At the 21st session of the United Nations Framework Convention on Climate Change (UNFCCC, COP21), a voluntary action plan, the '4 per 1000 Initiative: Soils for Food Security and Climate' was proposed under the Agenda for Action. The Initiative underlines the role of soil organic matter (SOM) in addressing the three-fold challenge of food and nutritional security, adaptation to climate change and mitigation of human-induced greenhouse gases (GHGs) emissions. It sets an ambitious aspirational target of a 4 per 1000 (i.e. 0.4%) rate of annual increase in global soil organic carbon (SOC) stocks, with a focus on agricultural lands where farmers would ensure the carbon stewardship of soils, like they manage day-to-day multipurpose production systems in a changing environment. In this paper, the opportunities and challenges for the 4 per 1000 initiative are discussed. We show that the 4 per 1000 target, calculated relative to global top soil SOC stocks, is consistent with literature estimates of the technical potential for SOC sequestration, though the achievable potential is likely to be substantially lower given socio-economic constraints. We calculate that land-based negative emissions from additional SOC sequestration could significantly contribute to reducing the anthropogenic CO 2 equivalent emission gap identified from Nationally Determined Contributions pledged by countries to stabilize global warming levels below 2°C or even 1.5°C under the Paris agreement on climate. The 4 per 1000 target could be implemented by taking into account differentiated SOC stock baselines, reversing the current trend of huge soil CO 2 losses, e.g. from agriculture encroaching peatland soils. We further discuss the potential benefits of SOC stewardship for both degraded and healthy soils along contrasting spatial scales (field, farm, landscape and country) and temporal (year to century) horizons. Last, we present some of the implications relative to non-CO 2 GHGs emissions, water and nutrients use as well as co-benefits for crop yields and climate change adaptation. We underline the considerable challenges associated with the non-permanence of SOC stocks and show how the rates of adoption and the duration of improved soil management practices could alter the global impacts of practices under the 4 per 1000 initiative. We conclude that the 4 per 1000 initiative has potential to support multiple sustainable development goals (SDGs) of the 2030 Agenda. It can be regarded as no-regret since increasing SOC in agricultural soils will contribute to food security benefits that will enhance resilience to climate change. However, social, economic and environmental safeguards will be needed to ensure an equitable and sustainable implementation of the 4 per 1000 target.

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

Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to C sequestration and GHG source/sink status. The abundance of simulation tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of C and N cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (46.2%), followed by limitations in the algorithms simulating the effects of management practices (33.1%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.7%). Innovative aspects have been identified for future development of C and N models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of N shortage on SOM decomposition, the improvements related to the production and consumption of gases and an adequate simulations of gas transport in soil. On these bases, the assessment of trends and gaps in the modelling approaches currently employed to represent biogeochemical cycles in crop and grassland systems appears an essential step for future research.

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Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate‐change studies. It is imperative to increase confidence in long‐term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process‐based C models by comparing simulations to experimental data from seven long‐term bare‐fallow (vegetation‐free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi‐year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge‐based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin‐up initialization of SOC. Changes in the multi‐model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.