Pub Date : 2021-01-01DOI: 10.1093/INSILICOPLANTS/DIAA012
E. V. van Oosterom, M. Kulathunga, K. Deifel, G. McLean, C. Barrasso, A. Wu, C. Messina, G. Hammer
Maize is considered less drought-tolerant than sorghum, but sorghum is commonly grown as a short triple dwarf (3dwarf) type, so difference in plant height confounds the species comparison. The objectives of this study were to experimentally determine effects of species and plant height differences on transpiration efficiency (TE) and transpiration rate per unit green leaf area (TGLA) and use findings to explain input parameters in a simulation study on the comparative adaptation of 3dwarf sorghum and maize in environments with contrasting water availability. Maize, tall double dwarf (2dwarf) and short 3dwarf sorghum genotypes were grown in two lysimeter experiments in 2011 in SE Queensland, Australia. Each plant was harvested after anthesis and total transpiration, shoot and root dry mass were measured to estimate TE. Daily TGLA was used to compare transpiration rates. Species and height had limited effect on TE, but significantly affected TGLA. This was associated with differences in biomass allocation. The similar TE but higher TGLA in maize compared with 3dwarf sorghum meant it potentially produces more biomass, consistent with published differences in biomass accumulation and radiation use efficiency (RUE). The simulation study, which used similar TE for maize and 3dwarf sorghum, but captured differences in TGLA through differences in RUE, predicted crossover interactions for grain yield between species and total water use. The greater TGLA of maize decreased grain yield in water-limited environments, but increased yields in well-watered situations. Results highlight that similarity in TE and differences in TGLA can influence comparative adaptation to water limitation.
{"title":"Dissecting and modelling the comparative adaptation to water limitation of sorghum and maize: role of transpiration efficiency, transpiration rate and height","authors":"E. V. van Oosterom, M. Kulathunga, K. Deifel, G. McLean, C. Barrasso, A. Wu, C. Messina, G. Hammer","doi":"10.1093/INSILICOPLANTS/DIAA012","DOIUrl":"https://doi.org/10.1093/INSILICOPLANTS/DIAA012","url":null,"abstract":"\u0000 Maize is considered less drought-tolerant than sorghum, but sorghum is commonly grown as a short triple dwarf (3dwarf) type, so difference in plant height confounds the species comparison. The objectives of this study were to experimentally determine effects of species and plant height differences on transpiration efficiency (TE) and transpiration rate per unit green leaf area (TGLA) and use findings to explain input parameters in a simulation study on the comparative adaptation of 3dwarf sorghum and maize in environments with contrasting water availability. Maize, tall double dwarf (2dwarf) and short 3dwarf sorghum genotypes were grown in two lysimeter experiments in 2011 in SE Queensland, Australia. Each plant was harvested after anthesis and total transpiration, shoot and root dry mass were measured to estimate TE. Daily TGLA was used to compare transpiration rates. Species and height had limited effect on TE, but significantly affected TGLA. This was associated with differences in biomass allocation. The similar TE but higher TGLA in maize compared with 3dwarf sorghum meant it potentially produces more biomass, consistent with published differences in biomass accumulation and radiation use efficiency (RUE). The simulation study, which used similar TE for maize and 3dwarf sorghum, but captured differences in TGLA through differences in RUE, predicted crossover interactions for grain yield between species and total water use. The greater TGLA of maize decreased grain yield in water-limited environments, but increased yields in well-watered situations. Results highlight that similarity in TE and differences in TGLA can influence comparative adaptation to water limitation.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/INSILICOPLANTS/DIAA012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45523248","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 : 2021-01-01DOI: 10.1093/INSILICOPLANTS/DIAB005
Fabrício Almeida-Silva, K. C. Moharana, T. M. Venancio
In the past decade, over 3000 samples of soybean transcriptomic data have accumulated in public repositories. Here, we review the state of the art in soybean transcriptomics, highlighting the major microarray and RNA-seq studies that investigated soybean transcriptional programs in different tissues and conditions. Further, we propose approaches for integrating such big data using gene coexpression network and outline important web resources that may facilitate soybean data acquisition and analysis, contributing to the acceleration of soybean breeding and functional genomics research.
{"title":"The state of the art in soybean transcriptomics resources and gene coexpression networks","authors":"Fabrício Almeida-Silva, K. C. Moharana, T. M. Venancio","doi":"10.1093/INSILICOPLANTS/DIAB005","DOIUrl":"https://doi.org/10.1093/INSILICOPLANTS/DIAB005","url":null,"abstract":"\u0000 In the past decade, over 3000 samples of soybean transcriptomic data have accumulated in public repositories. Here, we review the state of the art in soybean transcriptomics, highlighting the major microarray and RNA-seq studies that investigated soybean transcriptional programs in different tissues and conditions. Further, we propose approaches for integrating such big data using gene coexpression network and outline important web resources that may facilitate soybean data acquisition and analysis, contributing to the acceleration of soybean breeding and functional genomics research.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/INSILICOPLANTS/DIAB005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47327727","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 : 2021-01-01DOI: 10.1093/INSILICOPLANTS/DIAB014
L. Uys, J. Hofmeyr, J. Rohwer
The accompanying paper (Uys et al., in silico Plants, 2021: diab013) presented a core model of sucrose accumulation within the advection–diffusion–reaction framework, which is able to capture the spatio-temporal evolution of the system from a set of initial conditions. This paper presents a sensitivity analysis of this model. Because this is a non-steady-state model based on partial differential equations, we performed the sensitivity analysis using two approaches from engineering. The Morris method is based on a one-at-a-time design, perturbing parameters individually and calculating the influence on model output in terms of elementary effects. Fourier amplitude sensitivity test (FAST) is a global sensitivity analysis method, where all parameters are perturbed simultaneously, oscillating at different frequencies, enabling the calculation of the contribution of each parameter through Fourier analysis. Overall, both methods gave similar results. Perturbations in reactions tended to have a large influence on their own rate, as well as on directly connected metabolites. Sensitivities varied both with the time of the simulation and the position along the sugarcane stalk. Our results suggest that vacuolar sucrose concentrations are most sensitive to vacuolar invertase in the centre of the stalk, but that phloem unloading and vacuolar sucrose uptake also contribute, especially towards the stalk edges. Sucrose in the phloem was most sensitive to phloem loading at the nodes, but most sensitive to phloem unloading in the middle of the internodes. Sink concentrations of sucrose in the symplast were most sensitive to phloem unloading in the middle of the internodes, but at the nodes cytosolic invertase had the greatest effect.
{"title":"Coupling kinetic models and advection–diffusion equations. 2. Sensitivity analysis of an advection–diffusion–reaction model","authors":"L. Uys, J. Hofmeyr, J. Rohwer","doi":"10.1093/INSILICOPLANTS/DIAB014","DOIUrl":"https://doi.org/10.1093/INSILICOPLANTS/DIAB014","url":null,"abstract":"\u0000 The accompanying paper (Uys et al., in silico Plants, 2021: diab013) presented a core model of sucrose accumulation within the advection–diffusion–reaction framework, which is able to capture the spatio-temporal evolution of the system from a set of initial conditions. This paper presents a sensitivity analysis of this model. Because this is a non-steady-state model based on partial differential equations, we performed the sensitivity analysis using two approaches from engineering. The Morris method is based on a one-at-a-time design, perturbing parameters individually and calculating the influence on model output in terms of elementary effects. Fourier amplitude sensitivity test (FAST) is a global sensitivity analysis method, where all parameters are perturbed simultaneously, oscillating at different frequencies, enabling the calculation of the contribution of each parameter through Fourier analysis. Overall, both methods gave similar results. Perturbations in reactions tended to have a large influence on their own rate, as well as on directly connected metabolites. Sensitivities varied both with the time of the simulation and the position along the sugarcane stalk. Our results suggest that vacuolar sucrose concentrations are most sensitive to vacuolar invertase in the centre of the stalk, but that phloem unloading and vacuolar sucrose uptake also contribute, especially towards the stalk edges. Sucrose in the phloem was most sensitive to phloem loading at the nodes, but most sensitive to phloem unloading in the middle of the internodes. Sink concentrations of sucrose in the symplast were most sensitive to phloem unloading in the middle of the internodes, but at the nodes cytosolic invertase had the greatest effect.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/INSILICOPLANTS/DIAB014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42011389","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 : 2021-01-01DOI: 10.1093/INSILICOPLANTS/DIAB013
L. Uys, J. Hofmeyr, J. Rohwer
The sugarcane stalk, besides being the main structural component of the plant, is also the major storage organ for carbohydrates. Previous studies have modelled the sucrose accumulation pathway in the internodal storage parenchyma of sugarcane using kinetic models cast as systems of ordinary differential equations. To address the shortcomings of these models, which did not include subcellular compartmentation or spatial information, the present study extends the original models within an advection–diffusion–reaction framework, requiring the use of partial differential equations to model sucrose metabolism coupled to phloem translocation. We propose a kinetic model of a coupled reaction network where species can be involved in chemical reactions and/or be transported over long distances in a fluid medium by advection or diffusion. Darcy’s law is used to model fluid flow and allows a simplified, phenomenological approach to be applied to translocation in the phloem. Similarly, generic reversible Hill equations are used to model biochemical reaction rates. Numerical solutions to this formulation are demonstrated with time-course analysis of a simplified model of sucrose accumulation. The model shows sucrose accumulation in the vacuoles of stalk parenchyma cells, and is moreover able to demonstrate the upregulation of photosynthesis in response to a change in sink demand. The model presented is able to capture the spatio-temporal evolution of the system from a set of initial conditions by combining phloem flow, diffusion, transport of metabolites between compartments and biochemical enzyme-catalysed reactions in a rigorous, quantitative framework that can form the basis for future modelling and experimental design.
{"title":"Coupling kinetic models and advection–diffusion equations. 1. Framework development and application to sucrose translocation and metabolism in sugarcane","authors":"L. Uys, J. Hofmeyr, J. Rohwer","doi":"10.1093/INSILICOPLANTS/DIAB013","DOIUrl":"https://doi.org/10.1093/INSILICOPLANTS/DIAB013","url":null,"abstract":"\u0000 The sugarcane stalk, besides being the main structural component of the plant, is also the major storage organ for carbohydrates. Previous studies have modelled the sucrose accumulation pathway in the internodal storage parenchyma of sugarcane using kinetic models cast as systems of ordinary differential equations. To address the shortcomings of these models, which did not include subcellular compartmentation or spatial information, the present study extends the original models within an advection–diffusion–reaction framework, requiring the use of partial differential equations to model sucrose metabolism coupled to phloem translocation. We propose a kinetic model of a coupled reaction network where species can be involved in chemical reactions and/or be transported over long distances in a fluid medium by advection or diffusion. Darcy’s law is used to model fluid flow and allows a simplified, phenomenological approach to be applied to translocation in the phloem. Similarly, generic reversible Hill equations are used to model biochemical reaction rates. Numerical solutions to this formulation are demonstrated with time-course analysis of a simplified model of sucrose accumulation. The model shows sucrose accumulation in the vacuoles of stalk parenchyma cells, and is moreover able to demonstrate the upregulation of photosynthesis in response to a change in sink demand. The model presented is able to capture the spatio-temporal evolution of the system from a set of initial conditions by combining phloem flow, diffusion, transport of metabolites between compartments and biochemical enzyme-catalysed reactions in a rigorous, quantitative framework that can form the basis for future modelling and experimental design.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/INSILICOPLANTS/DIAB013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41884747","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 : 2021-01-01DOI: 10.1093/INSILICOPLANTS/DIAB017
A. Potgieter, Yan Zhao, P. Zarco-Tejada, K. Chenu, Yifan Zhang, K. Porker, B. Biddulph, Y. Dang, Tim Neale, Fred Roosta, Scott A. Chapman
The downside risk of crop production affects the entire supply chain of the agricultural industry nationally and globally. This also has a profound impact on food security, and thus livelihoods, in many parts of the world. The advent of high temporal, spatial and spectral resolution remote sensing platforms, specifically during the last 5 years, and the advancement in software pipelines and cloud computing have resulted in the collating, analysing and application of ‘BIG DATA’ systems, especially in agriculture. Furthermore, the application of traditional and novel computational and machine learning approaches is assisting in resolving complex interactions, to reveal components of ecophysiological systems that were previously deemed either ‘too difficult’ to solve or ‘unseen’. In this review, digital technologies encompass mathematical, computational, proximal and remote sensing technologies. Here, we review the current state of digital technologies and their application in broad-acre cropping systems globally and in Australia. More specifically, we discuss the advances in (i) remote sensing platforms, (ii) machine learning approaches to discriminate between crops and (iii) the prediction of crop phenological stages from both sensing and crop simulation systems for major Australian winter crops. An integrated solution is proposed to allow accurate development, validation and scalability of predictive tools for crop phenology mapping at within-field scales, across extensive cropping areas.
{"title":"Evolution and application of digital technologies to predict crop type and crop phenology in agriculture","authors":"A. Potgieter, Yan Zhao, P. Zarco-Tejada, K. Chenu, Yifan Zhang, K. Porker, B. Biddulph, Y. Dang, Tim Neale, Fred Roosta, Scott A. Chapman","doi":"10.1093/INSILICOPLANTS/DIAB017","DOIUrl":"https://doi.org/10.1093/INSILICOPLANTS/DIAB017","url":null,"abstract":"\u0000 The downside risk of crop production affects the entire supply chain of the agricultural industry nationally and globally. This also has a profound impact on food security, and thus livelihoods, in many parts of the world. The advent of high temporal, spatial and spectral resolution remote sensing platforms, specifically during the last 5 years, and the advancement in software pipelines and cloud computing have resulted in the collating, analysing and application of ‘BIG DATA’ systems, especially in agriculture. Furthermore, the application of traditional and novel computational and machine learning approaches is assisting in resolving complex interactions, to reveal components of ecophysiological systems that were previously deemed either ‘too difficult’ to solve or ‘unseen’. In this review, digital technologies encompass mathematical, computational, proximal and remote sensing technologies. Here, we review the current state of digital technologies and their application in broad-acre cropping systems globally and in Australia. More specifically, we discuss the advances in (i) remote sensing platforms, (ii) machine learning approaches to discriminate between crops and (iii) the prediction of crop phenological stages from both sensing and crop simulation systems for major Australian winter crops. An integrated solution is proposed to allow accurate development, validation and scalability of predictive tools for crop phenology mapping at within-field scales, across extensive cropping areas.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42376160","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 : 2021-01-01DOI: 10.1093/INSILICOPLANTS/DIAB008
Guénolé Boulch, Chloé Elmerich, A. Djemel, B. Lange
Soybean is a candidate crop to increase the independency of Europe in leguminous protein crops. However, its adaptation to northern European regions is not yet well defined due to the lack of long-term references. Herein, we simulated soybean yield potential in northern France and identified the main yield limiting factors under rainfed vs. irrigated conditions. Two cultivars representing maturity groups 000 and 00 were planted within three different trials. Leaf area index, shoot and pod biomass, main phenological stages and yield were recorded to evaluate CROPGRO-soybean model predictability. Adjustment of genetic coefficients was performed prior to simulate yield on 21-years weather database (1999–2018) at Beauvais (France, N 49.46°, E 2.07°) and Estrées-Mons (France, N 49.88°, E 3.01°) under different water regimes and planting dates. Predictions showed that adding irrigation at grain filling period would increase yield potential to the level of non-water limited scenarios. Although simulated yield variability is reduced with irrigation, the remaining variability suggests that water is not the only yield-limiting factor. A tentative explanation is proposed by deriving environmental covariates from the model. The analysis confirmed the importance of precipitation amount (optimum around 200 mm) and duration (optimum around 60 days) of the flowering to physiological maturity period under rainfed conditions. Under irrigated conditions, increasing evapotranspiration and average minimum temperature affected simulated yield positively while increasing the number of days below 10 °C had a negative impact. These results give insights for soybean crop management and bring indications to breeders for adapting the existing genetic material to northern Europe.
{"title":"Evaluation of soybean (Glycine max L.) adaptation to northern European regions under different agro-climatic scenarios","authors":"Guénolé Boulch, Chloé Elmerich, A. Djemel, B. Lange","doi":"10.1093/INSILICOPLANTS/DIAB008","DOIUrl":"https://doi.org/10.1093/INSILICOPLANTS/DIAB008","url":null,"abstract":"Soybean is a candidate crop to increase the independency of Europe in leguminous protein crops. However, its adaptation to northern European regions is not yet well defined due to the lack of long-term references. Herein, we simulated soybean yield potential in northern France and identified the main yield limiting factors under rainfed vs. irrigated conditions. Two cultivars representing maturity groups 000 and 00 were planted within three different trials. Leaf area index, shoot and pod biomass, main phenological stages and yield were recorded to evaluate CROPGRO-soybean model predictability. Adjustment of genetic coefficients was performed prior to simulate yield on 21-years weather database (1999–2018) at Beauvais (France, N 49.46°, E 2.07°) and Estrées-Mons (France, N 49.88°, E 3.01°) under different water regimes and planting dates. Predictions showed that adding irrigation at grain filling period would increase yield potential to the level of non-water limited scenarios. Although simulated yield variability is reduced with irrigation, the remaining variability suggests that water is not the only yield-limiting factor. A tentative explanation is proposed by deriving environmental covariates from the model. The analysis confirmed the importance of precipitation amount (optimum around 200 mm) and duration (optimum around 60 days) of the flowering to physiological maturity period under rainfed conditions. Under irrigated conditions, increasing evapotranspiration and average minimum temperature affected simulated yield positively while increasing the number of days below 10 °C had a negative impact. These results give insights for soybean crop management and bring indications to breeders for adapting the existing genetic material to northern Europe.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/INSILICOPLANTS/DIAB008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46881878","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 : 2020-12-30DOI: 10.1093/insilicoplants/diaa017
A. Burgess, Tiara Herman, Asgar Ali, E. Murchie
Increasing nitrogen use efficiency is a key target for yield improvement programs. Here we identify features of rice canopy architecture during altered N availability and link them to photosynthetic productivity. Empirical mathematical modelling, high-resolution 3-dimensional (3D) reconstruction and gas exchange measurements were employed to investigate the effect of a mild N deficiency vs. surplus N application on canopy architecture, light and photosynthesis distribution throughout development. Three contrasting rice lines: two Malaysian rice varieties (MR219 and MR253) and a high-yielding indica cultivar (IR64) were cultivated. 3D reconstruction indicated key N-dependent differences in plant architecture and canopy light distribution including changes to leaf area index (LAI), tiller number, leaf angle and modelled light extinction coefficients. Measured leaf photosynthetic capacity did not differ substantially between the high and reduced N treatments; however, modelled canopy photosynthesis rate indicated a higher carbon gain per unit leaf area for the reduced N treatment but a higher carbon gain per unit ground area for the high N treatment. This is a result of altered canopy structure leading to increased light distribution under reduced N which partially offsets the reduced LAI. Within rice, altered N availability results in the development of full photosynthetically functional leaves, but leads to altered canopy architecture, light distribution and overall productivity suggested that N availability can be fine-tuned to optimize biomass production. We propose wider use of 3D reconstruction to assess canopy architecture and productivity under differing N availabilities for a range of species.
{"title":"Interactions between nitrogen nutrition, canopy architecture and photosynthesis in rice, assessed using high-resolution 3D reconstruction","authors":"A. Burgess, Tiara Herman, Asgar Ali, E. Murchie","doi":"10.1093/insilicoplants/diaa017","DOIUrl":"https://doi.org/10.1093/insilicoplants/diaa017","url":null,"abstract":"\u0000 Increasing nitrogen use efficiency is a key target for yield improvement programs. Here we identify features of rice canopy architecture during altered N availability and link them to photosynthetic productivity. Empirical mathematical modelling, high-resolution 3-dimensional (3D) reconstruction and gas exchange measurements were employed to investigate the effect of a mild N deficiency vs. surplus N application on canopy architecture, light and photosynthesis distribution throughout development. Three contrasting rice lines: two Malaysian rice varieties (MR219 and MR253) and a high-yielding indica cultivar (IR64) were cultivated. 3D reconstruction indicated key N-dependent differences in plant architecture and canopy light distribution including changes to leaf area index (LAI), tiller number, leaf angle and modelled light extinction coefficients. Measured leaf photosynthetic capacity did not differ substantially between the high and reduced N treatments; however, modelled canopy photosynthesis rate indicated a higher carbon gain per unit leaf area for the reduced N treatment but a higher carbon gain per unit ground area for the high N treatment. This is a result of altered canopy structure leading to increased light distribution under reduced N which partially offsets the reduced LAI. Within rice, altered N availability results in the development of full photosynthetically functional leaves, but leads to altered canopy architecture, light distribution and overall productivity suggested that N availability can be fine-tuned to optimize biomass production. We propose wider use of 3D reconstruction to assess canopy architecture and productivity under differing N availabilities for a range of species.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/insilicoplants/diaa017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42933065","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 : 2020-12-28DOI: 10.1093/insilicoplants/diaa016
Mark Cooper, O. Powell, K. Voss-Fels, C. Messina, C. Gho, D. Podlich, F. Technow, S. Chapman, C. Beveridge, D. Ortiz-Barrientos, G. Hammer
Plant-breeding programs are designed and operated over multiple cycles to systematically change the genetic makeup of plants to achieve improved trait performance for a Target Population of Environments (TPE). Within each cycle, selection applied to the standing genetic variation within a structured reference population of genotypes (RPG) is the primary mechanism by which breeding programs make the desired genetic changes. Selection operates to change the frequencies of the alleles of the genes controlling trait variation within the RPG. The structure of the RPG and the TPE has important implications for the design of optimal breeding strategies. The breeder’s equation, together with the quantitative genetic theory behind the equation, informs many of the principles for design of breeding programs. The breeder’s equation can take many forms depending on the details of the breeding strategy. Through the genetic changes achieved by selection, the cultivated varieties of crops (cultivars) are improved for use in agriculture. From a breeding perspective, selection for specific trait combinations requires a quantitative link between the effects of the alleles of the genes impacted by selection and the trait phenotypes of plants and their breeding value. This gene-to-phenotype link function provides the G2P map for one to many traits. For complex traits controlled by many genes, the infinitesimal model for trait genetic variation is the dominant G2P model of quantitative genetics. Here we consider motivations and potential benefits of using the hierarchical structure of crop models as CGM-G2P trait link functions in combination with the infinitesimal model for the design and optimization of selection in breeding programs.
{"title":"Modelling selection response in plant-breeding programs using crop models as mechanistic gene-to-phenotype (CGM-G2P) multi-trait link functions","authors":"Mark Cooper, O. Powell, K. Voss-Fels, C. Messina, C. Gho, D. Podlich, F. Technow, S. Chapman, C. Beveridge, D. Ortiz-Barrientos, G. Hammer","doi":"10.1093/insilicoplants/diaa016","DOIUrl":"https://doi.org/10.1093/insilicoplants/diaa016","url":null,"abstract":"\u0000 Plant-breeding programs are designed and operated over multiple cycles to systematically change the genetic makeup of plants to achieve improved trait performance for a Target Population of Environments (TPE). Within each cycle, selection applied to the standing genetic variation within a structured reference population of genotypes (RPG) is the primary mechanism by which breeding programs make the desired genetic changes. Selection operates to change the frequencies of the alleles of the genes controlling trait variation within the RPG. The structure of the RPG and the TPE has important implications for the design of optimal breeding strategies. The breeder’s equation, together with the quantitative genetic theory behind the equation, informs many of the principles for design of breeding programs. The breeder’s equation can take many forms depending on the details of the breeding strategy. Through the genetic changes achieved by selection, the cultivated varieties of crops (cultivars) are improved for use in agriculture. From a breeding perspective, selection for specific trait combinations requires a quantitative link between the effects of the alleles of the genes impacted by selection and the trait phenotypes of plants and their breeding value. This gene-to-phenotype link function provides the G2P map for one to many traits. For complex traits controlled by many genes, the infinitesimal model for trait genetic variation is the dominant G2P model of quantitative genetics. Here we consider motivations and potential benefits of using the hierarchical structure of crop models as CGM-G2P trait link functions in combination with the infinitesimal model for the design and optimization of selection in breeding programs.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2020-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/insilicoplants/diaa016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43246167","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 : 2020-12-12DOI: 10.1093/insilicoplants/diaa014
Rachel G Shekar
Przemysław (Przemek) Prusinkiewicz is a Professor of Computer Science at the University of Calgary, Canada, where he creates models, simulations and visualizations of plant development. He received his MSc and PhD at the Technical University of Warsaw, where he studied Computer Science and Engineering under Prof. Stanislaw Budkowski.
{"title":"Researcher Profile: Przemysław Prusinkiewicz","authors":"Rachel G Shekar","doi":"10.1093/insilicoplants/diaa014","DOIUrl":"https://doi.org/10.1093/insilicoplants/diaa014","url":null,"abstract":"\u0000 Przemysław (Przemek) Prusinkiewicz is a Professor of Computer Science at the University of Calgary, Canada, where he creates models, simulations and visualizations of plant development. He received his MSc and PhD at the Technical University of Warsaw, where he studied Computer Science and Engineering under Prof. Stanislaw Budkowski.","PeriodicalId":36138,"journal":{"name":"in silico Plants","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/insilicoplants/diaa014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43428065","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 : 2020-12-01DOI: 10.1093/insilicoplants/diaa009
Honglong Zhao, Qiming Tang, Tian Chang, Yi Xiao, Xin-Guang Zhu
Overexpressing Calvin–Benson cycle (CBC) enzyme shown to limit the flow of CO2 through the cycle is a major approach to improve photosynthesis. Though control coefficients of CBC enzymes vary under different environmental and developmental conditions, it is usually implicitly assumed that enzymes in the CBC have a monotonic impact on the CBC fluxes. Here, with a dynamic systems model of the photosynthetic carbon metabolism, we show that, for glycerate-3-phosphate kinase (PGAK), fructose-1,6-bisphosphatase (FBPase), fructose-1,6-bisphosphate aldolase (FBA) and transketolase (TKa), individually increasing activity of these CBC enzymes theoretically leads to an initial increase then decrease in the fluxes through the CBC. Also, the inhibition constants of adenosine diphosphate (ADP) for PGAK and of fructose-6-phosphate (F6P) for FBPase influence the CBC flux in a biphasic manner. These predicted enzymes showing a biphasic manner are always located in different subcycles of the CBC, which consume the shared substrates in the early steps in the CBC and produce intermediates used as substrates for enzymes in the later reactions. We show that the excessive increase in activities of enzymes in one subcycle consuming the shared metabolite could cause low concentrations of metabolites in the other subcycles, which results in low reaction rates of the later reactions and hence lowers overall CBC flux. This study provides a model to explain the underlying reasons that overexpression of enzymes in the CBC sometimes can negatively impact photosynthesis. We find that balanced activities of enzymes in the subcycles of the CBC are required to gain a higher efficiency of the CBC.
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