• Energy Research

European managed grasslands are amongst the most productive in the world. Besides temperature and the amount and timing of precipitation, grass production is also highly controlled by applications of nitrogen fertilizers and land management to sustain a high productivity. Since management characteristics of pastures vary greatly across Europe, land-use intensity and their projections are critical input variables in earth system modeling when examining and predicting the effects of increasingly intensified agricultural and livestock systems on the environment. In this study, we aim to improve the representation of pastures in the dynamic global vegetation model LPJ-GUESS. This is done by incorporating daily carbon allocation for grasses as a foundation to further implement daily land management routines and land-use intensity data into the model to discriminate between intensively and extensively used regions. We further compare our new simulations with leaf area index observations, reported regional grassland productivity, and simulations conducted with the vegetation model ORCHIDEE-GM. Additionally, we analyze the implications of including pasture fertilization and daily management compared to the standard version of LPJ-GUESS. Our results demonstrate that grassland productivity cannot be adequately captured without including land-use intensity data in form of nitrogen applications. Using this type of information improved spatial patterns of grassland productivity significantly compared to standard LPJ-GUESS. In general, simulations for net primary productivity, net ecosystem carbon balance and nitrogen leaching were considerably increased in the extended version. Finally, the adapted version of LPJ-GUESS, driven with projections of climate and land-use intensity, simulated an increase in potential grassland productivity until 2050 for several agro-climatic regions, most notably for the Mediterranean North, the Mediterranean South, the Atlantic Central and the Atlantic South.

The climate impact of new cars in Sweden 2009 has been evaluated by the Swedish Transport Administration. Their report takes into account reduction factors to attribute the positive impact of renewable fuels on CO2 emissions. The Swedish Transport Administration recommends the public to buy cars that can run on biofuels. Besides acknowledging prevailing uncertainties for many of the input parameters to the index of new cars’ climate impact, reduction factors are based on calculations from point estimates of input parameters. A probabilistic re-assessment of the index is presented to find out the importance of these uncertainties and to assess whether the point estimated recommendation might be misguiding. Probabilistic reduction factors for CO2 emissions were derived with the same deterministic model proposed by the Swedish Transport Administration, were Bayesian probability distributions or intervals assigned by expert judgements were used to describe uncertainty in the model input parameters. The use of biofuels most likely reduces CO2 emissions. Probabilistic modelling indicated a CO2 reduction for E85 as a fuel of 30% (95% credibility interval = 10–52%) in the same order as the 20% given by the Swedish Transport Administration. The best estimate of 28% decrease for gas cars (95% credibility interval = 3–44%) and is lower than the originally proposed reduction of 42%, but still within a similar range. The difference is due to the large extent of optimistic values used in the assessment by the Swedish Transport Administration. The CO2 emissions from the production of the biofuel had most influence on the model results. We conclude that the recommendation of the Swedish Transport Administration to consumers is still valid after probabilistic recalculation.

In this study, a global vegetation model (LPJ-GUESS) is forced with spatial information (Nomenclature of Units for Territorial Statistics (NUTS) 2 level) of land-use intensity change in the form of nitrogen (N) fertilization derived from a model chain which informed the Common Agricultural Policy Regionalized Impact (CAPRI) model. We analysed the combined role of climate change and land-use intensity change for trade-offs between agricultural yield and N leaching in the European Union under two plausible scenarios up until 2040. Furthermore, we assessed both driver importance and uncertainty in future trends based on an alternative land-use intensity dataset derived from an integrated assessment model. LPJ-GUESS simulated an increase in wheat and maize yield but also N leaching for most regions when driven by changes in land-use intensity and climate under RCP 8.5. Under RCP 4.5, N leaching is reduced in 53% of the regions while there is a trade-off in crop productivity. The most important factors influencing yield were CO2 (wheat) and climate (maize), but N application almost equaled these in importance. For N leaching, N application was the most important factor, followed by climate. Therefore, using a constant N application dataset in the absence of future projections has a substantial effect on simulated ecosystem responses, especially for maize yield and N leaching. This study is a first assessment of future N leaching and yield responses based on projections of climate and land-use intensity. It further highlights the importance of accounting for changes in future N applications and land-use intensity in general when evaluating environmental impacts over long time periods.