Abstract: • This study combines crop, livestock and economic models.
• Models interaction is through use of relevant input and output variables.
• Future climate change will result in increased grass and wheat dry matter yields.
• Changes in grass, wheat and milk yields in future reduce farm emissions intensity.
• Changes in future dry matter yields and emissions lead to increased profitability.
There is a scientific consensus that the future climate change will affect grass and crop dry matter (DM) yields. Such yield changes may entail alterations to farm management practices to fulfill the feed requirements and reduce the farm greenhouse gas (GHG) emissions from dairy farms. While a large number of studies have focused on the impacts of projected climate change on a single farm output (e.g. GHG emissions or economic performance), several attempts have been made to combine bio-economic systems models with GHG accounting frameworks. In this study, we aimed to determine the physical impacts of future climate scenarios on grass and wheat DM yields, and demonstrate the effects such changes in future feed supply may have on farm GHG emissions and decision-making processes. For this purpose, we combined four models: BASGRA and CSM-CERES-Wheat models for simulating forage grass DM and wheat DM grain yields respectively; HolosNor for estimating the farm GHG emissions; and JORDMOD for calculating the impacts of changes in the climate and management on land use and farm economics. Four locations, with varying climate and soil conditions were included in the study: south-east Norway, south-west Norway, central Norway and northern Norway. Simulations were carried out for baseline (1961–1990) and future (2046–2065) climate conditions (projections based on two global climate models and the Special Report on Emissions Scenarios (SRES) A1B GHG emission scenario), and for production conditions with and without a milk quota. The GHG emissions intensities (kilogram carbon dioxide equivalent: kgCO2e emissions per kg fat and protein corrected milk: FPCM) varied between 0.8 kg and 1.23 kg CO2e (kg FPCM)− 1, with the lowest and highest emissions found in central Norway and south-east Norway, respectively. Emission intensities were generally lower under future compared to baseline conditions due mainly to higher future milk yields and to some extent to higher crop yields. The median seasonal above-ground timothy grass yield varied between 11,000 kg and 16,000 kg DM ha− 1 and was higher in all projected future climate conditions than in the baseline. The spring wheat grain DM yields simulated for the same weather conditions within each climate projection varied between 2200 kg and 6800 kg DM ha− 1. Similarly, the farm profitability as expressed by total national land rents varied between 1900 million Norwegian krone (NOK) for median yields under baseline climate conditions up to 3900 million NOK for median yield under future projected climate conditions.

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.