Pub Date : 2023-07-01DOI: 10.1093/insilicoplants/diad014
Yi-Chen Pao, Hartmut Stützel, Tsu-Wei Chen
Abstract Crop varieties differing in architectural characteristics (AC) vary in their intra-canopy light distribution. To optimize canopy photosynthesis, we hypothesize that varieties with contrasting AC possess different photosynthetic acclimation strategy (PAS) with respect to photosynthetic nitrogen (Np) partitioning. We firstly used in silico experiments to test this hypothesis and suggested a trade-off in Np partitioning between carboxylation and light harvesting to achieve optimal coordination between PAS, AC and growing light environment. Then, two cucumber (Cucumis sativus L.) cultivars, Aramon and SC-50, which were bred under greenhouse vertical single-stem and field creeping multi-branch canopy, were selected for studying their differences in AC and PAS using greenhouse and growth chamber experiments, respectively. In the greenhouse, more horizontal leaves of SC-50 resulted in steeper intra-canopy light gradient and a higher degree of self-shading, especially in the upper canopy layer. In growth chamber experiments, Aramon invested more leaf nitrogen into photosynthesis than SC-50, and the proportion (pNp) increased as light was reduced. In contrast, pNp of SC-50 did not respond to light but SC-50 partitioned its limited Np between carboxylation and light harvesting functions more effectively, showing a strategy particularly advantageous for canopies with a high degree of self-shading. This is further confirmed by additional in silico experiments showing that Np partitioning of SC-50 coped better with the impact of strong light competition caused by low light and by leaf clumping under high planting density. These findings provide a comprehensive perspective of genotypic variation in PAS, canopy architectures and their optimal coordination.
{"title":"Optimal coordination between photosynthetic acclimation strategy and canopy architecture in two contrasting cucumber cultivars","authors":"Yi-Chen Pao, Hartmut Stützel, Tsu-Wei Chen","doi":"10.1093/insilicoplants/diad014","DOIUrl":"https://doi.org/10.1093/insilicoplants/diad014","url":null,"abstract":"Abstract Crop varieties differing in architectural characteristics (AC) vary in their intra-canopy light distribution. To optimize canopy photosynthesis, we hypothesize that varieties with contrasting AC possess different photosynthetic acclimation strategy (PAS) with respect to photosynthetic nitrogen (Np) partitioning. We firstly used in silico experiments to test this hypothesis and suggested a trade-off in Np partitioning between carboxylation and light harvesting to achieve optimal coordination between PAS, AC and growing light environment. Then, two cucumber (Cucumis sativus L.) cultivars, Aramon and SC-50, which were bred under greenhouse vertical single-stem and field creeping multi-branch canopy, were selected for studying their differences in AC and PAS using greenhouse and growth chamber experiments, respectively. In the greenhouse, more horizontal leaves of SC-50 resulted in steeper intra-canopy light gradient and a higher degree of self-shading, especially in the upper canopy layer. In growth chamber experiments, Aramon invested more leaf nitrogen into photosynthesis than SC-50, and the proportion (pNp) increased as light was reduced. In contrast, pNp of SC-50 did not respond to light but SC-50 partitioned its limited Np between carboxylation and light harvesting functions more effectively, showing a strategy particularly advantageous for canopies with a high degree of self-shading. This is further confirmed by additional in silico experiments showing that Np partitioning of SC-50 coped better with the impact of strong light competition caused by low light and by leaf clumping under high planting density. These findings provide a comprehensive perspective of genotypic variation in PAS, canopy architectures and their optimal coordination.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135851977","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 : 2023-07-01DOI: 10.1093/insilicoplants/diad017
Hojae Yi, Charles T Anderson
Abstract Stomata are dynamic pores on plant surfaces that regulate photosynthesis and are thus of critical importance for understanding and leveraging the carbon-capturing and food-producing capabilities of plants. However, our understanding of the molecular underpinnings of stomatal kinetics and the biomechanical properties of the cell walls of stomatal guard cells that enable their dynamic responses to environmental and intrinsic stimuli is limited. Here, we built multiscale models that simulate regions of the guard cell wall, representing cellulose fibrils and matrix polysaccharides as discrete, interacting units, and used these models to help explain how molecular changes in wall composition and underlying architecture alter guard wall biomechanics that gives rise to stomatal responses in mutants with altered wall synthesis and modification. These results point to strategies for engineering guard cell walls to enhance stomatal response times and efficiency.
{"title":"Bottom-up Multiscale Modeling of Guard Cell Walls Reveals Molecular Mechanisms of Stomatal Biomechanics","authors":"Hojae Yi, Charles T Anderson","doi":"10.1093/insilicoplants/diad017","DOIUrl":"https://doi.org/10.1093/insilicoplants/diad017","url":null,"abstract":"Abstract Stomata are dynamic pores on plant surfaces that regulate photosynthesis and are thus of critical importance for understanding and leveraging the carbon-capturing and food-producing capabilities of plants. However, our understanding of the molecular underpinnings of stomatal kinetics and the biomechanical properties of the cell walls of stomatal guard cells that enable their dynamic responses to environmental and intrinsic stimuli is limited. Here, we built multiscale models that simulate regions of the guard cell wall, representing cellulose fibrils and matrix polysaccharides as discrete, interacting units, and used these models to help explain how molecular changes in wall composition and underlying architecture alter guard wall biomechanics that gives rise to stomatal responses in mutants with altered wall synthesis and modification. These results point to strategies for engineering guard cell walls to enhance stomatal response times and efficiency.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135855949","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 : 2023-07-01DOI: 10.1093/insilicoplants/diad012
Xiao-Ran Zhou, Andrea Schnepf, Jan Vanderborght, Daniel Leitner, Harry Vereecken, Guillaume Lobet
Abstract Plant growth and development involve the integration of numerous processes, influenced by both endogenous and exogenous factors. At any given time during a plant’s life cycle, the plant architecture is a readout of this continuous integration. However, untangling the individual factors and processes involved in the plant development and quantifying their influence on the plant developmental process is experimentally challenging. Here we used a combination of computational plant models (CPlantBox and PiafMunch) to help understand experimental findings about how local phloem anatomical features influence the root system architecture. Our hypothesis was that strong local phloem resistance would restrict local carbon flow and locally modify root growth patterns. To test this hypothesis, we simulated the mutual interplay between the root system architecture development and the carbohydrate distribution to provide a plausible mechanistic explanation for several experimental results. Our in silico experiments highlighted the strong influence of local phloem hydraulics on the root growth rates, growth duration and final length. The model result showed that a higher phloem resistivity leads to shorter roots due to the reduced flow of carbon within the root system. This effect was due to local properties of individual roots, and not linked to any of the pleiotropic effects at the root system level. Our results open a door to a better representation of growth processes in a plant computational model.
{"title":"Phloem anatomy restricts root system architecture development: theoretical clues from <i>in silico</i> experiments","authors":"Xiao-Ran Zhou, Andrea Schnepf, Jan Vanderborght, Daniel Leitner, Harry Vereecken, Guillaume Lobet","doi":"10.1093/insilicoplants/diad012","DOIUrl":"https://doi.org/10.1093/insilicoplants/diad012","url":null,"abstract":"Abstract Plant growth and development involve the integration of numerous processes, influenced by both endogenous and exogenous factors. At any given time during a plant’s life cycle, the plant architecture is a readout of this continuous integration. However, untangling the individual factors and processes involved in the plant development and quantifying their influence on the plant developmental process is experimentally challenging. Here we used a combination of computational plant models (CPlantBox and PiafMunch) to help understand experimental findings about how local phloem anatomical features influence the root system architecture. Our hypothesis was that strong local phloem resistance would restrict local carbon flow and locally modify root growth patterns. To test this hypothesis, we simulated the mutual interplay between the root system architecture development and the carbohydrate distribution to provide a plausible mechanistic explanation for several experimental results. Our in silico experiments highlighted the strong influence of local phloem hydraulics on the root growth rates, growth duration and final length. The model result showed that a higher phloem resistivity leads to shorter roots due to the reduced flow of carbon within the root system. This effect was due to local properties of individual roots, and not linked to any of the pleiotropic effects at the root system level. Our results open a door to a better representation of growth processes in a plant computational model.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135805130","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 : 2023-07-01DOI: 10.1093/insilicoplants/diad016
Jan Graefe, Richard Pauwels, Michael Bitterlich
Abstract Various analytical models that calculate the water flow either around or inside plant roots are available, but a combined analytical solution has not yet been derived. The classical solution of Landsberg and Fowkes for water flow within a root relates the second derivative of xylem water potential to the radial water influx term. This term can be linked to well-known steady state or steady rate-based solutions for computing soil water fluxes around roots. While neglecting lateral fluxes between local depletion zones around roots, we use this link to construct a system of continuous equations that combine root internal and external water flow that can be solved numerically for two boundary conditions (specified root collar water potential and zero distal influx) and one constraint (mean bulk matric flux potential). Furthermore, an iterative matrix solution for the stepwise analytical solution of homogeneous root segments is developed. Besides accounting for soil water flow iteratively, the intrinsic effect of variable axial conductance is accounted simultaneously. The reference and the iterative matrix solution are compared for different types of corn roots, soil textures and soil dryness states, which showed good correspondence. This also revealed the importance of accounting for variable axial conductance in more detail. The proposed reference solution can be used for the evaluation of different morphological and hydraulic designs of single or multiple parallel-connected roots operating in targeted soil environments. Some details of the iterative matrix solution may be adopted in analytical–numerical solutions of water flow in complex root systems.
{"title":"Water flow within and towards plant roots – a new concurrent solution","authors":"Jan Graefe, Richard Pauwels, Michael Bitterlich","doi":"10.1093/insilicoplants/diad016","DOIUrl":"https://doi.org/10.1093/insilicoplants/diad016","url":null,"abstract":"Abstract Various analytical models that calculate the water flow either around or inside plant roots are available, but a combined analytical solution has not yet been derived. The classical solution of Landsberg and Fowkes for water flow within a root relates the second derivative of xylem water potential to the radial water influx term. This term can be linked to well-known steady state or steady rate-based solutions for computing soil water fluxes around roots. While neglecting lateral fluxes between local depletion zones around roots, we use this link to construct a system of continuous equations that combine root internal and external water flow that can be solved numerically for two boundary conditions (specified root collar water potential and zero distal influx) and one constraint (mean bulk matric flux potential). Furthermore, an iterative matrix solution for the stepwise analytical solution of homogeneous root segments is developed. Besides accounting for soil water flow iteratively, the intrinsic effect of variable axial conductance is accounted simultaneously. The reference and the iterative matrix solution are compared for different types of corn roots, soil textures and soil dryness states, which showed good correspondence. This also revealed the importance of accounting for variable axial conductance in more detail. The proposed reference solution can be used for the evaluation of different morphological and hydraulic designs of single or multiple parallel-connected roots operating in targeted soil environments. Some details of the iterative matrix solution may be adopted in analytical–numerical solutions of water flow in complex root systems.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135856153","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 : 2023-07-01DOI: 10.1093/insilicoplants/diad013
Tommaso Stella, Heidi Webber, Ehsan Eyshi Rezaei, Senthold Asseng, Pierre Martre, Sibylle Dueri, Jose Rafael Guarin, Diego N L Pequeno, Daniel F Calderini, Matthew Reynolds, Gemma Molero, Daniel Miralles, Guillermo Garcia, Gustavo Slafer, Francesco Giunta, Yean-Uk Kim, Chenzhi Wang, Alex C Ruane, Frank Ewert
Abstract Increasing genetic wheat yield potential is considered by many as critical to increasing global wheat yields and production, baring major changes in consumption patterns. Climate change challenges breeding by making target environments less predictable, altering regional productivity and potentially increasing yield variability. Here we used a crop simulation model solution in the SIMPLACE framework to explore yield sensitivity to select trait characteristics (radiation use efficiency [RUE], fruiting efficiency and light extinction coefficient) across 34 locations representing the world’s wheat-producing environments, determining their relationship to increasing yields, yield variability and cultivar performance. The magnitude of the yield increase was trait-dependent and differed between irrigated and rainfed environments. RUE had the most prominent marginal effect on yield, which increased by about 45 % and 33 % in irrigated and rainfed sites, respectively, between the minimum and maximum value of the trait. Altered values of light extinction coefficient had the least effect on yield levels. Higher yields from improved traits were generally associated with increased inter-annual yield variability (measured by standard deviation), but the relative yield variability (as coefficient of variation) remained largely unchanged between base and improved genotypes. This was true under both current and future climate scenarios. In this context, our study suggests higher wheat yields from these traits would not increase climate risk for farmers and the adoption of cultivars with these traits would not be associated with increased yield variability.
{"title":"Wheat crop traits conferring high yield potential may also improve yield stability under climate change","authors":"Tommaso Stella, Heidi Webber, Ehsan Eyshi Rezaei, Senthold Asseng, Pierre Martre, Sibylle Dueri, Jose Rafael Guarin, Diego N L Pequeno, Daniel F Calderini, Matthew Reynolds, Gemma Molero, Daniel Miralles, Guillermo Garcia, Gustavo Slafer, Francesco Giunta, Yean-Uk Kim, Chenzhi Wang, Alex C Ruane, Frank Ewert","doi":"10.1093/insilicoplants/diad013","DOIUrl":"https://doi.org/10.1093/insilicoplants/diad013","url":null,"abstract":"Abstract Increasing genetic wheat yield potential is considered by many as critical to increasing global wheat yields and production, baring major changes in consumption patterns. Climate change challenges breeding by making target environments less predictable, altering regional productivity and potentially increasing yield variability. Here we used a crop simulation model solution in the SIMPLACE framework to explore yield sensitivity to select trait characteristics (radiation use efficiency [RUE], fruiting efficiency and light extinction coefficient) across 34 locations representing the world’s wheat-producing environments, determining their relationship to increasing yields, yield variability and cultivar performance. The magnitude of the yield increase was trait-dependent and differed between irrigated and rainfed environments. RUE had the most prominent marginal effect on yield, which increased by about 45 % and 33 % in irrigated and rainfed sites, respectively, between the minimum and maximum value of the trait. Altered values of light extinction coefficient had the least effect on yield levels. Higher yields from improved traits were generally associated with increased inter-annual yield variability (measured by standard deviation), but the relative yield variability (as coefficient of variation) remained largely unchanged between base and improved genotypes. This was true under both current and future climate scenarios. In this context, our study suggests higher wheat yields from these traits would not increase climate risk for farmers and the adoption of cultivars with these traits would not be associated with increased yield variability.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135807030","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 : 2023-07-01DOI: 10.1093/insilicoplants/diad015
Daniel R Kick, Jacob D Washburn
Abstract Predicting phenotypes accurately from genomic, environment and management factors is key to accelerating the development of novel cultivars with desirable traits. Inclusion of management and environmental factors enables in silico studies to predict the effect of specific management interventions or future climates. Despite the value such models would confer, much work remains to improve the accuracy of phenotypic predictions. Rather than advocate for a single specific modelling strategy, here we demonstrate within large multi-environment and multi-genotype maize trials that combining predictions from disparate models using simple ensemble approaches most often results in better accuracy than using any one of the models on their own. We investigated various ensemble combinations of different model types, model numbers and model weighting schemes to determine the accuracy of each. We find that ensembling generally improves performance even when combining only two models. The number and type of models included alter accuracy with improvements diminishing as the number of models included increases. Using a genetic algorithm to optimize ensemble composition reveals that, when weighted by the inverse of each model’s expected error, a combination of best linear unbiased predictor, linear fixed effects, deep learning, random forest and support vector regression models performed best on this dataset.
{"title":"Ensemble of Best Linear Unbiased Predictor, Machine Learning, and Deep Learning Models Predict Maize Yield Better Than Each Model Alone","authors":"Daniel R Kick, Jacob D Washburn","doi":"10.1093/insilicoplants/diad015","DOIUrl":"https://doi.org/10.1093/insilicoplants/diad015","url":null,"abstract":"Abstract Predicting phenotypes accurately from genomic, environment and management factors is key to accelerating the development of novel cultivars with desirable traits. Inclusion of management and environmental factors enables in silico studies to predict the effect of specific management interventions or future climates. Despite the value such models would confer, much work remains to improve the accuracy of phenotypic predictions. Rather than advocate for a single specific modelling strategy, here we demonstrate within large multi-environment and multi-genotype maize trials that combining predictions from disparate models using simple ensemble approaches most often results in better accuracy than using any one of the models on their own. We investigated various ensemble combinations of different model types, model numbers and model weighting schemes to determine the accuracy of each. We find that ensembling generally improves performance even when combining only two models. The number and type of models included alter accuracy with improvements diminishing as the number of models included increases. Using a genetic algorithm to optimize ensemble composition reveals that, when weighted by the inverse of each model’s expected error, a combination of best linear unbiased predictor, linear fixed effects, deep learning, random forest and support vector regression models performed best on this dataset.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135806925","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 : 2023-06-27DOI: 10.1093/insilicoplants/diad008
Sjoerd Hermes, J. van Heerwaarden, Pariya Behrouzi
Improving crop yields is one of the main goals of agronomy. However, yield is determined by a complex interplay between Genotypic, Environmental and Management factors (G × E × M) that varies across time and space. Therefore, identifying the fundamental relations underlying yield variation is a principal aim of agricultural research. A narrow, and not necessarily appropriate set of statistical methods tends to be used in the study of such relations, which is why we aim to introduce a diverse audience of agronomists, production ecologists, plant breeders and others interested in explaining yield variation to the use of graphical models. More specifically, we wish to demonstrate the usefulness of copula graphical models for heterogeneous mixed data. This new statistical learning technique provides a graphical representation of conditional independence relationships within data that is not necessarily normally distributed and consists of multiple groups for environments, management decisions, genotypes or abiotic stresses such as drought. This article introduces some basic graphical model terminology and theory, followed by an application on Ethiopian maize and wheat yield undergoing drought stress. The proposed method is accompanied with the R package heteromixgmhttps://CRAN.R-project.org/package=heteromixgm.
{"title":"Using Copula Graphical Models to Detect the Impact of Drought Stress on Maize and Wheat Yield","authors":"Sjoerd Hermes, J. van Heerwaarden, Pariya Behrouzi","doi":"10.1093/insilicoplants/diad008","DOIUrl":"https://doi.org/10.1093/insilicoplants/diad008","url":null,"abstract":"\u0000 Improving crop yields is one of the main goals of agronomy. However, yield is determined by a complex interplay between Genotypic, Environmental and Management factors (G × E × M) that varies across time and space. Therefore, identifying the fundamental relations underlying yield variation is a principal aim of agricultural research. A narrow, and not necessarily appropriate set of statistical methods tends to be used in the study of such relations, which is why we aim to introduce a diverse audience of agronomists, production ecologists, plant breeders and others interested in explaining yield variation to the use of graphical models. More specifically, we wish to demonstrate the usefulness of copula graphical models for heterogeneous mixed data. This new statistical learning technique provides a graphical representation of conditional independence relationships within data that is not necessarily normally distributed and consists of multiple groups for environments, management decisions, genotypes or abiotic stresses such as drought. This article introduces some basic graphical model terminology and theory, followed by an application on Ethiopian maize and wheat yield undergoing drought stress. The proposed method is accompanied with the R package heteromixgmhttps://CRAN.R-project.org/package=heteromixgm.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46895066","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 : 2023-06-16DOI: 10.1093/insilicoplants/diad007
Pratishtha Poudel, B. Naidenov, Charles Chen, P. Alderman, S. Welch
The Genome-to-Phenome (G2P) problem is one of the highest-priority challenges in applied biology. Ecophysiological crop models (ECM) and genomic prediction (GP) models are quantitative algorithms, which, when given information on a genotype and environment, can produce an accurate estimate of a phenotype of interest. In this article, we discuss how the GP algorithms can be used to estimate genotype-specific parameters (GSPs) in ECMs to develop robust prediction methods. In this approach, the numerical constants (GSPs) that ECMs use to distinguish and characterize crop cultivars/varieties are treated as quantitative traits to be predicted by genomic prediction models from underlying genetic information. In this article we provide information on which GP methods appear favorable for predicting different types of GSPs, such as vernalization sensitivity or potential radiation use efficiency. For each example GSP, we assess a number of GP methods in terms of their suitability using a set of three criteria grounded in genetic architecture, computational requirements, and the use of prior information. In general, we conclude that the most useful algorithms were dependent on both the nature of the particular GSP and the GP methods considered.
{"title":"Integrating genomic prediction and genotype specific parameter estimation in ecophysiological models: overview and perspectives","authors":"Pratishtha Poudel, B. Naidenov, Charles Chen, P. Alderman, S. Welch","doi":"10.1093/insilicoplants/diad007","DOIUrl":"https://doi.org/10.1093/insilicoplants/diad007","url":null,"abstract":"\u0000 The Genome-to-Phenome (G2P) problem is one of the highest-priority challenges in applied biology. Ecophysiological crop models (ECM) and genomic prediction (GP) models are quantitative algorithms, which, when given information on a genotype and environment, can produce an accurate estimate of a phenotype of interest. In this article, we discuss how the GP algorithms can be used to estimate genotype-specific parameters (GSPs) in ECMs to develop robust prediction methods. In this approach, the numerical constants (GSPs) that ECMs use to distinguish and characterize crop cultivars/varieties are treated as quantitative traits to be predicted by genomic prediction models from underlying genetic information. In this article we provide information on which GP methods appear favorable for predicting different types of GSPs, such as vernalization sensitivity or potential radiation use efficiency. For each example GSP, we assess a number of GP methods in terms of their suitability using a set of three criteria grounded in genetic architecture, computational requirements, and the use of prior information. In general, we conclude that the most useful algorithms were dependent on both the nature of the particular GSP and the GP methods considered.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47416135","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 : 2023-06-03DOI: 10.1093/insilicoplants/diad005
A. Schnepf, C. Black, V. Couvreur, B. Delory, C. Doussan, Adrien Heymans, M. Javaux, Deepanshu Khare, Axelle Koch, T. Koch, Christian W. Kuppe, M. Landl, D. Leitner, G. Lobet, F. Meunier, J. Postma, Ernst D Schäfer, Tobias Selzner, J. Vanderborght, H. Vereecken
Functional-structural root architecture models have evolved as tools for the design of improved agricultural management practices and for the selection of optimal root traits. In order to test their accuracy and reliability, we present the first benchmarking of root water uptake from soil using five well-established functional-structural root architecture models: DuMux, CPlantBox, R-SWMS, OpenSimRoot and SRI. The benchmark scenarios include basic tests for water flow in soil and roots as well as advanced tests for the coupled soil-root system. The reference solutions and the solutions of the different simulators are available through Jupyter Notebooks on a GitHub repository. All of the simulators were able to pass the basic tests and continued to perform well in the benchmarks for the coupled soil-plant system. For the advanced tests, we created an overview of the different ways of coupling the soil and the root domains as well as the different methods used to account for rhizosphere resistance to water flow. Although the methods used for coupling and modelling rhizosphere resistance were quite different, all simulators were in reasonably good agreement with the reference solution. During this benchmarking effort, individual simulators were able to learn about their strengths and challenges, while some were even able to improve their code. Some now include the benchmarks as standard tests within their codes. Additional model results may be added to the GitHub repository at any point in the future and will be automatically included in the comparison.
{"title":"Collaborative benchmarking of functional-structural root architecture models: Quantitative comparison of simulated root water uptake","authors":"A. Schnepf, C. Black, V. Couvreur, B. Delory, C. Doussan, Adrien Heymans, M. Javaux, Deepanshu Khare, Axelle Koch, T. Koch, Christian W. Kuppe, M. Landl, D. Leitner, G. Lobet, F. Meunier, J. Postma, Ernst D Schäfer, Tobias Selzner, J. Vanderborght, H. Vereecken","doi":"10.1093/insilicoplants/diad005","DOIUrl":"https://doi.org/10.1093/insilicoplants/diad005","url":null,"abstract":"\u0000 Functional-structural root architecture models have evolved as tools for the design of improved agricultural management practices and for the selection of optimal root traits. In order to test their accuracy and reliability, we present the first benchmarking of root water uptake from soil using five well-established functional-structural root architecture models: DuMux, CPlantBox, R-SWMS, OpenSimRoot and SRI. The benchmark scenarios include basic tests for water flow in soil and roots as well as advanced tests for the coupled soil-root system. The reference solutions and the solutions of the different simulators are available through Jupyter Notebooks on a GitHub repository. All of the simulators were able to pass the basic tests and continued to perform well in the benchmarks for the coupled soil-plant system. For the advanced tests, we created an overview of the different ways of coupling the soil and the root domains as well as the different methods used to account for rhizosphere resistance to water flow. Although the methods used for coupling and modelling rhizosphere resistance were quite different, all simulators were in reasonably good agreement with the reference solution. During this benchmarking effort, individual simulators were able to learn about their strengths and challenges, while some were even able to improve their code. Some now include the benchmarks as standard tests within their codes. Additional model results may be added to the GitHub repository at any point in the future and will be automatically included in the comparison.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43988840","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 : 2023-05-23DOI: 10.1093/insilicoplants/diad006
Andreas Enders, Murilo Vianna, T. Gaiser, Gunther Krauss, H. Webber, A. Srivastava, S. Seidel, Andreas H. J. Tewes, E. Rezaei, F. Ewert
Agricultural system analysis has considerably evolved over the last years, allowing scientists to quantify complex interactions in crops and agroecosystems. Computer-based models have become a central tool for such analysis, using formulated mathematical representations (algorithms) of different biophysical processes to simulate complex system behaviour. Nevertheless, the current large variety of algorithms in combination with non-standardization in their use limits rapid and rigorous model improvement and testing. This is particularly important because contextualization is a key aspect used to formulate the appropriate model structure for a specific research question, framing a clear demand for “next generation” models being modular and flexible. This paper aims to describe the Scientific Impact assessment and Modelling PLatform for Advanced Crop and Ecosystem management (SIMPLACE), which has been developed over the last decade to address the various aforementioned issues and support appropriate model formulations and interoperability. We describe its main technical implementation and features to develop customized model solutions that can be applied to a number of cropping systems with high flexibility, performance and transparency. A brief review of exemplary applications of SIMPLACE is provided covering the different topics, crops and cropping systems, spatial scales, and geographies. We stress that standardized documentation of modules, variables ontology, and data archives are key requirements to maintain and assist model development, and reproducibility. The increasing demand for more complex diversified and integrated production systems (e.g., intercropping, livestock-grazing, agroforestry) and the associated impacts on sustainable food systems also require the strong collaboration of a multidisciplinary community of modellers and stakeholders.
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