Quantified Evidence of Error Propagation
Abstract
Error propagation within models is an issue that requires a structured approach involving the testing of individual equations and evaluation of the consequences of error creation from imperfect equation and model structure on estimates of interest made by a model. This report briefly covers some of the key issues in error propagation and sets out several concepts, across a range of complexity, that may be used to organise an investigation into error propagation.
Full Text:
PDFReferences
Arras, K.O. 1998. An Introduction to Error Propagation: Derivation, Meaning and Examples of Equation Cy = FxCxFxT. Technical Report No. EPRL-ASL-TR-98-01 R3. Autonomous Systems Lab, Institute of Robotic Systems, Swiss Federal Institute of Technology Lausanne. http://www.nada.kth.se/~kai-a/papers/arrasTR-9801-R3.pdf
Marinelli, M.; Corner, R.; Wright, G. 2007. A comparison of error propagation analysis techniques applied to agricultural models. Edited by: Stafford, J. V. Conference: Precision agriculture '07. Papers presented at the 6th European Conference on Precision Agriculture, Skiathos, Greece, 3-6 June, 2007. Precision agriculture '07. Papers presented at the 6th European Conference on Precision Agriculture, Skiathos, Greece, 3-6 June, 2007 Pages: 215-222
Metselaar, K. (1999, February 2). Auditing predictive models: a case study in crop growth. WAU Dissertation no. 2570.
Nissanka, S., Karunaratne, A., Perera, R., Weerakoon, W. M. W., Thorburn, P., & Wallach, D. (2015). Calibration of the phenology sub model of APSIM -Oryza: going beyond goodness of fit. Ecological Modeling and Software, in press.
Rivington, M. and Koo, J. 2011. Report on the Meta-Analysis of Crop Modelling for Climate Change and Food Security Survey. Consultative Group on International Agricultural Research / Earth Systems Science Partnership Climate Change, Agriculture and Food Security Challenge Program. http://www.macaulay.ac.uk/climatechange/CC_CCAFS.php http://ccafs.cgiar.org/content/publications http://labs.harvestchoice.org/2011/02/meta-analysis-of-crop-modeling-for-climate-change-and-food-security/
Roux, S. Brun F. and Wallach D. 2014. Combining input uncertainty and residual error in crop model predictions: A case study on vineyards. European Journal of Agronomy 52, Part B, 191–197
Siegwart R. and Nourbakhsh I.R. 2004. Introduction to Autonomous Mobile Robots. Massachusetts Institute of Technology.
Wallach, D., 2011. Crop model calibration: A statistical perspective. Agronomy Journal, 103, pp.1144–1151.
Wallach D. Makowski D. and Jones J. 2006. Working with Dynamic Crop Models: Evaluation, Analysis, Parameterization and applications. Elsevier.
Wallach, D. and Rivington, M. 2014. A framework for assessing the uncertainty in crop model predictions. MACSUR CropM Deliverable Report C4.1.2.
Previous issues and volumes can be found in the 'Archives' section.
You can refer to a paper published in this series in the following format Author (2013) Title. FACCE MACSUR Reports 2: D-C1.3, where "D-C1.3" is the article ID en lieu of page range.