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Sándor, R., Ehrhardt, F., Basso, B., Bellocchi, G., Bhatia, A., Brilli, L., et al. (2016). C and N models Intercomparison – benchmark and ensemble model estimates for grassland production. Advances in Animal Biosciences, 7(03), 245–247.
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Acutis, M., & Bellocchi, G. (2013). Briefing on CropM-LiveM model intercomparison protocol..
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Ben Touhami, H., & Bellocchi, G. (2015). Bayesian calibration of the Pasture Simulation model (PaSim) to simulate European grasslands under water stress. Ecological Informatics, 30, 356–364.
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.
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Ben Touhami, H., & Bellocchi, G. (2014). Bayesian calibration of the Pasture Simulation model (PaSim) to simulate emissions from long-term grassland sites: a European perspective..
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Ben Touhami, H., Lardy, R., Klumpp, K., & Bellocchi, G. (2013). Bayesian calibration of the Pasture Simulation model (PaSim) to simulate emissions from long-term European grassland sites: a case study at Laqueuille (France)..
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