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Reactive nitrogen (Nr) is not only an important nutrient for plant growth, thereby safeguarding human alimentation, but it also heavily disturbs natural systems. To mitigate air, land, aquatic, and atmospheric pollution caused by the excessive availability of Nr, it is crucial to understand the long-term development of the global agricultural Nr cycle. For our analysis, we combine a material flow model with a land-use optimization model. In a first step we estimate the state of the Nr cycle in 1995. In a second step we create four scenarios for the 21st century in line with the SRES storylines. Our results indicate that in 1995 only half of the Nr applied to croplands was incorporated into plant biomass. Moreover, less than 10 per cent of all Nr in cropland plant biomass and grazed pasture was consumed by humans. In our scenarios a strong surge of the Nr cycle occurs in the first half of the 21st century, even in the environmentally oriented scenarios. Nitrous oxide (N2O) emissions rise from 3 Tg N2O-N in 1995 to 7-9 in 2045 and 5-12 Tg in 2095. Reinforced Nr pollution mitigation efforts are therefore required. [ABSTRACT FROM AUTHOR]

Reactive nitrogen (Nr) is not only an important nutrient for plant growth, thereby safeguarding human alimentation, but it also heavily disturbs natural systems. To mitigate air, land, aquatic, and atmospheric pollution caused by the excessive availability of Nr, it is crucial to understand the long term development of the global agricultural Nr cycle. For our analysis, we combine a material flow model with a land-use-optimization model. In a first step we estimate the state of the Nr cycle in 1995. In a second step we create four scenarios for the 21st century in line with the SRES storylines. Our results indicate that in 1995 only half of the Nr applied to croplands was incorporated into cropland biomass. Moreover, less than per cent of all Nr in cropland biomass and grazed pasture was consumed by humans. In our scenarios a strong surge of the Nr cycle occurs in the first half of the 21st century, even in the environmentally oriented scenarios. Nitrous oxide (N2O) emissions rise from 3 Tg N2O-N in 1995 to 7-9 in 2045 and 5-15 Tg in 2095. Reinforced Nr pollution mitigation efforts are therefore required. [ABSTRACT FROM AUTHOR]

The nutrition transition transforms food systems globally and shapes public health and environmental change. Here we provide a global forward-looking assessment of a continued nutrition transition and its interlinked symptoms in respect to food consumption. These symptoms range from underweight and unbalanced diets to obesity, food waste and environmental pressure. We find that by 2050, 45% (39-52%) of the world population will be overweight and 16% (13-20%) obese, compared to 29% and 9% in 2010 respectively. The prevalence of underweight approximately halves but absolute numbers stagnate at 0.4-0.7 billion. Aligned, dietary composition shifts towards animal-source foods and empty calories, while the consumption of vegetables, fruits and nuts increases insufficiently. Population growth, ageing, increasing body mass and more wasteful consumption patterns are jointly pushing global food demand from 30 to 45 (43-47) Exajoules. Our comprehensive open dataset and model provides the interfaces necessary for integrated studies of global health, food systems, and environmental change. Achieving zero hunger, healthy diets, and a food demand compatible with environmental boundaries necessitates a coordinated redirection of the nutrition transition. Reducing household waste, animal-source foods, and overweight could synergistically address multiple symptoms at once, while eliminating underweight would not substantially increase food demand.

Increasing global levels of meat consumption are a threat to the environment and to human health. To identify measures that may change consumption patterns towards more plant-based foods, it is necessary to improve our understanding of the causes behind the demand for meat. In this paper we use data from 137 different countries to identify and assess factors that influence meat consumption at the national level using a cross-country multivariate regression analysis. We specify either total meat or ruminant meat as the dependent variable and we consider a broad range of potential drivers of meat consumption. The combination of explanatory variables we use is new for this type of analysis. In addition, we estimate the relative importance of the different drivers. We find that income per capita followed by rate of urbanisation are the two most important drivers of total meat consumption per capita. Income per capita and natural endowment factors are major drivers of ruminant meat consumption per capita. Other drivers are Western culture, Muslim religion, female labour participation, economic and social globalisation and meat prices. The main identified drivers of meat demand are difficult to influence through direct policy intervention. Thus, acting indirectly on consumers' preferences and consumption habits (for instance through information, education policy and increased availability of ready-made plant based products) could be of key importance for mitigating the rise of meat consumption per capita all over the world., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Simulation models quantify the impacts on carbon (C) and nitrogen (N) cycling in grassland systems caused by changes in management practices. To support agricultural policies, it is however important to contrast the responses of alternative models, which can differ greatly in their treatment of key processes and in their response to management. We applied eight biogeochemical models at five grassland sites (in France, New Zealand, Switzerland, United Kingdom and United States) to compare the sensitivity of modelled C and N fluxes to changes in the density of grazing animals (from 100% to 50% of the original livestock densities), also in combination with decreasing N fertilization levels (reduced to zero from the initial levels). Simulated multi-model median values indicated that input reduction would lead to an increase in the C sink strength (negative net ecosystem C exchange) in intensive grazing systems: -64 ± 74 g C m -2  yr -1 (animal density reduction) and -81 ± 74 g C m -2  yr -1 (N and animal density reduction), against the baseline of -30.5 ± 69.5 g C m -2  yr -1 (LSU [livestock units] ≥ 0.76 ha -1  yr -1 ). Simulations also indicated a strong effect of N fertilizer reduction on N fluxes, e.g. N 2 O-N emissions decreased from 0.34 ± 0.22 (baseline) to 0.1 ± 0.05 g N m -2  yr -1 (no N fertilization). Simulated decline in grazing intensity had only limited impact on the N balance. The simulated pattern of enteric methane emissions was dominated by high model-to-model variability. The reduction in simulated offtake (animal intake + cut biomass) led to a doubling in net primary production per animal (increased by 11.6 ± 8.1 t C LSU -1  yr -1 across sites). The highest N 2 O-N intensities (N 2 O-N/offtake) were simulated at mown and extensively grazed arid sites. We show the possibility of using grassland models to determine sound mitigation practices while quantifying the uncertainties associated with the simulated outputs., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N 2 O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N 2 O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N 2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N 2 O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N 2 O emissions. Yield-scaled N 2 O emissions (N 2 O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N 2 O emissions at field scale is discussed., (© 2017 John Wiley & Sons Ltd.)

High temperatures are detrimental to crop yields and could lead to global warming-driven reductions in agricultural productivity. To assess future threats, the majority of studies used process-based crop models, but their ability to represent effects of high temperature has been questioned. Here we show that an ensemble of nine crop models reproduces the observed average temperature responses of US maize, soybean and wheat yields. Each day >30 °C diminishes maize and soybean yields by up to 6% under rainfed conditions. Declines observed in irrigated areas, or simulated assuming full irrigation, are weak. This supports the hypothesis that water stress induced by high temperatures causes the decline. For wheat a negative response to high temperature is neither observed nor simulated under historical conditions, since critical temperatures are rarely exceeded during the growing season. In the future, yields are modelled to decline for all three crops at temperatures >30 °C. Elevated CO 2 can only weakly reduce these yield losses, in contrast to irrigation.

The land use sector of agriculture, forestry, and other land use (AFOLU) plays a central role in ambitious climate change mitigation efforts. Yet, mitigation policies in agriculture may be in conflict with food security related targets. Using a global agro-economic model, we analyze the impacts on food prices under mitigation policies targeting either incentives for producers (e.g., through taxes) or consumer preferences (e.g., through education programs). Despite having a similar reduction potential of 43-44% in 2100, the two types of policy instruments result in opposite outcomes for food prices. Incentive-based mitigation, such as protecting carbon-rich forests or adopting low-emission production techniques, increase land scarcity and production costs and thereby food prices. Preference-based mitigation, such as reduced household waste or lower consumption of animal-based products, decreases land scarcity, prevents emissions leakage, and concentrates production on the most productive sites and consequently lowers food prices. Whereas agricultural emissions are further abated in the combination of these mitigation measures, the synergy of strategies fails to substantially lower food prices. Additionally, we demonstrate that the efficiency of agricultural emission abatement is stable across a range of greenhouse-gas (GHG) tax levels, while resulting food prices exhibit a disproportionally larger spread.

Irrigation water demand (IWD) is increasing worldwide, including in regions such as Germany that are characterized with low precipitation levels, yet grow water-demanding crops such as sugar beets, potatoes, and vegetables. This study aimed to calculate and analyze the spatial and temporal changes in the IWD of four crops-spring barley, oat, winter wheat, and potato-between 1902 and 2010 in Germany by using the modeling software AgroHyd Farmmodel. Climatic conditions in Germany continued to change over the investigation period, with an increase in temperature of 0.01K/yr and an increase in precipitation of 1mm/yr. Nevertheless, no significant increasing or decreasing trend in IWD was noted in the analysis. The IWD for the investigated crops in the area of the current "Federal Republic of Germany" over the 109years was 112mm/yr, varying between 100 and 127mm/yr. Changes in cropping pattern and cultivated area over the last century caused large differences in the IWD calculated for each administrative district. The mean annual IWD of over the study period (which was divided into 4 parts) varied between 13,455Mm(3)/yr in the earliest period (1902-1919) and 4717Mm(3)/yr in the latest period (1990-2010). Policy and management measures to adapt to climate change are currently being debated in Germany. The presented results suggest that the effects of the choice of crops (in this case, changes in cropping pattern in the German nation states) had a stronger influence on regional water resources than those of climate variability. Thus, the influence of climate change on water resources is relativized which brings an important input into the debate., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Grassland-based ruminant production systems are integral to sustainable food production in Europe, converting plant materials indigestible to humans into nutritious food, while providing a range of environmental and cultural benefits. Climate change poses significant challenges for such systems, their productivity and the wider benefits they supply. In this context, grassland models have an important role in predicting and understanding the impacts of climate change on grassland systems, and assessing the efficacy of potential adaptation and mitigation strategies. In order to identify the key challenges for European grassland modelling under climate change, modellers and researchers from across Europe were consulted via workshop and questionnaire. Participants identified fifteen challenges and considered the current state of modelling and priorities for future research in relation to each. A review of literature was undertaken to corroborate and enrich the information provided during the horizon scanning activities. Challenges were in four categories relating to: 1) the direct and indirect effects of climate change on the sward 2) climate change effects on grassland systems outputs 3) mediation of climate change impacts by site, system and management and 4) cross-cutting methodological issues. While research priorities differed between challenges, an underlying theme was the need for accessible, shared inventories of models, approaches and data, as a resource for stakeholders and to stimulate new research. Developing grassland models to effectively support efforts to tackle climate change impacts, while increasing productivity and enhancing ecosystem services, will require engagement with stakeholders and policy-makers, as well as modellers and experimental researchers across many disciplines. The challenges and priorities identified are intended to be a resource 1) for grassland modellers and experimental researchers, to stimulate the development of new research directions and collaborative opportunities, and 2) for policy-makers involved in shaping the research agenda for European grassland modelling under climate change., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Long-term food demand scenarios are an important tool for studying global food security and for analysing the environmental impacts of agriculture. We provide a simple and transparent method to create scenarios for future plant-based and animal-based calorie demand, using time-dependent regression models between calorie demand and income. The scenarios can be customized to a specific storyline by using different input data for gross domestic product (GDP) and population projections and by assuming different functional forms of the regressions. Our results confirm that total calorie demand increases with income, but we also found a non-income related positive time-trend. The share of animal-based calories is estimated to rise strongly with income for low-income groups. For high income groups, two ambiguous relations between income and the share of animal-based products are consistent with historical data: First, a positive relation with a strong negative time-trend and second a negative relation with a slight negative time-trend. The fits of our regressions are highly significant and our results compare well to other food demand estimates. The method is exemplarily used to construct four food demand scenarios until the year 2100 based on the storylines of the IPCC Special Report on Emissions Scenarios (SRES). We find in all scenarios a strong increase of global food demand until 2050 with an increasing share of animal-based products, especially in developing countries.