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The stability of soil organic matter (SOM) is a major source of uncertainty in predicting atmospheric CO2 concentration during the 21st century. Isolating the stable soil carbon (C) from other, more labile, C fractions in soil is of prime importance for calibrating soil C simulation models, and gaining insights into the mechanisms that lead to soil C stability. Long-term experiments with continuous bare fallow (vegetation-free) treatments in which the decay of soil C is monitored for decades after all inputs of C have stopped, provide a unique opportunity to assess the quantity of stable soil C. We analyzed data from six bare fallow experiments of long-duration (>30 yrs), covering a range of soil types and climate conditions, and sited at Askov (Denmark), Grignon and Versailles (France), Kursk (Russia), Rothamsted (UK), and Ultuna (Sweden). A conceptual three pool model dividing soil C into a labile pool (turnover time of a several years), an intermediate pool (turnover time of a several decades) and a stable pool (turnover time of a several centuries or more) fits well with the long term C decline observed in the bare fallow soils. The estimate of stable C ranged from 2.7 g C kg−1 at Rothamsted to 6.8 g C kg−1 at Grignon. The uncertainty associated with estimates of the stable pool was large due to the short duration of the fallow treatments relative to the turnover time of stable soil C. At Versailles, where there is least uncertainty associated with the determination of a stable pool, the soil contains predominantly stable C after 80 years of continuous bare fallow. Such a site represents a unique research platform for characterization of the nature of stable SOM and its vulnerability to global change.

Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984-2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO2 offering no yield benefit in these years.

The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.

Food security relies on the resilience of staple food crops to climatic variability and extremes, but the climate resilience of European wheat is unknown. A diversity of responses to disturbance is considered a key determinant of resilience. The capacity of a sole crop genotype to perform well under climatic variability is limited; therefore, a set of cultivars with diverse responses to weather conditions critical to crop yield is required. Here, we show a decline in the response diversity of wheat in farmers’ fields in most European countries after 2002–2009 based on 101,000 cultivar yield observations. Similar responses to weather were identified in cultivar trials among central European countries and southern European countries. A response diversity hotspot appeared in the trials in Slovakia, while response diversity “deserts” were identified in Czechia and Germany and for durum wheat in southern Europe. Positive responses to abundant precipitation were lacking. This assessment suggests that current breeding programs and cultivar selection practices do not sufficiently prepare for climatic uncertainty and variability. Consequently, the demand for climate resilience of staple food crops such as wheat must be better articulated. Assessments and communication of response diversity enable collective learning across supply chains. Increased awareness could foster governance of resilience through research and breeding programs, incentives, and regulation.

Projections of climate change impacts on crop yields are inherently uncertain(1). Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate(2). However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models(1,3) are difficult(4). Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking.

Nature conservation on privately owned land depends on land managers’ decision-making. Interactions between values, rules and knowledge (vrk) underpin decision-making, thus, it is important to understand these interactions to support conservation intentions. We investigated how different sets of vrk determine the decision-making context regarding the management and conservation of renosterveld, a critically endangered ecosystem in the Cape Floristic Region, and how this relates to land managers’ perceptions of nature’s contributions to people (NCP). From interviews with thirty land managers, we identified nine value types, four rule types, three knowledge types and 13 different NCP. We found that different vrk combinations can be grouped into three decision-making contexts: Bottom-up conservation, Top-down conservation and Utility. Each context is associated with the perception of different beneficial and detrimental NCP. Regulating NCP are perceived across all contexts, whereas more non-material NCP are associated with a Bottom-up conservation context and relational values, such as family ties. The prevalence of relational values in Bottom-up and Top-down conservation contexts illustrates the complexity and non-substitutability of the dynamic relationships between renosterveld and people. This indicates the importance of plural valuation in nature conservation to foster diverse NCP provided by renosterveld.

Genetic improvement through breeding is one of the key approaches to increasing biomass supply. This paper documents the breeding progress to date for four perennial biomass crops (PBCs) that have high output–input energy ratios: namely Panicum virgatum (switchgrass), species of the genera Miscanthus (miscanthus), Salix (willow) and Populus (poplar). For each crop, we report on the size of germplasm collections, the efforts to date to phenotype and genotype, the diversity available for breeding and on the scale of breeding work as indicated by number of attempted crosses. We also report on the development of faster and more precise breeding using molecular breeding techniques. Poplar is the model tree for genetic studies and is furthest ahead in terms of biological knowledge and genetic resources. Linkage maps, transgenesis and genome editing methods are now being used in commercially focused poplar breeding. These are in development in switchgrass, miscanthus and willow generating large genetic and phenotypic data sets requiring concomitant efforts in informatics to create summaries that can be accessed and used by practical breeders. Cultivars of switchgrass and miscanthus can be seed‐based synthetic populations, semihybrids or clones. Willow and poplar cultivars are commercially deployed as clones. At local and regional level, the most advanced cultivars in each crop are at technology readiness levels which could be scaled to planting rates of thousands of hectares per year in about 5 years with existing commercial developers. Investment in further development of better cultivars is subject to current market failure and the long breeding cycles. We conclude that sustained public investment in breeding plays a key role in delivering future mass‐scale deployment of PBCs. Plant breeding links the research effort with commercial mass upscaling. The authors’ assessment of development status of the four species is shown (poplar having two: one for short rotation coppice (SRC) poplar and one for the more traditional short rotation forestry (SRF)). Mass scale deployment needs developments outside the breeding arenas to drive breeding activities more rapidly and extensively.

Intensive agriculture is dominated by monocultures of high-yielding plants that receive large applications of nitrogen (N) fertilizers to boost plant productivity. However, these systems have low N use efficiency (NUE) as fertilized plants generally take up less than half of the N applied. A large fraction of the remainder N is susceptible to be lost from the agroecosystem generating a cascade of environmental and socio-economic problems. Climate change and projected global increases in fertilizer use pose further risks to N losses and yield stability. We review and translate concepts from ecology in natural systems to demonstrate that NUE in intensive agroecosystems can be strongly increased by fine-tuning the traits of the plant communities to the levels of N fertilization intensity. We present key plant traits of importance for N-cycling (architectural, morphological and physiological traits, as well as symbiotic associations and exudation patterns); discuss ecological (with soil fauna and N-cycling microbial communities) and agronomic interactions of this approach; propose interdisciplinary methodologies for future research ranging from pot to global scales; and highlight possible solutions leading to an optimal balance between N fertilizer use and productivity. Synthesis and applications. By showing the strong links between plant traits and nitrogen (N) cycling, our work opens possibilities to test ecologically informed hypotheses across gradients of soil fertility and N fertilizer management intensity, setting a research agenda for the coming years. Accordingly, the choice of plant species based on their functional traits will play a central role for the development of modern and productive agroecosystems that retain and use N more efficiently.

The inefficient use of phosphorus (P) in the food chain is a threat to the global aquatic environment and the health and well-being of citizens, and it is depleting an essential finite natural resource critical for future food security and ecosystem function. We outline a strategic framework of 5R stewardship (Re-align P inputs, Reduce P losses, Recycle P in bioresources, Recover P in wastes, and Redefine P in food systems) to help identify and deliver a range of integrated, cost-effective, and feasible technological innovations to improve P use efficiency in society and reduce Europe's dependence on P imports. Their combined adoption facilitated by interactive policies, co-operation between upstream and downstream stakeholders (researchers, investors, producers, distributors, and consumers), and more harmonized approaches to P accounting would maximize the resource and environmental benefits and help deliver a more competitive, circular, and sustainable European economy. The case of Europe provides a blueprint for global P stewardship.

Land-use change and intensification threaten bee populations worldwide, imperilling pollination services. Global models are needed to better characterise, project, and mitigate bees' responses to these human impacts. The available data are, however, geographically and taxonomically unrepresentative; most data are from North America and Western Europe, overrepresenting bumblebees and raising concerns that model results may not be generalizable to other regions and taxa. To assess whether the geographic and taxonomic biases of data could undermine effectiveness of models for conservation policy, we have collated from the published literature a global dataset of bee diversity at sites facing land-use change and intensification, and assess whether bee responses to these pressures vary across 11 regions (Western, Northern, Eastern and Southern Europe; North, Central and South America; Australia and New Zealand; South East Asia; Middle and Southern Africa) and between bumblebees and other bees. Our analyses highlight strong regionally-based responses of total abundance, species richness and Simpson's diversity to land use, caused by variation in the sensitivity of species and potentially in the nature of threats. These results suggest that global extrapolation of models based on geographically and taxonomically restricted data may underestimate the true uncertainty, increasing the risk of ecological surprises.