Ming Wang1,2, Neil White2,3, Jim Hanan2, Di He4, Enli Wang4, Bronwen Cribb5,6, Darren J Kriticos1,6, Dean Paini1, Volker Grimm7,8. 1. Commonwealth Scientific and Industrial Research Organisation (CSIRO), Health & Biosecurity, Canberra, Australia. 2. The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Centre for Horticultural Science, Brisbane, Australia. 3. Department of Agriculture and Fisheries, Toowoomba, Australia. 4. Commonwealth Scientific and Industrial Research Organisation (CSIRO), Agriculture & Food, Canberra, Australia. 5. The University of Queensland, Centre for Microscopy and Microanalysis, Brisbane, Australia. 6. The University of Queensland, School of Biological Sciences, Brisbane, Australia. 7. Helmholtz Centre for Environmental Research-UFZ, Department of Ecological Modelling, Permoserstr, Germany. 8. University of Potsdam, Department of Plant Ecology and Nature Conservation, Am Mühlenberg, Germany.
Abstract
BACKGROUND AND AIMS: Functional-structural plant (FSP) models provide insights into the complex interactions between plant architecture and underlying developmental mechanisms. However, parameter estimation of FSP models remains challenging. We therefore used pattern-oriented modelling (POM) to test whether parameterization of FSP models can be made more efficient, systematic and powerful. With POM, a set of weak patterns is used to determine uncertain parameter values, instead of measuring them in experiments or observations, which often is infeasible. METHODS: We used an existing FSP model of avocado (Persea americana 'Hass') and tested whether POM parameterization would converge to an existing manual parameterization. The model was run for 10 000 parameter sets and model outputs were compared with verification patterns. Each verification pattern served as a filter for rejecting unrealistic parameter sets. The model was then validated by running it with the surviving parameter sets that passed all filters and then comparing their pooled model outputs with additional validation patterns that were not used for parameterization. KEY RESULTS: POM calibration led to 22 surviving parameter sets. Within these sets, most individual parameters varied over a large range. One of the resulting sets was similar to the manually parameterized set. Using the entire suite of surviving parameter sets, the model successfully predicted all validation patterns. However, two of the surviving parameter sets could not make the model predict all validation patterns. CONCLUSIONS: Our findings suggest strong interactions among model parameters and their corresponding processes, respectively. Using all surviving parameter sets takes these interactions into account fully, thereby improving model performance regarding validation and model output uncertainty. We conclude that POM calibration allows FSP models to be developed in a timely manner without having to rely on field or laboratory experiments, or on cumbersome manual parameterization. POM also increases the predictive power of FSP models.
BACKGROUND AND AIMS: Functional-structural plant (FSP) models provide insights into the complex interactions between plant architecture and underlying developmental mechanisms. However, parameter estimation of FSP models remains challenging. We therefore used pattern-oriented modelling (POM) to test whether parameterization of FSP models can be made more efficient, systematic and powerful. With POM, a set of weak patterns is used to determine uncertain parameter values, instead of measuring them in experiments or observations, which often is infeasible. METHODS: We used an existing FSP model of avocado (Persea americana 'Hass') and tested whether POM parameterization would converge to an existing manual parameterization. The model was run for 10 000 parameter sets and model outputs were compared with verification patterns. Each verification pattern served as a filter for rejecting unrealistic parameter sets. The model was then validated by running it with the surviving parameter sets that passed all filters and then comparing their pooled model outputs with additional validation patterns that were not used for parameterization. KEY RESULTS: POM calibration led to 22 surviving parameter sets. Within these sets, most individual parameters varied over a large range. One of the resulting sets was similar to the manually parameterized set. Using the entire suite of surviving parameter sets, the model successfully predicted all validation patterns. However, two of the surviving parameter sets could not make the model predict all validation patterns. CONCLUSIONS: Our findings suggest strong interactions among model parameters and their corresponding processes, respectively. Using all surviving parameter sets takes these interactions into account fully, thereby improving model performance regarding validation and model output uncertainty. We conclude that POM calibration allows FSP models to be developed in a timely manner without having to rely on field or laboratory experiments, or on cumbersome manual parameterization. POM also increases the predictive power of FSP models.
Authors: Ming Wang; Neil White; Volker Grimm; Helen Hofman; David Doley; Grant Thorp; Bronwen Cribb; Ella Wherritt; Liqi Han; John Wilkie; Jim Hanan Journal: Ann Bot Date: 2018-04-18 Impact factor: 4.357