Abstract: This report provides an overview of how grasslands are represented in six different farmscale  models represented in MACSUR. A survey was conducted, followed by a workshop in  which modellers discussed the results of the survey, and identified research challenges and  knowledge gaps. The workshop was attended by grassland as well as livestock specialists.  The investigated models differed largely with respect to how grasslands were represented,  e.g. as regards weather and management factors accounted for, spatial and temporal  resolution, and output variables. All models had grassland modules that simulate DM yield  and herbage N content (or crude protein (CP) content = N content x 6.25). Many models  also simulate P content, whereas only one simulate K content. About half of the model  simulate herbage energy value and/or herbage fibre content and fibre and/or dry matter  digestibility. Critical input data required from grassland models to simulate ruminant  productivity and GHG emissions at farm scale was identified by the workshop participants.  The different types of input data required were ranked in order of importance as regards  their influence on important system outputs. For simulation of ruminant productivity and  GHG emissions, herbage DM yield was ranked as the most important input variable from  grassland models, followed by CP content together with at least one variable describing  herbage fibre characteristics. These findings suggest that work on improving the ability of  the current grassland models with respect to simulation of fibre/energy should be  prioritized in farm-scale modelling aiming at quantifying livestock production and GHG  emissions under different management regimes and climate conditions. More work is also needed on model evaluation, a task that has not been prioritized yet for some models.

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