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Author Ben Touhami, H.; Bellocchi, G.
Title (down) Bayesian calibration of the Pasture Simulation model (PaSim) to simulate European grasslands under water stress Type Journal Article
Year 2015 Publication Ecological Informatics Abbreviated Journal Ecological Informatics
Volume 30 Issue Pages 356-364
Keywords Bayesian calibration framework; Grasslands; Pasture Simulation model; (PaSim); integrated assessment models; chain monte-carlo; climate-change; computation; impacts; vulnerability; likelihoods; france
Abstract As modeling becomes a more widespread practice in the agro-environmental sciences, scientists need reliable tools to calibrate models against ever more complex and detailed data. We present a generic Bayesian computation framework for grassland simulation, which enables parameter estimation in the Bayesian formalism by using Monte Carlo approaches. We outline the underlying rationale, discuss the computational issues, and provide results from an application of the Pasture Simulation model (PaSim) to three European grasslands. The framework was suited to investigate the challenging problem of calibrating complex biophysical models to data from altered scenarios generated by precipitation reduction (water stress conditions). It was used to infer the parameters of manipulated grassland systems and to assess the gain in uncertainty reduction by updating parameter distributions using measurements of the output variables.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1574-9541 ISBN Medium Article
Area Expedition Conference
Notes CropM, LiveM, ft_macsur Approved no
Call Number MA @ admin @ Serial 4697
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Author Makowski, D.
Title (down) A simple Bayesian method for adjusting ensemble of crop model outputs to yield observations Type Journal Article
Year 2017 Publication European Journal of Agronomy Abbreviated Journal Europ. J. Agron.
Volume 88 Issue Pages 76-83
Keywords Bayesian method; Climate change; Ensemble modelling; Uncertainty; Yield; Linear-Approach; Climate-Change; CO2
Abstract Multi-model forecasting has drawn some attention in crop science for evaluating effect of climate change on crop yields. The principle is to run several individual process-based crop models under several climate scenarios in order to generate ensembles of output values. This paper describes a simple Bayesian method – called Bayes linear method- for updating ensemble of crop model outputs using yield observations. The principle is to summarize the ensemble of crop model outputs by its mean and variance, and then to adjust these two quantities to yield observations in order to reduce uncertainty. The adjusted mean and variance combine two sources of information, i.e., the ensemble of crop model outputs and the observations. Interestingly, with this method, observations collected under a given climate scenario can be used to adjust mean and variance of the model ensemble under a different scenario. Another advantage of the proposed method is that it does not rely on a separate calibration of each individual crop model. The uncertainty reduction resulting from the adjustment of an ensemble of crop models to observations was assessed in a numerical application. The implementation of the Bayes linear method systematically reduced uncertainty, but the results showed the effectiveness of this method varied in function of several factors, especially the accuracy of the yield observation, and the covariance between the crop model output and the observation. (C) 2015 Elsevier B.V. All rights reserved.
Address 2017-08-07
Corporate Author Thesis
Publisher Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1161-0301 ISBN Medium Article
Area Expedition Conference
Notes CropM, ft_macsur Approved no
Call Number MA @ admin @ Serial 5171
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