• Energy Research

AbstractAgricultural production has replaced natural ecosystems across the planet, becoming a major driver of carbon emissions, biodiversity loss, and freshwater consumption. Here we combined global crop yield and environmental data in a ~1-million-dimensional mathematical optimisation framework to determine how optimising the spatial distribution of global croplands could reduce environmental impacts whilst maintaining current crop production levels. We estimate that relocating current croplands to optimal locations, whilst allowing ecosystems in then-abandoned areas to regenerate, could simultaneously decrease the current carbon, biodiversity, and irrigation water footprint of global crop production by 71%, 87%, and 100%, respectively, assuming high-input farming on newly established sites. The optimal global distribution of crops is largely similar for current and end-of-century climatic conditions across emission scenarios. Substantial impact reductions could already be achieved by relocating only a small proportion of worldwide crop production, relocating croplands only within national borders, and assuming less intensive farming systems.

AbstractWarming-induced droughts caused tree growth loss across the globe, leading to substantial carbon loss to the atmosphere. Drought-induced growth loss, however, can be regulated by changes in diurnal temperature ranges. Here, we investigated long term radial growth responses of 23 widespread distributed tree species from 2327 sites over the world and found that species’ drought tolerances were significantly and positively correlated with diurnal temperature range-growth loss relationships for the period 1901-1940. Since 1940, this relationship has continued to fade, likely due to asymmetric day and night warming trends and the species’ ability to deal with them. The alleviation of reduced diurnal temperature ranges on drought-induced growth loss was mainly found for drought resistant tree species. Overall, our results highlight the need to carefully consider diurnal temperature ranges and species-specific responses to daytime and nighttime warming to explore tree growth responses to current and future warmer and drier climates.

Palm oil has been widely criticised for its high environmental impacts, leading to calls to replace it with alternative vegetable oils in food and cosmetic products. However, substituting palm oil would be environmentally beneficial only if the environmental footprint per litre oil were lower than those of alternative vegetable oils. Whether this is the case is not obvious, given the high oil yields of oil palm of up to 10 times those of alternative crops. Here, we combine global agricultural and environmental datasets to show that, among the world’s seven major vegetable oil crops (oil palm, soybean, rapeseed, sunflower, groundnut, coconut, olive), oil palm has the lowest average species richness and carbon footprint associated with an annual production of one litre of vegetable oil. For each crop, these yield-adjusted footprints differ substantially between major producer countries, which we find to be largely the result of differences in crop management. Closing agricultural yield gaps of oil crops through improved management practices would significantly reduce the environmental footprints per oil yield. This would minimise the need for further land conversion to oil cropland and indeed could increase production to such an extent that a significant area of oil croplands could be ecologically restored.

Future climate change and increasing atmospheric CO 2 are expected to cause major changes in vegetation structure and function over large fractions of the global land surface. Seven global vegetation models are used to analyze possible responses to future climate simulated by a range of general circulation models run under all four representative concentration pathway scenarios of changing concentrations of greenhouse gases. All 110 simulations predict an increase in global vegetation carbon to 2100, but with substantial variation between vegetation models. For example, at 4 °C of global land surface warming (510–758 ppm of CO 2 ), vegetation carbon increases by 52–477 Pg C (224 Pg C mean), mainly due to CO 2 fertilization of photosynthesis. Simulations agree on large regional increases across much of the boreal forest, western Amazonia, central Africa, western China, and southeast Asia, with reductions across southwestern North America, central South America, southern Mediterranean areas, southwestern Africa, and southwestern Australia. Four vegetation models display discontinuities across 4 °C of warming, indicating global thresholds in the balance of positive and negative influences on productivity and biomass. In contrast to previous global vegetation model studies, we emphasize the importance of uncertainties in projected changes in carbon residence times. We find, when all seven models are considered for one representative concentration pathway × general circulation model combination, such uncertainties explain 30% more variation in modeled vegetation carbon change than responses of net primary productivity alone, increasing to 151% for non-HYBRID4 models. A change in research priorities away from production and toward structural dynamics and demographic processes is recommended.

Climate change may pose a high risk of change to Earth’s ecosystems: shifting climatic boundaries may induce changes in the biogeochemical functioning and structures of ecosystems that render it difficult for endemic plant and animal species to survive in their current habitats. Here we aggregate changes in the biogeochemical ecosystem state as a proxy for the risk of these shifts at different levels of global warming. Estimates are based on simulations from seven global vegetation models (GVMs) driven by future climate scenarios, allowing for a quantification of the related uncertainties. 5–19% of the naturally vegetated land surface is projected to be at risk of severe ecosystem change at 2 ° C of global warming (ΔGMT) above 1980–2010 levels. However, there is limited agreement across the models about which geographical regions face the highest risk of change. The extent of regions at risk of severe ecosystem change is projected to rise with ΔGMT, approximately doubling between ΔGMT = 2 and 3 ° C, and reaching a median value of 35% of the naturally vegetated land surface for ΔGMT = 4 °C. The regions projected to face the highest risk of severe ecosystem changes above ΔGMT = 4 °C or earlier include the tundra and shrublands of the Tibetan Plateau, grasslands of eastern India, the boreal forests of northern Canada and Russia, the savanna region in the Horn of Africa, and the Amazon rainforest.