Bayesian multi-purpose modelling of plant growth and development across scales
| dc.contributor.advisor | Streck, Thilo | |
| dc.contributor.author | Viswanathan, Michelle | |
| dc.date.accepted | 2023-12-21 | |
| dc.date.accessioned | 2024-10-09T10:37:47Z | |
| dc.date.available | 2024-10-09T10:37:47Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Crop models are invaluable tools for predicting the impact of climate change on crop production and assessing the fate of agrochemicals in the environment. To ensure robust predictions of crop yield, for example, models are usually calibrated to observations of plant growth and phenological development using different methods. However, various sources of uncertainty exist in the model inputs, parameters, equations, observations, etc., which need to be quantified, especially when model predictions influence decision-making. Bayesian inference is suitable for this purpose since it enables different uncertainties to be taken into account, while also incorporating prior knowledge. Thus, Bayesian methods are used for model calibration to improve the model and enhance prediction quality. However, this improvement in the model and its prediction quality does not always occur due to the presence of model errors. These errors are a result of incomplete knowledge or simplifying assumptions made to reduce model complexity and computational costs. For instance, crop models are used for regional scale simulations thereby assuming that these point-based models are able to represent processes that act at regional scale. Additionally, simple statistical assumptions are made about uncertainty in model errors during Bayesian calibration. In this work, the problems arising from such applications are analysed and other Bayesian approaches are investigated as potential solutions. A conceptually simple Bayesian approach of sequentially updating a maize phenology model, an important component in plant models, was investigated as yearly observation data were gathered. In this approach, model parameters and their uncertainty were estimated while accounting for observation uncertainty. As the model was calibrated to increasing amounts of observation data, the uncertainty in the model parameters reduced as expected. However, the prediction quality of the calibrated model did not always improve in spite of more data being available for potentially improving the model. This discrepancy was attributed to the presence of errors in the model structure, possibly due to missing environmental dependencies that were ignored during calibration. As a potential solution, the model was calibrated using Bayesian multi-level modelling which could account for model errors. Furthermore, this approach accounted for the hierarchical data structure of cultivars nested within maize ripening groups, thus simultaneously obtaining model parameter estimates for the species, ripening groups and cultivars. Applying this approach improved the model's calibration quality and further aided in identifying possible model deficits related to temperature effects in the post-flowering phase of development and soil moisture. As another potential solution, an alternative calibration strategy was tested which accounted for model errors by relaxing the strict statistical assumptions in classical Bayesian inference. This was done by first acknowledging that due to model errors, different data sets may yield diverse solutions to the calibration problem. Thus, instead of fitting the model to all data sets together and finding a compromise solution, a fit was found to each data set. This was implemented by modifying the likelihood, a term that accounts for information content of the data. An additive rather than the classical multiplicative strategy was used to combine likelihood values from different data sets. This approach resulted in conservative but more reliable predictions than the classical approach in most cases. The classical approach resulted in better predictions only when the prediction target represented an average of the calibration data. The above-mentioned results show that Bayesian methods with representative error assumptions lead to improved model performance and a more realistic quantification of uncertainties. This is a step towards the effective application of process-based crop models for developing suitable adaptation and mitigation strategies. | en |
| dc.identifier.uri | https://hohpublica.uni-hohenheim.de/handle/123456789/15937 | |
| dc.identifier.uri | https://doi.org/10.60848/10890 | |
| dc.language.iso | eng | |
| dc.rights.license | cc_by | |
| dc.subject | crop model | |
| dc.subject | Bayesian calibration | |
| dc.subject | phenology | |
| dc.subject | silage maize | |
| dc.subject | data-model integration | |
| dc.subject.ddc | 630 | |
| dc.title | Bayesian multi-purpose modelling of plant growth and development across scales | en |
| dc.type.dini | DoctoralThesis | |
| local.export.bibtex | @phdthesis{Viswanathan2024, url = {https://hohpublica.uni-hohenheim.de/handle/123456789/15937}, author = {Viswanathan, Michelle}, title = {Bayesian Multi-Purpose Modelling of Plant Growth and Development Across Scales}, year = {2024}, } | |
| local.export.bibtexAuthor | Viswanathan, Michelle | |
| local.export.bibtexKey | Viswanathan2024 | |
| local.export.bibtexType | @phdthesis | |
| local.faculty.number | 2 | |
| local.institute.number | 310 |