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| Hauptseite > Publikationsdatenbank > Parameter sensitivity analysis of a root system architecture model based on virtual field sampling > print |
| Hauptseite > Publikationsdatenbank > Parameter sensitivity analysis of a root system architecture model based on virtual field sampling > print |
| 001 | 861707 | ||
| 005 | 20210131030920.0 | ||
| 024 | 7 | _ | |a 10.1007/s11104-019-03993-3 |2 doi |
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| 100 | 1 | _ | |a Morandage, Tharaka Shehan |0 P:(DE-Juel1)168106 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Parameter sensitivity analysis of a root system architecture model based on virtual field sampling |
| 260 | _ | _ | |a Dordrecht [u.a.] |c 2019 |b Springer Science + Business Media B.V |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a AimsTraits of the plant root system architecture (RSA) play a key role in crop performance. Therefore, architectural root traits are becoming increasingly important in plant phenotyping. In this study, we use a mathematical model to investigate the sensitivity of characteristic root system measures, obtained from different classical field root sampling schemes, to RSA parameters.MethodsRoot systems of wheat and maize were simulated and sampled virtually to mimic real field experiments using the root system architecture (RSA) model CRootBox. By means of a sensitivity analysis, we found RSA parameters that significantly influenced the virtual field sampling results. To identify correlations between sensitivities, we carried out a principal component analysis.ResultsWe found that the parameters of zero order roots are the most sensitive, and parameters of higher order roots are less sensitive. Moreover, different characteristic root system measures showed different sensitivity to RSA parameters. RSA parameters that could be derived independently from different types of field observations were identified.ConclusionsSelection of characteristic root system measures and parameters is essential to reduce the problem of parameter equifinality in inverse modeling with multi-parameter models and is an important step in the characterization of root traits from field observations. |
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| 773 | _ | _ | |a 10.1007/s11104-019-03993-3 |0 PERI:(DE-600)1478535-3 |n 1-2 |p 101-126 |t Plant and soil |v 438 |y 2019 |x 0032-079X |
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