|
|
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.
|
|
|
|
Lehtonen, H., Liu, X., & Purola, T. (2015). Balancing Climate Change Mitigation and Adaptation with Socio-Economic Goals at Farms in Northern Europe. In A. Paloviita, & M. Järvelä (Eds.),. Climate Adaptation, Policy and Food Supply Chain Management in Europe. Routledge.
|
|
|
|
Dono, G. (2015). Awareness of climate change for adaptation of the farm sector (Vol. 4).
|
|
|
|
Sinabell, F., Schönhart, M., & Schmid, E. (2015). Austrian Agriculture 2010-2050. Quantitative Effects of Climate Change Mitigation Measures. An analysis of the scenarios WEM, WAM, WAM+ and a sensitivity analysis of scenario WEM. Vienna, Austria.
|
|
|
|
Bodin, P. (2015). Assessing modelling approaches for simulating the effect of high temperature stress on yield (Vol. 5).
Abstract: High temperature events can have a large negative effect on crop yields, and the effects of these events are strongly dependent on not only the maximum temperature but also on the length and timing of these heat stress events. In future climate the likelihood of these types of events are expected to increase and thus make it crucial to be able to correctly assess not only the effect of changes in mean temperature but also the effect of changes in climate extremes. Crop models are often employed to predict yield responses to a changing climate, and traditionally they have not included the effect of heat stress events. In recent years more and more models have come to include the effect of high temperature stress on crop yield. Here we implement three of these approaches (APSIM, GAEZ and CERES-Wheat) into the Crop-DGVM: LPJ-GUESS and results from an initial sensitivity analysis are presented. Results show a large difference in year to year variability in simulated yield for the different approaches, and also on differences in sensitivity in relation to temperature change. No Label
|
|
