• Energy Research

AbstractSimulation 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 (N2O) 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 N2O 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 N2O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2O 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 N2O emissions. Yield‐scaled N2O emissions (N2O 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 N2O emissions at field scale is discussed.

Abstract Perennial species with the C4 pathway hold promise for biomass-based energy sources. We have explored the extent that CO2 uptake of such species may be limited by light in a temperate climate. One energetic cost of the C4 pathway is the leakiness (ϕ) of bundle sheath tissues, whereby a variable proportion of the CO2, concentrated in bundle sheath cells, retrodiffuses back to the mesophyll. In this study, we scale ϕ from leaf to canopy level of a Miscanthus crop (Miscanthus × giganteus hybrid) under field conditions and model the likely limitations to CO2 fixation. At the leaf level, measurements of photosynthesis coupled to online carbon isotope discrimination showed that leaves within a 3.3-m canopy (leaf area index = 8.3) show a progressive increase in both carbon isotope discrimination and ϕ as light decreases. A similar increase was observed at the ecosystem scale when we used eddy covariance net ecosystem CO2 fluxes, together with isotopic profiles, to partition photosynthetic and respiratory isotopic flux densities (isofluxes) and derive canopy carbon isotope discrimination as an integrated proxy for ϕ at the canopy level. Modeled values of canopy CO2 fixation using leaf-level measurements of ϕ suggest that around 32% of potential photosynthetic carbon gain is lost due to light limitation, whereas using ϕ determined independently from isofluxes at the canopy level the reduction in canopy CO2 uptake is estimated at 14%. Based on these results, we identify ϕ as an important limitation to CO2 uptake of crops with the C4 pathway.

This study argues that several metrics are necessary to build up a picture of yield gain and nitrogen losses for ryegrass sheep pastures. Metrics of resource use efficiency, nitrous oxide emission factor, leached and emitted nitrogen per unit product are used to encompass yield gain and losses relating to nitrogen. These metrics are calculated from field system simulations using the DAYCENT model, validated from field sensor measurements and observations relating to crop yield, fertilizer applied, ammonium in soil and nitrate in soil and water, nitrous oxide and soil moisture. Three ryegrass pastures with traditional management for sheep grazing and silage are studied. As expected, the metrics between long-term ryegrass swards in this study are not very dissimilar. Slight differences between simulations of different field systems likely result from varying soil bulk density, as revealed by a sensitivity analysis applied to DAYCENT. The field with the highest resource use efficiency was also the field with the lowest leached inorganic nitrogen per unit product, and vice versa. Field system simulation using climate projections indicates an increase in nitrogen loss to water and air, with a corresponding increase in biomass. If we simulate both nitrogen loss by leaching and by gaseous emission, we obtain a fuller picture. Under climate projections, the field with the lowest determined nitrous oxide emissions factor, had a relatively high leached nitrogen per product amongst the three fields. When management differences were investigated, the amount of nitrous oxide per unit biomass was found to be significantly higher for an annual management of grazing only, than a silage harvest plus grazing, likely relating to the increased period of livestock on pasture. This work emphasizes how several metrics validated by auto-sampled data provide a measure of nitrogen loss, efficiency and best management practise.

Systems approaches have great potential for application in predictive ecology. In this paper, we present a range of examples, where systems approaches are being developed and applied at a range of scales in the field of global change and biogeochemical cycling. Systems approaches range from Bayesian calibration techniques at plot scale, through data assimilation methods at regional to continental scales, to multi-disciplinary numerical model applications at country to global scales. We provide examples from a range of studies and show how these approaches are being used to address current topics in global change and biogeochemical research, such as the interaction between carbon and nitrogen cycles, terrestrial carbon feedbacks to climate change and the attribution of observed global changes to various drivers of change. We examine how transferable the methods and techniques might be to other areas of ecosystem science and ecology.

Greenhouse gas (GHG) emissions from agriculture, including crop and livestock production, forestry and associated land use changes, are responsible for a significant fraction of anthropogenic emissions, up to 30% according to the Intergovernmental Panel on Climate Change (IPCC). Yet while emissions from fossil fuels are updated yearly and by multiple sources—including national-level statistics from the International Energy Agency (IEA)—no comparable efforts for reporting global statistics for agriculture, forestry and other land use (AFOLU) emissions exist: the latest complete assessment was the 2007 IPCC report, based on 2005 emission data. This gap is critical for several reasons. First, potentially large climate funding could be linked in coming decades to more precise estimates of emissions and mitigation potentials. For many developing countries, and especially the least developed ones, this requires improved assessments of AFOLU emissions. Second, growth in global emissions from fossil fuels has outpaced that from AFOLU during every decade of the period 1961–2010, so the relative contribution of the latter to total climate forcing has diminished over time, with a need for regular updates. We present results from a new GHG database developed at FAO, providing a complete and coherent time series of emission statistics over a reference period 1961–2010, at country level, based on FAOSTAT activity data and IPCC Tier 1 methodology. We discuss results at global and regional level, focusing on trends in the agriculture sector and net deforestation. Our results complement those available from the IPCC, extending trend analysis to a longer historical period and, critically, beyond 2005 to more recent years. In particular, from 2000 to 2010, we find that agricultural emissions increased by 1.1% annually, reaching 4.6 Gt CO _2 yr ^−1 in 2010 (up to 5.4–5.8 Gt CO _2 yr ^−1 with emissions from biomass burning and organic soils included). Over the same decade 2000–2010, the ratio of agriculture to fossil fuel emissions has decreased, from 17.2% to 13.7%, and the decrease is even greater for the ratio of net deforestation to fossil fuel emissions: from 19.1% to 10.1%. In fact, in the year 2000, emissions from agriculture have been consistently larger—about 1.2 Gt CO _2 yr ^−1 in 2010—than those from net deforestation.