Abstract: Land use and management intensity can influence provision of ecosystem services (ES). We argue that forest/agroforestry production systems are characterized by relatively higher C:O/C:N and ES value compared to arable production systems. Field investigations on C:N/C:O and 15 ES were determined in three diverse production systems: wheat monoculture (Cwheat), a combined food and energy system (CFE) and a beech forest in Denmark. The C:N/C:O ratios were 194.1/1.68, 94.1/1.57 and 59.5/1.45 for beech forest, CFE and Cwheat, respectively. The economic value of the non-marketed ES was also highest in beech forest (US$ 1089 ha(-1) yr(-1)) followed by CFE (US$ 800 ha(-1) yr(-1)) and Cwheat (US$ 339 ha(-1) yr(-1)). The combined economic value was highest in the CFE (US$ 3143 ha(-1) yr(-1)) as compared to the Cwheat (US$ 2767 ha(-1) yr(-1)) and beech forest (US$ 2365 ha(-1) yr(-1)). We argue that C:N/C:O can be used as a proxy of ES, particularly for the non-marketed ES, such as regulating, supporting and cultural services. These ES play a vital role in the sustainable production of food and energy. Therefore, they should be considered in decision making and developing appropriate policy responses for land use management.

Abstract: Climate change is anticipated to affect European agriculture, including the risk of emerging or re-emerging feed and food hazards. Indirectly, climate change may influence such hazards (e.g. the occurrence of mycotoxins) due to geographic shifts in the distribution of major cereal cropping systems and the consequences this may have for crop rotations. This paper analyses the impact of climate on cropping shares of maize, oat and wheat on a 50-km square grid across Europe (45-65°N) and provides model-based estimates of the changes in cropping shares in response to changes in temperature and precipitation as projected for the time period around 2040 by two regional climate models (RCM) with a moderate and a strong climate change signal, respectively. The projected cropping shares are based on the output from the two RCMs and on algorithms derived for the relation between meteorological data and observed cropping shares of maize, oat and wheat. The observed cropping shares show a south-to-north gradient, where maize had its maximum at 45-55°N, oat had its maximum at 55-65°N, and wheat was more evenly distributed along the latitudes in Europe. Under the projected climate changes, there was a general increase in maize cropping shares, whereas for oat no areas showed distinct increases. For wheat, the projected changes indicated a tendency towards higher cropping shares in the northern parts and lower cropping shares in the southern parts of the study area. The present modelling approach represents a simplification of factors determining the distribution of cereal crops, and also some uncertainties in the data basis were apparent. A promising way of future model improvement could be through a systematic analysis and inclusion of other variables, such as key soil properties and socio-economic conditions, influencing the comparative advantages of specific crops.

Abstract: The impacts of global climate change on different aspects of humanity’s diverse life-support systems are complex and often difficult to predict. To facilitate policy decisions on mitigation and adaptation strategies, it is necessary to understand, quantify, and synthesize these climate-change impacts, taking into account their uncertainties. Crucial to these decisions is an understanding of how impacts in different sectors overlap, as overlapping impacts increase exposure, lead to interactions of impacts, and are likely to raise adaptation pressure. As a first step we develop herein a framework to study coinciding impacts and identify regional exposure hotspots. This framework can then be used as a starting point for regional case studies on vulnerability and multifaceted adaptation strategies. We consider impacts related to water, agriculture, ecosystems, and malaria at different levels of global warming. Multisectoral overlap starts to be seen robustly at a mean global warming of 3 °C above the 1980-2010 mean, with 11% of the world population subject to severe impacts in at least two of the four impact sectors at 4 °C. Despite these general conclusions, we find that uncertainty arising from the impact models is considerable, and larger than that from the climate models. In a low probability-high impact worst-case assessment, almost the whole inhabited world is at risk for multisectoral pressures. Hence, there is a pressing need for an increased research effort to develop a more comprehensive understanding of impacts, as well as for the development of policy measures under existing uncertainty.