Pub Date : 2024-07-12DOI: 10.1093/insilicoplants/diae011
R. J. Rutjens, J. B. Evers, L. R. Band, M. D. Jones, M. R. Owen
Performing global sensitivity analysis on functional-structural plant models (FSP models) can greatly benefit both model development and analysis by identifying the relevance of parameters for specific model outputs. Setting unimportant parameters to a fixed value decreases dimensionality of the typically large model parameter space. Efforts can then be concentrated on accurately estimating the most important input parameters. In this work we apply the Elementary Effects method for dimensional models with arbitrary input types, adapting the method to models with inherent randomness. Our FSP model simulated a maize stand for 160 days of growth, considering three outputs: yield, peak biomass and peak leaf area index (LAI). Of 52 input parameters, 12 were identified as important for yield and peak biomass and 14 for LAI. Over 70% of parameters were deemed unimportant for the outputs under consideration, including most parameters relating to crop architecture. Parameters governing shade avoidance response and leaf appearance rate (phyllochron) were also unimportant; variations in these physiological and developmental parameters do lead to visible changes in plant architecture, but not to significant changes in yield, biomass or LAI. Some inputs identified as unimportant due to their low sensitivity index have a relatively high standard deviation of effects, with high fluctuations around a low mean, which could indicate non-linearity or interaction effects. Consequently, parameters with low sensitivity index but high standard deviation should be investigated further. Our study demonstrates that global sensitivity analysis can reveal which parameter values have the most influence on key outputs, predicting specific parameter estimates that need to be carefully characterised.
{"title":"Are we focusing on the right parameters? Insights from Global Sensitivity Analysis of a Functional-Structural Plant Model","authors":"R. J. Rutjens, J. B. Evers, L. R. Band, M. D. Jones, M. R. Owen","doi":"10.1093/insilicoplants/diae011","DOIUrl":"https://doi.org/10.1093/insilicoplants/diae011","url":null,"abstract":"\u0000 Performing global sensitivity analysis on functional-structural plant models (FSP models) can greatly benefit both model development and analysis by identifying the relevance of parameters for specific model outputs. Setting unimportant parameters to a fixed value decreases dimensionality of the typically large model parameter space. Efforts can then be concentrated on accurately estimating the most important input parameters. In this work we apply the Elementary Effects method for dimensional models with arbitrary input types, adapting the method to models with inherent randomness. Our FSP model simulated a maize stand for 160 days of growth, considering three outputs: yield, peak biomass and peak leaf area index (LAI). Of 52 input parameters, 12 were identified as important for yield and peak biomass and 14 for LAI. Over 70% of parameters were deemed unimportant for the outputs under consideration, including most parameters relating to crop architecture. Parameters governing shade avoidance response and leaf appearance rate (phyllochron) were also unimportant; variations in these physiological and developmental parameters do lead to visible changes in plant architecture, but not to significant changes in yield, biomass or LAI. Some inputs identified as unimportant due to their low sensitivity index have a relatively high standard deviation of effects, with high fluctuations around a low mean, which could indicate non-linearity or interaction effects. Consequently, parameters with low sensitivity index but high standard deviation should be investigated further. Our study demonstrates that global sensitivity analysis can reveal which parameter values have the most influence on key outputs, predicting specific parameter estimates that need to be carefully characterised.","PeriodicalId":505799,"journal":{"name":"in silico Plants","volume":"81 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141652987","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 : 2024-07-02DOI: 10.1093/insilicoplants/diae010
Mercy Appiah, G. Bracho-Mujica, Simon Svane, M. Styczen, K. Kersebaum, R. Rötter
Crop model-aided ideotyping can accelerate the breeding of resilient barley cultivars. Yet, the accuracy of process descriptions in the crop models still requires substantial improvement, which is only possible with high-quality experimental data. Despite being demanded frequently, such data is still rarely available, especially for Northern European barley production. This study is one of the first to contribute to closing this existing data gap through the targeted collection of high-quality experimental data in pluri-annual, multi-location spring barley field trials in Denmark. With this data the prediction accuracy of APSIM significantly increased in contrast to commonly utilized lower quality datasets. Using this data for model calibration resulted in more accurate predictions of in-season plant development and important state variables (e.g. final grain yield and biomass). The model’s prediction accuracy can ultimately be further improved by examining remaining model weaknesses that were discoverable with the high quality data. Process descriptions regarding, e.g., early and late leaf development, soil water dynamics and respective plant response appeared to require further improvement. By illustrating the effect of data quality on model performance we reinforce the need for more model-guided field experiments.
{"title":"Insights from utilizing data of different quality levels for simulating barley performance under Nordic conditions: APSIM model evaluation","authors":"Mercy Appiah, G. Bracho-Mujica, Simon Svane, M. Styczen, K. Kersebaum, R. Rötter","doi":"10.1093/insilicoplants/diae010","DOIUrl":"https://doi.org/10.1093/insilicoplants/diae010","url":null,"abstract":"\u0000 Crop model-aided ideotyping can accelerate the breeding of resilient barley cultivars. Yet, the accuracy of process descriptions in the crop models still requires substantial improvement, which is only possible with high-quality experimental data. Despite being demanded frequently, such data is still rarely available, especially for Northern European barley production. This study is one of the first to contribute to closing this existing data gap through the targeted collection of high-quality experimental data in pluri-annual, multi-location spring barley field trials in Denmark. With this data the prediction accuracy of APSIM significantly increased in contrast to commonly utilized lower quality datasets. Using this data for model calibration resulted in more accurate predictions of in-season plant development and important state variables (e.g. final grain yield and biomass). The model’s prediction accuracy can ultimately be further improved by examining remaining model weaknesses that were discoverable with the high quality data. Process descriptions regarding, e.g., early and late leaf development, soil water dynamics and respective plant response appeared to require further improvement. By illustrating the effect of data quality on model performance we reinforce the need for more model-guided field experiments.","PeriodicalId":505799,"journal":{"name":"in silico Plants","volume":"32 49","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141685449","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 : 2024-06-06DOI: 10.1093/insilicoplants/diae008
A. M. Chan, Miao Lin Pay, Jesper Christensen, Fei He, Laura C. Roden, Hafiz Ahmed, Mathias Foo
In smart greenhouse farming, the impact of light qualities on plant growth and development is crucial but lacks systematic identification of optimal combinations. This study addresses this gap by analysing various light properties’ effects (photoperiod, intensity, ratio, light-dark order) on Arabidopsis thaliana growth using days-to-flower (DTF) and hypocotyl length as proxies to measure plant growth and development. After establishing suitable ranges through comprehensive literature review, these properties were varied within those ranges. Compared to white light, a 16-hour cycle of blue light reduces DTF and hypocotyl length by 12% and 3%, respectively. Interestingly, similar results can be achieved using a shorter photoperiod of 14-hour light (composed of 8 hours of a mixture of 66.7 µmol/m2s−1 red and 800 µmol/m2s−1 blue lights (i.e., blue: red ratio of 12:1) followed by 6 hours of monochromatic red light and 10-hour dark. These findings offer potential for efficient growth light recipes in smart greenhouse farming, optimising productivity while minimising energy consumption.
{"title":"Red, Blue or Mix: Choice of Optimal Light Qualities for Enhanced Plant Growth and Development through in silico Analysis","authors":"A. M. Chan, Miao Lin Pay, Jesper Christensen, Fei He, Laura C. Roden, Hafiz Ahmed, Mathias Foo","doi":"10.1093/insilicoplants/diae008","DOIUrl":"https://doi.org/10.1093/insilicoplants/diae008","url":null,"abstract":"\u0000 In smart greenhouse farming, the impact of light qualities on plant growth and development is crucial but lacks systematic identification of optimal combinations. This study addresses this gap by analysing various light properties’ effects (photoperiod, intensity, ratio, light-dark order) on Arabidopsis thaliana growth using days-to-flower (DTF) and hypocotyl length as proxies to measure plant growth and development. After establishing suitable ranges through comprehensive literature review, these properties were varied within those ranges. Compared to white light, a 16-hour cycle of blue light reduces DTF and hypocotyl length by 12% and 3%, respectively. Interestingly, similar results can be achieved using a shorter photoperiod of 14-hour light (composed of 8 hours of a mixture of 66.7 µmol/m2s−1 red and 800 µmol/m2s−1 blue lights (i.e., blue: red ratio of 12:1) followed by 6 hours of monochromatic red light and 10-hour dark. These findings offer potential for efficient growth light recipes in smart greenhouse farming, optimising productivity while minimising energy consumption.","PeriodicalId":505799,"journal":{"name":"in silico Plants","volume":"2 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141379309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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