitized 3D plant models and add realism and diversity to plant phenotyping studies.
{"title":"Autonomous construction of parameterizable 3D leaf models from scanned sweet pepper leaves with deep generative networks","authors":"T. Moon, H. Choi, Dongpil Kim, I. Hwang, Jaewoo Kim, Jiyong Shin, J. Son","doi":"10.1093/insilicoplants/diac015","DOIUrl":"https://doi.org/10.1093/insilicoplants/diac015","url":null,"abstract":"\u0000 Visible traits can be criteria for selecting a suitable crop. Three-dimensional (3D)-scanned plant models can be used to extract visible traits; however, collecting scanned data and physically manipulating point-cloud structures of the scanned models are difficult. Recently, deep generative models have shown high performance in learning and creating target data. Deep generative models can improve the versatility of scanned models. The objectives of this study were to generate sweet pepper (Capsicum annuum L.) leaf models and to extract their traits by using deep generative models. The leaves were scanned, preprocessed, and used to train the deep generative models. The variational autoencoder, generative adversarial network (GAN), and latent space GAN were used to generate the desired leaves. The optimal number of latent variables in the model was selected via the Jensen‒Shannon divergence (JSD). The generated leaves were evaluated by using the JSD, coverage, and minimum matching distance to determine the best model for leaf generation. Among the deep generative models, a modified GAN showed the highest performance. Sweet pepper leaves with various shapes were generated from eight latent variables following a normal distribution, and the morphological traits of the leaves were controlled through linear interpolation and simple arithmetic operations in latent space. Simple arithmetic operations and gradual changes in the latent space modified the leaf traits. Deep generative models can parametrize and generate morphological traits in digitized 3D plant models and add realism and diversity to plant phenotyping studies.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48071748","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 : 2022-07-28DOI: 10.1093/insilicoplants/diac013
Arthur Couturier, E. Frak, Quentin Rambaud, G. Louarn, R. Barillot, J. Durand, A. Escobar-Gutiérrez, D. Combes
Red:far-red ratio (R:FR) plays an important role in the architectural dynamics of vegetation. The integration of its effects into the crop model and/or into modelling work on plant dynamics over years requires new methods for describing R:FR spatial and temporal variability. This study assesses the sensitivity of simulating plant morphogenesis to the methods of R:FR modelling. The approach consisted in using a generic individual-based legume model coupled with radiative transfer models allowing the computation of R:FR values. Three methods of computation of R:FR were evaluated based on reference radiative transfer model CANESTRA and on turbid-medium model RIRI. The effects of R:FR simulated by the three methods on the simulation of plant morphogenesis were evaluated for the petiole and internodes of two contrasted plant architectures (alfalfa and white clover) at different stages of plant development and density. Plant morphogenesis expressed by the simulated petiole and internode length was sensitive to R:FR values provided by the models, especially at an early stage of development. Contrasted plant architectures exhibit different ranges of sensitivity to R:FR computed by the different models. However, this sensitivity follows a similar pattern between the two types of plant architecture, also for other conditions such as density or stage of development. This study highlights that the choice of radiative transfer model is of main importance for modelling plant morphogenetical responses, in particular at an early stage of plant development. The role of coupling of the FSP and radiative transfer models to address photomorphogenetic issues in order to consider plant-to-plant interactions is discussed.
{"title":"How much do radiative transfer models influence red:far-red simulation and subsequent plant photomorphogenesis modelling ?","authors":"Arthur Couturier, E. Frak, Quentin Rambaud, G. Louarn, R. Barillot, J. Durand, A. Escobar-Gutiérrez, D. Combes","doi":"10.1093/insilicoplants/diac013","DOIUrl":"https://doi.org/10.1093/insilicoplants/diac013","url":null,"abstract":"\u0000 Red:far-red ratio (R:FR) plays an important role in the architectural dynamics of vegetation. The integration of its effects into the crop model and/or into modelling work on plant dynamics over years requires new methods for describing R:FR spatial and temporal variability. This study assesses the sensitivity of simulating plant morphogenesis to the methods of R:FR modelling. The approach consisted in using a generic individual-based legume model coupled with radiative transfer models allowing the computation of R:FR values. Three methods of computation of R:FR were evaluated based on reference radiative transfer model CANESTRA and on turbid-medium model RIRI. The effects of R:FR simulated by the three methods on the simulation of plant morphogenesis were evaluated for the petiole and internodes of two contrasted plant architectures (alfalfa and white clover) at different stages of plant development and density.\u0000 Plant morphogenesis expressed by the simulated petiole and internode length was sensitive to R:FR values provided by the models, especially at an early stage of development. Contrasted plant architectures exhibit different ranges of sensitivity to R:FR computed by the different models. However, this sensitivity follows a similar pattern between the two types of plant architecture, also for other conditions such as density or stage of development. This study highlights that the choice of radiative transfer model is of main importance for modelling plant morphogenetical responses, in particular at an early stage of plant development. The role of coupling of the FSP and radiative transfer models to address photomorphogenetic issues in order to consider plant-to-plant interactions is discussed.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46299417","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 : 2022-07-01DOI: 10.1093/insilicoplants/diac011
{"title":"Correction to: A conserved cellular mechanism for cotton fibre diameter and length control","authors":"","doi":"10.1093/insilicoplants/diac011","DOIUrl":"https://doi.org/10.1093/insilicoplants/diac011","url":null,"abstract":"","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46667759","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 : 2022-05-26DOI: 10.1093/insilicoplants/diac009
Raphaël P A Perez, Rémi Vezy, L. Brancheriau, F. Boudon, François Grand, Merlin Ramel, Doni Artanto Raharjo, J. Caliman, J. Dauzat
Functional-structural plant modelling approaches (FSPM) explore the relationships between the 3D structure and the physiological functioning of plants in relation to environmental conditions. In this study, we present a methodological approach that integrated architectural responses to planting design in an oil palm FSPM, and test the impact of planting design and architectural plasticity on physiological responses such as light interception and carbon assimilation. LiDAR-derived and direct measurements were performed on five planting designs to assess the phenotypic plasticity of architectural traits, and allowed evaluating the variations of the main parameters of an existing 3D plant model. Accordingly, we proposed a neighborhood index (NI) as a simple explanatory variable of architectural plasticity, and used NI-based allometries to simulate architectural variations in 3D virtual plants. Light interception and carbon assimilation were then simulated on virtual plots reproducing the five studied designs. We found that the main traits affected by plant proximity were leaf dimensions, leaf weight, and leaf erectness, whereas other structural traits like the frequency of leaflets along the rachis or biomechanical properties of leaves remained unchanged. Our simulation study highlighted model compliance to reproduce architectural plasticity and illustrated how architectural plasticity improved light interception via leaf area expansion, but how the competition for light imposed by the design can counter-balance this benefit in terms of carbon assimilation at stand scale. We conclude on the importance of planting patterns for plants with low architectural plasticity such as oil palm, and how in silico experiments can help in designing innovative planting patterns.
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