The ongoing nutrition transition thwarts long-term targets for food security, public health and environmental protection

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.

To what extent is climate change adaptation a novel challenge for agricultural modellers?

Modelling is key to adapting agriculture to climate change (CC), facilitating evaluation of the impacts and efficacy of adaptation measures, and the design of optimal strategies. Although there are many challenges to modelling agricultural CC adaptation, it is unclear whether these are novel or, whether adaptation merely adds new motivations to old challenges. Here, qualitative analysis of modellers' views revealed three categories of challenge: Content, Use, and Capacity. Triangulation of findings with reviews of agricultural modelling and Climate Change Risk Assessment was then used to highlight challenges specific to modelling adaptation. These were refined through literature review, focussing attention on how the progressive nature of CC affects the role and impact of modelling. Specific challenges identified were: Scope of adaptations modelled, Information on future adaptation, Collaboration to tackle novel challenges, Optimisation under progressive change with thresholds, and Responsibility given the sensitivity of future outcomes to initial choices under progressive change.

Drivers of meat consumption

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.

The use of biogeochemical models to evaluate mitigation of greenhouse gas emissions from managed grasslands

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. N₂O-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 N₂O-N intensities (N₂O-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.

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2 O emissions

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 (N₂ 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 N₂ 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 N₂ O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N₂ 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 N₂ O emissions. Yield-scaled N₂ O emissions (N₂ 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 N₂ O emissions at field scale is discussed.

Consistent negative response of US crops to high temperatures in observations and crop models

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 CO₂ can only weakly reduce these yield losses, in contrast to irrigation.

Future agricultural productivity is threatened by high temperatures. Here, using 9 crop models, Schauberger et al. find that yield losses due to temperatures >30 °C are captured by current models where yield losses by mild heat stress occur mainly due to water stress and can be buffered by irrigation.

Mitigation Strategies for Greenhouse Gas Emissions from Agriculture and Land-Use Change: Consequences for Food Prices

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 of selected agricultural crops in Germany between 1902 and 2010

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.

Key challenges and priorities for modelling European grasslands under climate change

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.

Global Food Demand Scenarios for the 21st Century

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.

Land-use and carbon cycle responses to moderate climate change: implications for land-based mitigation?

Climate change has impacts on agricultural yields, which could alter cropland requirements and hence deforestation rates. Thus, land-use responses to climate change might influence terrestrial carbon stocks. Moreover, climate change could alter the carbon storage capacity of the terrestrial biosphere and hence the land-based mitigation potential. We use a global spatially explicit economic land-use optimization model to (a) estimate the mitigation potential of a climate policy that provides economic incentives for carbon stock conservation and enhancement, (b) simulate land-use and carbon cycle responses to moderate climate change (RCP2.6), and (c) investigate the combined effects throughout the 21st century. The climate policy immediately stops deforestation and strongly increases afforestation, resulting in a global mitigation potential of 191 GtC in 2100. Climate change increases terrestrial carbon stocks not only directly through enhanced carbon sequestration (62 GtC by 2100) but also indirectly through less deforestation due to higher crop yields (16 GtC by 2100). However, such beneficial climate impacts increase the potential of the climate policy only marginally, as the potential is already large under static climatic conditions. In the broader picture, this study highlights the importance of land-use dynamics for modeling carbon cycle responses to climate change in integrated assessment modeling.

Phytoplankton appearance in particle size spectra - deriving conversion functions between microscopic and particle counter measurements

Analysis of aquatic field samples by particle counters are a widespread method but the representation of phytoplankton abundance and of size classes in which phytoplankton appears in the resulting size spectra is not well studied. To address this gap, two freshwater phytoplankton species were analysed in a particle counter and using a microscope: the colony forming Asterionella formosa (Bacillariophyceae) and the single-celled Microcystis aeruginosa (Cyanobacteria). Field samples, growth experiments and model approaches were used to study the image of phytoplankton derived by two different commonly used and standardized counting methods. In our results, the colony forming A. formosa had to be considered in units of colonies because the counting device enumerated only 23% of the single cells but 85% of the colonies that were determined under the microscope. Furthermore, the size class representation in the particle counter of both taxa appeared in much smaller ranges than expected from microscopic size measurements. Model simulations of movements and rotations of phytoplankton in the measuring device can explain half of the size shift. We deduce that about 86% of the cell areas of both studied species are transparent from two approaches. First, areas derived from simulations of rotated phytoplankton colonies equal the measured particle spectra of the laboratory cultures when the shadow areas are reduced to 14%. Secondly, field counts of A. formosa can be integrated into particle size spectra of the total particulate material when the same reduction factor is applied. For the considered optical counting device, field samples of A. formosa can be detected in particle size spectra when colony sizes as well as transparency of the cells and reduction of cell sizes by rotations are taken into account.

Two genes in Arabidopsis thaliana encoding GDP-L-galactose phosphorylase are required for ascorbate biosynthesis and seedling viability

Plants synthesize ascorbate from guanosine diphosphate (GDP)-mannose via L-galactose/L-gulose, although uronic acids have also been proposed as precursors. Genes encoding all the enzymes of the GDP-mannose pathway have previously been identified, with the exception of the step that converts GDP-L-galactose to L-galactose 1-P. We show that a GDP-L-galactose phosphorylase, encoded by the Arabidopsis thaliana VTC2 gene, catalyses this step in the ascorbate biosynthetic pathway. Furthermore, a homologue of VTC2, At5g55120, encodes a second GDP-L-galactose phosphorylase with similar properties to VTC2. Two At5g55120 T-DNA insertion mutants (vtc5-1 and vtc5-2) have 80% of the wild-type ascorbate level. Double mutants were produced by crossing the loss-of-function vtc2-1 mutant with each of the two vtc5 alleles. These show growth arrest immediately upon germination and the cotyledons subsequently bleach. Normal growth was restored by supplementation with ascorbate or L-galactose, indicating that both enzymes are necessary for ascorbate generation. vtc2-1 leaves contain more mannose 6-P than wild-type. We conclude that the GDP-mannose pathway is the only significant source of ascorbate in A. thaliana seedlings, and that ascorbate is essential for seedling growth. A. thaliana leaves accumulate more ascorbate after acclimatization to high light intensity. VTC2 expression and GDP-L-galactose phosphorylase activity rapidly increase on transfer to high light, but the activity of other enzymes in the GDP-mannose pathway is little affected. VTC2 and At5g55120 (VTC5) expression also peak in at the beginning of the light cycle and are controlled by the circadian clock. The GDP-L-galactose phosphorylase step may therefore play an important role in controlling ascorbate biosynthesis.

Identifying cardinal dates in phytoplankton time series to enable the analysis of long-term trends

Phenology and seasonal succession in aquatic ecosystems are strongly dependent on physical factors. In order to promote investigations into this coupling, methods of characterising annual time series of phytoplankton were derived and applied to a 31-year data set from Saidenbach Reservoir (Saxony, Germany). Field data are often scarce and irregularly sampled, particularly in the transition period from winter to spring, so reliable methods of determining cardinal dates in the time series are necessary. The proposed methods were used to determine the beginning, maximum and end of the spring mass development of phytoplankton by estimating the inflexion points (A), fitting a Weibull-type function (B) and fitting linear segments to the logarithmic values (C). For the data set from Saidenbach Reservoir, all three methods proved to be relevant to the analysis of long-term trends. Differences between the maxima determined by the different methods seemed small, but there were deviations when the maximum was related to physical factors such as ice-out. The Weibull-type fit gave the most reliable and comprehensible results and is recommended for trend analyses. For all methods, long-term analysis of the duration of the spring mass development and the duration of the spring full circulation revealed a period of consistently low values (1975-1990) followed by a period of higher values (1990-2005). These periods were also identified for the date of ice-out, although in this case there was a period of high values followed by a period of low values. A sensitivity analysis that compared results from subsampled time series with increasing time intervals indicated that a minimum of one sample every three weeks is needed to obtain reliable results.

Arabidopsis thaliana VTC4 encodes L-galactose-1-P phosphatase, a plant ascorbic acid biosynthetic enzyme

In plants, a proposed ascorbate (vitamin C) biosynthesis pathway occurs via GDP-D-mannose (GDP-D-Man), GDP-L-galactose (GDP-L-Gal), and L-galactose. However, the steps involved in the synthesis of L-Gal from GDP-L-Gal in planta are not fully characterized. Here we present evidence for an in vivo role for L-Gal-1-P phosphatase in plant ascorbate biosynthesis. We have characterized a low ascorbate mutant (vtc4-1) of Arabidopsis thaliana, which exhibits decreased ascorbate biosynthesis. Genetic mapping and sequencing of the VTC4 locus identified a mutation (P92L) in a gene with predicted L-Gal-1-P phosphatase activity (At3g02870). Pro-92 is within a beta-bulge that is conserved in related myo-inositol monophosphatases. The mutation is predicted to disrupt the positioning of catalytic amino acid residues within the active site. Accordingly, L-Gal-1-P phosphatase activity in vtc4-1 was approximately 50% of wild-type plants. In addition, vtc4-1 plants incorporate significantly more radiolabel from [2-(3)H]Man into L-galactosyl residues suggesting that the mutation increases the availability of GDP-L-Gal for polysaccharide synthesis. Finally, a homozygous T-DNA insertion line, which lacks a functional At3g02870 gene product, is also ascorbate-deficient (50% of wild type) and deficient in L-Gal-1-P phosphatase activity. Genetic complementation tests revealed that the insertion mutant and VTC4-1 are alleles of the same genetic locus. The significantly lower ascorbate and perturbed L-Gal metabolism in vtc4-1 and the T-DNA insertion mutant indicate that L-Gal-1-P phosphatase plays a role in plant ascorbate biosynthesis. The presence of ascorbate in the T-DNA insertion mutant suggests there is a bypass to this enzyme or that other pathways also contribute to ascorbate biosynthesis.