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Historical and current approaches to decompose uncertainty in crop model predictions

Citation

Chapagain, R and Ojeda, J and Mohammed, C and Brown, J and Remenyi, T and Harris, R, Historical and current approaches to decompose uncertainty in crop model predictions, iCROPM2020 Book of Abstracts, 3-5 February 2020, Montpellier, France, pp. 555-556. (2020) [Conference Extract]


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Abstract

Cropping systems models have become an essential tool to simulate crop growth and yield at different scales to produce actionable information related to climate change, food security, land use and market dynamics (e.g. Waha et al., 2015; Wallach et al., 2016; Porwollik et al., 2017). Despite their increased usage and importance, various sources of uncertainty exist in the modelling process due to the "impossibility to model the cropping system with complete determinism" (Ramirez-Villegas et al. 2017). In this article, by uncertainty we mean "any departure from the unachievable ideal of complete determinism" (Walker et al., 2003). Uncertainty is prevalent in every step of crop modelling, starting from the field observations used for model development to value of inputs and parameters to the structure and design of model (Fig. 1).

Item Details

Item Type:Conference Extract
Keywords:modelling, input uncertainty, structure uncertainty, parameter uncertainty, data
Research Division:Agricultural and Veterinary Sciences
Research Group:Agriculture, Land and Farm Management
Research Field:Agricultural Spatial Analysis and Modelling
Objective Division:Environment
Objective Group:Climate and Climate Change
Objective Field:Climate Change Models
UTAS Author:Chapagain, R (Miss Ranju Chapagain)
UTAS Author:Ojeda, J (Dr Jonathan Ojeda)
UTAS Author:Mohammed, C (Professor Caroline Mohammed)
UTAS Author:Remenyi, T (Dr Tom Remenyi)
UTAS Author:Harris, R (Dr Rebecca Harris)
ID Code:138082
Year Published:2020
Deposited By:Agriculture and Food Systems
Deposited On:2020-03-24
Last Modified:2020-03-24
Downloads:0

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